diff --git a/SavingsAI/DeepLearningDolfin.ipynb b/SavingsAI/DeepLearningDolfin.ipynb
new file mode 100644
index 0000000..06aa859
--- /dev/null
+++ b/SavingsAI/DeepLearningDolfin.ipynb
@@ -0,0 +1,1018 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "#Libs\n",
+        "import pandas as pd"
+      ],
+      "metadata": {
+        "id": "x99rV5Hmm3Mt"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def getSampleData():\n",
+        "  return pd.read_csv(\"/content/drive/MyDrive/Sample getTransactions API data.csv\")"
+      ],
+      "metadata": {
+        "id": "7_WJ-eC2nBTq"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "nFJdQADyvLZ3",
+        "outputId": "baf9e4ac-3a84-4480-db08-909802228700"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           type                                    id  status  \\\n",
+              "0   transaction  4957bcf6-f18f-43d7-94df-71f44a0bcf32  posted   \n",
+              "1   transaction  0d6e11ab-e28e-4de0-a152-600cc44fb61c  posted   \n",
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+              "7   transaction  55dbdd71-9976-43ec-a724-7cf620d279d4  posted   \n",
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+              "10  transaction  6ba3dd59-4268-419d-b5aa-49df88c11c71  posted   \n",
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+              "13  transaction  9cf8a7ac-0e8b-4699-81e6-98181bdf18cf  posted   \n",
+              "14  transaction  d5a36da2-5b9f-4dbf-aa95-eb518d163f0f  posted   \n",
+              "15  transaction  70aa3766-93eb-4da6-ab69-299b16507794  posted   \n",
+              "16  transaction  6b8f916b-3317-4a5a-818b-82b008f67d84  posted   \n",
+              "17  transaction  7dbf9e0f-e172-4474-9427-69fbb2d46752  posted   \n",
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+              "19  transaction  e24adcd4-4a91-4a0e-bc57-48256635e051  posted   \n",
+              "20  transaction  a5fd3de7-1d3b-44e9-8821-d01dc852a0d7  posted   \n",
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+              "22  transaction  7aa1234e-4132-4dcb-8cd2-c2adeeb43891  posted   \n",
+              "23  transaction  793923f1-00e1-4181-b518-13feac5adca9  posted   \n",
+              "24  transaction  305aba74-a09e-470d-9079-f386936b08b8  posted   \n",
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+              "29  transaction  1fe000a0-ccc3-4752-9fbc-f939a9473300  posted   \n",
+              "\n",
+              "                                         description   amount  \\\n",
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+              "1                       Non Hooli ATM Withdrawal Fee     -2.5   \n",
+              "2   Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU   -200.0   \n",
+              "3                       Non Hooli ATM Withdrawal Fee     -2.5   \n",
+              "4                    Transfer Platnm Homeloan 346454  -3852.5   \n",
+              "5                  TFR From Transaction to CC Acc 13  -9406.0   \n",
+              "6                             Payroll WFRMS 15439393  17098.0   \n",
+              "7                            Manly Maths Tutor Wages    201.0   \n",
+              "8                                  MANLY WEST SCHOOL  -1422.2   \n",
+              "9                                  MANLY WEST SCHOOL    -51.0   \n",
+              "10                           Funds Transfer Transfer   3001.0   \n",
+              "11                           Manly Maths Tutor Wages    201.0   \n",
+              "12                             TFR Acc14000 TO 12389   -500.0   \n",
+              "13                       AGL RETAIL ENERGY LTD (GAS)    -78.0   \n",
+              "14  Wdl ATM CBA ATM  SEAFORTH       NSW 225101   AUS   -750.0   \n",
+              "15                   Transfer Platnm Homeloan 346454  -3860.0   \n",
+              "16                 TFR From Transaction to CC Acc 13  -9398.5   \n",
+              "17                            Payroll WFRMS 15439393  17098.0   \n",
+              "18            CTRLINK CARERS Ref: 998R6789201610974V     19.6   \n",
+              "19                           Manly Maths Tutor Wages    201.0   \n",
+              "20                                 MANLY WEST SCHOOL  -1422.2   \n",
+              "21                                 MANLY WEST SCHOOL    -51.0   \n",
+              "22                           Manly Maths Tutor Wages    201.0   \n",
+              "23                             TFR Acc14000 TO 12389   -500.0   \n",
+              "24                       AGL RETAIL ENERGY LTD (GAS)    -78.0   \n",
+              "25            CTRLINK CARERS Ref: 998R6789201610974V     26.0   \n",
+              "26                   Transfer Platnm Homeloan 346454  -3867.5   \n",
+              "27                 TFR From Transaction to CC Acc 13  -9391.0   \n",
+              "28                            Payroll WFRMS 15439393  17098.0   \n",
+              "29                                 MANLY WEST SCHOOL    -20.0   \n",
+              "\n",
+              "                                 account   balance direction            class  \\\n",
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+              "\n",
+              "   institution                            connection  enrich transactionDate  \\\n",
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+              "      <td>2023-08-03T00:07:36</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>{\"account\":\"https://au-api.basiq.io/users/6a52...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>0d6e11ab-e28e-4de0-a152-600cc44fb61c</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Non Hooli ATM Withdrawal Fee</td>\n",
+              "      <td>-2.5</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>22309.56</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>bank-fee</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3/08/2023</td>\n",
+              "      <td>2023-08-03T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"card\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>323f6d42-a38e-4c46-83d2-6c8f3e999686</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU</td>\n",
+              "      <td>-200.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>22312.06</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>cash-withdrawal</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3/08/2023</td>\n",
+              "      <td>2023-08-03T00:00:00Z</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>{\\self\\\":\\\"https://au-api.basiq.io/users/bca4b...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>1ab3a3c5-faeb-4de3-b5aa-612e5bc76fd5</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Non Hooli ATM Withdrawal Fee</td>\n",
+              "      <td>-2.5</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>22512.06</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>bank-fee</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3/08/2023</td>\n",
+              "      <td>2023-08-03T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"card\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>d12df6a2-48b6-4f5f-a718-ccb377aa330c</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Transfer Platnm Homeloan 346454</td>\n",
+              "      <td>-3852.5</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>22514.56</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>29/07/2023</td>\n",
+              "      <td>2023-07-29T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Auxiliary Finance and Investment S...</td>\n",
+              "      <td>\\\"code\\\":\\\"641\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>08e6f7c9-f359-48f2-b086-a55f468f2f60</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>TFR From Transaction to CC Acc 13</td>\n",
+              "      <td>-9406.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>26367.06</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>28/07/2023</td>\n",
+              "      <td>2023-07-28T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Civic</td>\n",
+              "      <td>Professional and Other Interest Group Services\\\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>53570fde-c731-4e33-8224-0583650eeaa7</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Payroll WFRMS 15439393</td>\n",
+              "      <td>17098.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>35773.06</td>\n",
+              "      <td>credit</td>\n",
+              "      <td>transfer</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>28/07/2023</td>\n",
+              "      <td>2023-07-28T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Unknown\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"0\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>55dbdd71-9976-43ec-a724-7cf620d279d4</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Manly Maths Tutor Wages</td>\n",
+              "      <td>201.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>18675.06</td>\n",
+              "      <td>credit</td>\n",
+              "      <td>transfer</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>17/07/2023</td>\n",
+              "      <td>2023-07-17T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Educational Support Services\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"822\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>17b710a5-da3d-42f8-ae31-f9d601f95c6d</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>MANLY WEST SCHOOL</td>\n",
+              "      <td>-1422.2</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>18474.06</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>16/07/2023</td>\n",
+              "      <td>2023-07-16T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"School Education\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"802\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>d93fb495-8a7a-4997-8a16-b3581b778471</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>MANLY WEST SCHOOL</td>\n",
+              "      <td>-51.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>19896.26</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>16/07/2023</td>\n",
+              "      <td>2023-07-16T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"School Education\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"802\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>6ba3dd59-4268-419d-b5aa-49df88c11c71</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Funds Transfer Transfer</td>\n",
+              "      <td>3001.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>19947.26</td>\n",
+              "      <td>credit</td>\n",
+              "      <td>transfer</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>13/07/2023</td>\n",
+              "      <td>2023-07-13T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Auxiliary Finance and Investment S...</td>\n",
+              "      <td>\\\"code\\\":\\\"641\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>7d9413b0-694f-4b62-aac9-c178a6f069c6</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Manly Maths Tutor Wages</td>\n",
+              "      <td>201.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>16946.26</td>\n",
+              "      <td>credit</td>\n",
+              "      <td>transfer</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>13/07/2023</td>\n",
+              "      <td>2023-07-13T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Educational Support Services\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"822\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>4f3bb9a5-9597-4d71-80db-fae35b70edcd</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>TFR Acc14000 TO 12389</td>\n",
+              "      <td>-500.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>16745.26</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>12/07/2023</td>\n",
+              "      <td>2023-07-12T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Legal and Accounting Services\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"693\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>9cf8a7ac-0e8b-4699-81e6-98181bdf18cf</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>AGL RETAIL ENERGY LTD (GAS)</td>\n",
+              "      <td>-78.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>17245.26</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>9/07/2023</td>\n",
+              "      <td>2023-07-09T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Electricity Distribution\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"263\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>d5a36da2-5b9f-4dbf-aa95-eb518d163f0f</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Wdl ATM CBA ATM  SEAFORTH       NSW 225101   AUS</td>\n",
+              "      <td>-750.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>17323.26</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>cash-withdrawal</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3/07/2023</td>\n",
+              "      <td>2023-07-03T00:00:00Z</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>{\\self\\\":\\\"https://au-api.basiq.io/users/bca4b...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>70aa3766-93eb-4da6-ab69-299b16507794</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Transfer Platnm Homeloan 346454</td>\n",
+              "      <td>-3860.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>18073.26</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>28/06/2023</td>\n",
+              "      <td>2023-06-28T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Auxiliary Finance and Investment S...</td>\n",
+              "      <td>\\\"code\\\":\\\"641\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>6b8f916b-3317-4a5a-818b-82b008f67d84</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>TFR From Transaction to CC Acc 13</td>\n",
+              "      <td>-9398.5</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>21933.26</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>27/06/2023</td>\n",
+              "      <td>2023-06-27T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Civic</td>\n",
+              "      <td>Professional and Other Interest Group Services\\\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>7dbf9e0f-e172-4474-9427-69fbb2d46752</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Payroll WFRMS 15439393</td>\n",
+              "      <td>17098.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>31331.76</td>\n",
+              "      <td>credit</td>\n",
+              "      <td>transfer</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>27/06/2023</td>\n",
+              "      <td>2023-06-27T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Unknown\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"0\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>d4f55027-9c2f-4288-8580-0969738f4d78</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>CTRLINK CARERS Ref: 998R6789201610974V</td>\n",
+              "      <td>19.6</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>14233.76</td>\n",
+              "      <td>credit</td>\n",
+              "      <td>transfer</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>22/06/2023</td>\n",
+              "      <td>2023-06-22T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Unknown\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"0\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>e24adcd4-4a91-4a0e-bc57-48256635e051</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Manly Maths Tutor Wages</td>\n",
+              "      <td>201.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>14214.16</td>\n",
+              "      <td>credit</td>\n",
+              "      <td>transfer</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>19/06/2023</td>\n",
+              "      <td>2023-06-19T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Educational Support Services\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"822\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>a5fd3de7-1d3b-44e9-8821-d01dc852a0d7</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>MANLY WEST SCHOOL</td>\n",
+              "      <td>-1422.2</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>14013.16</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>19/06/2023</td>\n",
+              "      <td>2023-06-18T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"School Education\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"802\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>358ed9c0-46c0-486e-bedc-fe05cb2d7030</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>MANLY WEST SCHOOL</td>\n",
+              "      <td>-51.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>15435.36</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>18/06/2023</td>\n",
+              "      <td>2023-06-18T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"School Education\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"802\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>7aa1234e-4132-4dcb-8cd2-c2adeeb43891</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Manly Maths Tutor Wages</td>\n",
+              "      <td>201.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>15486.36</td>\n",
+              "      <td>credit</td>\n",
+              "      <td>transfer</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>15/06/2023</td>\n",
+              "      <td>2023-06-15T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Educational Support Services\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"822\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>793923f1-00e1-4181-b518-13feac5adca9</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>TFR Acc14000 TO 12389</td>\n",
+              "      <td>-500.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>15285.36</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>14/06/2023</td>\n",
+              "      <td>2023-06-14T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Legal and Accounting Services\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"693\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>305aba74-a09e-470d-9079-f386936b08b8</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>AGL RETAIL ENERGY LTD (GAS)</td>\n",
+              "      <td>-78.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>15785.36</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>11/06/2023</td>\n",
+              "      <td>2023-06-11T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Electricity Distribution\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"263\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>32f52553-c38d-473f-994d-3e6dcf949268</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>CTRLINK CARERS Ref: 998R6789201610974V</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>15863.36</td>\n",
+              "      <td>credit</td>\n",
+              "      <td>transfer</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1/06/2023</td>\n",
+              "      <td>2023-06-01T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Unknown\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"0\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>78a8f7f6-5978-4985-a3d8-66f01838978c</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Transfer Platnm Homeloan 346454</td>\n",
+              "      <td>-3867.5</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>15837.36</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>31/05/2023</td>\n",
+              "      <td>2023-05-31T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Auxiliary Finance and Investment S...</td>\n",
+              "      <td>\\\"code\\\":\\\"641\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>0b31ba00-2629-49d0-b5a3-ff18398efc7a</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>TFR From Transaction to CC Acc 13</td>\n",
+              "      <td>-9391.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>19704.86</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>30/05/2023</td>\n",
+              "      <td>2023-05-30T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Civic</td>\n",
+              "      <td>Professional and Other Interest Group Services\\\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>d5944ba8-8211-4036-a412-aa25def5c61f</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>Payroll WFRMS 15439393</td>\n",
+              "      <td>17098.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>29095.86</td>\n",
+              "      <td>credit</td>\n",
+              "      <td>transfer</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>30/05/2023</td>\n",
+              "      <td>2023-05-30T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"Unknown\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"0\\\"}\"</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>transaction</td>\n",
+              "      <td>1fe000a0-ccc3-4752-9fbc-f939a9473300</td>\n",
+              "      <td>posted</td>\n",
+              "      <td>MANLY WEST SCHOOL</td>\n",
+              "      <td>-20.0</td>\n",
+              "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+              "      <td>11997.86</td>\n",
+              "      <td>debit</td>\n",
+              "      <td>payment</td>\n",
+              "      <td>AU00000</td>\n",
+              "      <td>1bd6c08e-8457-4a74-bdd9-514df489d27e</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>28/05/2023</td>\n",
+              "      <td>2023-05-28T00:00:00Z</td>\n",
+              "      <td>{\\title\\\":\\\"School Education\\\"</td>\n",
+              "      <td>\\\"code\\\":\\\"802\\\"}\"</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a15c85d-c3f2-453b-8cd6-5bb58334671c')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-700198b5-4b63-4ab7-b663-3debe356838c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-700198b5-4b63-4ab7-b663-3debe356838c')\"\n",
+              "              title=\"Suggest charts.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "    <script>\n",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-700198b5-4b63-4ab7-b663-3debe356838c button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-1a15c85d-c3f2-453b-8cd6-5bb58334671c button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-1a15c85d-c3f2-453b-8cd6-5bb58334671c');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ],
+      "source": [
+        "raw = getSampleData()\n",
+        "raw.head(30)"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/SavingsAI/LSTM_savings_prediction.py b/SavingsAI/LSTM_savings_prediction.py
new file mode 100644
index 0000000..1486983
--- /dev/null
+++ b/SavingsAI/LSTM_savings_prediction.py
@@ -0,0 +1,95 @@
+import pandas as pd
+import numpy as np
+from datetime import datetime
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import MinMaxScaler
+from statsmodels.tsa.holtwinters import ExponentialSmoothing
+from sklearn.model_selection import train_test_split
+from keras.models import Sequential
+from keras.layers import LSTM, Dense, Dropout
+from keras.optimizers import Adam
+from keras.regularizers import l2
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+import pytz
+
+# load csv data for testing
+data = pd.read_csv('Sample data.csv')
+
+# Convert "postDate" column to datetime format
+data['postDate'] = pd.to_datetime(data['postDate'], utc=True)
+
+# Sort the DataFrame by "postDate"
+data = data.sort_values(by='postDate')
+
+# Function to extract and normalize time components
+def preprocess_timestamp(timestamp):
+    year = timestamp.year
+    month = timestamp.month
+    day = timestamp.day
+    hour = timestamp.hour
+    minute = timestamp.minute
+
+    # Normalize time components
+    normalized_year = (year - min_year) / (max_year - min_year)
+    normalized_month = (month - 1) / 11  # Month range: 1-12
+    normalized_day = (day - 1) / 30      # Assuming 30 days in a month
+    normalized_hour = hour / 23
+    normalized_minute = minute / 59
+
+    return [normalized_year, normalized_month, normalized_day, normalized_hour, normalized_minute]
+
+# Calculate min_year and max_year from your dataset
+min_year = data["postDate"].apply(lambda x: x.year).min()
+max_year = data["postDate"].apply(lambda x: x.year).max()
+
+# Apply preprocessing function to the "postDate" column and create new columns
+data[["year", "month", "day", "hour", "minute"]] = data["postDate"].apply(preprocess_timestamp).apply(pd.Series)
+
+# Print the updated DataFrame
+#print(data)
+
+# Feature Engineering
+# Currently using 'amount', 'balance' and date fields as features
+features = ['amount', 'balance', 'year', 'month', 'day']
+X = data[features].values
+scaler = StandardScaler()
+X = scaler.fit_transform(X)
+
+# Creating Sequences
+sequence_length = 10
+X_sequences = []
+y = []
+for i in range(len(X) - sequence_length):
+    X_sequences.append(X[i:i+sequence_length])
+    y.append(X[i+sequence_length, 1])  # 'balance' is the target variable
+
+X_sequences = np.array(X_sequences)
+y = np.array(y)
+
+# Train-Validation-Test Split
+X_train, X_temp, y_train, y_temp = train_test_split(X_sequences, y, test_size=0.3, shuffle=False)
+X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, shuffle=False)
+
+# Build LSTM Model
+model = Sequential()
+model.add(LSTM(50, activation='relu', input_shape=(sequence_length, len(features))))
+model.add(Dense(1))
+model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')
+
+# Training
+model.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_val, y_val))
+
+# Evaluation
+y_pred = model.predict(X_test)
+mse = mean_squared_error(y_test, y_pred)
+print(f"Mean Squared Error: {mse}")
+
+# Mean Absolute Error
+mae = np.mean(np.abs(y_test - y_pred))
+print(f"Mean Absolute Error: {mae}")
+
+# Root Mean Squared Error
+rmse = np.sqrt(np.mean((y_test - y_pred)**2))
+print(f"Root Mean Squared Error: {rmse}")
+
+model.save('LSTM_savings_model.h5')
\ No newline at end of file
diff --git a/SavingsAI/Sample getTransactions API data.csv b/SavingsAI/Sample getTransactions API data.csv
new file mode 100644
index 0000000..703bd99
--- /dev/null
+++ b/SavingsAI/Sample getTransactions API data.csv	
@@ -0,0 +1,290 @@
+type,id,status,description,amount,account,balance,direction,class,institution,connection,enrich,transactionDate,postDate,subClass,links
+transaction,4957bcf6-f18f-43d7-94df-71f44a0bcf32,posted,Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU,-200,d3de1ca1,22109.56,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,3/08/2023,2023-08-03T00:07:36,null,"{""account"":""https://au-api.basiq.io/users/6a52015e/accounts/31eb30a0"",""institution"":""https://au-api.basiq.io/institutions/AU00000"",""self"":""https://au-api.basiq.io/users/6a52015e/transactions/2082c765""}"
+transaction,0d6e11ab-e28e-4de0-a152-600cc44fb61c,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,22309.56,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,3/08/2023,2023-08-03T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,323f6d42-a38e-4c46-83d2-6c8f3e999686,posted,Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,22312.06,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,3/08/2023,2023-08-03T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/323f6d42-a38e-4c46-83d2-6c8f3e999686\"""
+transaction,1ab3a3c5-faeb-4de3-b5aa-612e5bc76fd5,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,22512.06,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,3/08/2023,2023-08-03T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,d12df6a2-48b6-4f5f-a718-ccb377aa330c,posted,Transfer Platnm Homeloan 346454,-3852.5,070c1d68-0ee0-477a-9679-294ea7059939,22514.56,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,29/07/2023,2023-07-29T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,08e6f7c9-f359-48f2-b086-a55f468f2f60,posted,TFR From Transaction to CC Acc 13,-9406,070c1d68-0ee0-477a-9679-294ea7059939,26367.06,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/07/2023,2023-07-28T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,53570fde-c731-4e33-8224-0583650eeaa7,posted,Payroll WFRMS 15439393,17098,070c1d68-0ee0-477a-9679-294ea7059939,35773.06,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/07/2023,2023-07-28T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,55dbdd71-9976-43ec-a724-7cf620d279d4,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,18675.06,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,17/07/2023,2023-07-17T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,17b710a5-da3d-42f8-ae31-f9d601f95c6d,posted,MANLY WEST SCHOOL,-1422.2,070c1d68-0ee0-477a-9679-294ea7059939,18474.06,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,16/07/2023,2023-07-16T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,d93fb495-8a7a-4997-8a16-b3581b778471,posted,MANLY WEST SCHOOL,-51,070c1d68-0ee0-477a-9679-294ea7059939,19896.26,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,16/07/2023,2023-07-16T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,6ba3dd59-4268-419d-b5aa-49df88c11c71,posted,Funds Transfer Transfer,3001,070c1d68-0ee0-477a-9679-294ea7059939,19947.26,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/07/2023,2023-07-13T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,7d9413b0-694f-4b62-aac9-c178a6f069c6,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,16946.26,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/07/2023,2023-07-13T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,4f3bb9a5-9597-4d71-80db-fae35b70edcd,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,16745.26,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/07/2023,2023-07-12T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,9cf8a7ac-0e8b-4699-81e6-98181bdf18cf,posted,AGL RETAIL ENERGY LTD (GAS),-78,070c1d68-0ee0-477a-9679-294ea7059939,17245.26,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,9/07/2023,2023-07-09T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,d5a36da2-5b9f-4dbf-aa95-eb518d163f0f,posted,Wdl ATM CBA ATM  SEAFORTH       NSW 225101   AUS,-750,070c1d68-0ee0-477a-9679-294ea7059939,17323.26,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,3/07/2023,2023-07-03T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/d5a36da2-5b9f-4dbf-aa95-eb518d163f0f\"""
+transaction,70aa3766-93eb-4da6-ab69-299b16507794,posted,Transfer Platnm Homeloan 346454,-3860,070c1d68-0ee0-477a-9679-294ea7059939,18073.26,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/06/2023,2023-06-28T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,6b8f916b-3317-4a5a-818b-82b008f67d84,posted,TFR From Transaction to CC Acc 13,-9398.5,070c1d68-0ee0-477a-9679-294ea7059939,21933.26,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/06/2023,2023-06-27T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,7dbf9e0f-e172-4474-9427-69fbb2d46752,posted,Payroll WFRMS 15439393,17098,070c1d68-0ee0-477a-9679-294ea7059939,31331.76,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/06/2023,2023-06-27T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,d4f55027-9c2f-4288-8580-0969738f4d78,posted,CTRLINK CARERS Ref: 998R6789201610974V,19.6,070c1d68-0ee0-477a-9679-294ea7059939,14233.76,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,22/06/2023,2023-06-22T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,e24adcd4-4a91-4a0e-bc57-48256635e051,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,14214.16,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,19/06/2023,2023-06-19T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,a5fd3de7-1d3b-44e9-8821-d01dc852a0d7,posted,MANLY WEST SCHOOL,-1422.2,070c1d68-0ee0-477a-9679-294ea7059939,14013.16,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,19/06/2023,2023-06-18T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,358ed9c0-46c0-486e-bedc-fe05cb2d7030,posted,MANLY WEST SCHOOL,-51,070c1d68-0ee0-477a-9679-294ea7059939,15435.36,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,18/06/2023,2023-06-18T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,7aa1234e-4132-4dcb-8cd2-c2adeeb43891,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,15486.36,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,15/06/2023,2023-06-15T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,793923f1-00e1-4181-b518-13feac5adca9,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,15285.36,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/06/2023,2023-06-14T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,305aba74-a09e-470d-9079-f386936b08b8,posted,AGL RETAIL ENERGY LTD (GAS),-78,070c1d68-0ee0-477a-9679-294ea7059939,15785.36,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/06/2023,2023-06-11T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,32f52553-c38d-473f-994d-3e6dcf949268,posted,CTRLINK CARERS Ref: 998R6789201610974V,26,070c1d68-0ee0-477a-9679-294ea7059939,15863.36,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,1/06/2023,2023-06-01T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,78a8f7f6-5978-4985-a3d8-66f01838978c,posted,Transfer Platnm Homeloan 346454,-3867.5,070c1d68-0ee0-477a-9679-294ea7059939,15837.36,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,31/05/2023,2023-05-31T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,0b31ba00-2629-49d0-b5a3-ff18398efc7a,posted,TFR From Transaction to CC Acc 13,-9391,070c1d68-0ee0-477a-9679-294ea7059939,19704.86,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,30/05/2023,2023-05-30T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,d5944ba8-8211-4036-a412-aa25def5c61f,posted,Payroll WFRMS 15439393,17098,070c1d68-0ee0-477a-9679-294ea7059939,29095.86,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,30/05/2023,2023-05-30T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,1fe000a0-ccc3-4752-9fbc-f939a9473300,posted,MANLY WEST SCHOOL,-20,070c1d68-0ee0-477a-9679-294ea7059939,11997.86,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/05/2023,2023-05-28T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,5564eeb5-2aad-49ac-93bc-93d0c78c4ebc,posted,AGL RETAIL ENERGY LTD (GAS),-384.5,070c1d68-0ee0-477a-9679-294ea7059939,12017.86,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,22/05/2023,2023-05-22T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,e50bf6c2-6ab1-4005-8c2e-590221581418,posted,AGL RETAIL ENERGY LTD,-186.79,070c1d68-0ee0-477a-9679-294ea7059939,12402.36,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,22/05/2023,2023-05-22T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,3447238a-2fdb-4ece-80d3-64a1eb9b9324,posted,Funds Transfer transfer,3001,070c1d68-0ee0-477a-9679-294ea7059939,12589.15,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,22/05/2023,2023-05-22T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,205e2a5b-dcd1-4fbe-8375-9d25a0366697,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,9588.15,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/05/2023,2023-05-14T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,b82187ad-dfcd-4d03-ada2-c0675dd270c4,posted,Funds Transfer topup,1301,070c1d68-0ee0-477a-9679-294ea7059939,10088.15,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/05/2023,2023-05-11T00:00:00Z,"{\title\"":\""Telecommunications Services\""","\""code\"":\""580\""}"""
+transaction,2f18608b-d9ec-4f7c-aab1-4d3ef53fe4d8,posted,AGL RETAIL ENERGY LTD (GAS),-78,070c1d68-0ee0-477a-9679-294ea7059939,8787.15,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,10/05/2023,2023-05-10T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,51ce8653-b79f-4047-a20f-5829d53e9a34,posted,Transfer Platnm Homeloan 346454,-3875,070c1d68-0ee0-477a-9679-294ea7059939,8865.15,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,30/04/2023,2023-04-30T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,650ee7e3-59fb-4c4e-aba8-f061b15c96d5,posted,TFR From Transaction to CC Acc 13,-9383.5,070c1d68-0ee0-477a-9679-294ea7059939,12740.15,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,29/04/2023,2023-04-29T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,c88e1126-3e99-40fc-b733-d2c71cc15b8b,posted,Payroll WFRMS 15439393,17098,070c1d68-0ee0-477a-9679-294ea7059939,22123.65,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,29/04/2023,2023-04-29T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,536e0439-f9e1-4ebc-a256-22565dd19afc,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,5025.65,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/04/2023,2023-04-14T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,6e7b9ddc-f5f0-4a33-ab7f-82198378e043,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,4824.65,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/04/2023,2023-04-13T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,5267154d-9096-4e8b-9b43-8f8072fb0882,posted,AGL RETAIL LTD 005473584884,-78,070c1d68-0ee0-477a-9679-294ea7059939,5324.65,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,9/04/2023,2023-04-09T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,ee7de86b-39f3-4cbf-b2e7-f20e1d46f841,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,5402.65,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/04/2023,2023-04-07T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/ee7de86b-39f3-4cbf-b2e7-f20e1d46f841\"""
+transaction,5fa9e625-332c-476a-88d1-5f930f074177,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,5602.65,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/04/2023,2023-04-07T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,ee7de86b-39f3-4cbf-b2e7-f20e1d46f841,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,5402.65,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/04/2023,2023-04-07T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/ee7de86b-39f3-4cbf-b2e7-f20e1d46f841\"""
+transaction,5fa9e625-332c-476a-88d1-5f930f074177,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,5602.65,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/04/2023,2023-04-07T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,ee7de86b-39f3-4cbf-b2e7-f20e1d46f841,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,5402.65,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/04/2023,2023-04-07T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/ee7de86b-39f3-4cbf-b2e7-f20e1d46f841\"""
+transaction,5fa9e625-332c-476a-88d1-5f930f074177,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,5602.65,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/04/2023,2023-04-07T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,f3470ce7-8fc9-42c6-8205-53cc78215d99,posted,AGL RETAIL ENERGY LTD (GAS),-18.22,070c1d68-0ee0-477a-9679-294ea7059939,6010.15,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,6/04/2023,2023-04-06T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,3c68c201-c670-417e-a55a-d9ac0b05bd20,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-450,070c1d68-0ee0-477a-9679-294ea7059939,6028.37,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,4/04/2023,2023-04-04T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/3c68c201-c670-417e-a55a-d9ac0b05bd20\"""
+transaction,986c390f-7251-45f1-b359-a1b6c3c6de60,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,6478.37,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,4/04/2023,2023-04-04T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,3b3deb96-19b0-4e87-b177-586cad15bff7,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-300,070c1d68-0ee0-477a-9679-294ea7059939,6480.87,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,4/04/2023,2023-04-04T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/3b3deb96-19b0-4e87-b177-586cad15bff7\"""
+transaction,3b3deb96-19b0-4e87-b177-586cad15bff7,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-300,070c1d68-0ee0-477a-9679-294ea7059939,6480.87,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,4/04/2033,2023-04-04T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/3b3deb96-19b0-4e87-b177-586cad15bff7\"""
+transaction,b8f96f64-c37b-41ae-8270-c9a48cf9109e,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,6780.87,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,4/04/2023,2023-04-04T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,cf105523-0ffc-41ba-b213-94c65d5b34d1,posted,Wdl ATM CBA ATM  BALGOWLAH A    NSW 210901   AUS,-200,070c1d68-0ee0-477a-9679-294ea7059939,6783.37,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,4/04/2023,2023-04-04T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/cf105523-0ffc-41ba-b213-94c65d5b34d1\"""
+transaction,188b6447-9cee-48e8-865d-1020d6e804c1,posted,Transfer Platnm Homeloan 346454,-3882.5,070c1d68-0ee0-477a-9679-294ea7059939,6983.37,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,30/03/2023,2023-03-30T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,ae019070-fd2f-4fa0-b34f-c1a6acb61bd7,posted,TFR From Transaction to CC Acc 13,-9376,070c1d68-0ee0-477a-9679-294ea7059939,10865.87,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,29/03/2023,2023-03-29T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,11f9508d-cf0e-48c7-b0d6-30248995ed64,posted,Payroll WFRMS 15439393,17098,070c1d68-0ee0-477a-9679-294ea7059939,20241.87,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,29/03/2023,2023-03-29T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,5fa7efe5-6c42-49fb-bc99-6fc0b1557826,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,3143.87,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,19/03/2023,2023-03-19T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,83536773-a3a6-4555-82f9-33f958233f0a,posted,MANLY WEST SCHOOL,-2530.81,070c1d68-0ee0-477a-9679-294ea7059939,2942.87,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,17/03/2023,2023-03-17T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,e78643d6-4029-4c7b-be92-7d2e0b864b0e,posted,MANLY WEST SCHOOL,-101,070c1d68-0ee0-477a-9679-294ea7059939,5473.68,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,17/03/2023,2023-03-17T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,4baf3412-5b84-4610-8748-2279e7ec9f3e,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,5574.68,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,15/03/2023,2023-03-15T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,f6d88111-52f6-4458-8cd0-86645a04fb6f,posted,Wdl ATM CBA ATM  MANLY D        NSW 219704   AUS,-420,070c1d68-0ee0-477a-9679-294ea7059939,5373.68,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/03/2023,2023-03-14T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/f6d88111-52f6-4458-8cd0-86645a04fb6f\"""
+transaction,f5848553-2d3a-4832-bcff-033ec1365669,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,5793.68,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/03/2023,2023-03-14T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,71db7860-ba64-4da1-99c0-2f0c6007266b,posted,AGL RETAIL ENERGY LTD (GAS),-95,070c1d68-0ee0-477a-9679-294ea7059939,6293.68,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/03/2023,2023-03-13T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,9da8b932-0fe7-44be-9325-fbab17790e48,posted,AGL RETAIL ENERGY LTD (GAS),-78,070c1d68-0ee0-477a-9679-294ea7059939,6388.68,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/03/2023,2023-03-13T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,ed597d14-0d6b-4be7-b6c7-54792ead565f,posted,AGL RETAIL ENERGY LTD (GAS),-95,070c1d68-0ee0-477a-9679-294ea7059939,6466.68,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/03/2023,2023-03-12T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,0d5bf67f-3b32-4063-b78f-9d7e3d1d47e2,posted,AGL RETAIL ENERGY LTD (GAS),-78,070c1d68-0ee0-477a-9679-294ea7059939,6561.68,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/03/2023,2023-03-12T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,fca582af-f723-48c9-9713-b1dbb0cae56f,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-350,070c1d68-0ee0-477a-9679-294ea7059939,6639.68,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,5/03/2023,2023-03-05T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/fca582af-f723-48c9-9713-b1dbb0cae56f\"""
+transaction,e9ca7a17-2125-4f9c-a614-552269097b06,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,6989.68,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,5/03/2023,2023-03-05T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,3f224d3d-544b-43d7-b7cf-7c0c342edb84,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-350,070c1d68-0ee0-477a-9679-294ea7059939,6992.18,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,5/03/2023,2023-03-05T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/3f224d3d-544b-43d7-b7cf-7c0c342edb84\"""
+transaction,81822baa-3400-47fc-8371-2a5b7bffc578,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,7342.18,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,5/03/2023,2023-03-05T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,74362203-4b97-49fd-b2d7-af8e2ee84cb2,posted,Transfer Platnm Homeloan 346454,-3890,070c1d68-0ee0-477a-9679-294ea7059939,7344.68,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/02/2023,2023-02-28T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,5204bdbd-024a-4220-a667-302df31ccc97,posted,TFR From Transaction to CC Acc 13,-9368.5,070c1d68-0ee0-477a-9679-294ea7059939,11234.68,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/02/2023,2023-02-27T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,2e6f6ed8-0069-401f-80b1-ad9ce86e59cb,posted,Payroll WFRMS 15439393,17098,070c1d68-0ee0-477a-9679-294ea7059939,20603.18,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/02/2023,2023-02-27T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,fe783962-54a6-4741-89bc-160d4c48601a,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,3505.18,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/02/2023,2023-02-14T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,40875b6b-1c5a-4df7-aec8-07a65a7d2fbf,posted,MANLY WEST SCHOOL,-2530.81,070c1d68-0ee0-477a-9679-294ea7059939,3304.18,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/02/2023,2023-02-14T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,4d633d46-7f31-401c-a5db-b6649deefa9b,posted,MANLY WEST SCHOOL,-101,070c1d68-0ee0-477a-9679-294ea7059939,5834.99,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/02/2023,2023-02-14T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,fe8c63f6-a1e4-4154-95f8-1dec02a99fd3,posted,CTRLINK CARERS Ref: 998R6789201610974V,171,070c1d68-0ee0-477a-9679-294ea7059939,5935.99,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/02/2023,2023-02-14T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,002dad4d-9c8c-4da0-921f-2bc1087f0b1f,posted,CTRLINK CARERS Ref: 998R6789201610974V,35,070c1d68-0ee0-477a-9679-294ea7059939,5764.99,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/02/2023,2023-02-13T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,59403070-bb5b-447b-9cdf-b82c4d136290,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,5729.99,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/02/2023,2023-02-11T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,100c5516-ccc1-4383-b660-0972ca7126f9,posted,Wdl ATM Redi ATM CEXP BERKSHIREBerkshire      AU,-100,070c1d68-0ee0-477a-9679-294ea7059939,6229.99,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,10/02/2023,2023-02-10T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/100c5516-ccc1-4383-b660-0972ca7126f9\"""
+transaction,ca51ee81-f26e-4bb3-a287-7c5028f7754c,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,6329.99,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,10/02/2023,2023-02-10T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,79518563-7890-44d6-9c6f-2b0dcf62766a,posted,AGL RETAIL ENERGY LTD (GAS),-95,070c1d68-0ee0-477a-9679-294ea7059939,6332.49,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,8/02/2023,2023-02-08T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,e77bc235-2cf0-4ce0-b862-2550511552bc,posted,AGL RETAIL ENERGY LTD (GAS),-78,070c1d68-0ee0-477a-9679-294ea7059939,6427.49,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,8/02/2023,2023-02-08T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,9f0350cf-9d59-4c63-a11e-7b9f5c352ba1,posted,CTRLINK CARERS Ref: 998R6789201610974V,61,070c1d68-0ee0-477a-9679-294ea7059939,6505.49,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,30/01/2023,2023-01-30T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,19c34929-bb8d-4fd4-9648-82a0fbb31bb0,posted,Transfer Platnm Homeloan 346454,-3897.5,070c1d68-0ee0-477a-9679-294ea7059939,6444.49,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/01/2023,2023-01-28T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,74f74fef-4577-482d-a89d-8306ba11d69f,posted,TFR From Transaction to CC Acc 13,-9361,070c1d68-0ee0-477a-9679-294ea7059939,10341.99,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/01/2023,2023-01-27T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,8242021c-e160-4a7b-ba79-24902175ba57,posted,Payroll WFRMS 15439393,17098,070c1d68-0ee0-477a-9679-294ea7059939,19702.99,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/01/2023,2023-01-27T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,33d05927-387a-4377-bbae-2b05abbcdd30,posted,Wdl ATM CBA ATM  MANLY D        NSW 219704   AUS,-300,070c1d68-0ee0-477a-9679-294ea7059939,2604.99,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,17/01/2023,2023-01-17T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/33d05927-387a-4377-bbae-2b05abbcdd30\"""
+transaction,3070f91a-cde3-466e-a90e-20244be35bb8,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,2904.99,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,17/01/2023,2023-01-17T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,92f01adb-0ad3-4340-b9af-3043b44eb6ed,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,2703.99,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,16/01/2023,2023-01-16T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/92f01adb-0ad3-4340-b9af-3043b44eb6ed\"""
+transaction,f88a8a0b-5738-4914-8c2e-38c42fac99e9,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,2903.99,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,16/01/2023,2023-01-16T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,5a85f687-741e-4b44-8cb8-9b5a2039f04c,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-100,070c1d68-0ee0-477a-9679-294ea7059939,2906.49,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,16/01/2023,2023-01-16T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/5a85f687-741e-4b44-8cb8-9b5a2039f04c\"""
+transaction,6df8ae78-9429-4c88-a997-4119ea707572,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,3006.49,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,16/01/2023,2023-01-16T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,ead14f6c-0c6d-46b5-bcc3-96384756cfc7,posted,MANLY WEST SCHOOL,-2530.81,070c1d68-0ee0-477a-9679-294ea7059939,3008.99,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,16/01/2023,2023-01-16T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,4dcf54d8-f53c-48ea-8036-8fec24c8df58,posted,MANLY WEST SCHOOL,-101,070c1d68-0ee0-477a-9679-294ea7059939,5539.8,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,16/01/2023,2023-01-16T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,6214ed97-ac6d-4c1c-bc77-3bd37e435e42,posted,Wdl ATM CBA ATM  MANLY E        NSW 219705   AUS,-100,070c1d68-0ee0-477a-9679-294ea7059939,5640.8,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,15/01/2023,2023-01-15T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/6214ed97-ac6d-4c1c-bc77-3bd37e435e42\"""
+transaction,e5008e57-b692-4d6e-a1ba-7a2593997ca5,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,5740.8,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/01/2023,2023-01-13T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,e0cf7437-9d41-4f80-91c8-dc7809f0545a,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,5539.8,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/01/2023,2023-01-12T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,d55a0a51-92de-4a96-aef1-c6b58dec14f7,posted,AGL RETAIL ENERGY LTD,-95,070c1d68-0ee0-477a-9679-294ea7059939,6039.8,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,9/01/2023,2023-01-09T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,7aed3afd-5405-41ea-904e-5d625bfb96d4,posted,AGL RETAIL ENERGY LTD (GAS),-78,070c1d68-0ee0-477a-9679-294ea7059939,6134.8,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,9/01/2023,2023-01-09T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,c8356982-4ded-44b0-baca-c24d8411f314,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-100,070c1d68-0ee0-477a-9679-294ea7059939,6212.8,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,2/01/2023,2023-01-02T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/c8356982-4ded-44b0-baca-c24d8411f314\"""
+transaction,68f1e960-e566-4f23-b9f2-a69ba2a05e5f,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,6312.8,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,2/01/2023,2023-01-02T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,ea4f83f5-41f8-481d-bccc-7d83f73ebdd2,posted,Transfer Platnm Homeloan 346454,-3905,070c1d68-0ee0-477a-9679-294ea7059939,6315.3,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,29/12/2022,2022-12-29T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,fb847112-209c-4850-ad0a-2e16f138a3b1,posted,Wdl ATM CBA ATM  BALGOWLAH A    NSW 210901   AUS,-200,070c1d68-0ee0-477a-9679-294ea7059939,10220.3,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/12/2022,2022-12-28T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/fb847112-209c-4850-ad0a-2e16f138a3b1\"""
+transaction,289dffa5-44c5-42cc-895f-d349b396e2c5,posted,Wdl ATM CBA ATM  BALGOWLAH A    NSW 210901   AUS,-100,070c1d68-0ee0-477a-9679-294ea7059939,10420.3,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/12/2022,2022-12-28T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/289dffa5-44c5-42cc-895f-d349b396e2c5\"""
+transaction,abc39e1b-ef56-44bb-baa4-cab8a408c0a6,posted,TFR From Transaction to CC Acc 13,-9353.5,070c1d68-0ee0-477a-9679-294ea7059939,10520.3,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/12/2022,2022-12-28T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,9564a887-1a69-47aa-a889-6a4bdafa9f83,posted,Payroll WFRMS 15439393,17098,070c1d68-0ee0-477a-9679-294ea7059939,19873.8,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/12/2022,2022-12-28T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,b5609663-4a3b-462c-b02b-84fd5d057223,posted,Wdl ATM CBA ATM  MANLY A        NSW 219701   AUS,-400,070c1d68-0ee0-477a-9679-294ea7059939,2775.8,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,26/12/2022,2022-12-26T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/b5609663-4a3b-462c-b02b-84fd5d057223\"""
+transaction,315a3d59-28ac-456e-a8f4-f52d98bda0eb,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,3175.8,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,25/12/2022,2022-12-25T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/315a3d59-28ac-456e-a8f4-f52d98bda0eb\"""
+transaction,0a744a39-f174-46f8-a319-72a9ff49d247,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,3375.8,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,25/12/2022,2022-12-25T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,a3669631-200b-4515-9394-20d0dd3d3c1f,posted,Wdl ATM CBA ATM  MANLY B        NSW 219702   AUS,-200,070c1d68-0ee0-477a-9679-294ea7059939,3378.3,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,18/12/2022,2022-12-18T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/a3669631-200b-4515-9394-20d0dd3d3c1f\"""
+transaction,bdf88236-e22b-4b03-928a-c78e475f7705,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,3578.3,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,15/12/2022,2022-12-15T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,a084ff4f-7d58-49c3-a9f3-704ee7d916aa,posted,MANLY WEST SCHOOL,-2530.81,070c1d68-0ee0-477a-9679-294ea7059939,3377.3,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,15/12/2022,2022-12-15T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,8d5cb9e3-39ed-4c03-942e-4bc1326d2829,posted,MANLY WEST SCHOOL,-101,070c1d68-0ee0-477a-9679-294ea7059939,5908.11,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,15/12/2022,2022-12-15T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,80d0cd8a-3170-4f77-b9ab-5818843af2e9,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,6009.11,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/12/2022,2022-12-13T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,62c8daa1-da9a-4227-a3eb-05c7cd66feea,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,5808.11,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/12/2022,2022-12-12T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,3bfd5ebe-8945-4bbe-aa36-819cfee1f121,posted,AGL RETAIL ENERGY LTD,-95,070c1d68-0ee0-477a-9679-294ea7059939,6308.11,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,9/12/2022,2022-12-09T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,679660a0-8ee5-460a-bf8f-edacee09b304,posted,AGL RETAIL ENERGY LTD,-78,070c1d68-0ee0-477a-9679-294ea7059939,6403.11,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,9/12/2022,2022-12-09T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,d37e589e-c0af-4522-b207-a519c64fb61c,posted,Transfer Platnm Homeloan 346454,-3912.5,070c1d68-0ee0-477a-9679-294ea7059939,6481.11,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/11/2022,2022-11-28T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,e3535b29-cfe5-4c8f-91fb-12949e83715b,posted,TFR From Transaction to CC Acc 13,-9346,070c1d68-0ee0-477a-9679-294ea7059939,10393.61,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/11/2022,2022-11-27T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,572916d7-eb3b-4656-b5d0-13fb4cdd1524,posted,Payroll WFRMS 15439393,17098,070c1d68-0ee0-477a-9679-294ea7059939,19739.61,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/11/2022,2022-11-27T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,9cf42451-de4a-494d-8133-78be85ef4a4b,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,2641.61,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,15/11/2022,2022-11-15T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,60c1ca71-1eb7-4ac7-9b53-c99d8b7a290b,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,2440.61,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/11/2022,2022-11-14T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/60c1ca71-1eb7-4ac7-9b53-c99d8b7a290b\"""
+transaction,a63a9d5b-f98d-46cc-b88f-43b20d07434a,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,2640.61,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/11/2022,2022-11-14T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,7c4bc206-9afc-4aca-a661-e01c34e40ba8,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-500,070c1d68-0ee0-477a-9679-294ea7059939,2643.11,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/11/2022,2022-11-14T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/7c4bc206-9afc-4aca-a661-e01c34e40ba8\"""
+transaction,429b55ce-d04e-4142-9dd7-add457afc847,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,3143.11,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/11/2022,2022-11-14T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,e8c9c275-8700-4aa1-9edb-93a33ed3589d,posted,MANLY WEST SCHOOL,-2530.81,070c1d68-0ee0-477a-9679-294ea7059939,3145.61,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/11/2022,2022-11-14T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,ebbf2bf9-6142-45a9-bad3-4acab91db622,posted,MANLY WEST SCHOOL,-101,070c1d68-0ee0-477a-9679-294ea7059939,5676.42,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/11/2022,2022-11-14T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,0fca9914-d026-4f49-b122-03bbe2aef60d,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,5777.42,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/11/2022,2022-11-12T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,d72c17f1-cb68-49d8-ac90-4e8cfa862dfc,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,5576.42,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/11/2022,2022-11-11T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,d176ac7d-c5a0-49d4-bd06-b8adc10b9028,posted,AGL RETAIL ENERGY LTD,-95,070c1d68-0ee0-477a-9679-294ea7059939,6076.42,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,8/11/2022,2022-11-08T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
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+transaction,73e09d4a-eabe-4fb1-b95b-d95753322a42,posted,V9193 04/07 FOREIGN CURRENCY TRAN FEE     Ref: 74973807186,-2.02,070c1d68-0ee0-477a-9679-294ea7059939,6249.42,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,3/11/2022,2022-11-03T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,8ad93bb2-f202-408b-b2db-99592a3ba93d,posted,V9193 04/07 FOREIGN CURRENCY TRAN FEE     Ref: 74974007186,-1.31,070c1d68-0ee0-477a-9679-294ea7059939,6251.44,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,3/11/2022,2022-11-03T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,49788910-9c61-4b72-a103-85441ea6b680,posted,Transfer Platnm Homeloan 346454,-3920,070c1d68-0ee0-477a-9679-294ea7059939,6252.75,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/10/2022,2022-10-28T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,b4baec80-264c-4ea5-a2b3-28a0f601f4cf,posted,TFR From Transaction to CC Acc 13,-9338.5,070c1d68-0ee0-477a-9679-294ea7059939,10172.75,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/10/2022,2022-10-27T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,f0a3da07-f77d-4881-9386-cbe5d72e51b4,posted,Payroll WFRMS 15439393,15985,070c1d68-0ee0-477a-9679-294ea7059939,19511.25,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/10/2022,2022-10-27T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,cf24f795-323f-4df6-8705-25e157b54f4e,posted,Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,3526.25,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,18/10/2022,2022-10-18T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/cf24f795-323f-4df6-8705-25e157b54f4e\"""
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+transaction,ccc7cf56-bc5c-4130-84da-edd888036813,posted,MANLY WEST SCHOOL,-2530.81,070c1d68-0ee0-477a-9679-294ea7059939,3527.75,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,17/10/2022,2022-10-17T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,55af5e4f-5ef1-4ffe-9650-07e7ed053da9,posted,MANLY WEST SCHOOL,-101,070c1d68-0ee0-477a-9679-294ea7059939,6058.56,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,17/10/2022,2022-10-17T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,ea19ebe1-87d7-43c1-80cf-1dacd83f15a4,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,6159.56,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/10/2022,2022-10-13T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,dbff00b8-6fde-4a83-9522-a948e3eb0035,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,5958.56,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/10/2022,2022-10-12T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,e032bda9-1c0a-47d3-9f0f-0588424a1f89,posted,AGL RETAIL ENERGY LTD (GAS),-95,070c1d68-0ee0-477a-9679-294ea7059939,6458.56,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/10/2022,2022-10-11T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,4618ef43-67d7-43a7-98ba-f93be3505677,posted,AGL RETAIL ENERGY LTD (GAS),-78,070c1d68-0ee0-477a-9679-294ea7059939,6553.56,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/10/2022,2022-10-11T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,acbec3d2-a932-43e0-bc12-3c26d45e3bd2,posted,Transfer Platnm Homeloan 346454,-3927.5,070c1d68-0ee0-477a-9679-294ea7059939,6631.56,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/09/2022,2022-09-28T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,3b3b98cc-bfd7-468e-840b-f4ce9c2a01d4,posted,TFR From Transaction to CC Acc 13,-9331,070c1d68-0ee0-477a-9679-294ea7059939,10559.06,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/09/2022,2022-09-27T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,1b9aad04-5271-4714-b1d7-2f8c1883e4c4,posted,Payroll WFRMS 15439393,15985,070c1d68-0ee0-477a-9679-294ea7059939,19890.06,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/09/2022,2022-09-27T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,56d93429-dee0-49a6-8350-2bf40671c8d8,posted,Wdl ATM BOQ BP Connect BalgowlaBalgowlahNS    AU,-900,070c1d68-0ee0-477a-9679-294ea7059939,3905.06,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,25/09/2022,2022-09-25T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/56d93429-dee0-49a6-8350-2bf40671c8d8\"""
+transaction,6c23e85a-dbe4-4ae3-a0a0-aacdfa0a4224,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,4805.06,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,25/09/2022,2022-09-25T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,bef47dda-f657-48a3-9e75-a03ef2815253,posted,MANLY WEST SCHOOL,-31,070c1d68-0ee0-477a-9679-294ea7059939,4807.56,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,25/09/2022,2022-09-25T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,a24f8cbe-0eb1-4463-8de2-c76eccba54be,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,4838.56,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/09/2022,2022-09-14T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,b2d80403-be36-46ed-87ca-c35d6ffd9cd6,posted,MANLY WEST SCHOOL,-2530.81,070c1d68-0ee0-477a-9679-294ea7059939,4637.56,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/09/2022,2022-09-14T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,3900db8b-3871-483f-8301-d318d3a9bb86,posted,MANLY WEST SCHOOL,-101,070c1d68-0ee0-477a-9679-294ea7059939,7168.37,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/09/2022,2022-09-14T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,4ab3b32f-c109-4da4-95f6-15f82c4189ac,posted,MANLY WEST SCHOOL,-70.69,070c1d68-0ee0-477a-9679-294ea7059939,7269.37,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/09/2022,2022-09-12T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,1232cd68-fbe4-455c-9970-9b8fb433654a,posted,Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU,-500,070c1d68-0ee0-477a-9679-294ea7059939,7340.06,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/09/2022,2022-09-12T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/1232cd68-fbe4-455c-9970-9b8fb433654a\"""
+transaction,295b0279-0c49-44a4-a1c2-2a0a024e287b,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,7840.06,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/09/2022,2022-09-12T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,1c452615-16d6-4947-bfa4-2896d4de5707,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,7842.56,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/09/2022,2022-09-12T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,7b8ca779-9832-4246-82e5-cfcf2b018fb1,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,7641.56,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/09/2022,2022-09-11T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,4fb3ae73-e089-4301-9ddd-4e1d0aeb297b,posted,AGL RETAIL ENERGY LTD (GAS),-95,070c1d68-0ee0-477a-9679-294ea7059939,8141.56,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,8/09/2022,2022-09-08T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,917ca690-59e8-4c08-b97c-db881b318731,posted,AGL RETAIL ENERGY LTD (GAS),-69,070c1d68-0ee0-477a-9679-294ea7059939,8236.56,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,8/09/2022,2022-09-08T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,c6fb51c0-fcff-463b-8e53-aaf3a10bb90e,posted,Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,8305.56,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/09/2022,2022-09-07T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/c6fb51c0-fcff-463b-8e53-aaf3a10bb90e\"""
+transaction,e11785ce-e82c-44ec-89fd-d40d6f8bf141,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,8505.56,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/09/2022,2022-09-07T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,9e876d17-2d29-433c-b6a7-2807549120ea,posted,MEDIC ALERT,-36.35,070c1d68-0ee0-477a-9679-294ea7059939,8508.06,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,1/09/2022,2022-09-01T00:00:00Z,"{\title\"":\""Other Machinery and Equipment Wholesaling\""","\""code\"":\""349\""}"""
+transaction,57f45596-3277-4d8d-bb02-253f70a05d8c,posted,Wdl ATM CBA ATM  BALGOWLAH A    NSW 210901   AUS,-400,070c1d68-0ee0-477a-9679-294ea7059939,8544.41,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,31/08/2022,2022-08-31T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/57f45596-3277-4d8d-bb02-253f70a05d8c\"""
+transaction,a422deed-be40-451e-b48a-86b0a835a222,posted,Transfer Platnm Homeloan 346454,-3935,070c1d68-0ee0-477a-9679-294ea7059939,8944.41,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/08/2022,2022-08-28T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,0c0997ce-f1ea-436a-9323-7f3b74f7d923,posted,TFR From Transaction to CC Acc 13,-9323.5,070c1d68-0ee0-477a-9679-294ea7059939,12879.41,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/08/2022,2022-08-27T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,db612ca6-20fe-4478-af7b-0bb858ca122a,posted,Payroll WFRMS 15439393,15985,070c1d68-0ee0-477a-9679-294ea7059939,22202.91,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/08/2022,2022-08-27T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,097c304f-b09d-4266-ad4b-fbb0ac6ed68d,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,6217.91,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,17/08/2022,2022-08-17T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,836c4dfb-88c9-48d2-b8ce-fa7ff36499d7,posted,MANLY WEST SCHOOL,-2530.81,070c1d68-0ee0-477a-9679-294ea7059939,6016.91,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,16/08/2022,2022-08-16T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,35a0061c-1567-4763-beba-93974897abf9,posted,MANLY WEST SCHOOL,-101,070c1d68-0ee0-477a-9679-294ea7059939,8547.72,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,16/08/2022,2022-08-16T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,f2b7baa0-eca7-4a90-9876-022732097d98,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,8648.72,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/08/2022,2022-08-13T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,73189bf4-4c0c-4207-8a19-8b3b3dc28a08,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,8447.72,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/08/2022,2022-08-12T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,1dd272f5-6c58-465b-8ab6-015344cd19a9,posted,AGL RETAIL ENERGY LTD,-160,070c1d68-0ee0-477a-9679-294ea7059939,8947.72,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,9/08/2022,2022-08-09T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,67ab5af7-d851-4b01-a858-683a12bb2bf4,posted,AGL RETAIL ENERGY LTD (GAS),-69,070c1d68-0ee0-477a-9679-294ea7059939,9107.72,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,9/08/2022,2022-08-09T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,acf03d71-894a-494f-9bf3-a1160ce5a8ac,posted,Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,9176.72,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,3/08/2022,2022-08-03T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/acf03d71-894a-494f-9bf3-a1160ce5a8ac\"""
+transaction,93c5d592-1b45-47fc-891d-54e3eca900aa,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,9376.72,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,3/08/2022,2022-08-03T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,ea93880b-31cc-4be3-ae4e-5f6d20efa543,posted,Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,9379.22,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,3/08/2022,2022-08-03T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/ea93880b-31cc-4be3-ae4e-5f6d20efa543\"""
+transaction,43208918-2e36-4379-aa52-816aa609401e,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,9579.22,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,3/08/2022,2022-08-03T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,3dc2ec2a-9524-446d-a7ce-d102b0affda9,posted,Transfer Platnm Homeloan 346454,-3942.5,070c1d68-0ee0-477a-9679-294ea7059939,9581.72,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,29/07/2022,2022-07-29T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,b1350666-6edb-4b10-bc51-6da9143504b7,posted,TFR From Transaction to CC Acc 13,-9316,070c1d68-0ee0-477a-9679-294ea7059939,13524.22,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/07/2022,2022-07-28T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,8fbf5f95-1841-4cb5-aee4-c3480d918262,posted,Payroll WFRMS 15439393,15985,070c1d68-0ee0-477a-9679-294ea7059939,22840.22,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/07/2022,2022-07-28T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,de890a88-4cf4-4b0f-9f01-7ad22cbb867a,posted,CTRLINK CARERS Ref: 998R6789201610974V,61,070c1d68-0ee0-477a-9679-294ea7059939,6855.22,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/07/2022,2022-07-28T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,59c5fe04-af69-4e16-a287-c828453da377,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,6794.22,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,17/07/2022,2022-07-17T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,4606cb79-a73d-4eb0-a84a-cfeb0b84db0a,posted,MANLY WEST SCHOOL,-2530.81,070c1d68-0ee0-477a-9679-294ea7059939,6593.22,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,15/07/2022,2022-07-15T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,3566b1fa-e743-4319-b694-cf5c39c544bd,posted,MANLY WEST SCHOOL,-101,070c1d68-0ee0-477a-9679-294ea7059939,9124.03,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,15/07/2022,2022-07-15T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,8c9fcd11-ac87-4828-97b9-7678fb9b7c89,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,9225.03,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/07/2022,2022-07-13T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,fb915385-cfc3-4b51-816c-4bd3b52e596f,posted,MANLY WEST SCHOOL,-26,070c1d68-0ee0-477a-9679-294ea7059939,9024.03,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/07/2022,2022-07-12T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,f454e2db-dbd3-4336-a180-3a140519f038,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,9050.03,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,12/07/2022,2022-07-12T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,bc55cadd-f1ca-40d2-a1d1-5f89195bafb8,posted,AGL RETAIL ENERGY LTD,-160,070c1d68-0ee0-477a-9679-294ea7059939,9550.03,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/07/2022,2022-07-11T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,13e41e9c-c154-45f1-a85f-e3ed838cfc6b,posted,AGL RETAIL ENERGY LTD (GAS),-69,070c1d68-0ee0-477a-9679-294ea7059939,9710.03,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/07/2022,2022-07-11T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,961110b0-d016-431e-b76b-14d9c8a8e288,posted,CTRLINK CARERS Ref: 998R6789201610974V,14,070c1d68-0ee0-477a-9679-294ea7059939,9779.03,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,6/07/2022,2022-07-06T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,2ab79b95-8a27-4266-8012-c7106cd46d81,posted,Wdl ATM CBA ATM  SEAFORTH       NSW 225101   AUS,-750,070c1d68-0ee0-477a-9679-294ea7059939,9765.03,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,3/07/2022,2022-07-03T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/2ab79b95-8a27-4266-8012-c7106cd46d81\"""
+transaction,a6c203a1-4adf-4e8d-a054-4a26f50b19b8,posted,Transfer Platnm Homeloan 346454,-3950,070c1d68-0ee0-477a-9679-294ea7059939,10515.03,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,28/06/2022,2022-06-28T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,8d3bf474-d04e-48f7-9b22-947556e44f28,posted,TFR From Transaction to CC Acc 13,-9308.5,070c1d68-0ee0-477a-9679-294ea7059939,14465.03,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/06/2022,2022-06-27T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,6caa5971-4e99-4329-b82b-ba6b8edc5133,posted,Payroll WFRMS 15439393,15985,070c1d68-0ee0-477a-9679-294ea7059939,23773.53,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/06/2022,2022-06-27T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,49203edf-3758-4db5-a3f5-f3bd6a47c8ec,posted,MANLY WEST SCHOOL,-2533.56,070c1d68-0ee0-477a-9679-294ea7059939,7788.53,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/06/2022,2022-06-27T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,31ca2f55-c341-447b-9a81-c00eb925636b,posted,MANLY WEST SCHOOL,-103.75,070c1d68-0ee0-477a-9679-294ea7059939,10322.09,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,27/06/2022,2022-06-27T00:00:00Z,"{\title\"":\""School Education\""","\""code\"":\""802\""}"""
+transaction,56ed3c4b-88bc-425e-9105-0e5ce157aa54,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,10425.84,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,19/06/2022,2022-06-19T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,31958121-4752-4e3c-ac75-97dc541df3d7,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,10224.84,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,15/06/2022,2022-06-15T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,1e359899-06cd-48be-8b45-31f4cfbf0998,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,10023.84,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,16/05/2022,2022-06-14T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,9a3814e8-c12a-47c7-a467-8e8b62532ec6,posted,AGL RETAIL ENERGY LTD,-160,070c1d68-0ee0-477a-9679-294ea7059939,10523.84,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/06/2022,2022-06-13T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,a4ef6948-bc5e-495a-bc67-2b0123459b57,posted,AGL RETAIL ENERGY LTD (GAS),-69,070c1d68-0ee0-477a-9679-294ea7059939,10683.84,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/06/2022,2022-06-13T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,69a89726-9c99-4e37-9d67-36e679ba7851,posted,CTRLINK CARERS Ref: 998R6789201610974V,752.4,070c1d68-0ee0-477a-9679-294ea7059939,10752.84,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,9/06/2022,2022-06-09T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,6ea9a089-043a-4f0f-ad80-cee9c66e731b,posted,Transfer Platnm Homeloan 346454,-3957.5,070c1d68-0ee0-477a-9679-294ea7059939,10000.44,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,31/05/2022,2022-05-31T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,e8375d96-10be-46ab-8a82-2c61605aaffc,posted,CTRLINK CARERS Ref: 998R6789201610974V,261.78,070c1d68-0ee0-477a-9679-294ea7059939,13957.94,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,31/05/2022,2022-05-31T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,e8ab4144-c266-4bae-8878-08056292aef9,posted,TFR From Transaction to CC Acc 13,-9301,070c1d68-0ee0-477a-9679-294ea7059939,13696.16,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,30/05/2022,2022-05-30T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,f71aa57c-ddbe-4805-897d-81c133d72239,posted,Payroll WFRMS 15439393,15985,070c1d68-0ee0-477a-9679-294ea7059939,22997.16,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,30/05/2022,2022-05-30T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,a8842522-fc0a-4b79-a85d-4437511e01ae,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,7012.16,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,17/05/2022,2022-05-17T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,d3e5cd4d-cb33-4ab0-aaa2-4027762b6611,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,6811.16,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,15/05/2022,2022-05-15T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,cf21b11b-9be2-464e-b2e8-574840276adc,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,6610.16,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/05/2022,2022-05-14T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,78b3be1b-4fd7-4fc7-9561-62365570b319,posted,AGL RETAIL ENERGY LTD,-160,070c1d68-0ee0-477a-9679-294ea7059939,7110.16,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/05/2022,2022-05-11T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,63028cbd-42c2-4f45-bc7e-dae4c135831e,posted,AGL RETAIL ENERGY LTD (GAS),-69,070c1d68-0ee0-477a-9679-294ea7059939,7270.16,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/05/2022,2022-05-11T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,7d86d624-14d3-42ea-a00a-ad3cac8036ce,posted,Transfer Platnm Homeloan 346454,-3965,070c1d68-0ee0-477a-9679-294ea7059939,7339.16,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,30/04/2022,2022-04-30T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,c64a8892-b51e-4720-96d7-1a33dd140e3b,posted,TFR From Transaction to CC Acc 13,-9293.5,070c1d68-0ee0-477a-9679-294ea7059939,11304.16,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,29/04/2022,2022-04-29T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,f8370a2f-e894-4ee8-ab4d-474e5744e349,posted,Payroll WFRMS 15439393,15985,070c1d68-0ee0-477a-9679-294ea7059939,20597.66,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,29/04/2022,2022-04-29T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
+transaction,2da800d4-d0a4-4c90-8ba4-018351ff7b0b,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,4612.66,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,18/04/2022,2022-04-18T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,4f45a037-33c1-4301-bfc9-490927257eab,posted,Manly Maths Tutor Wages,201,070c1d68-0ee0-477a-9679-294ea7059939,4411.66,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,14/04/2022,2022-04-14T00:00:00Z,"{\title\"":\""Educational Support Services\""","\""code\"":\""822\""}"""
+transaction,8eaedc76-de2f-4ea5-bfb4-5da5bdaccedf,posted,TFR Acc14000 TO 12389,-500,070c1d68-0ee0-477a-9679-294ea7059939,4210.66,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,13/04/2022,2022-04-13T00:00:00Z,"{\title\"":\""Legal and Accounting Services\""","\""code\"":\""693\""}"""
+transaction,307a484f-94e2-4ef7-959f-aa05f3db8720,posted,AGL RETAIL ENERGY LTD,-160,070c1d68-0ee0-477a-9679-294ea7059939,4710.66,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/04/2022,2022-04-11T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,e973beb5-99dd-427d-b11c-c7b7c3e30503,posted,AGL RETAIL ENERGY LTD (GAS),-69,070c1d68-0ee0-477a-9679-294ea7059939,4870.66,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,11/04/2022,2022-04-11T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,2dbfa683-412d-42f3-9fad-c1cafdf63327,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,4939.66,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/04/2022,2022-04-07T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/2dbfa683-412d-42f3-9fad-c1cafdf63327\"""
+transaction,8fb5f001-6228-4f93-8dea-35fc10351a21,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,5139.66,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/04/2022,2022-04-07T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,42e4c47e-4f76-429a-981d-c65adec252dc,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,5142.16,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/04/2022,2022-04-07T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/42e4c47e-4f76-429a-981d-c65adec252dc\"""
+transaction,05c0ced1-8883-4fd9-861c-0065defadb6a,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,5342.16,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/04/2022,2022-04-07T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,90d85046-2813-4d01-9df2-4a51250cf667,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-200,070c1d68-0ee0-477a-9679-294ea7059939,5344.66,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/04/2022,2022-04-07T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/90d85046-2813-4d01-9df2-4a51250cf667\"""
+transaction,848d1832-2d76-4353-8d2e-fac0003e2da3,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,5544.66,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,7/04/2022,2022-04-07T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,767006a0-98ae-4b57-8a30-99f60bde2e52,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-450,070c1d68-0ee0-477a-9679-294ea7059939,5547.16,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,4/04/2022,2022-04-04T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/767006a0-98ae-4b57-8a30-99f60bde2e52\"""
+transaction,55047046-dc28-4a1a-a37a-81cd0cc10bf1,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,5997.16,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,4/04/2022,2022-04-04T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,d754e878-de08-461e-a835-c82360e11ce7,posted,Wdl ATM WESTPAC IGA BALGOWLAH  BALGOWL        AU,-300,070c1d68-0ee0-477a-9679-294ea7059939,5999.66,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,4/04/2022,2022-04-04T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/d754e878-de08-461e-a835-c82360e11ce7\"""
+transaction,0d2d0484-45ec-483d-800b-9f5b55484b7c,posted,Non Hooli ATM Withdrawal Fee,-2.5,070c1d68-0ee0-477a-9679-294ea7059939,6299.66,debit,bank-fee,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,4/04/2022,2022-04-04T00:00:00Z,"{\title\"":\""\""","\""code\"":\""card\""}"""
+transaction,9d46d1fe-384a-49ae-9b87-83e18d68c7db,posted,Wdl ATM CBA ATM  BALGOWLAH A    NSW 210901   AUS,-200,070c1d68-0ee0-477a-9679-294ea7059939,6302.16,debit,cash-withdrawal,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,4/04/2022,2022-04-04T00:00:00Z,null,"{\self\"":\""https://au-api.basiq.io/users/bca4b9cc-b7e2-4458-83bc-4855c147bd70/transactions/9d46d1fe-384a-49ae-9b87-83e18d68c7db\"""
+transaction,d7a77217-eb85-404c-97af-051fb96f7468,posted,Transfer Platnm Homeloan 346454,-3972.5,070c1d68-0ee0-477a-9679-294ea7059939,6502.16,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,30/03/2022,2022-03-30T00:00:00Z,"{\title\"":\""Auxiliary Finance and Investment Services\""","\""code\"":\""641\""}"""
+transaction,ccd6f0ee-6b5f-479b-84b5-cd6068a1237e,posted,TFR From Transaction to CC Acc 13,-9286,070c1d68-0ee0-477a-9679-294ea7059939,10474.66,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,29/03/2022,2022-03-29T00:00:00Z,"{\title\"":\""Civic","Professional and Other Interest Group Services\"""
+transaction,c336eeb3-2a0f-4079-919c-ac45d7edcb57,posted,Payroll WFRMS 15439393,15985,070c1d68-0ee0-477a-9679-294ea7059939,19760.66,credit,transfer,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,29/03/2022,2022-03-29T00:00:00Z,"{\title\"":\""Unknown\""","\""code\"":\""0\""}"""
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+transaction,414279bc-9226-45c5-9722-b18269374373,posted,AGL RETAIL ENERGY LTD (GAS),-160,070c1d68-0ee0-477a-9679-294ea7059939,-259.34,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,10/12/2021,2021-12-10T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
+transaction,312885c2-6ff6-4d96-89c9-0bf1b2987f46,posted,AGL RETAIL ENERGY LTD (GAS),-92,070c1d68-0ee0-477a-9679-294ea7059939,-99.34,debit,payment,AU00000,1bd6c08e-8457-4a74-bdd9-514df489d27e,null,10/12/2021,2021-12-10T00:00:00Z,"{\title\"":\""Electricity Distribution\""","\""code\"":\""263\""}"""
diff --git a/SavingsAI/random forrest savings model.ipynb b/SavingsAI/random forrest savings model.ipynb
new file mode 100644
index 0000000..16815d4
--- /dev/null
+++ b/SavingsAI/random forrest savings model.ipynb	
@@ -0,0 +1,1551 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import GridSearchCV, train_test_split, cross_val_score\n",
+    "from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge, QuantileRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"Dummy Data/sample_data.csv\")\n",
+    "df = df.sort_values(by=\"postDate\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"postDate\"] = pd.to_datetime(df[\"postDate\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Year\"] = df[\"postDate\"].apply(lambda time: time.year)\n",
+    "\n",
+    "df[\"Month\"] = df[\"postDate\"].apply(lambda time: time.month)\n",
+    "\n",
+    "df[\"Day\"] = df[\"postDate\"].apply(lambda time: time.day)\n",
+    "\n",
+    "df[\"Hour\"] = df[\"postDate\"].apply(lambda time: time.hour)\n",
+    "\n",
+    "df[\"Minute\"] = df[\"postDate\"].apply(lambda time: time.minute)\n",
+    "\n",
+    "df[\"Second\"] = df[\"postDate\"].apply(lambda time: time.second)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</style>\n",
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+       "  <thead>\n",
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+       "      <th></th>\n",
+       "      <th>type</th>\n",
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+       "      <td>AGL RETAIL ENERGY LTD (GAS)</td>\n",
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+       "      <td>transaction</td>\n",
+       "      <td>1973ec3d-7680-45dd-ac2e-e1eb97c97ea3</td>\n",
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+       "      <td>Manly Maths Tutor Wages</td>\n",
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+       "      <td>AU00000</td>\n",
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+       "      <td>2021-12-16 00:00:00+00:00</td>\n",
+       "      <td>{\\title\\\":\\\"Educational Support Services\\\"</td>\n",
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+       "      <th>4</th>\n",
+       "      <td>transaction</td>\n",
+       "      <td>d12df6a2-48b6-4f5f-a718-ccb377aa330c</td>\n",
+       "      <td>posted</td>\n",
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+       "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
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+       "      <td>payment</td>\n",
+       "      <td>AU00000</td>\n",
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+       "    <tr>\n",
+       "      <th>0</th>\n",
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+       "      <td>3/08/2023</td>\n",
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+       "      <td>{\"account\":\"https://au-api.basiq.io/users/6a52...</td>\n",
+       "      <td>2023</td>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>289 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            type                                    id  status  \\\n",
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+       "\n",
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+       "284                           Manly Maths Tutor Wages   201.0   \n",
+       "..                                                ...     ...   \n",
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+       "1                        Non Hooli ATM Withdrawal Fee    -2.5   \n",
+       "0    Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU  -200.0   \n",
+       "\n",
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+       "..                                    ...       ...       ...   \n",
+       "4    070c1d68-0ee0-477a-9679-294ea7059939  22514.56     debit   \n",
+       "3    070c1d68-0ee0-477a-9679-294ea7059939  22512.06     debit   \n",
+       "2    070c1d68-0ee0-477a-9679-294ea7059939  22312.06     debit   \n",
+       "1    070c1d68-0ee0-477a-9679-294ea7059939  22309.56     debit   \n",
+       "0                                d3de1ca1  22109.56     debit   \n",
+       "\n",
+       "               class institution  ... transactionDate  \\\n",
+       "288          payment     AU00000  ...      10/12/2021   \n",
+       "287          payment     AU00000  ...      10/12/2021   \n",
+       "286          payment     AU00000  ...      13/12/2021   \n",
+       "285         transfer     AU00000  ...      14/12/2021   \n",
+       "284         transfer     AU00000  ...      26/12/2021   \n",
+       "..               ...         ...  ...             ...   \n",
+       "4            payment     AU00000  ...      29/07/2023   \n",
+       "3           bank-fee     AU00000  ...       3/08/2023   \n",
+       "2    cash-withdrawal     AU00000  ...       3/08/2023   \n",
+       "1           bank-fee     AU00000  ...       3/08/2023   \n",
+       "0    cash-withdrawal     AU00000  ...       3/08/2023   \n",
+       "\n",
+       "                      postDate  \\\n",
+       "288  2021-12-10 00:00:00+00:00   \n",
+       "287  2021-12-10 00:00:00+00:00   \n",
+       "286  2021-12-13 00:00:00+00:00   \n",
+       "285  2021-12-14 00:00:00+00:00   \n",
+       "284  2021-12-16 00:00:00+00:00   \n",
+       "..                         ...   \n",
+       "4    2023-07-29 00:00:00+00:00   \n",
+       "3    2023-08-03 00:00:00+00:00   \n",
+       "2    2023-08-03 00:00:00+00:00   \n",
+       "1    2023-08-03 00:00:00+00:00   \n",
+       "0          2023-08-03 00:07:36   \n",
+       "\n",
+       "                                              subClass  \\\n",
+       "288             {\\title\\\":\\\"Electricity Distribution\\\"   \n",
+       "287             {\\title\\\":\\\"Electricity Distribution\\\"   \n",
+       "286        {\\title\\\":\\\"Legal and Accounting Services\\\"   \n",
+       "285         {\\title\\\":\\\"Educational Support Services\\\"   \n",
+       "284         {\\title\\\":\\\"Educational Support Services\\\"   \n",
+       "..                                                 ...   \n",
+       "4    {\\title\\\":\\\"Auxiliary Finance and Investment S...   \n",
+       "3                                       {\\title\\\":\\\"\\\"   \n",
+       "2                                                  NaN   \n",
+       "1                                       {\\title\\\":\\\"\\\"   \n",
+       "0                                                  NaN   \n",
+       "\n",
+       "                                                 links  Year Month  Day  Hour  \\\n",
+       "288                                 \\\"code\\\":\\\"263\\\"}\"  2021    12   10     0   \n",
+       "287                                 \\\"code\\\":\\\"263\\\"}\"  2021    12   10     0   \n",
+       "286                                 \\\"code\\\":\\\"693\\\"}\"  2021    12   13     0   \n",
+       "285                                 \\\"code\\\":\\\"822\\\"}\"  2021    12   14     0   \n",
+       "284                                 \\\"code\\\":\\\"822\\\"}\"  2021    12   16     0   \n",
+       "..                                                 ...   ...   ...  ...   ...   \n",
+       "4                                   \\\"code\\\":\\\"641\\\"}\"  2023     7   29     0   \n",
+       "3                                  \\\"code\\\":\\\"card\\\"}\"  2023     8    3     0   \n",
+       "2    {\\self\\\":\\\"https://au-api.basiq.io/users/bca4b...  2023     8    3     0   \n",
+       "1                                  \\\"code\\\":\\\"card\\\"}\"  2023     8    3     0   \n",
+       "0    {\"account\":\"https://au-api.basiq.io/users/6a52...  2023     8    3     0   \n",
+       "\n",
+       "     Minute  Second  \n",
+       "288       0       0  \n",
+       "287       0       0  \n",
+       "286       0       0  \n",
+       "285       0       0  \n",
+       "284       0       0  \n",
+       "..      ...     ...  \n",
+       "4         0       0  \n",
+       "3         0       0  \n",
+       "2         0       0  \n",
+       "1         0       0  \n",
+       "0         7      36  \n",
+       "\n",
+       "[289 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop unnecessary columns\n",
+    "df.drop([\"type\", \"id\", \"status\", \"description\", \"account\", \"direction\", \"class\", \"institution\", \"connection\", \"enrich\", \"transactionDate\", \"postDate\", \"subClass\", \"links\"], axis=1, inplace=True)\n",
+    "\n",
+    "# Handle missing values (if any)\n",
+    "df.dropna(inplace=True)\n",
+    "\n",
+    "# Split the data into features (X) and target (y)\n",
+    "X = df[[\"amount\", \"Year\", \"Month\", \"Day\", \"Hour\", \"Minute\", \"Second\"]]\n",
+    "y = df[[\"balance\"]]\n",
+    "\n",
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = RandomForestRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n",
+      "[CV 1/5; 1/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 1/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-1493402.778 total time=   0.0s\n",
+      "[CV 2/5; 1/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 1/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2750005.758 total time=   0.0s\n",
+      "[CV 3/5; 1/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 1/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-5310391.691 total time=   0.0s\n",
+      "[CV 4/5; 1/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 1/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2747839.631 total time=   0.0s\n",
+      "[CV 5/5; 1/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 1/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2740293.576 total time=   0.0s\n",
+      "[CV 1/5; 2/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 2/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-1496368.775 total time=   0.1s\n",
+      "[CV 2/5; 2/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 2/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2596092.958 total time=   0.1s\n",
+      "[CV 3/5; 2/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 2/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-4928806.941 total time=   0.1s\n",
+      "[CV 4/5; 2/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 2/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2394061.243 total time=   0.1s\n",
+      "[CV 5/5; 2/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 2/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2312933.049 total time=   0.1s\n",
+      "[CV 1/5; 3/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 3/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-1419002.978 total time=   0.2s\n",
+      "[CV 2/5; 3/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 3/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2529619.247 total time=   0.2s\n",
+      "[CV 3/5; 3/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 3/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-4937640.309 total time=   0.2s\n",
+      "[CV 4/5; 3/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 3/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2525517.028 total time=   0.2s\n",
+      "[CV 5/5; 3/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 3/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2368631.156 total time=   0.2s\n",
+      "[CV 1/5; 4/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 4/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-1840145.549 total time=   0.0s\n",
+      "[CV 2/5; 4/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 4/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-3941048.311 total time=   0.0s\n",
+      "[CV 3/5; 4/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 4/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-6383640.844 total time=   0.0s\n",
+      "[CV 4/5; 4/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 4/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-2692294.121 total time=   0.0s\n",
+      "[CV 5/5; 4/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 4/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-4102034.444 total time=   0.0s\n",
+      "[CV 1/5; 5/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 5/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-1805122.762 total time=   0.1s\n",
+      "[CV 2/5; 5/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 5/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-3014625.017 total time=   0.1s\n",
+      "[CV 3/5; 5/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 5/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-6525079.574 total time=   0.1s\n",
+      "[CV 4/5; 5/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 5/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-2870465.188 total time=   0.1s\n",
+      "[CV 5/5; 5/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 5/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-3904485.170 total time=   0.1s\n",
+      "[CV 1/5; 6/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 6/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-1753689.156 total time=   0.1s\n",
+      "[CV 2/5; 6/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 6/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-2962084.387 total time=   0.1s\n",
+      "[CV 3/5; 6/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 6/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-6336992.704 total time=   0.2s\n",
+      "[CV 4/5; 6/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 6/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-2874510.367 total time=   0.1s\n",
+      "[CV 5/5; 6/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 6/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-3920707.873 total time=   0.1s\n",
+      "[CV 1/5; 7/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 7/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-2509165.350 total time=   0.0s\n",
+      "[CV 2/5; 7/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 7/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-4195604.261 total time=   0.0s\n",
+      "[CV 3/5; 7/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 7/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-9173050.988 total time=   0.0s\n",
+      "[CV 4/5; 7/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 7/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-3451923.985 total time=   0.0s\n",
+      "[CV 5/5; 7/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 7/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-6006056.970 total time=   0.0s\n",
+      "[CV 1/5; 8/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 8/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-2143375.610 total time=   0.1s\n",
+      "[CV 2/5; 8/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 8/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-4299446.307 total time=   0.0s\n",
+      "[CV 3/5; 8/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 8/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-8639693.060 total time=   0.1s\n",
+      "[CV 4/5; 8/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 8/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-3721174.793 total time=   0.0s\n",
+      "[CV 5/5; 8/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 8/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-5830153.882 total time=   0.1s\n",
+      "[CV 1/5; 9/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 9/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-2105206.307 total time=   0.1s\n",
+      "[CV 2/5; 9/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 9/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-4293538.685 total time=   0.1s\n",
+      "[CV 3/5; 9/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 9/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-8803493.693 total time=   0.1s\n",
+      "[CV 4/5; 9/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 9/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-3532328.266 total time=   0.1s\n",
+      "[CV 5/5; 9/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 9/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-5594827.430 total time=   0.1s\n",
+      "[CV 1/5; 10/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 10/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-1876215.858 total time=   0.0s\n",
+      "[CV 2/5; 10/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 10/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-2964099.561 total time=   0.0s\n",
+      "[CV 3/5; 10/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 10/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-6301099.747 total time=   0.0s\n",
+      "[CV 4/5; 10/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 10/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-2512242.085 total time=   0.0s\n",
+      "[CV 5/5; 10/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 10/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-3696921.509 total time=   0.0s\n",
+      "[CV 1/5; 11/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 11/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-2010601.430 total time=   0.1s\n",
+      "[CV 2/5; 11/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 11/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-2882896.760 total time=   0.1s\n",
+      "[CV 3/5; 11/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 11/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-6095140.275 total time=   0.1s\n",
+      "[CV 4/5; 11/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 11/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-2795932.970 total time=   0.1s\n",
+      "[CV 5/5; 11/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 11/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-3828886.532 total time=   0.1s\n",
+      "[CV 1/5; 12/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 12/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-1907845.364 total time=   0.1s\n",
+      "[CV 2/5; 12/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 12/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-2926750.187 total time=   0.2s\n",
+      "[CV 3/5; 12/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 12/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-6305383.776 total time=   0.2s\n",
+      "[CV 4/5; 12/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 12/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-2479278.986 total time=   0.2s\n",
+      "[CV 5/5; 12/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 12/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-3782521.262 total time=   0.2s\n",
+      "[CV 1/5; 13/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 13/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-2349198.835 total time=   0.0s\n",
+      "[CV 2/5; 13/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 13/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-3694356.150 total time=   0.0s\n",
+      "[CV 3/5; 13/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 13/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-7043366.687 total time=   0.0s\n",
+      "[CV 4/5; 13/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 13/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-2876993.079 total time=   0.0s\n",
+      "[CV 5/5; 13/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 13/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-4269065.312 total time=   0.0s\n",
+      "[CV 1/5; 14/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 14/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-2171661.195 total time=   0.1s\n",
+      "[CV 2/5; 14/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 14/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-3262489.781 total time=   0.1s\n",
+      "[CV 3/5; 14/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 14/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-6469971.389 total time=   0.1s\n",
+      "[CV 4/5; 14/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 14/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-2813928.399 total time=   0.1s\n",
+      "[CV 5/5; 14/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 14/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-4139265.396 total time=   0.1s\n",
+      "[CV 1/5; 15/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 15/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-2071552.766 total time=   0.1s\n",
+      "[CV 2/5; 15/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 15/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-3481549.212 total time=   0.1s\n",
+      "[CV 3/5; 15/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 15/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-6825376.927 total time=   0.1s\n",
+      "[CV 4/5; 15/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 15/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-2639414.647 total time=   0.1s\n",
+      "[CV 5/5; 15/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 15/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-4288743.718 total time=   0.1s\n",
+      "[CV 1/5; 16/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 16/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-2378780.030 total time=   0.0s\n",
+      "[CV 2/5; 16/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 16/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-4334288.139 total time=   0.0s\n",
+      "[CV 3/5; 16/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 16/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-8600489.220 total time=   0.0s\n",
+      "[CV 4/5; 16/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 16/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-3688305.170 total time=   0.0s\n",
+      "[CV 5/5; 16/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 16/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-5806074.784 total time=   0.0s\n",
+      "[CV 1/5; 17/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 17/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-2191149.033 total time=   0.1s\n",
+      "[CV 2/5; 17/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 17/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-4528502.633 total time=   0.1s\n",
+      "[CV 3/5; 17/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 17/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-8837158.372 total time=   0.1s\n",
+      "[CV 4/5; 17/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 17/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-3590169.157 total time=   0.0s\n",
+      "[CV 5/5; 17/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 17/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-5820377.497 total time=   0.0s\n",
+      "[CV 1/5; 18/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 18/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-2381478.487 total time=   0.1s\n",
+      "[CV 2/5; 18/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 18/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-4321764.943 total time=   0.1s\n",
+      "[CV 3/5; 18/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 18/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-8528247.692 total time=   0.1s\n",
+      "[CV 4/5; 18/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 18/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-3814581.630 total time=   0.1s\n",
+      "[CV 5/5; 18/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 18/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-5626121.690 total time=   0.1s\n",
+      "[CV 1/5; 19/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 19/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-2374117.976 total time=   0.0s\n",
+      "[CV 2/5; 19/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 19/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-4520294.058 total time=   0.0s\n",
+      "[CV 3/5; 19/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 19/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-9082949.130 total time=   0.0s\n",
+      "[CV 4/5; 19/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 19/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-4198571.423 total time=   0.0s\n",
+      "[CV 5/5; 19/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 19/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-5512735.448 total time=   0.0s\n",
+      "[CV 1/5; 20/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 20/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-2461452.697 total time=   0.0s\n",
+      "[CV 2/5; 20/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 20/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-4581327.622 total time=   0.1s\n",
+      "[CV 3/5; 20/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 20/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-9383936.192 total time=   0.1s\n",
+      "[CV 4/5; 20/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 20/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-4135248.094 total time=   0.1s\n",
+      "[CV 5/5; 20/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 20/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-5986956.112 total time=   0.1s\n",
+      "[CV 1/5; 21/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 21/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-2301804.550 total time=   0.1s\n",
+      "[CV 2/5; 21/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 21/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-4595011.445 total time=   0.1s\n",
+      "[CV 3/5; 21/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 21/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-8798886.267 total time=   0.2s\n",
+      "[CV 4/5; 21/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 21/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-4255982.974 total time=   0.1s\n",
+      "[CV 5/5; 21/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 21/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-5983131.281 total time=   0.1s\n",
+      "[CV 1/5; 22/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 22/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-2362429.026 total time=   0.0s\n",
+      "[CV 2/5; 22/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 22/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-4960556.908 total time=   0.0s\n",
+      "[CV 3/5; 22/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 22/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-9288360.107 total time=   0.0s\n",
+      "[CV 4/5; 22/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 22/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-4450417.496 total time=   0.0s\n",
+      "[CV 5/5; 22/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 22/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-5920687.167 total time=   0.0s\n",
+      "[CV 1/5; 23/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 23/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-2254473.069 total time=   0.1s\n",
+      "[CV 2/5; 23/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 23/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-4643777.236 total time=   0.1s\n",
+      "[CV 3/5; 23/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 23/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-8867270.852 total time=   0.1s\n",
+      "[CV 4/5; 23/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 23/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-4411057.171 total time=   0.0s\n",
+      "[CV 5/5; 23/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 23/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-5695005.125 total time=   0.1s\n",
+      "[CV 1/5; 24/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 24/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-2306370.602 total time=   0.1s\n",
+      "[CV 2/5; 24/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 24/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-4557703.325 total time=   0.1s\n",
+      "[CV 3/5; 24/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 24/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-8771592.616 total time=   0.1s\n",
+      "[CV 4/5; 24/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 24/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-4276237.999 total time=   0.1s\n",
+      "[CV 5/5; 24/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 24/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-5853814.567 total time=   0.1s\n",
+      "[CV 1/5; 25/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 25/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-2539661.839 total time=   0.0s\n",
+      "[CV 2/5; 25/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 25/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-4884760.136 total time=   0.0s\n",
+      "[CV 3/5; 25/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 25/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-9182908.353 total time=   0.0s\n",
+      "[CV 4/5; 25/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 25/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-4318155.042 total time=   0.0s\n",
+      "[CV 5/5; 25/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 25/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-6097153.511 total time=   0.0s\n",
+      "[CV 1/5; 26/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 26/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-2451114.732 total time=   0.0s\n",
+      "[CV 2/5; 26/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 26/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-4774984.041 total time=   0.1s\n",
+      "[CV 3/5; 26/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 26/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-9151134.499 total time=   0.1s\n",
+      "[CV 4/5; 26/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 26/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-4545120.851 total time=   0.0s\n",
+      "[CV 5/5; 26/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 26/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-5893008.813 total time=   0.1s\n",
+      "[CV 1/5; 27/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 27/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-2479328.669 total time=   0.1s\n",
+      "[CV 2/5; 27/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 27/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-4840317.179 total time=   0.1s\n",
+      "[CV 3/5; 27/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 27/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-9719609.684 total time=   0.1s\n",
+      "[CV 4/5; 27/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 27/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-4591330.267 total time=   0.1s\n",
+      "[CV 5/5; 27/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 27/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-6390299.625 total time=   0.1s\n",
+      "[CV 1/5; 28/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 28/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-1404857.934 total time=   0.0s\n",
+      "[CV 2/5; 28/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 28/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2394298.087 total time=   0.0s\n",
+      "[CV 3/5; 28/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 28/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-5129238.585 total time=   0.0s\n",
+      "[CV 4/5; 28/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 28/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2554567.718 total time=   0.0s\n",
+      "[CV 5/5; 28/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 28/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2505905.140 total time=   0.0s\n",
+      "[CV 1/5; 29/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 29/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-1422505.332 total time=   0.1s\n",
+      "[CV 2/5; 29/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 29/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2444795.675 total time=   0.1s\n",
+      "[CV 3/5; 29/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 29/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-5021190.193 total time=   0.1s\n",
+      "[CV 4/5; 29/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 29/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2584575.950 total time=   0.1s\n",
+      "[CV 5/5; 29/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 29/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2491680.906 total time=   0.1s\n",
+      "[CV 1/5; 30/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 30/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-1433487.097 total time=   0.2s\n",
+      "[CV 2/5; 30/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 30/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2542683.044 total time=   0.2s\n",
+      "[CV 3/5; 30/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 30/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-4969822.376 total time=   0.2s\n",
+      "[CV 4/5; 30/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 30/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2477421.725 total time=   0.2s\n",
+      "[CV 5/5; 30/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 30/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2496189.284 total time=   0.2s\n",
+      "[CV 1/5; 31/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 31/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-1853747.958 total time=   0.0s\n",
+      "[CV 2/5; 31/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 31/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-3189702.186 total time=   0.0s\n",
+      "[CV 3/5; 31/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 31/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-6544549.356 total time=   0.0s\n",
+      "[CV 4/5; 31/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 31/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-2577271.331 total time=   0.0s\n",
+      "[CV 5/5; 31/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 31/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-3903079.386 total time=   0.0s\n",
+      "[CV 1/5; 32/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 32/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-1816812.809 total time=   0.1s\n",
+      "[CV 2/5; 32/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 32/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-3152393.029 total time=   0.1s\n",
+      "[CV 3/5; 32/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 32/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-6452415.889 total time=   0.1s\n",
+      "[CV 4/5; 32/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 32/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-2898142.093 total time=   0.1s\n",
+      "[CV 5/5; 32/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 32/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-3744507.190 total time=   0.1s\n",
+      "[CV 1/5; 33/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 33/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-2011416.197 total time=   0.2s\n",
+      "[CV 2/5; 33/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 33/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-2990783.654 total time=   0.1s\n",
+      "[CV 3/5; 33/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 33/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-6416164.852 total time=   0.2s\n",
+      "[CV 4/5; 33/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 33/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-2673837.105 total time=   0.1s\n",
+      "[CV 5/5; 33/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 33/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-3842677.045 total time=   0.2s\n",
+      "[CV 1/5; 34/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 34/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-2225716.562 total time=   0.0s\n",
+      "[CV 2/5; 34/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 34/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-4044574.792 total time=   0.0s\n",
+      "[CV 3/5; 34/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 34/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-8699907.278 total time=   0.0s\n",
+      "[CV 4/5; 34/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 34/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-3674477.759 total time=   0.0s\n",
+      "[CV 5/5; 34/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 34/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-5691508.091 total time=   0.0s\n",
+      "[CV 1/5; 35/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 35/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-2284714.457 total time=   0.1s\n",
+      "[CV 2/5; 35/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 35/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-4144365.211 total time=   0.1s\n",
+      "[CV 3/5; 35/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 35/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-8780152.577 total time=   0.0s\n",
+      "[CV 4/5; 35/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 35/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-3605747.573 total time=   0.1s\n",
+      "[CV 5/5; 35/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 35/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-5603469.158 total time=   0.1s\n",
+      "[CV 1/5; 36/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 36/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-2295267.677 total time=   0.1s\n",
+      "[CV 2/5; 36/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 36/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-4203551.909 total time=   0.1s\n",
+      "[CV 3/5; 36/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 36/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-8466137.662 total time=   0.1s\n",
+      "[CV 4/5; 36/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 36/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-3453709.840 total time=   0.1s\n",
+      "[CV 5/5; 36/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 36/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-5662423.192 total time=   0.1s\n",
+      "[CV 1/5; 37/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 37/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-2088111.699 total time=   0.0s\n",
+      "[CV 2/5; 37/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 37/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-2826814.170 total time=   0.0s\n",
+      "[CV 3/5; 37/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 37/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-6425600.613 total time=   0.0s\n",
+      "[CV 4/5; 37/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 37/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-2930163.989 total time=   0.0s\n",
+      "[CV 5/5; 37/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 37/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-3625491.565 total time=   0.0s\n",
+      "[CV 1/5; 38/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 38/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-1877014.480 total time=   0.1s\n",
+      "[CV 2/5; 38/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 38/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-3250664.987 total time=   0.1s\n",
+      "[CV 3/5; 38/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 38/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-6223106.167 total time=   0.1s\n",
+      "[CV 4/5; 38/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 38/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-2794833.376 total time=   0.1s\n",
+      "[CV 5/5; 38/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 38/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-3594023.828 total time=   0.1s\n",
+      "[CV 1/5; 39/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 39/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-1888385.876 total time=   0.1s\n",
+      "[CV 2/5; 39/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 39/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-3056692.958 total time=   0.1s\n",
+      "[CV 3/5; 39/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 39/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-6390406.744 total time=   0.2s\n",
+      "[CV 4/5; 39/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 39/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-2540730.512 total time=   0.1s\n",
+      "[CV 5/5; 39/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 39/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-3582576.894 total time=   0.1s\n",
+      "[CV 1/5; 40/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 40/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-2231447.111 total time=   0.0s\n",
+      "[CV 2/5; 40/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 40/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-3425616.891 total time=   0.0s\n",
+      "[CV 3/5; 40/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 40/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-7370805.375 total time=   0.0s\n",
+      "[CV 4/5; 40/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 40/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-2995313.345 total time=   0.0s\n",
+      "[CV 5/5; 40/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 40/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-4233828.274 total time=   0.0s\n",
+      "[CV 1/5; 41/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 41/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-1927857.284 total time=   0.1s\n",
+      "[CV 2/5; 41/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 41/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-3348989.832 total time=   0.1s\n",
+      "[CV 3/5; 41/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 41/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-6611179.068 total time=   0.1s\n",
+      "[CV 4/5; 41/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 41/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-2724016.301 total time=   0.1s\n",
+      "[CV 5/5; 41/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 41/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-4124312.085 total time=   0.1s\n",
+      "[CV 1/5; 42/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 42/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-2136789.889 total time=   0.1s\n",
+      "[CV 2/5; 42/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 42/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-3280183.701 total time=   0.1s\n",
+      "[CV 3/5; 42/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 42/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-6979050.614 total time=   0.1s\n",
+      "[CV 4/5; 42/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 42/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-2714924.802 total time=   0.1s\n",
+      "[CV 5/5; 42/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 42/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-4404054.371 total time=   0.1s\n",
+      "[CV 1/5; 43/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 43/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-2469132.382 total time=   0.0s\n",
+      "[CV 2/5; 43/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 43/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-4322613.386 total time=   0.0s\n",
+      "[CV 3/5; 43/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 43/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-8521262.580 total time=   0.0s\n",
+      "[CV 4/5; 43/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 43/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-3841363.922 total time=   0.0s\n",
+      "[CV 5/5; 43/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 43/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-5785715.233 total time=   0.0s\n",
+      "[CV 1/5; 44/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 44/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-2330431.082 total time=   0.0s\n",
+      "[CV 2/5; 44/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 44/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-4300544.592 total time=   0.0s\n",
+      "[CV 3/5; 44/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 44/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-8682119.214 total time=   0.0s\n",
+      "[CV 4/5; 44/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 44/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-3625717.485 total time=   0.0s\n",
+      "[CV 5/5; 44/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 44/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-5874735.358 total time=   0.0s\n",
+      "[CV 1/5; 45/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 45/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-2389134.502 total time=   0.1s\n",
+      "[CV 2/5; 45/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 45/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-4512117.133 total time=   0.1s\n",
+      "[CV 3/5; 45/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 45/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-8430115.077 total time=   0.1s\n",
+      "[CV 4/5; 45/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 45/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-3664299.554 total time=   0.1s\n",
+      "[CV 5/5; 45/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 45/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-5825148.569 total time=   0.2s\n",
+      "[CV 1/5; 46/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 46/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-2457137.314 total time=   0.0s\n",
+      "[CV 2/5; 46/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 46/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-4843628.559 total time=   0.0s\n",
+      "[CV 3/5; 46/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 46/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-9748007.016 total time=   0.0s\n",
+      "[CV 4/5; 46/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 46/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-4616961.652 total time=   0.0s\n",
+      "[CV 5/5; 46/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 46/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-5900710.145 total time=   0.0s\n",
+      "[CV 1/5; 47/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 47/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-2336570.980 total time=   0.1s\n",
+      "[CV 2/5; 47/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 47/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-4760874.488 total time=   0.0s\n",
+      "[CV 3/5; 47/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 47/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-8915376.194 total time=   0.1s\n",
+      "[CV 4/5; 47/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 47/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-4526590.634 total time=   0.0s\n",
+      "[CV 5/5; 47/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 47/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-6004628.785 total time=   0.1s\n",
+      "[CV 1/5; 48/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 48/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-2375650.924 total time=   0.1s\n",
+      "[CV 2/5; 48/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 48/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-4597000.020 total time=   0.1s\n",
+      "[CV 3/5; 48/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 48/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-9094563.025 total time=   0.1s\n",
+      "[CV 4/5; 48/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 48/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-4395906.696 total time=   0.1s\n",
+      "[CV 5/5; 48/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 48/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-5817221.959 total time=   0.1s\n",
+      "[CV 1/5; 49/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 49/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-2433832.479 total time=   0.0s\n",
+      "[CV 2/5; 49/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 49/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-5035902.284 total time=   0.0s\n",
+      "[CV 3/5; 49/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 49/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-9469212.201 total time=   0.0s\n",
+      "[CV 4/5; 49/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 49/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-4345456.148 total time=   0.0s\n",
+      "[CV 5/5; 49/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 49/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-5851051.509 total time=   0.0s\n",
+      "[CV 1/5; 50/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 50/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-2380775.406 total time=   0.1s\n",
+      "[CV 2/5; 50/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 50/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-4738411.753 total time=   0.1s\n",
+      "[CV 3/5; 50/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 50/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-8937773.602 total time=   0.1s\n",
+      "[CV 4/5; 50/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 50/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-4401972.368 total time=   0.1s\n",
+      "[CV 5/5; 50/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 50/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-5544569.498 total time=   0.1s\n",
+      "[CV 1/5; 51/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 51/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-2332160.411 total time=   0.1s\n",
+      "[CV 2/5; 51/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 51/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-4758147.684 total time=   0.1s\n",
+      "[CV 3/5; 51/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 51/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-8716376.385 total time=   0.1s\n",
+      "[CV 4/5; 51/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 51/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-4389424.573 total time=   0.2s\n",
+      "[CV 5/5; 51/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 51/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-6186010.293 total time=   0.2s\n",
+      "[CV 1/5; 52/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 52/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-2602299.086 total time=   0.0s\n",
+      "[CV 2/5; 52/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 52/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-5040662.441 total time=   0.0s\n",
+      "[CV 3/5; 52/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 52/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-9623295.751 total time=   0.0s\n",
+      "[CV 4/5; 52/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 52/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-4672669.722 total time=   0.0s\n",
+      "[CV 5/5; 52/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 52/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-6397985.502 total time=   0.0s\n",
+      "[CV 1/5; 53/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 53/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-2436856.881 total time=   0.1s\n",
+      "[CV 2/5; 53/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 53/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-4819283.579 total time=   0.1s\n",
+      "[CV 3/5; 53/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 53/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-9398619.385 total time=   0.1s\n",
+      "[CV 4/5; 53/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 53/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-4319891.504 total time=   0.1s\n",
+      "[CV 5/5; 53/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 53/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-6139660.756 total time=   0.1s\n",
+      "[CV 1/5; 54/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 54/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-2437369.233 total time=   0.1s\n",
+      "[CV 2/5; 54/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 54/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-4818437.840 total time=   0.1s\n",
+      "[CV 3/5; 54/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 54/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-9469143.240 total time=   0.1s\n",
+      "[CV 4/5; 54/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 54/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-4445199.083 total time=   0.1s\n",
+      "[CV 5/5; 54/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 54/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-6054686.027 total time=   0.1s\n",
+      "[CV 1/5; 55/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 55/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-1522632.505 total time=   0.0s\n",
+      "[CV 2/5; 55/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 55/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2622997.222 total time=   0.0s\n",
+      "[CV 3/5; 55/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 55/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-5197832.561 total time=   0.0s\n",
+      "[CV 4/5; 55/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 55/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2363710.171 total time=   0.0s\n",
+      "[CV 5/5; 55/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 55/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2304188.350 total time=   0.0s\n",
+      "[CV 1/5; 56/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 56/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-1493320.837 total time=   0.1s\n",
+      "[CV 2/5; 56/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 56/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2595480.762 total time=   0.1s\n",
+      "[CV 3/5; 56/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 56/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-5017004.844 total time=   0.1s\n",
+      "[CV 4/5; 56/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 56/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2406621.570 total time=   0.1s\n",
+      "[CV 5/5; 56/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 56/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2312787.231 total time=   0.1s\n",
+      "[CV 1/5; 57/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 57/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-1392114.556 total time=   0.2s\n",
+      "[CV 2/5; 57/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 57/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2668332.860 total time=   0.2s\n",
+      "[CV 3/5; 57/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 57/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-5040127.420 total time=   0.2s\n",
+      "[CV 4/5; 57/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 57/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2436760.873 total time=   0.2s\n",
+      "[CV 5/5; 57/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 57/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2656136.826 total time=   0.2s\n",
+      "[CV 1/5; 58/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 58/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-1871264.975 total time=   0.0s\n",
+      "[CV 2/5; 58/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 58/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-2745168.424 total time=   0.0s\n",
+      "[CV 3/5; 58/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 58/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-6546987.620 total time=   0.0s\n",
+      "[CV 4/5; 58/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 58/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-2893055.736 total time=   0.0s\n",
+      "[CV 5/5; 58/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 58/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-3718122.365 total time=   0.0s\n",
+      "[CV 1/5; 59/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 59/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-1804995.542 total time=   0.1s\n",
+      "[CV 2/5; 59/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 59/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-3131835.005 total time=   0.1s\n",
+      "[CV 3/5; 59/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 59/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-6576874.979 total time=   0.1s\n",
+      "[CV 4/5; 59/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 59/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-2781179.286 total time=   0.1s\n",
+      "[CV 5/5; 59/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 59/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-3771878.879 total time=   0.1s\n",
+      "[CV 1/5; 60/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 60/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-1757698.024 total time=   0.2s\n",
+      "[CV 2/5; 60/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 60/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-2923551.305 total time=   0.2s\n",
+      "[CV 3/5; 60/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 60/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-6597271.838 total time=   0.2s\n",
+      "[CV 4/5; 60/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 60/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-2777036.559 total time=   0.2s\n",
+      "[CV 5/5; 60/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 60/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-3908512.000 total time=   0.2s\n",
+      "[CV 1/5; 61/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 61/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-2340652.343 total time=   0.0s\n",
+      "[CV 2/5; 61/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 61/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-4234904.923 total time=   0.0s\n",
+      "[CV 3/5; 61/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 61/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-9167303.308 total time=   0.0s\n",
+      "[CV 4/5; 61/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 61/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-3590950.218 total time=   0.0s\n",
+      "[CV 5/5; 61/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 61/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-6059543.427 total time=   0.0s\n",
+      "[CV 1/5; 62/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 62/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-2322492.941 total time=   0.1s\n",
+      "[CV 2/5; 62/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 62/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-4068594.363 total time=   0.1s\n",
+      "[CV 3/5; 62/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 62/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-8505464.766 total time=   0.1s\n",
+      "[CV 4/5; 62/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 62/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-3549009.335 total time=   0.1s\n",
+      "[CV 5/5; 62/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 62/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-5499965.723 total time=   0.1s\n",
+      "[CV 1/5; 63/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 63/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-2308982.970 total time=   0.1s\n",
+      "[CV 2/5; 63/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 63/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-4217323.407 total time=   0.1s\n",
+      "[CV 3/5; 63/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 63/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-8579596.754 total time=   0.1s\n",
+      "[CV 4/5; 63/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 63/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-3468000.555 total time=   0.1s\n",
+      "[CV 5/5; 63/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 63/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-5710202.437 total time=   0.1s\n",
+      "[CV 1/5; 64/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 64/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-1792152.700 total time=   0.0s\n",
+      "[CV 2/5; 64/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 64/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-2714379.451 total time=   0.0s\n",
+      "[CV 3/5; 64/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 64/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-6094780.005 total time=   0.0s\n",
+      "[CV 4/5; 64/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 64/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-2585323.912 total time=   0.0s\n",
+      "[CV 5/5; 64/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 64/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-3549744.436 total time=   0.0s\n",
+      "[CV 1/5; 65/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 65/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-2060479.578 total time=   0.1s\n",
+      "[CV 2/5; 65/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 65/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-3141610.263 total time=   0.1s\n",
+      "[CV 3/5; 65/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 65/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-6161299.416 total time=   0.1s\n",
+      "[CV 4/5; 65/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 65/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-2751210.485 total time=   0.1s\n",
+      "[CV 5/5; 65/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 65/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-3505681.680 total time=   0.1s\n",
+      "[CV 1/5; 66/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 66/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-1825925.821 total time=   0.1s\n",
+      "[CV 2/5; 66/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 66/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-2875418.295 total time=   0.1s\n",
+      "[CV 3/5; 66/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 66/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-6322623.775 total time=   0.2s\n",
+      "[CV 4/5; 66/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 66/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-2684852.637 total time=   0.2s\n",
+      "[CV 5/5; 66/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 66/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-3537577.789 total time=   0.2s\n",
+      "[CV 1/5; 67/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 67/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-1990128.515 total time=   0.0s\n",
+      "[CV 2/5; 67/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 67/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-3200841.343 total time=   0.0s\n",
+      "[CV 3/5; 67/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 67/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-6886191.116 total time=   0.0s\n",
+      "[CV 4/5; 67/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 67/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-2730828.062 total time=   0.0s\n",
+      "[CV 5/5; 67/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 67/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-4111211.345 total time=   0.0s\n",
+      "[CV 1/5; 68/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 68/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-2130734.025 total time=   0.1s\n",
+      "[CV 2/5; 68/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 68/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-3504664.583 total time=   0.1s\n",
+      "[CV 3/5; 68/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 68/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-6902168.005 total time=   0.1s\n",
+      "[CV 4/5; 68/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 68/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-2873493.372 total time=   0.1s\n",
+      "[CV 5/5; 68/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 68/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-4044493.806 total time=   0.1s\n",
+      "[CV 1/5; 69/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 69/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-2108440.562 total time=   0.1s\n",
+      "[CV 2/5; 69/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 69/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-3310901.316 total time=   0.1s\n",
+      "[CV 3/5; 69/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 69/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-6609343.229 total time=   0.1s\n",
+      "[CV 4/5; 69/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 69/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-2743035.393 total time=   0.2s\n",
+      "[CV 5/5; 69/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 69/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-4231282.186 total time=   0.2s\n",
+      "[CV 1/5; 70/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 70/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-2257077.740 total time=   0.0s\n",
+      "[CV 2/5; 70/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 70/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-4510419.410 total time=   0.0s\n",
+      "[CV 3/5; 70/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 70/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-8331260.043 total time=   0.0s\n",
+      "[CV 4/5; 70/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 70/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-3447285.839 total time=   0.0s\n",
+      "[CV 5/5; 70/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 70/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-6031546.717 total time=   0.0s\n",
+      "[CV 1/5; 71/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 71/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-2187350.901 total time=   0.1s\n",
+      "[CV 2/5; 71/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 71/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-4416361.876 total time=   0.1s\n",
+      "[CV 3/5; 71/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 71/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-8370732.370 total time=   0.1s\n",
+      "[CV 4/5; 71/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 71/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-3788809.529 total time=   0.1s\n",
+      "[CV 5/5; 71/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 71/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-5702316.729 total time=   0.1s\n",
+      "[CV 1/5; 72/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 72/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-2259412.629 total time=   0.1s\n",
+      "[CV 2/5; 72/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 72/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-4184100.724 total time=   0.1s\n",
+      "[CV 3/5; 72/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 72/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-8841639.658 total time=   0.1s\n",
+      "[CV 4/5; 72/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 72/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-3595737.008 total time=   0.1s\n",
+      "[CV 5/5; 72/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 72/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-5783939.973 total time=   0.2s\n",
+      "[CV 1/5; 73/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 73/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-2468380.898 total time=   0.0s\n",
+      "[CV 2/5; 73/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 73/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-4830021.364 total time=   0.0s\n",
+      "[CV 3/5; 73/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 73/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-8302006.398 total time=   0.0s\n",
+      "[CV 4/5; 73/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 73/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-4468858.924 total time=   0.0s\n",
+      "[CV 5/5; 73/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 73/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-5845330.969 total time=   0.0s\n",
+      "[CV 1/5; 74/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 74/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-2319276.920 total time=   0.1s\n",
+      "[CV 2/5; 74/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 74/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-4688519.886 total time=   0.1s\n",
+      "[CV 3/5; 74/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 74/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-8548601.425 total time=   0.1s\n",
+      "[CV 4/5; 74/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 74/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-4388957.149 total time=   0.1s\n",
+      "[CV 5/5; 74/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 74/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-5734983.790 total time=   0.1s\n",
+      "[CV 1/5; 75/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 75/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-2325365.802 total time=   0.1s\n",
+      "[CV 2/5; 75/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 75/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-4754953.217 total time=   0.2s\n",
+      "[CV 3/5; 75/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 75/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-9193975.360 total time=   0.1s\n",
+      "[CV 4/5; 75/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 75/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-4285933.238 total time=   0.1s\n",
+      "[CV 5/5; 75/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 75/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-5828800.816 total time=   0.2s\n",
+      "[CV 1/5; 76/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 76/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-2419932.194 total time=   0.0s\n",
+      "[CV 2/5; 76/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 76/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-4960857.593 total time=   0.0s\n",
+      "[CV 3/5; 76/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 76/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-8290513.458 total time=   0.0s\n",
+      "[CV 4/5; 76/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 76/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-4513477.179 total time=   0.0s\n",
+      "[CV 5/5; 76/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 76/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-5990879.062 total time=   0.0s\n",
+      "[CV 1/5; 77/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 77/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-2347744.404 total time=   0.1s\n",
+      "[CV 2/5; 77/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 77/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-4488678.416 total time=   0.1s\n",
+      "[CV 3/5; 77/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 77/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-8824173.485 total time=   0.1s\n",
+      "[CV 4/5; 77/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 77/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-4308000.319 total time=   0.1s\n",
+      "[CV 5/5; 77/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 77/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-6030549.580 total time=   0.1s\n",
+      "[CV 1/5; 78/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 78/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-2320048.657 total time=   0.1s\n",
+      "[CV 2/5; 78/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 78/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-4805967.487 total time=   0.2s\n",
+      "[CV 3/5; 78/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 78/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-9068806.161 total time=   0.2s\n",
+      "[CV 4/5; 78/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 78/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-4354969.645 total time=   0.1s\n",
+      "[CV 5/5; 78/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 78/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-5893096.800 total time=   0.2s\n",
+      "[CV 1/5; 79/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 79/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-2488687.488 total time=   0.0s\n",
+      "[CV 2/5; 79/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 79/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-4940879.856 total time=   0.0s\n",
+      "[CV 3/5; 79/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 79/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-8915486.626 total time=   0.0s\n",
+      "[CV 4/5; 79/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 79/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-4307381.769 total time=   0.0s\n",
+      "[CV 5/5; 79/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 79/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-6334559.644 total time=   0.0s\n",
+      "[CV 1/5; 80/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 80/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-2407408.471 total time=   0.1s\n",
+      "[CV 2/5; 80/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 80/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-4931269.940 total time=   0.1s\n",
+      "[CV 3/5; 80/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 80/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-9639498.469 total time=   0.1s\n",
+      "[CV 4/5; 80/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 80/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-4821884.748 total time=   0.1s\n",
+      "[CV 5/5; 80/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 80/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-6391560.037 total time=   0.1s\n",
+      "[CV 1/5; 81/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 81/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-2501672.396 total time=   0.1s\n",
+      "[CV 2/5; 81/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 81/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-4958982.743 total time=   0.2s\n",
+      "[CV 3/5; 81/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 81/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-9157851.809 total time=   0.1s\n",
+      "[CV 4/5; 81/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 81/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-4496835.000 total time=   0.1s\n",
+      "[CV 5/5; 81/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 81/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-6133967.703 total time=   0.1s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
+       "             param_grid={&#x27;max_depth&#x27;: [None, 10, 20],\n",
+       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
+       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
+       "                         &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
+       "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
+       "             param_grid={&#x27;max_depth&#x27;: [None, 10, 20],\n",
+       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
+       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
+       "                         &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
+       "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=10)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
+       "             param_grid={'max_depth': [None, 10, 20],\n",
+       "                         'min_samples_leaf': [1, 2, 4],\n",
+       "                         'min_samples_split': [2, 5, 10],\n",
+       "                         'n_estimators': [100, 200, 300]},\n",
+       "             scoring='neg_mean_squared_error', verbose=10)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param_grid = {\n",
+    "    'n_estimators': [100, 200, 300],\n",
+    "    'max_depth': [None, 10, 20],\n",
+    "    'min_samples_split': [2, 5, 10],\n",
+    "    'min_samples_leaf': [1, 2, 4]\n",
+    "}\n",
+    "\n",
+    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, n_jobs=1, verbose=10, scoring='neg_mean_squared_error')\n",
+    "grid_search.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 200}\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_params = grid_search.best_params_\n",
+    "best_model = grid_search.best_estimator_\n",
+    "print(best_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error: 1953048.7644503405\n",
+      "R-squared: 0.9117987566088667\n",
+      "Root Mean Squared Error: 1397.5152108117966\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = best_model.predict(X_test)\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "rmse = np.sqrt(mse)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "print(\"Mean Squared Error:\", mse)\n",
+    "print(\"R-squared:\", r2)\n",
+    "print(\"Root Mean Squared Error:\", rmse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model.fit(X_train, y_train)\n",
+    "\n",
+    "# # Predict on the test set\n",
+    "# y_pred = model.predict(X_test)\n",
+    "\n",
+    "# # Evaluate the model\n",
+    "# mse = mean_squared_error(y_test, y_pred)\n",
+    "# r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "# print(\"Mean Squared Error:\", mse)\n",
+    "# print(\"R-squared:\", r2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# models = {\n",
+    "#     \"Linear Regression\": LinearRegression(),\n",
+    "#     \"Decision Tree Regression\": DecisionTreeRegressor(),\n",
+    "#     \"Random Forest Regression\": RandomForestRegressor(),\n",
+    "#     \"Stochastic Gradient Descent\": SGDRegressor(), \n",
+    "#     \"Ridge Regression\": Ridge(), \n",
+    "#     \"Quantile Regression\": QuantileRegressor()\n",
+    "# }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for model_name, model in models.items():\n",
+    "#     model.fit(X_train, y_train)\n",
+    "#     y_pred = model.predict(X_test)\n",
+    "\n",
+    "#     # Evaluate the model\n",
+    "#     mse = mean_squared_error(y_test, y_pred)\n",
+    "#     r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "#     print(f\"Model: {model_name}\")\n",
+    "#     print(\"Mean Squared Error:\", mse)\n",
+    "#     print(\"R-squared:\", r2)\n",
+    "#     print(\"------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Scatter plot for y_test in blue color\n",
+    "plt.scatter(X_test[\"amount\"], y_test, color='blue', label='Actual (y_test)')\n",
+    "\n",
+    "# Scatter plot for y_pred in red color\n",
+    "plt.scatter(X_test[\"amount\"], y_pred, color='red', label='Predicted (y_pred)')\n",
+    "\n",
+    "plt.xlabel(\"Amount\")\n",
+    "plt.ylabel(\"Balance\")\n",
+    "plt.title(\"Actual vs Predicted\")\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/SavingsAI/rf_CV_model.ipynb b/SavingsAI/rf_CV_model.ipynb
new file mode 100644
index 0000000..78a6c3e
--- /dev/null
+++ b/SavingsAI/rf_CV_model.ipynb
@@ -0,0 +1,1587 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import GridSearchCV, train_test_split, cross_val_score\n",
+    "from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge, QuantileRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"Dummy Data/sample_data.csv\")\n",
+    "df = df.sort_values(by=\"postDate\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"postDate\"] = pd.to_datetime(df[\"postDate\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[\"Year\"] = df[\"postDate\"].apply(lambda time: time.year)\n",
+    "\n",
+    "df[\"Month\"] = df[\"postDate\"].apply(lambda time: time.month)\n",
+    "\n",
+    "df[\"Day\"] = df[\"postDate\"].apply(lambda time: time.day)\n",
+    "\n",
+    "df[\"Hour\"] = df[\"postDate\"].apply(lambda time: time.hour)\n",
+    "\n",
+    "df[\"Minute\"] = df[\"postDate\"].apply(lambda time: time.minute)\n",
+    "\n",
+    "df[\"Second\"] = df[\"postDate\"].apply(lambda time: time.second)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>type</th>\n",
+       "      <th>id</th>\n",
+       "      <th>status</th>\n",
+       "      <th>description</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>account</th>\n",
+       "      <th>balance</th>\n",
+       "      <th>direction</th>\n",
+       "      <th>class</th>\n",
+       "      <th>institution</th>\n",
+       "      <th>...</th>\n",
+       "      <th>transactionDate</th>\n",
+       "      <th>postDate</th>\n",
+       "      <th>subClass</th>\n",
+       "      <th>links</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>Day</th>\n",
+       "      <th>Hour</th>\n",
+       "      <th>Minute</th>\n",
+       "      <th>Second</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>288</th>\n",
+       "      <td>transaction</td>\n",
+       "      <td>312885c2-6ff6-4d96-89c9-0bf1b2987f46</td>\n",
+       "      <td>posted</td>\n",
+       "      <td>AGL RETAIL ENERGY LTD (GAS)</td>\n",
+       "      <td>-92.0</td>\n",
+       "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+       "      <td>-99.34</td>\n",
+       "      <td>debit</td>\n",
+       "      <td>payment</td>\n",
+       "      <td>AU00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10/12/2021</td>\n",
+       "      <td>2021-12-10 00:00:00+00:00</td>\n",
+       "      <td>{\\title\\\":\\\"Electricity Distribution\\\"</td>\n",
+       "      <td>\\\"code\\\":\\\"263\\\"}\"</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>12</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>287</th>\n",
+       "      <td>transaction</td>\n",
+       "      <td>414279bc-9226-45c5-9722-b18269374373</td>\n",
+       "      <td>posted</td>\n",
+       "      <td>AGL RETAIL ENERGY LTD (GAS)</td>\n",
+       "      <td>-160.0</td>\n",
+       "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+       "      <td>-259.34</td>\n",
+       "      <td>debit</td>\n",
+       "      <td>payment</td>\n",
+       "      <td>AU00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>10/12/2021</td>\n",
+       "      <td>2021-12-10 00:00:00+00:00</td>\n",
+       "      <td>{\\title\\\":\\\"Electricity Distribution\\\"</td>\n",
+       "      <td>\\\"code\\\":\\\"263\\\"}\"</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>12</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>286</th>\n",
+       "      <td>transaction</td>\n",
+       "      <td>8598b570-b80f-42a6-9a52-e73425b3ccd0</td>\n",
+       "      <td>posted</td>\n",
+       "      <td>TFR Acc14000 TO 12389</td>\n",
+       "      <td>-500.0</td>\n",
+       "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+       "      <td>-759.34</td>\n",
+       "      <td>debit</td>\n",
+       "      <td>payment</td>\n",
+       "      <td>AU00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>13/12/2021</td>\n",
+       "      <td>2021-12-13 00:00:00+00:00</td>\n",
+       "      <td>{\\title\\\":\\\"Legal and Accounting Services\\\"</td>\n",
+       "      <td>\\\"code\\\":\\\"693\\\"}\"</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>285</th>\n",
+       "      <td>transaction</td>\n",
+       "      <td>1973ec3d-7680-45dd-ac2e-e1eb97c97ea3</td>\n",
+       "      <td>posted</td>\n",
+       "      <td>Manly Maths Tutor Wages</td>\n",
+       "      <td>201.0</td>\n",
+       "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+       "      <td>-558.34</td>\n",
+       "      <td>credit</td>\n",
+       "      <td>transfer</td>\n",
+       "      <td>AU00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14/12/2021</td>\n",
+       "      <td>2021-12-14 00:00:00+00:00</td>\n",
+       "      <td>{\\title\\\":\\\"Educational Support Services\\\"</td>\n",
+       "      <td>\\\"code\\\":\\\"822\\\"}\"</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>12</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>transaction</td>\n",
+       "      <td>5b20b38e-6c98-4bfc-97bc-7c9ea2ccc983</td>\n",
+       "      <td>posted</td>\n",
+       "      <td>Manly Maths Tutor Wages</td>\n",
+       "      <td>201.0</td>\n",
+       "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+       "      <td>-357.34</td>\n",
+       "      <td>credit</td>\n",
+       "      <td>transfer</td>\n",
+       "      <td>AU00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26/12/2021</td>\n",
+       "      <td>2021-12-16 00:00:00+00:00</td>\n",
+       "      <td>{\\title\\\":\\\"Educational Support Services\\\"</td>\n",
+       "      <td>\\\"code\\\":\\\"822\\\"}\"</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>12</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>transaction</td>\n",
+       "      <td>d12df6a2-48b6-4f5f-a718-ccb377aa330c</td>\n",
+       "      <td>posted</td>\n",
+       "      <td>Transfer Platnm Homeloan 346454</td>\n",
+       "      <td>-3852.5</td>\n",
+       "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+       "      <td>22514.56</td>\n",
+       "      <td>debit</td>\n",
+       "      <td>payment</td>\n",
+       "      <td>AU00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>29/07/2023</td>\n",
+       "      <td>2023-07-29 00:00:00+00:00</td>\n",
+       "      <td>{\\title\\\":\\\"Auxiliary Finance and Investment S...</td>\n",
+       "      <td>\\\"code\\\":\\\"641\\\"}\"</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>7</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>transaction</td>\n",
+       "      <td>1ab3a3c5-faeb-4de3-b5aa-612e5bc76fd5</td>\n",
+       "      <td>posted</td>\n",
+       "      <td>Non Hooli ATM Withdrawal Fee</td>\n",
+       "      <td>-2.5</td>\n",
+       "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+       "      <td>22512.06</td>\n",
+       "      <td>debit</td>\n",
+       "      <td>bank-fee</td>\n",
+       "      <td>AU00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3/08/2023</td>\n",
+       "      <td>2023-08-03 00:00:00+00:00</td>\n",
+       "      <td>{\\title\\\":\\\"\\\"</td>\n",
+       "      <td>\\\"code\\\":\\\"card\\\"}\"</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>transaction</td>\n",
+       "      <td>323f6d42-a38e-4c46-83d2-6c8f3e999686</td>\n",
+       "      <td>posted</td>\n",
+       "      <td>Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU</td>\n",
+       "      <td>-200.0</td>\n",
+       "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+       "      <td>22312.06</td>\n",
+       "      <td>debit</td>\n",
+       "      <td>cash-withdrawal</td>\n",
+       "      <td>AU00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3/08/2023</td>\n",
+       "      <td>2023-08-03 00:00:00+00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{\\self\\\":\\\"https://au-api.basiq.io/users/bca4b...</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>transaction</td>\n",
+       "      <td>0d6e11ab-e28e-4de0-a152-600cc44fb61c</td>\n",
+       "      <td>posted</td>\n",
+       "      <td>Non Hooli ATM Withdrawal Fee</td>\n",
+       "      <td>-2.5</td>\n",
+       "      <td>070c1d68-0ee0-477a-9679-294ea7059939</td>\n",
+       "      <td>22309.56</td>\n",
+       "      <td>debit</td>\n",
+       "      <td>bank-fee</td>\n",
+       "      <td>AU00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3/08/2023</td>\n",
+       "      <td>2023-08-03 00:00:00+00:00</td>\n",
+       "      <td>{\\title\\\":\\\"\\\"</td>\n",
+       "      <td>\\\"code\\\":\\\"card\\\"}\"</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>transaction</td>\n",
+       "      <td>4957bcf6-f18f-43d7-94df-71f44a0bcf32</td>\n",
+       "      <td>posted</td>\n",
+       "      <td>Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU</td>\n",
+       "      <td>-200.0</td>\n",
+       "      <td>d3de1ca1</td>\n",
+       "      <td>22109.56</td>\n",
+       "      <td>debit</td>\n",
+       "      <td>cash-withdrawal</td>\n",
+       "      <td>AU00000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3/08/2023</td>\n",
+       "      <td>2023-08-03 00:07:36+00:00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>{\"account\":\"https://au-api.basiq.io/users/6a52...</td>\n",
+       "      <td>2023</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>289 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            type                                    id  status  \\\n",
+       "288  transaction  312885c2-6ff6-4d96-89c9-0bf1b2987f46  posted   \n",
+       "287  transaction  414279bc-9226-45c5-9722-b18269374373  posted   \n",
+       "286  transaction  8598b570-b80f-42a6-9a52-e73425b3ccd0  posted   \n",
+       "285  transaction  1973ec3d-7680-45dd-ac2e-e1eb97c97ea3  posted   \n",
+       "284  transaction  5b20b38e-6c98-4bfc-97bc-7c9ea2ccc983  posted   \n",
+       "..           ...                                   ...     ...   \n",
+       "4    transaction  d12df6a2-48b6-4f5f-a718-ccb377aa330c  posted   \n",
+       "3    transaction  1ab3a3c5-faeb-4de3-b5aa-612e5bc76fd5  posted   \n",
+       "2    transaction  323f6d42-a38e-4c46-83d2-6c8f3e999686  posted   \n",
+       "1    transaction  0d6e11ab-e28e-4de0-a152-600cc44fb61c  posted   \n",
+       "0    transaction  4957bcf6-f18f-43d7-94df-71f44a0bcf32  posted   \n",
+       "\n",
+       "                                          description  amount  \\\n",
+       "288                       AGL RETAIL ENERGY LTD (GAS)   -92.0   \n",
+       "287                       AGL RETAIL ENERGY LTD (GAS)  -160.0   \n",
+       "286                             TFR Acc14000 TO 12389  -500.0   \n",
+       "285                           Manly Maths Tutor Wages   201.0   \n",
+       "284                           Manly Maths Tutor Wages   201.0   \n",
+       "..                                                ...     ...   \n",
+       "4                     Transfer Platnm Homeloan 346454 -3852.5   \n",
+       "3                        Non Hooli ATM Withdrawal Fee    -2.5   \n",
+       "2    Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU  -200.0   \n",
+       "1                        Non Hooli ATM Withdrawal Fee    -2.5   \n",
+       "0    Wdl ATM WES IGA BALGOWLAH HGT  BALGOWL        AU  -200.0   \n",
+       "\n",
+       "                                  account   balance direction  \\\n",
+       "288  070c1d68-0ee0-477a-9679-294ea7059939    -99.34     debit   \n",
+       "287  070c1d68-0ee0-477a-9679-294ea7059939   -259.34     debit   \n",
+       "286  070c1d68-0ee0-477a-9679-294ea7059939   -759.34     debit   \n",
+       "285  070c1d68-0ee0-477a-9679-294ea7059939   -558.34    credit   \n",
+       "284  070c1d68-0ee0-477a-9679-294ea7059939   -357.34    credit   \n",
+       "..                                    ...       ...       ...   \n",
+       "4    070c1d68-0ee0-477a-9679-294ea7059939  22514.56     debit   \n",
+       "3    070c1d68-0ee0-477a-9679-294ea7059939  22512.06     debit   \n",
+       "2    070c1d68-0ee0-477a-9679-294ea7059939  22312.06     debit   \n",
+       "1    070c1d68-0ee0-477a-9679-294ea7059939  22309.56     debit   \n",
+       "0                                d3de1ca1  22109.56     debit   \n",
+       "\n",
+       "               class institution  ... transactionDate  \\\n",
+       "288          payment     AU00000  ...      10/12/2021   \n",
+       "287          payment     AU00000  ...      10/12/2021   \n",
+       "286          payment     AU00000  ...      13/12/2021   \n",
+       "285         transfer     AU00000  ...      14/12/2021   \n",
+       "284         transfer     AU00000  ...      26/12/2021   \n",
+       "..               ...         ...  ...             ...   \n",
+       "4            payment     AU00000  ...      29/07/2023   \n",
+       "3           bank-fee     AU00000  ...       3/08/2023   \n",
+       "2    cash-withdrawal     AU00000  ...       3/08/2023   \n",
+       "1           bank-fee     AU00000  ...       3/08/2023   \n",
+       "0    cash-withdrawal     AU00000  ...       3/08/2023   \n",
+       "\n",
+       "                     postDate  \\\n",
+       "288 2021-12-10 00:00:00+00:00   \n",
+       "287 2021-12-10 00:00:00+00:00   \n",
+       "286 2021-12-13 00:00:00+00:00   \n",
+       "285 2021-12-14 00:00:00+00:00   \n",
+       "284 2021-12-16 00:00:00+00:00   \n",
+       "..                        ...   \n",
+       "4   2023-07-29 00:00:00+00:00   \n",
+       "3   2023-08-03 00:00:00+00:00   \n",
+       "2   2023-08-03 00:00:00+00:00   \n",
+       "1   2023-08-03 00:00:00+00:00   \n",
+       "0   2023-08-03 00:07:36+00:00   \n",
+       "\n",
+       "                                              subClass  \\\n",
+       "288             {\\title\\\":\\\"Electricity Distribution\\\"   \n",
+       "287             {\\title\\\":\\\"Electricity Distribution\\\"   \n",
+       "286        {\\title\\\":\\\"Legal and Accounting Services\\\"   \n",
+       "285         {\\title\\\":\\\"Educational Support Services\\\"   \n",
+       "284         {\\title\\\":\\\"Educational Support Services\\\"   \n",
+       "..                                                 ...   \n",
+       "4    {\\title\\\":\\\"Auxiliary Finance and Investment S...   \n",
+       "3                                       {\\title\\\":\\\"\\\"   \n",
+       "2                                                  NaN   \n",
+       "1                                       {\\title\\\":\\\"\\\"   \n",
+       "0                                                  NaN   \n",
+       "\n",
+       "                                                 links  Year Month  Day  Hour  \\\n",
+       "288                                 \\\"code\\\":\\\"263\\\"}\"  2021    12   10     0   \n",
+       "287                                 \\\"code\\\":\\\"263\\\"}\"  2021    12   10     0   \n",
+       "286                                 \\\"code\\\":\\\"693\\\"}\"  2021    12   13     0   \n",
+       "285                                 \\\"code\\\":\\\"822\\\"}\"  2021    12   14     0   \n",
+       "284                                 \\\"code\\\":\\\"822\\\"}\"  2021    12   16     0   \n",
+       "..                                                 ...   ...   ...  ...   ...   \n",
+       "4                                   \\\"code\\\":\\\"641\\\"}\"  2023     7   29     0   \n",
+       "3                                  \\\"code\\\":\\\"card\\\"}\"  2023     8    3     0   \n",
+       "2    {\\self\\\":\\\"https://au-api.basiq.io/users/bca4b...  2023     8    3     0   \n",
+       "1                                  \\\"code\\\":\\\"card\\\"}\"  2023     8    3     0   \n",
+       "0    {\"account\":\"https://au-api.basiq.io/users/6a52...  2023     8    3     0   \n",
+       "\n",
+       "     Minute  Second  \n",
+       "288       0       0  \n",
+       "287       0       0  \n",
+       "286       0       0  \n",
+       "285       0       0  \n",
+       "284       0       0  \n",
+       "..      ...     ...  \n",
+       "4         0       0  \n",
+       "3         0       0  \n",
+       "2         0       0  \n",
+       "1         0       0  \n",
+       "0         7      36  \n",
+       "\n",
+       "[289 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop unnecessary columns\n",
+    "df.drop([\"type\", \"id\", \"status\", \"description\", \"account\", \"direction\", \"class\", \"institution\", \"connection\", \"enrich\", \"transactionDate\", \"postDate\", \"subClass\", \"links\"], axis=1, inplace=True)\n",
+    "\n",
+    "# Handle missing values (if any)\n",
+    "df.dropna(inplace=True)\n",
+    "\n",
+    "# Split the data into features (X) and target (y)\n",
+    "X = df[[\"amount\", \"Year\", \"Month\", \"Day\", \"Hour\", \"Minute\", \"Second\"]]\n",
+    "y = df[[\"balance\"]]\n",
+    "\n",
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = RandomForestRegressor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 81 candidates, totalling 405 fits\n",
+      "[CV 1/5; 1/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 1/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-1486274.140 total time=   0.0s\n",
+      "[CV 2/5; 1/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 1/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2378515.147 total time=   0.0s\n",
+      "[CV 3/5; 1/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 1/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-5172717.531 total time=   0.0s\n",
+      "[CV 4/5; 1/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 1/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2421784.002 total time=   0.0s\n",
+      "[CV 5/5; 1/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 1/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2452441.321 total time=   0.0s\n",
+      "[CV 1/5; 2/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 2/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-1443274.643 total time=   0.1s\n",
+      "[CV 2/5; 2/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 2/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2641389.239 total time=   0.1s\n",
+      "[CV 3/5; 2/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 2/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-4984241.449 total time=   0.1s\n",
+      "[CV 4/5; 2/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 2/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2432877.320 total time=   0.1s\n",
+      "[CV 5/5; 2/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 2/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2432380.885 total time=   0.1s\n",
+      "[CV 1/5; 3/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 3/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-1426695.729 total time=   0.2s\n",
+      "[CV 2/5; 3/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 3/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2361624.367 total time=   0.2s\n",
+      "[CV 3/5; 3/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 3/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-5003866.809 total time=   0.2s\n",
+      "[CV 4/5; 3/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 3/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2530638.939 total time=   0.2s\n",
+      "[CV 5/5; 3/81] START max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 3/81] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2469204.242 total time=   0.2s\n",
+      "[CV 1/5; 4/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 4/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-2084773.639 total time=   0.0s\n",
+      "[CV 2/5; 4/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 4/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-3008264.660 total time=   0.0s\n",
+      "[CV 3/5; 4/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 4/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-6387845.678 total time=   0.0s\n",
+      "[CV 4/5; 4/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 4/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-2699045.221 total time=   0.0s\n",
+      "[CV 5/5; 4/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 4/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-3867873.747 total time=   0.0s\n",
+      "[CV 1/5; 5/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 5/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-1665990.221 total time=   0.1s\n",
+      "[CV 2/5; 5/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 5/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-2775133.273 total time=   0.1s\n",
+      "[CV 3/5; 5/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 5/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-6472082.828 total time=   0.1s\n",
+      "[CV 4/5; 5/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 5/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-2507484.103 total time=   0.1s\n",
+      "[CV 5/5; 5/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 5/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-3880702.469 total time=   0.1s\n",
+      "[CV 1/5; 6/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 6/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-1802474.118 total time=   0.2s\n",
+      "[CV 2/5; 6/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 6/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-3116798.519 total time=   0.2s\n",
+      "[CV 3/5; 6/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 6/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-6596518.449 total time=   0.1s\n",
+      "[CV 4/5; 6/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 6/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-2639416.796 total time=   0.2s\n",
+      "[CV 5/5; 6/81] START max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 6/81] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-3926916.301 total time=   0.1s\n",
+      "[CV 1/5; 7/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 7/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-2515457.433 total time=   0.0s\n",
+      "[CV 2/5; 7/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 7/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-4235171.679 total time=   0.0s\n",
+      "[CV 3/5; 7/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 7/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-8318997.656 total time=   0.0s\n",
+      "[CV 4/5; 7/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 7/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-3694882.717 total time=   0.0s\n",
+      "[CV 5/5; 7/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 7/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-5680636.893 total time=   0.0s\n",
+      "[CV 1/5; 8/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 8/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-2204649.912 total time=   0.1s\n",
+      "[CV 2/5; 8/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 8/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-4323647.958 total time=   0.1s\n",
+      "[CV 3/5; 8/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 8/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-8249375.030 total time=   0.1s\n",
+      "[CV 4/5; 8/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 8/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-3553381.772 total time=   0.1s\n",
+      "[CV 5/5; 8/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 8/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-5711381.723 total time=   0.1s\n",
+      "[CV 1/5; 9/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 9/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-2210481.982 total time=   0.1s\n",
+      "[CV 2/5; 9/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 9/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-4386383.460 total time=   0.1s\n",
+      "[CV 3/5; 9/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 9/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-8631425.063 total time=   0.1s\n",
+      "[CV 4/5; 9/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 9/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-3524975.146 total time=   0.1s\n",
+      "[CV 5/5; 9/81] START max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 9/81] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-5685714.988 total time=   0.2s\n",
+      "[CV 1/5; 10/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 10/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-2008707.087 total time=   0.0s\n",
+      "[CV 2/5; 10/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 10/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-3068848.032 total time=   0.0s\n",
+      "[CV 3/5; 10/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 10/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-6645188.668 total time=   0.0s\n",
+      "[CV 4/5; 10/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 10/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-2600396.836 total time=   0.0s\n",
+      "[CV 5/5; 10/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 10/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-3371741.924 total time=   0.0s\n",
+      "[CV 1/5; 11/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 11/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-1894194.361 total time=   0.1s\n",
+      "[CV 2/5; 11/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 11/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-2768241.923 total time=   0.1s\n",
+      "[CV 3/5; 11/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 11/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-5933545.828 total time=   0.1s\n",
+      "[CV 4/5; 11/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 11/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-2533264.264 total time=   0.1s\n",
+      "[CV 5/5; 11/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 11/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-3855207.813 total time=   0.1s\n",
+      "[CV 1/5; 12/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 12/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-1886332.297 total time=   0.1s\n",
+      "[CV 2/5; 12/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 12/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-2959482.400 total time=   0.1s\n",
+      "[CV 3/5; 12/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 12/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-6185375.878 total time=   0.1s\n",
+      "[CV 4/5; 12/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 12/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-2611944.893 total time=   0.1s\n",
+      "[CV 5/5; 12/81] START max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 12/81] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-3702299.731 total time=   0.1s\n",
+      "[CV 1/5; 13/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 13/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-2342416.810 total time=   0.0s\n",
+      "[CV 2/5; 13/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 13/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-3600886.892 total time=   0.0s\n",
+      "[CV 3/5; 13/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 13/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-7060163.532 total time=   0.0s\n",
+      "[CV 4/5; 13/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 13/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-2740094.218 total time=   0.0s\n",
+      "[CV 5/5; 13/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 13/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-4467653.069 total time=   0.0s\n",
+      "[CV 1/5; 14/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 14/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-2131680.159 total time=   0.1s\n",
+      "[CV 2/5; 14/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 14/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-3214169.252 total time=   0.1s\n",
+      "[CV 3/5; 14/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 14/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-6540955.754 total time=   0.1s\n",
+      "[CV 4/5; 14/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 14/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-2866875.593 total time=   0.1s\n",
+      "[CV 5/5; 14/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 14/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-4007878.690 total time=   0.1s\n",
+      "[CV 1/5; 15/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 15/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-1949890.295 total time=   0.1s\n",
+      "[CV 2/5; 15/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 15/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-3309281.711 total time=   0.1s\n",
+      "[CV 3/5; 15/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 15/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-6868636.683 total time=   0.1s\n",
+      "[CV 4/5; 15/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 15/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-2953672.545 total time=   0.1s\n",
+      "[CV 5/5; 15/81] START max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 15/81] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-4178832.981 total time=   0.1s\n",
+      "[CV 1/5; 16/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 16/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-2587491.728 total time=   0.0s\n",
+      "[CV 2/5; 16/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 16/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-4658665.557 total time=   0.0s\n",
+      "[CV 3/5; 16/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 16/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-8533706.965 total time=   0.0s\n",
+      "[CV 4/5; 16/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 16/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-3651137.208 total time=   0.0s\n",
+      "[CV 5/5; 16/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 16/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-5774730.863 total time=   0.0s\n",
+      "[CV 1/5; 17/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 17/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-2227941.386 total time=   0.1s\n",
+      "[CV 2/5; 17/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 17/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-4132064.449 total time=   0.1s\n",
+      "[CV 3/5; 17/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 17/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-8425758.071 total time=   0.0s\n",
+      "[CV 4/5; 17/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 17/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-3412412.306 total time=   0.0s\n",
+      "[CV 5/5; 17/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 17/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-6057397.385 total time=   0.0s\n",
+      "[CV 1/5; 18/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 18/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-2348627.280 total time=   0.1s\n",
+      "[CV 2/5; 18/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 18/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-4519188.050 total time=   0.1s\n",
+      "[CV 3/5; 18/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 18/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-8843161.720 total time=   0.1s\n",
+      "[CV 4/5; 18/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 18/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-3594069.984 total time=   0.1s\n",
+      "[CV 5/5; 18/81] START max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 18/81] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-5731966.618 total time=   0.1s\n",
+      "[CV 1/5; 19/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 19/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-2316859.834 total time=   0.0s\n",
+      "[CV 2/5; 19/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 19/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-4672389.969 total time=   0.0s\n",
+      "[CV 3/5; 19/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 19/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-8818988.159 total time=   0.0s\n",
+      "[CV 4/5; 19/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 19/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-4535042.938 total time=   0.0s\n",
+      "[CV 5/5; 19/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 19/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-6005716.110 total time=   0.0s\n",
+      "[CV 1/5; 20/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 20/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-2337874.088 total time=   0.1s\n",
+      "[CV 2/5; 20/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 20/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-4543844.767 total time=   0.1s\n",
+      "[CV 3/5; 20/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 20/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-8895599.364 total time=   0.1s\n",
+      "[CV 4/5; 20/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 20/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-4471939.477 total time=   0.1s\n",
+      "[CV 5/5; 20/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 20/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-5844315.724 total time=   0.1s\n",
+      "[CV 1/5; 21/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 21/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-2302876.533 total time=   0.1s\n",
+      "[CV 2/5; 21/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 21/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-4616896.810 total time=   0.1s\n",
+      "[CV 3/5; 21/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 21/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-8979530.381 total time=   0.1s\n",
+      "[CV 4/5; 21/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 21/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-4224766.569 total time=   0.1s\n",
+      "[CV 5/5; 21/81] START max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 21/81] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-5863902.731 total time=   0.1s\n",
+      "[CV 1/5; 22/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 22/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-2519229.591 total time=   0.0s\n",
+      "[CV 2/5; 22/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 22/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-4875590.255 total time=   0.0s\n",
+      "[CV 3/5; 22/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 22/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-9296304.511 total time=   0.0s\n",
+      "[CV 4/5; 22/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 22/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-4275457.295 total time=   0.0s\n",
+      "[CV 5/5; 22/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 22/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-6062200.223 total time=   0.0s\n",
+      "[CV 1/5; 23/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 23/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-2380752.710 total time=   0.0s\n",
+      "[CV 2/5; 23/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 23/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-4559955.779 total time=   0.0s\n",
+      "[CV 3/5; 23/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 23/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-9010755.682 total time=   0.0s\n",
+      "[CV 4/5; 23/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 23/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-4396849.830 total time=   0.1s\n",
+      "[CV 5/5; 23/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 23/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-5788710.096 total time=   0.1s\n",
+      "[CV 1/5; 24/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 24/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-2273337.784 total time=   0.1s\n",
+      "[CV 2/5; 24/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 24/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-4479724.918 total time=   0.1s\n",
+      "[CV 3/5; 24/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 24/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-8987806.321 total time=   0.1s\n",
+      "[CV 4/5; 24/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 24/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-4306969.452 total time=   0.1s\n",
+      "[CV 5/5; 24/81] START max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 24/81] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-5753464.445 total time=   0.1s\n",
+      "[CV 1/5; 25/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 25/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-2415871.644 total time=   0.0s\n",
+      "[CV 2/5; 25/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 25/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-5231895.422 total time=   0.0s\n",
+      "[CV 3/5; 25/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 25/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-10360138.340 total time=   0.0s\n",
+      "[CV 4/5; 25/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 25/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-4408753.270 total time=   0.0s\n",
+      "[CV 5/5; 25/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 25/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-6067599.289 total time=   0.0s\n",
+      "[CV 1/5; 26/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 26/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-2387366.995 total time=   0.1s\n",
+      "[CV 2/5; 26/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 26/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-5050008.942 total time=   0.1s\n",
+      "[CV 3/5; 26/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 26/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-9252597.002 total time=   0.1s\n",
+      "[CV 4/5; 26/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 26/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-4467716.145 total time=   0.1s\n",
+      "[CV 5/5; 26/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 26/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-6176428.121 total time=   0.1s\n",
+      "[CV 1/5; 27/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 27/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-2403897.025 total time=   0.1s\n",
+      "[CV 2/5; 27/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 27/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-4826633.962 total time=   0.1s\n",
+      "[CV 3/5; 27/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 27/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-9259581.899 total time=   0.1s\n",
+      "[CV 4/5; 27/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 27/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-4405708.646 total time=   0.1s\n",
+      "[CV 5/5; 27/81] START max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 27/81] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-6258746.888 total time=   0.1s\n",
+      "[CV 1/5; 28/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 28/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-1534598.811 total time=   0.0s\n",
+      "[CV 2/5; 28/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 28/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2884736.038 total time=   0.0s\n",
+      "[CV 3/5; 28/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 28/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-4902837.865 total time=   0.0s\n",
+      "[CV 4/5; 28/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 28/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2260020.006 total time=   0.0s\n",
+      "[CV 5/5; 28/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 28/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2532227.900 total time=   0.0s\n",
+      "[CV 1/5; 29/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 29/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-1365064.600 total time=   0.1s\n",
+      "[CV 2/5; 29/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 29/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2647394.869 total time=   0.1s\n",
+      "[CV 3/5; 29/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 29/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-4985734.914 total time=   0.1s\n",
+      "[CV 4/5; 29/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 29/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2429814.401 total time=   0.1s\n",
+      "[CV 5/5; 29/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 29/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2500400.931 total time=   0.1s\n",
+      "[CV 1/5; 30/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 30/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-1411451.822 total time=   0.2s\n",
+      "[CV 2/5; 30/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 30/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2530552.672 total time=   0.2s\n",
+      "[CV 3/5; 30/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 30/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-5069418.937 total time=   0.2s\n",
+      "[CV 4/5; 30/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 30/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2477231.521 total time=   0.2s\n",
+      "[CV 5/5; 30/81] START max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 30/81] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2459990.235 total time=   0.2s\n",
+      "[CV 1/5; 31/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 31/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-2079976.640 total time=   0.0s\n",
+      "[CV 2/5; 31/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 31/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-3136012.162 total time=   0.0s\n",
+      "[CV 3/5; 31/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 31/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-6777515.085 total time=   0.0s\n",
+      "[CV 4/5; 31/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 31/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-2553017.006 total time=   0.0s\n",
+      "[CV 5/5; 31/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 31/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-3934275.732 total time=   0.0s\n",
+      "[CV 1/5; 32/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 32/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-2008979.625 total time=   0.1s\n",
+      "[CV 2/5; 32/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 32/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-2754696.798 total time=   0.1s\n",
+      "[CV 3/5; 32/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 32/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-6348958.686 total time=   0.1s\n",
+      "[CV 4/5; 32/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 32/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-2656654.596 total time=   0.1s\n",
+      "[CV 5/5; 32/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 32/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-3774808.682 total time=   0.1s\n",
+      "[CV 1/5; 33/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 33/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-1857913.213 total time=   0.1s\n",
+      "[CV 2/5; 33/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 33/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-3053646.312 total time=   0.1s\n",
+      "[CV 3/5; 33/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 33/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-6430073.379 total time=   0.1s\n",
+      "[CV 4/5; 33/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 33/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-2610328.259 total time=   0.1s\n",
+      "[CV 5/5; 33/81] START max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 33/81] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-3934656.857 total time=   0.2s\n",
+      "[CV 1/5; 34/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 34/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-2252145.216 total time=   0.0s\n",
+      "[CV 2/5; 34/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 34/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-4178175.515 total time=   0.0s\n",
+      "[CV 3/5; 34/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 34/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-8031994.302 total time=   0.0s\n",
+      "[CV 4/5; 34/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 34/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-3694909.302 total time=   0.0s\n",
+      "[CV 5/5; 34/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 34/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-5674673.247 total time=   0.0s\n",
+      "[CV 1/5; 35/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 35/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-2212642.099 total time=   0.1s\n",
+      "[CV 2/5; 35/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 35/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-4511958.250 total time=   0.1s\n",
+      "[CV 3/5; 35/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 35/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-9103290.296 total time=   0.1s\n",
+      "[CV 4/5; 35/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 35/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-3826780.569 total time=   0.1s\n",
+      "[CV 5/5; 35/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 35/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-5762082.395 total time=   0.1s\n",
+      "[CV 1/5; 36/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 36/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-2211922.462 total time=   0.1s\n",
+      "[CV 2/5; 36/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 36/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-4129984.887 total time=   0.1s\n",
+      "[CV 3/5; 36/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 36/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-8649770.300 total time=   0.1s\n",
+      "[CV 4/5; 36/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 36/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-3644823.690 total time=   0.1s\n",
+      "[CV 5/5; 36/81] START max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 36/81] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-5700457.029 total time=   0.1s\n",
+      "[CV 1/5; 37/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 37/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-1755708.503 total time=   0.0s\n",
+      "[CV 2/5; 37/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 37/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-3036761.029 total time=   0.0s\n",
+      "[CV 3/5; 37/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 37/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-6076885.109 total time=   0.0s\n",
+      "[CV 4/5; 37/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 37/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-2517492.286 total time=   0.0s\n",
+      "[CV 5/5; 37/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 37/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-3438626.670 total time=   0.0s\n",
+      "[CV 1/5; 38/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 38/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-1902258.868 total time=   0.1s\n",
+      "[CV 2/5; 38/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 38/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-2868842.701 total time=   0.1s\n",
+      "[CV 3/5; 38/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 38/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-6058267.150 total time=   0.1s\n",
+      "[CV 4/5; 38/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 38/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-2698765.110 total time=   0.1s\n",
+      "[CV 5/5; 38/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 38/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-3749829.407 total time=   0.1s\n",
+      "[CV 1/5; 39/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 39/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-1926151.227 total time=   0.1s\n",
+      "[CV 2/5; 39/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 39/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-2911922.617 total time=   0.2s\n",
+      "[CV 3/5; 39/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 39/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-6317871.825 total time=   0.2s\n",
+      "[CV 4/5; 39/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 39/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-2692841.207 total time=   0.2s\n",
+      "[CV 5/5; 39/81] START max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 39/81] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-3550182.626 total time=   0.1s\n",
+      "[CV 1/5; 40/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 40/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-2274433.388 total time=   0.0s\n",
+      "[CV 2/5; 40/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 40/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-3596746.289 total time=   0.0s\n",
+      "[CV 3/5; 40/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 40/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-7573948.914 total time=   0.0s\n",
+      "[CV 4/5; 40/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 40/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-2907322.813 total time=   0.0s\n",
+      "[CV 5/5; 40/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 40/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-4319284.837 total time=   0.0s\n",
+      "[CV 1/5; 41/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 41/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-2010692.263 total time=   0.1s\n",
+      "[CV 2/5; 41/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 41/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-3215234.441 total time=   0.1s\n",
+      "[CV 3/5; 41/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 41/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-6885378.482 total time=   0.1s\n",
+      "[CV 4/5; 41/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 41/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-2924659.617 total time=   0.1s\n",
+      "[CV 5/5; 41/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 41/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-4192195.167 total time=   0.1s\n",
+      "[CV 1/5; 42/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 42/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-2213698.247 total time=   0.1s\n",
+      "[CV 2/5; 42/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 42/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-3319891.345 total time=   0.2s\n",
+      "[CV 3/5; 42/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 42/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-6549052.870 total time=   0.1s\n",
+      "[CV 4/5; 42/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 42/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-2858591.894 total time=   0.1s\n",
+      "[CV 5/5; 42/81] START max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 42/81] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-4238862.304 total time=   0.1s\n",
+      "[CV 1/5; 43/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 43/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-2321305.695 total time=   0.0s\n",
+      "[CV 2/5; 43/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 43/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-4182624.678 total time=   0.0s\n",
+      "[CV 3/5; 43/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 43/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-8203130.318 total time=   0.0s\n",
+      "[CV 4/5; 43/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 43/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-3819685.556 total time=   0.0s\n",
+      "[CV 5/5; 43/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 43/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-5882084.151 total time=   0.0s\n",
+      "[CV 1/5; 44/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 44/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-2211199.414 total time=   0.1s\n",
+      "[CV 2/5; 44/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 44/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-4418408.190 total time=   0.1s\n",
+      "[CV 3/5; 44/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 44/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-8585957.554 total time=   0.0s\n",
+      "[CV 4/5; 44/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 44/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-3792829.722 total time=   0.0s\n",
+      "[CV 5/5; 44/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 44/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-5951815.180 total time=   0.1s\n",
+      "[CV 1/5; 45/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 45/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-2347640.820 total time=   0.1s\n",
+      "[CV 2/5; 45/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 45/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-4457781.040 total time=   0.1s\n",
+      "[CV 3/5; 45/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 45/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-8554979.147 total time=   0.1s\n",
+      "[CV 4/5; 45/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 45/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-3633805.362 total time=   0.1s\n",
+      "[CV 5/5; 45/81] START max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 45/81] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-5708662.720 total time=   0.1s\n",
+      "[CV 1/5; 46/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 46/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-2206021.377 total time=   0.0s\n",
+      "[CV 2/5; 46/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 46/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-4723617.178 total time=   0.0s\n",
+      "[CV 3/5; 46/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 46/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-8850577.735 total time=   0.0s\n",
+      "[CV 4/5; 46/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 46/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-4517705.342 total time=   0.0s\n",
+      "[CV 5/5; 46/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 46/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-5883334.004 total time=   0.0s\n",
+      "[CV 1/5; 47/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 47/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-2230204.988 total time=   0.0s\n",
+      "[CV 2/5; 47/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 47/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-4830722.330 total time=   0.1s\n",
+      "[CV 3/5; 47/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 47/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-8973203.952 total time=   0.1s\n",
+      "[CV 4/5; 47/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 47/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-4466476.972 total time=   0.1s\n",
+      "[CV 5/5; 47/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 47/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-5842186.389 total time=   0.1s\n",
+      "[CV 1/5; 48/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 48/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-2387670.460 total time=   0.1s\n",
+      "[CV 2/5; 48/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 48/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-4801714.307 total time=   0.1s\n",
+      "[CV 3/5; 48/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 48/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-8687949.070 total time=   0.1s\n",
+      "[CV 4/5; 48/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 48/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-4640804.905 total time=   0.2s\n",
+      "[CV 5/5; 48/81] START max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 48/81] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-5780485.777 total time=   0.1s\n",
+      "[CV 1/5; 49/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 49/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-2348460.689 total time=   0.0s\n",
+      "[CV 2/5; 49/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 49/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-4922417.374 total time=   0.0s\n",
+      "[CV 3/5; 49/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 49/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-8599394.685 total time=   0.0s\n",
+      "[CV 4/5; 49/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 49/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-4525605.525 total time=   0.0s\n",
+      "[CV 5/5; 49/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 49/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-5676671.096 total time=   0.0s\n",
+      "[CV 1/5; 50/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 50/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-2377329.486 total time=   0.1s\n",
+      "[CV 2/5; 50/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 50/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-4709170.948 total time=   0.1s\n",
+      "[CV 3/5; 50/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 50/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-8994952.640 total time=   0.1s\n",
+      "[CV 4/5; 50/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 50/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-4266322.557 total time=   0.1s\n",
+      "[CV 5/5; 50/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 50/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-5690055.809 total time=   0.1s\n",
+      "[CV 1/5; 51/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 51/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-2406926.209 total time=   0.2s\n",
+      "[CV 2/5; 51/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 51/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-4641881.505 total time=   0.1s\n",
+      "[CV 3/5; 51/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 51/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-8809762.172 total time=   0.1s\n",
+      "[CV 4/5; 51/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 51/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-4303096.166 total time=   0.1s\n",
+      "[CV 5/5; 51/81] START max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 51/81] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-5956786.754 total time=   0.2s\n",
+      "[CV 1/5; 52/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 52/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-2385220.040 total time=   0.0s\n",
+      "[CV 2/5; 52/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 52/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-4764222.839 total time=   0.0s\n",
+      "[CV 3/5; 52/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 52/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-9414839.655 total time=   0.0s\n",
+      "[CV 4/5; 52/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 52/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-4803351.464 total time=   0.0s\n",
+      "[CV 5/5; 52/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 52/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-5921679.925 total time=   0.0s\n",
+      "[CV 1/5; 53/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 53/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-2511508.469 total time=   0.1s\n",
+      "[CV 2/5; 53/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 53/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-4754586.350 total time=   0.1s\n",
+      "[CV 3/5; 53/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 53/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-9546746.025 total time=   0.1s\n",
+      "[CV 4/5; 53/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 53/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-4411456.106 total time=   0.1s\n",
+      "[CV 5/5; 53/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 53/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-6278186.504 total time=   0.1s\n",
+      "[CV 1/5; 54/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 54/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-2418635.822 total time=   0.1s\n",
+      "[CV 2/5; 54/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 54/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-4850699.481 total time=   0.1s\n",
+      "[CV 3/5; 54/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 54/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-9846354.115 total time=   0.1s\n",
+      "[CV 4/5; 54/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 54/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-4809160.441 total time=   0.1s\n",
+      "[CV 5/5; 54/81] START max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 54/81] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-6320778.274 total time=   0.1s\n",
+      "[CV 1/5; 55/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 55/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-1351806.635 total time=   0.0s\n",
+      "[CV 2/5; 55/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 55/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2551399.171 total time=   0.0s\n",
+      "[CV 3/5; 55/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 55/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-5054003.070 total time=   0.0s\n",
+      "[CV 4/5; 55/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 55/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2596466.435 total time=   0.0s\n",
+      "[CV 5/5; 55/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 55/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100;, score=-2730956.423 total time=   0.0s\n",
+      "[CV 1/5; 56/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 56/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-1351198.762 total time=   0.1s\n",
+      "[CV 2/5; 56/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 56/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2405882.826 total time=   0.1s\n",
+      "[CV 3/5; 56/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 56/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-5106543.429 total time=   0.1s\n",
+      "[CV 4/5; 56/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 56/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2448569.843 total time=   0.1s\n",
+      "[CV 5/5; 56/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 56/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200;, score=-2552569.919 total time=   0.1s\n",
+      "[CV 1/5; 57/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 57/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-1545122.161 total time=   0.2s\n",
+      "[CV 2/5; 57/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 57/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2627691.117 total time=   0.2s\n",
+      "[CV 3/5; 57/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 57/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-5274611.498 total time=   0.2s\n",
+      "[CV 4/5; 57/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 57/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2458029.711 total time=   0.2s\n",
+      "[CV 5/5; 57/81] START max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 57/81] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300;, score=-2380402.882 total time=   0.2s\n",
+      "[CV 1/5; 58/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 58/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-2091677.947 total time=   0.0s\n",
+      "[CV 2/5; 58/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 58/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-2930001.694 total time=   0.0s\n",
+      "[CV 3/5; 58/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 58/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-6214967.054 total time=   0.0s\n",
+      "[CV 4/5; 58/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 58/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-2796873.371 total time=   0.0s\n",
+      "[CV 5/5; 58/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 58/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100;, score=-3576157.826 total time=   0.0s\n",
+      "[CV 1/5; 59/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 59/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-2066102.799 total time=   0.1s\n",
+      "[CV 2/5; 59/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 59/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-3221384.582 total time=   0.1s\n",
+      "[CV 3/5; 59/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 59/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-6171919.269 total time=   0.1s\n",
+      "[CV 4/5; 59/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 59/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-2634327.309 total time=   0.1s\n",
+      "[CV 5/5; 59/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 59/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200;, score=-3836116.603 total time=   0.1s\n",
+      "[CV 1/5; 60/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 60/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-1786696.136 total time=   0.2s\n",
+      "[CV 2/5; 60/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 60/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-3081084.058 total time=   0.2s\n",
+      "[CV 3/5; 60/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 60/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-6348601.836 total time=   0.2s\n",
+      "[CV 4/5; 60/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 60/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-2582581.951 total time=   0.2s\n",
+      "[CV 5/5; 60/81] START max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 60/81] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300;, score=-3847933.942 total time=   0.2s\n",
+      "[CV 1/5; 61/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 61/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-2317998.536 total time=   0.0s\n",
+      "[CV 2/5; 61/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 61/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-4367814.349 total time=   0.0s\n",
+      "[CV 3/5; 61/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 61/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-8693404.904 total time=   0.0s\n",
+      "[CV 4/5; 61/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 61/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-3806143.357 total time=   0.0s\n",
+      "[CV 5/5; 61/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 61/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100;, score=-5482144.781 total time=   0.0s\n",
+      "[CV 1/5; 62/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 62/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-2181597.458 total time=   0.1s\n",
+      "[CV 2/5; 62/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 62/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-4134532.826 total time=   0.1s\n",
+      "[CV 3/5; 62/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 62/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-8108594.963 total time=   0.1s\n",
+      "[CV 4/5; 62/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 62/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-3588028.094 total time=   0.1s\n",
+      "[CV 5/5; 62/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 62/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200;, score=-5647398.606 total time=   0.1s\n",
+      "[CV 1/5; 63/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 63/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-2240212.129 total time=   0.2s\n",
+      "[CV 2/5; 63/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 63/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-4337669.224 total time=   0.2s\n",
+      "[CV 3/5; 63/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 63/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-8660876.157 total time=   0.1s\n",
+      "[CV 4/5; 63/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 63/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-3650599.160 total time=   0.2s\n",
+      "[CV 5/5; 63/81] START max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 63/81] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300;, score=-5701321.685 total time=   0.1s\n",
+      "[CV 1/5; 64/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 64/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-1883285.956 total time=   0.0s\n",
+      "[CV 2/5; 64/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 64/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-3207018.035 total time=   0.0s\n",
+      "[CV 3/5; 64/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 64/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-6256647.026 total time=   0.0s\n",
+      "[CV 4/5; 64/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 64/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-2845995.798 total time=   0.0s\n",
+      "[CV 5/5; 64/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 64/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100;, score=-3920085.781 total time=   0.0s\n",
+      "[CV 1/5; 65/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 65/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-1945119.454 total time=   0.1s\n",
+      "[CV 2/5; 65/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 65/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-3019047.948 total time=   0.1s\n",
+      "[CV 3/5; 65/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 65/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-5897230.109 total time=   0.1s\n",
+      "[CV 4/5; 65/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 65/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-2604923.649 total time=   0.1s\n",
+      "[CV 5/5; 65/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 65/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200;, score=-3766618.486 total time=   0.1s\n",
+      "[CV 1/5; 66/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 66/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-1770000.558 total time=   0.1s\n",
+      "[CV 2/5; 66/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 66/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-3027178.318 total time=   0.1s\n",
+      "[CV 3/5; 66/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 66/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-6140926.510 total time=   0.1s\n",
+      "[CV 4/5; 66/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 66/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-2660261.910 total time=   0.1s\n",
+      "[CV 5/5; 66/81] START max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 66/81] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300;, score=-3805596.536 total time=   0.2s\n",
+      "[CV 1/5; 67/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 67/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-2028326.992 total time=   0.0s\n",
+      "[CV 2/5; 67/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 67/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-3174149.134 total time=   0.0s\n",
+      "[CV 3/5; 67/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 67/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-7218787.951 total time=   0.0s\n",
+      "[CV 4/5; 67/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 67/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-2534268.205 total time=   0.0s\n",
+      "[CV 5/5; 67/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 67/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100;, score=-4104507.684 total time=   0.0s\n",
+      "[CV 1/5; 68/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 68/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-2011787.494 total time=   0.1s\n",
+      "[CV 2/5; 68/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 68/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-3146421.470 total time=   0.1s\n",
+      "[CV 3/5; 68/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 68/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-7138734.981 total time=   0.1s\n",
+      "[CV 4/5; 68/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 68/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-2775527.707 total time=   0.1s\n",
+      "[CV 5/5; 68/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 68/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200;, score=-4185397.413 total time=   0.1s\n",
+      "[CV 1/5; 69/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 69/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-1984245.902 total time=   0.1s\n",
+      "[CV 2/5; 69/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 69/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-3351158.757 total time=   0.1s\n",
+      "[CV 3/5; 69/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 69/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-6581878.496 total time=   0.1s\n",
+      "[CV 4/5; 69/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 69/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-2782117.904 total time=   0.1s\n",
+      "[CV 5/5; 69/81] START max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 69/81] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300;, score=-4192436.029 total time=   0.1s\n",
+      "[CV 1/5; 70/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 70/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-2275688.956 total time=   0.0s\n",
+      "[CV 2/5; 70/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 70/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-4428392.041 total time=   0.0s\n",
+      "[CV 3/5; 70/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 70/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-8945955.105 total time=   0.0s\n",
+      "[CV 4/5; 70/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 70/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-3475157.884 total time=   0.0s\n",
+      "[CV 5/5; 70/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 70/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100;, score=-5507432.034 total time=   0.0s\n",
+      "[CV 1/5; 71/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 71/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-2273675.103 total time=   0.1s\n",
+      "[CV 2/5; 71/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 71/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-4246140.639 total time=   0.1s\n",
+      "[CV 3/5; 71/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 71/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-8414974.479 total time=   0.1s\n",
+      "[CV 4/5; 71/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 71/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-3723417.716 total time=   0.1s\n",
+      "[CV 5/5; 71/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 71/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200;, score=-5731027.168 total time=   0.0s\n",
+      "[CV 1/5; 72/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 72/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-2384202.169 total time=   0.1s\n",
+      "[CV 2/5; 72/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 72/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-4237881.831 total time=   0.1s\n",
+      "[CV 3/5; 72/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 72/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-8770038.981 total time=   0.1s\n",
+      "[CV 4/5; 72/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 72/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-3807171.725 total time=   0.1s\n",
+      "[CV 5/5; 72/81] START max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 72/81] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300;, score=-5748272.563 total time=   0.1s\n",
+      "[CV 1/5; 73/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 1/5; 73/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-2361827.435 total time=   0.0s\n",
+      "[CV 2/5; 73/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 2/5; 73/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-4561762.868 total time=   0.0s\n",
+      "[CV 3/5; 73/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 3/5; 73/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-9161871.160 total time=   0.0s\n",
+      "[CV 4/5; 73/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 4/5; 73/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-4080407.409 total time=   0.0s\n",
+      "[CV 5/5; 73/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100\n",
+      "[CV 5/5; 73/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100;, score=-6126654.653 total time=   0.0s\n",
+      "[CV 1/5; 74/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 1/5; 74/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-2386149.091 total time=   0.0s\n",
+      "[CV 2/5; 74/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 2/5; 74/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-4539614.622 total time=   0.0s\n",
+      "[CV 3/5; 74/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 3/5; 74/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-8595811.506 total time=   0.0s\n",
+      "[CV 4/5; 74/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 4/5; 74/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-4528860.909 total time=   0.0s\n",
+      "[CV 5/5; 74/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200\n",
+      "[CV 5/5; 74/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200;, score=-5719698.170 total time=   0.0s\n",
+      "[CV 1/5; 75/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 1/5; 75/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-2333718.823 total time=   0.1s\n",
+      "[CV 2/5; 75/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 2/5; 75/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-4645044.878 total time=   0.1s\n",
+      "[CV 3/5; 75/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 3/5; 75/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-9164314.430 total time=   0.1s\n",
+      "[CV 4/5; 75/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 4/5; 75/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-4411749.046 total time=   0.1s\n",
+      "[CV 5/5; 75/81] START max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300\n",
+      "[CV 5/5; 75/81] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300;, score=-5704299.266 total time=   0.1s\n",
+      "[CV 1/5; 76/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 1/5; 76/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-2455772.697 total time=   0.0s\n",
+      "[CV 2/5; 76/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 2/5; 76/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-4739799.174 total time=   0.0s\n",
+      "[CV 3/5; 76/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 3/5; 76/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-8632797.812 total time=   0.0s\n",
+      "[CV 4/5; 76/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 4/5; 76/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-4465747.294 total time=   0.0s\n",
+      "[CV 5/5; 76/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100\n",
+      "[CV 5/5; 76/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100;, score=-5815916.764 total time=   0.0s\n",
+      "[CV 1/5; 77/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 1/5; 77/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-2379045.360 total time=   0.0s\n",
+      "[CV 2/5; 77/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 2/5; 77/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-5010839.579 total time=   0.1s\n",
+      "[CV 3/5; 77/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 3/5; 77/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-8980610.806 total time=   0.1s\n",
+      "[CV 4/5; 77/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 4/5; 77/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-4554499.443 total time=   0.0s\n",
+      "[CV 5/5; 77/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200\n",
+      "[CV 5/5; 77/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200;, score=-6210936.926 total time=   0.0s\n",
+      "[CV 1/5; 78/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 1/5; 78/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-2304289.322 total time=   0.1s\n",
+      "[CV 2/5; 78/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 2/5; 78/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-4604533.317 total time=   0.1s\n",
+      "[CV 3/5; 78/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 3/5; 78/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-8914630.047 total time=   0.2s\n",
+      "[CV 4/5; 78/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 4/5; 78/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-4439496.164 total time=   0.1s\n",
+      "[CV 5/5; 78/81] START max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300\n",
+      "[CV 5/5; 78/81] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300;, score=-6055701.915 total time=   0.1s\n",
+      "[CV 1/5; 79/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 1/5; 79/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-2436154.047 total time=   0.0s\n",
+      "[CV 2/5; 79/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 2/5; 79/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-5018603.438 total time=   0.0s\n",
+      "[CV 3/5; 79/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 3/5; 79/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-9532896.641 total time=   0.0s\n",
+      "[CV 4/5; 79/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 4/5; 79/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-5114693.865 total time=   0.0s\n",
+      "[CV 5/5; 79/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100\n",
+      "[CV 5/5; 79/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100;, score=-5899070.027 total time=   0.0s\n",
+      "[CV 1/5; 80/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 1/5; 80/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-2552620.894 total time=   0.1s\n",
+      "[CV 2/5; 80/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 2/5; 80/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-4558388.255 total time=   0.0s\n",
+      "[CV 3/5; 80/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 3/5; 80/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-9416243.836 total time=   0.0s\n",
+      "[CV 4/5; 80/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 4/5; 80/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-4557965.501 total time=   0.0s\n",
+      "[CV 5/5; 80/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200\n",
+      "[CV 5/5; 80/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200;, score=-6247100.862 total time=   0.0s\n",
+      "[CV 1/5; 81/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 1/5; 81/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-2346398.773 total time=   0.1s\n",
+      "[CV 2/5; 81/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 2/5; 81/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-4902085.008 total time=   0.1s\n",
+      "[CV 3/5; 81/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 3/5; 81/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-9269438.831 total time=   0.1s\n",
+      "[CV 4/5; 81/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 4/5; 81/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-4328182.684 total time=   0.1s\n",
+      "[CV 5/5; 81/81] START max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300\n",
+      "[CV 5/5; 81/81] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300;, score=-6296748.118 total time=   0.1s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
+       "             param_grid={&#x27;max_depth&#x27;: [None, 10, 20],\n",
+       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
+       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
+       "                         &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
+       "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
+       "             param_grid={&#x27;max_depth&#x27;: [None, 10, 20],\n",
+       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
+       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
+       "                         &#x27;n_estimators&#x27;: [100, 200, 300]},\n",
+       "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=10)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=1,\n",
+       "             param_grid={'max_depth': [None, 10, 20],\n",
+       "                         'min_samples_leaf': [1, 2, 4],\n",
+       "                         'min_samples_split': [2, 5, 10],\n",
+       "                         'n_estimators': [100, 200, 300]},\n",
+       "             scoring='neg_mean_squared_error', verbose=10)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param_grid = {\n",
+    "    'n_estimators': [100, 200, 300],\n",
+    "    'max_depth': [None, 10, 20],\n",
+    "    'min_samples_split': [2, 5, 10],\n",
+    "    'min_samples_leaf': [1, 2, 4]\n",
+    "}\n",
+    "\n",
+    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, n_jobs=1, verbose=10, scoring='neg_mean_squared_error')\n",
+    "grid_search.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 300}\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_params = grid_search.best_params_\n",
+    "best_model = grid_search.best_estimator_\n",
+    "print(best_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Squared Error: 1844826.441998729\n",
+      "R-squared: 0.9166861631993493\n",
+      "Root Mean Squared Error: 1358.2438816349327\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = best_model.predict(X_test)\n",
+    "mse = mean_squared_error(y_test, y_pred)\n",
+    "rmse = np.sqrt(mse)\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "print(\"Mean Squared Error:\", mse)\n",
+    "print(\"R-squared:\", r2)\n",
+    "print(\"Root Mean Squared Error:\", rmse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model.fit(X_train, y_train)\n",
+    "\n",
+    "# # Predict on the test set\n",
+    "# y_pred = model.predict(X_test)\n",
+    "\n",
+    "# # Evaluate the model\n",
+    "# mse = mean_squared_error(y_test, y_pred)\n",
+    "# r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "# print(\"Mean Squared Error:\", mse)\n",
+    "# print(\"R-squared:\", r2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# models = {\n",
+    "#     \"Linear Regression\": LinearRegression(),\n",
+    "#     \"Decision Tree Regression\": DecisionTreeRegressor(),\n",
+    "#     \"Random Forest Regression\": RandomForestRegressor(),\n",
+    "#     \"Stochastic Gradient Descent\": SGDRegressor(), \n",
+    "#     \"Ridge Regression\": Ridge(), \n",
+    "#     \"Quantile Regression\": QuantileRegressor()\n",
+    "# }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for model_name, model in models.items():\n",
+    "#     model.fit(X_train, y_train)\n",
+    "#     y_pred = model.predict(X_test)\n",
+    "\n",
+    "#     # Evaluate the model\n",
+    "#     mse = mean_squared_error(y_test, y_pred)\n",
+    "#     r2 = r2_score(y_test, y_pred)\n",
+    "\n",
+    "#     print(f\"Model: {model_name}\")\n",
+    "#     print(\"Mean Squared Error:\", mse)\n",
+    "#     print(\"R-squared:\", r2)\n",
+    "#     print(\"------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "# Scatter plot for y_test in blue color\n",
+    "plt.scatter(X_test[\"amount\"], y_test, color='blue', label='Actual (y_test)')\n",
+    "\n",
+    "# Scatter plot for y_pred in red color\n",
+    "plt.scatter(X_test[\"amount\"], y_pred, color='red', label='Predicted (y_pred)')\n",
+    "\n",
+    "plt.xlabel(\"Amount\")\n",
+    "plt.ylabel(\"Balance\")\n",
+    "plt.title(\"Actual vs Predicted\")\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"Dummy Data/sample_data.csv\")\n",
+    "# Find the starting index of the test set\n",
+    "test_start_index = len(X_train)\n",
+    "\n",
+    "# Extract corresponding dates for the test set\n",
+    "dates_test = df.iloc[test_start_index : test_start_index + len(X_test)]['postDate']\n",
+    "# plot\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(dates_test, y_test, label='Actual Values', marker='o')\n",
+    "plt.plot(dates_test, y_pred, label='Predicted Values', marker='x')\n",
+    "plt.xlabel('postDate')\n",
+    "plt.ylabel('Balance')\n",
+    "plt.title('Actual vs. Predicted Values Over Time')\n",
+    "plt.legend()\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}