Fradulence_detector.ipynb

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              "   Transaction_id Transfer_type    Amount Sender_account  Initial_balance_p1  \\\n",
              "0               1       PAYMENT   9839.64    ACC10204397           170136.00   \n",
              "1               2       PAYMENT   1864.28    ACC36787793            21249.00   \n",
              "2               3      TRANSFER    181.00    ACC69442839              181.00   \n",
              "3               4      CASH_OUT    181.00    ACC13673580              181.00   \n",
              "4               5       PAYMENT  11668.14    ACC46873273            41554.00   \n",
              "5               6       PAYMENT   7817.71    ACC84508864            53860.00   \n",
              "6               7       PAYMENT   7107.77    ACC43559957           183195.00   \n",
              "7               8       PAYMENT   7861.64    ACC81042647           176087.23   \n",
              "8               9       PAYMENT   4024.36    ACC41426604             2671.00   \n",
              "9              10         DEBIT   5337.77    ACC95900894            41720.00   \n",
              "\n",
              "   new_balance_1 Recipient_account  Initial_balance_p2  new_balance_2  \\\n",
              "0      160296.36       ACC18330495                 0.0           0.00   \n",
              "1       19384.72       ACC43829436                 0.0           0.00   \n",
              "2           0.00       ACC19495987                 0.0           0.00   \n",
              "3           0.00       ACC94944418             21182.0           0.00   \n",
              "4       29885.86       ACC17787237                 0.0           0.00   \n",
              "5       46042.29       ACC67530981                 0.0           0.00   \n",
              "6      176087.23       ACC86690831                 0.0           0.00   \n",
              "7      168225.59       ACC97891146                 0.0           0.00   \n",
              "8           0.00       ACC74713816                 0.0           0.00   \n",
              "9       36382.23       ACC60296984             41898.0       40348.79   \n",
              "\n",
              "   isFraud  isFlaggedFraud Transaction_Date Transaction_Time  \\\n",
              "0        0               0         06-06-24         00:34:36   \n",
              "1        0               0         16-07-24         19:13:41   \n",
              "2        1               0         24-02-24         07:29:34   \n",
              "3        1               0         30-12-24         04:48:05   \n",
              "4        0               0         17-04-24         05:54:56   \n",
              "5        0               0         25-09-24         11:26:13   \n",
              "6        0               0         28-04-24         18:29:42   \n",
              "7        0               0         27-02-24         13:05:46   \n",
              "8        0               0         13-04-24         11:26:05   \n",
              "9        0               0         02-07-24         15:49:31   \n",
              "\n",
              "                                 Coordinates    ip_addresses  \n",
              "0  (43.550740883275296, -141.72974723173664)   15.246.64.212  \n",
              "1   (78.74630788793377, -2.8864833917104136)   29.13.180.155  \n",
              "2    (-48.70562123207855, 3.433940326483139)    132.7.36.190  \n",
              "3   (-54.63748785685925, 60.509022769098294)   153.105.24.95  \n",
              "4   (-57.96291770179832, -99.23770861640779)  161.203.207.51  \n",
              "5    (21.923751935157796, 135.6968762975912)  141.227.154.31  \n",
              "6   (-83.83903479292408, -136.8807093894966)    18.151.66.37  \n",
              "7    (-25.95184756465001, 79.06697397817305)    104.9.12.103  \n",
              "8     (67.30427834734033, 64.66394988831104)  139.50.143.112  \n",
              "9   (34.92976887714575, -63.005535432983436)   98.145.54.164  "
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              "      <th></th>\n",
              "      <th>Transaction_id</th>\n",
              "      <th>Transfer_type</th>\n",
              "      <th>Amount</th>\n",
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              "      <th>isFraud</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>9839.64</td>\n",
              "      <td>ACC10204397</td>\n",
              "      <td>170136.00</td>\n",
              "      <td>160296.36</td>\n",
              "      <td>ACC18330495</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>06-06-24</td>\n",
              "      <td>00:34:36</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>ACC36787793</td>\n",
              "      <td>21249.00</td>\n",
              "      <td>19384.72</td>\n",
              "      <td>ACC43829436</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16-07-24</td>\n",
              "      <td>19:13:41</td>\n",
              "      <td>(78.74630788793377, -2.8864833917104136)</td>\n",
              "      <td>29.13.180.155</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>181.00</td>\n",
              "      <td>ACC69442839</td>\n",
              "      <td>181.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>ACC19495987</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
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              "      <td>24-02-24</td>\n",
              "      <td>07:29:34</td>\n",
              "      <td>(-48.70562123207855, 3.433940326483139)</td>\n",
              "      <td>132.7.36.190</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>181.00</td>\n",
              "      <td>ACC13673580</td>\n",
              "      <td>181.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>ACC94944418</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>30-12-24</td>\n",
              "      <td>04:48:05</td>\n",
              "      <td>(-54.63748785685925, 60.509022769098294)</td>\n",
              "      <td>153.105.24.95</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>11668.14</td>\n",
              "      <td>ACC46873273</td>\n",
              "      <td>41554.00</td>\n",
              "      <td>29885.86</td>\n",
              "      <td>ACC17787237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>17-04-24</td>\n",
              "      <td>05:54:56</td>\n",
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              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>7817.71</td>\n",
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              "      <td>53860.00</td>\n",
              "      <td>46042.29</td>\n",
              "      <td>ACC67530981</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25-09-24</td>\n",
              "      <td>11:26:13</td>\n",
              "      <td>(21.923751935157796, 135.6968762975912)</td>\n",
              "      <td>141.227.154.31</td>\n",
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              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>7107.77</td>\n",
              "      <td>ACC43559957</td>\n",
              "      <td>183195.00</td>\n",
              "      <td>176087.23</td>\n",
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              "      <td>28-04-24</td>\n",
              "      <td>18:29:42</td>\n",
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              "      <th>7</th>\n",
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              "      <td>PAYMENT</td>\n",
              "      <td>7861.64</td>\n",
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              "      <td>176087.23</td>\n",
              "      <td>168225.59</td>\n",
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              "      <td>27-02-24</td>\n",
              "      <td>13:05:46</td>\n",
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              "      <td>41898.0</td>\n",
              "      <td>40348.79</td>\n",
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              "      <td>02-07-24</td>\n",
              "      <td>15:49:31</td>\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-77d14fde-c310-4dd7-9f9c-6efcf6f5cb68 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 699,\n  \"fields\": [\n    {\n      \"column\": \"Transaction_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 201,\n        \"min\": 1,\n        \"max\": 699,\n        \"num_unique_values\": 699,\n        \"samples\": [\n          159,\n          500,\n          397\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"TRANSFER\",\n          \"CASH_IN\",\n          \"CASH_OUT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260036.97996259067,\n        \"min\": 8.73,\n        \"max\": 2545478.01,\n        \"num_unique_values\": 696,\n        \"samples\": [\n          10648.06,\n          9686.85,\n          188238.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sender_account\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 699,\n        \"samples\": [\n          \"ACC71151265\",\n          \"ACC50520744\",\n          \"ACC41019414\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Initial_balance_p1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2399672.6713369135,\n        \"min\": 0.0,\n        \"max\": 9716462.14,\n        \"num_unique_values\": 493,\n        \"samples\": [\n          7243810.89,\n          71636.0,\n          6185786.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"new_balance_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2455110.700869648,\n        \"min\": 0.0,\n        \"max\": 9987286.56,\n        \"num_unique_values\": 409,\n        \"samples\": [\n          3401668.32,\n          1056.3,\n          9446.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recipient_account\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 699,\n        \"samples\": [\n          \"ACC81959731\",\n          \"ACC16212824\",\n          \"ACC94200769\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Initial_balance_p2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2166217.2851094683,\n        \"min\": 0.0,\n        \"max\": 17000998.23,\n        \"num_unique_values\": 416,\n        \"samples\": [\n          608612.15,\n          2441173.94,\n          402063.39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"new_balance_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3707628.9148058435,\n        \"min\": 0.0,\n        \"max\": 19169204.93,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          54985.05,\n          9940339.29,\n          10588885.27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isFraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isFlaggedFraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transaction_Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 316,\n        \"samples\": [\n          \"17-12-24\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transaction_Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 693,\n        \"samples\": [\n          \"08:11:55\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coordinates\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 699,\n        \"samples\": [\n          \"(64.18058489539649, -124.96356708496765)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ip_addresses\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 699,\n        \"samples\": [\n          \"34.210.92.96\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 253
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import *\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import seaborn as sns\n",
        "import pickle\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "\n",
        "\n",
        "data= pd.read_csv('/content/Training_data.csv')\n",
        "data_test = pd.read_csv('/content/Testing_data .csv')\n",
        "data.head(10)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 254,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fjj6XdwpIe44",
        "outputId": "93d55003-3c0f-4665-b8c1-8467e3628196"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 699 entries, 0 to 698\n",
            "Data columns (total 15 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Transaction_id      699 non-null    int64  \n",
            " 1   Transfer_type       699 non-null    object \n",
            " 2   Amount              699 non-null    float64\n",
            " 3   Sender_account      699 non-null    object \n",
            " 4   Initial_balance_p1  699 non-null    float64\n",
            " 5   new_balance_1       699 non-null    float64\n",
            " 6   Recipient_account   699 non-null    object \n",
            " 7   Initial_balance_p2  699 non-null    float64\n",
            " 8   new_balance_2       699 non-null    float64\n",
            " 9   isFraud             699 non-null    int64  \n",
            " 10  isFlaggedFraud      699 non-null    int64  \n",
            " 11  Transaction_Date    699 non-null    object \n",
            " 12  Transaction_Time    699 non-null    object \n",
            " 13  Coordinates         699 non-null    object \n",
            " 14  ip_addresses        699 non-null    object \n",
            "dtypes: float64(5), int64(3), object(7)\n",
            "memory usage: 82.0+ KB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQU_7-NNOF9M"
      },
      "source": [
        "Data preprocessing part"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 255,
      "metadata": {
        "id": "jqAUuONiR82x"
      },
      "outputs": [],
      "source": [
        "data.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.countplot(x=\"Transfer_type\", data=data, palette=\"YlOrBr\")\n",
        "plt.title(\"Visualizing type of online transaction\")\n",
        "plt.xlabel(\"Type of online transaction\")\n",
        "plt.ylabel(\"count of online transaction type \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "cYuUHE-iggPd",
        "outputId": "163db71c-54b0-4887-99f3-3d33664f65da"
      },
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'count of online transaction type ')"
            ]
          },
          "metadata": {},
          "execution_count": 256
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Fraud (x):\n",
        "    if x ==1:\n",
        "        return \"Fraudulent\"\n",
        "    else:\n",
        "        return \"not Fraudulent\"\n",
        "\n",
        "\n",
        "data[\"fraud_transaction_label\"] = data[\"isFraud\"].apply(Fraud)\n",
        "\n",
        "\n",
        "plt.figure(figsize = (10,5))\n",
        "plt.title (\"Fraudulent Transactions\")\n",
        "data.fraud_transaction_label.value_counts().plot.pie(autopct='%1.1f%%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "J8pJEeDJhwad",
        "outputId": "bcd543fb-b679-4ded-d3a2-be51c79722b1"
      },
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Fraudulent Transactions'}, ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 257
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.fraud_transaction_label.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "Ciaqr5XMiRFs",
        "outputId": "7ef0ab8e-d6a4-4e18-9549-b2f3a13128b9"
      },
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fraud_transaction_label\n",
              "not Fraudulent    693\n",
              "Fraudulent          6\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fraud_transaction_label</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>not Fraudulent</th>\n",
              "      <td>693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fraudulent</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fradulent_part =6/693 *100\n",
        "\n",
        "\n",
        "fradulent_part\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qf21xYPiX1P",
        "outputId": "1ed8257e-ddee-4443-eb96-2c99c2a4466a"
      },
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8658008658008658"
            ]
          },
          "metadata": {},
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Here it means 86.6% approx. of the data in the dataset is tagged as fradulent\n",
        "\n",
        "!pip install cmocean"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbIxagr7ii5F",
        "outputId": "bbf2f5d2-f933-42e6-8152-bb8a3d261470"
      },
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: cmocean in /usr/local/lib/python3.10/dist-packages (4.0.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from cmocean) (3.7.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from cmocean) (1.26.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from cmocean) (24.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->cmocean) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->cmocean) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->cmocean) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->cmocean) (1.4.7)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->cmocean) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->cmocean) (3.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->cmocean) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->cmocean) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import cmocean\n",
        "\n",
        "sns.barplot(x='Transfer_type',y='Amount',data=data, palette = 'YlOrBr')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "BhF4W1Yhi6Ln",
        "outputId": "626657d7-98f7-434f-8b74-1da7465f8baf"
      },
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Transfer_type', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 261
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "plt.scatter(x='Amount',y='fraud_transaction_label',data=data)\n",
        "plt.xlabel('Amount')\n",
        "plt.ylabel('fraud_transaction_label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "GyYxwcKRi6JI",
        "outputId": "89f4156c-4adf-4560-bf95-761e1377c345"
      },
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'fraud_transaction_label')"
            ]
          },
          "metadata": {},
          "execution_count": 262
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.countplot(x='fraud_transaction_label',data=data,hue='Transfer_type')\n",
        "plt.legend(loc=[0.85,0.8])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        },
        "id": "a4mnFeWii6GR",
        "outputId": "4649fcf6-f77e-4671-de98-ae76bbc535f4"
      },
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ad9589c7d60>"
            ]
          },
          "metadata": {},
          "execution_count": 263
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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ZWebNm7dW9a5cuTLXXHNNunXrlvr162fLLbdM//798+STlf9oMXLkyOy+++5VXj9r1qyUlZVl+vTpGTx4cMrKyj51a9++/VrVtCEI7QAAAJu5WbNmpUePHnnsscdy5ZVX5qWXXsrEiROz//77Z+jQoZX6jh8/PiNGjMj48eOrjHPJJZfkmmuuyaWXXppXX301jz/+eE499dS1DuJrq6KiIscee2xGjRqVs846K6+99lomT56cdu3apXfv3rn//vvXabzrrrsus2fPLm1Jcsstt5T2n3vuuWqtf124PB4AAGAzd8YZZ6SsrCzPPvtsGjVqVGrv2rVrTjrppNL+lClTsnjx4owaNSq33XZbnnrqqey9996l4w888EDOOOOMHHXUUaW27t27V3u9d999d+6999488MADGTBgQKn9v/7rv/LBBx/k5JNPzkEHHVRpLp+lWbNmadasWaW25s2bp7y8vFrrXh9W2gEAADZjH374YSZOnJihQ4euMeQ2b9689PO4ceNy3HHHZYsttshxxx2XcePGVepbXl6exx57LO+///4GrfmOO+7IzjvvXCmwr3bOOefkgw8+yCOPPLJBa9hYhHYAAIDN2FtvvZWKiop06tTpM/stWLAg9957b0488cQkyYknnpi77747CxcuLPW5+uqr8/7776e8vDy77bZbTjvttPzud7+rMtaDDz6Yxo0bV9r69++/1jX/5S9/SefOndd4bHX7X/7yl7Uer8iEdgAAgM1YRUXFWvW78847s+OOO5Yud999992z/fbb56677ir16dKlS15++eU8/fTTOemkkzJ37twMGDAgJ598cqWx9t9//0yfPr3S9smH1VVn3Zs6oR0AAGAzttNOO6WsrCyvv/76Z/YbN25cXnnlldSpU6e0vfrqq1UeSFerVq307NkzZ599dn71q19lwoQJGTduXGbOnFnq06hRo3Ts2LHSts0226x1zTvvvHNee+21NR5b3b7zzjsnSZo2bZr58+dX6bf64Xj/ei970QjtAAAAm7EWLVqkb9++GTt2bBYtWlTl+Lx58/LSSy/l+eefz+TJkyutjk+ePDlTp079zMDfpUuXJFnj2Ovr2GOPzZtvvpnf/OY3VY5dddVV2WqrrXLQQQclSXbZZZf87W9/y3vvvVep3wsvvJD69etnu+22q7a6NgRPjwcAANjMjR07Nvvss0+++tWvZtSoUdltt92yYsWKPPLII7npppvSt2/ffPWrX82+++5b5bU9e/bMuHHjcuWVV+bII4/MPvvsk7333jvl5eWZOXNmLrjgguy8886fe8/8ujj22GNzzz33ZNCgQbnyyitzwAEHZMGCBRk7dmweeOCB3HPPPaWH6vXt2ze77LJLjjvuuPzoRz9KeXl5Xnjhhfzwhz/MWWedldq1a1dbXRuC0A4AALCBPXnmkzVdwmfaYYcd8sILL+Syyy7LOeeck9mzZ6dly5bp0aNHrrvuuhx//PE5//zz1/jagQMH5qqrrsrll1+evn375s4778zo0aMzf/78lJeXp0+fPhk5cmTq1Km++FlWVpa777471157ba655pqcccYZqV+/fnr16pXJkydnn332KfWtU6dOHn744Xz/+9/Pcccdl/fffz8dOnTIWWedleHDh1dbTRtKWcXmcvf+Z1iwYEGaNWuW+fPnp2nTpjVdzjrpcd5tNV3CJmPald+u6RIAAPgSW7JkSWbOnJkOHTqkfv36NV0OG8EX+Z2vbQ51TzsAAAAUlNAOAABAofTv37/K97iv3i6//PKaLm+jck87AAAAhfKzn/0sixcvXuOxFi1abORqapbQDgAAQKGsy3e2f9m5PB4AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKytPjAQAANrAp++630c613x+mbLRzseFZaQcAANjMDR48OGVlZSkrK8sWW2yR1q1b56CDDsr48eOzatWqUr/27duX+n1yGzNmTJJk1qxZldrr1q2bjh075kc/+lEqKipK44wcOTK77777Z465ehs8ePDGfCsKx0o7AAAA6devX2655ZasXLky7733XiZOnJizzjor9957bx544IHUqfPP+Dhq1KiccsoplV7bpEmTSvuPPvpounbtmqVLl+aJJ57IySefnDZt2mTIkCFVzvvcc89l5cqVSZKnnnoqAwcOzBtvvJGmTZsmSRo0aLAhprvJENoBAABIvXr1Ul5eniTZZpttsscee+RrX/taDjjggEyYMCEnn3xykn8G9NX9Ps1WW21V6rP99tvnlltuyQsvvLDG0N6yZcvSzy1atEiStGrVKs2bN6+OaW3yXB4PAADAGvXp0yfdu3fPr371q/Ue4/nnn8+0adOy1157VWNlmw+hHQAAgE/VqVOnzJo1q7R//vnnp3HjxpW2P/7xj5Ves/fee6dx48apW7duevbsmaOPPjrf/va3N3LlXw4ujwcAAOBTVVRUpKysrLR/3nnnVXk43DbbbFNp/6677krnzp2zfPnyvPzyyznzzDOz5ZZblh5Yx9qr0ZX20aNHp2fPnmnSpElatWqVI444Im+88UalPr17967y9MDTTjutUp+33347hx56aBo2bJhWrVrlvPPOy4oVKzbmVAAAAL6UXnvttXTo0KG0v/XWW6djx46Vtn99WFy7du3SsWPHdO7cOUcddVTOPvvsXHXVVVmyZMnGLn+TV6OhfcqUKRk6dGiefvrpPPLII1m+fHkOPvjgLFq0qFK/U045JbNnzy5tV1xxRenYypUrc+ihh2bZsmV56qmncuutt2bChAm56KKLNvZ0AAAAvlQee+yxvPTSSxk4cOAXGqd27dpZsWJFli1bVk2VbT5q9PL4iRMnVtqfMGFCWrVqlWnTpmXfffcttTds2PBTn0748MMP59VXX82jjz6a1q1bZ/fdd8+ll16a888/PyNHjkzdunU36BwAAAC+DJYuXZo5c+ZU+sq30aNH57DDDqt0P/pHH32UOXPmVHptw4YNS1/RliQffPBB5syZkxUrVuSll17Kddddl/33379SH9ZOoe5pnz9/fpL/e8z/arfffnv+53/+J+Xl5RkwYEAuvPDCNGzYMEkyderUdOvWLa1bty7179u3b04//fS88sor+cpXvlLlPEuXLs3SpUtL+wsWLNgQ0wEAAEiS7PeHKTVdwueaOHFi2rRpkzp16mTLLbdM9+7dc/3112fQoEGpVev/LtK+6KKLqlzZ/B//8R+5+eabS/sHHnhgkn+usLdp0yaHHHJILrvsso0zkS+ZwoT2VatW5eyzz84+++yTXXfdtdR+/PHHZ/vtt0/btm3z5z//Oeeff37eeOON0lcOzJkzp1JgT1La/9e//qw2evToXHLJJRtoJgAAAJuWCRMmZMKECZ/b75NPkV+T9u3bp6Ki4nPHGTlyZEaOHFmlvXfv3mv1+s1JYUL70KFD8/LLL+eJJ56o1H7qqaeWfu7WrVvatGmTAw44IDNmzMiOO+64Xue64IILMnz48NL+ggUL0q5du/UrHAAAADaQQnxP+7Bhw/Lggw/m8ccfz7bbbvuZfffaa68kyVtvvZUkKS8vz3vvvVepz+r9T7sPvl69emnatGmlDQAAAIqmRkN7RUVFhg0blvvuuy+PPfZYpa8R+DTTp09PkrRp0yZJ0qtXr7z00kuZO3duqc8jjzySpk2bpkuXLhukbgAAANgYavTy+KFDh+aOO+7Ir3/96zRp0qR0D3qzZs3SoEGDzJgxI3fccUcOOeSQbLXVVvnzn/+c733ve9l3332z2267JUkOPvjgdOnSJd/61rdyxRVXZM6cOfnhD3+YoUOHpl69ejU5PQAAAPhCanSl/aabbsr8+fPTu3fvtGnTprTdddddSZK6devm0UcfzcEHH5xOnTrlnHPOycCBA/Ob3/ymNEbt2rXz4IMPpnbt2unVq1dOPPHEfPvb386oUaNqaloAAABQLWp0pf3zngrYrl27TJny+V+NsP322+e3v/1tdZUFAAAAhVCIB9EBAAAAVQntAAAAUFBCOwAAABRUjd7TDgAAsDn46Tm/+fxO1WTYVQM22rnY8Ky0AwAAkDlz5uTMM8/MDjvskHr16qVdu3YZMGBAJk2aVKnf6NGjU7t27Vx55ZVVxli5cmXGjBmTTp06pUGDBmnRokX22muv/OxnPyv1GTx4cI444ogqr508eXLKysoyb968z631X/uu3u/atWtWrlxZqW/z5s0zYcKEzx2zqIR2AACAzdysWbPSo0ePPPbYY7nyyivz0ksvZeLEidl///0zdOjQSn3Hjx+fESNGZPz48VXGueSSS3LNNdfk0ksvzauvvprHH388p5566loF8erwv//7v7nttts2yrk2FpfHAwAAbObOOOOMlJWV5dlnn02jRo1K7V27ds1JJ51U2p8yZUoWL16cUaNG5bbbbstTTz2Vvffeu3T8gQceyBlnnJGjjjqq1Na9e/eNM4kkZ555Zi6++OIcf/zxqVev3kY774ZkpR0AAGAz9uGHH2bixIkZOnRopcC+WvPmzUs/jxs3Lscdd1y22GKLHHfccRk3blylvuXl5Xnsscfy/vvvb+iy1+jss8/OihUrcsMNN9TI+TcEoR0AAGAz9tZbb6WioiKdOnX6zH4LFizIvffemxNPPDFJcuKJJ+buu+/OwoULS32uvvrqvP/++ykvL89uu+2W0047Lb/73e+qjPXggw+mcePGlbb+/ft/4bk0bNgwF198cUaPHp358+d/4fGKQGgHAADYjFVUVKxVvzvvvDM77rhj6XL33XffPdtvv33uuuuuUp8uXbrk5ZdfztNPP52TTjopc+fOzYABA3LyySdXGmv//ffP9OnTK22ffFjdFzFkyJBstdVW+fGPf1wt49U0oR0AAGAzttNOO6WsrCyvv/76Z/YbN25cXnnlldSpU6e0vfrqq1UeSFerVq307NkzZ599dn71q19lwoQJGTduXGbOnFnq06hRo3Ts2LHSts0221TLfOrUqZPLLrss1113Xd59991qGbMmCe0AAACbsRYtWqRv374ZO3ZsFi1aVOX4vHnz8tJLL+X555/P5MmTK62OT548OVOnTv3MwN+lS5ckWePYG8pRRx2Vrl275pJLLtlo59xQPD0eAABgMzd27Njss88++epXv5pRo0Zlt912y4oVK/LII4/kpptuSt++ffPVr341++67b5XX9uzZM+PGjcuVV16ZI488Mvvss0/23nvvlJeXZ+bMmbnggguy8847f+4989VtzJgx6du370Y954YgtAMAAGxgw64aUNMlfKYddtghL7zwQi677LKcc845mT17dlq2bJkePXrkuuuuy/HHH5/zzz9/ja8dOHBgrrrqqlx++eXp27dv7rzzztKD4MrLy9OnT5+MHDkydeps3PjZp0+f9OnTJw8//PBGPW91K6tY26cOfIktWLAgzZo1y/z589O0adOaLmed9DjvtpouYZMx7cpv13QJAAB8iS1ZsiQzZ85Mhw4dUr9+/Zouh43gi/zO1zaHuqcdAAAACkpoBwAAoFD69+9f5XvcV2+XX355TZe3UbmnHQAAgEL52c9+lsWLF6/xWIsWLTZyNTVLaAcAAKBQqus7278MXB4PAABQjTzre/OxMX7XQjsAAEA12GKLLZIkH3/8cQ1Xwsay+ne9+ne/Ibg8HgAAoBrUrl07zZs3z9y5c5MkDRs2TFlZWQ1XxYZQUVGRjz/+OHPnzk3z5s1Tu3btDXYuoR0AAKCalJeXJ0kpuPPl1rx589LvfEMR2gEAAKpJWVlZ2rRpk1atWmX58uU1XQ4b0BZbbLFBV9hXE9oBAACqWe3atTdKoOPLz4PoAAAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAghLaAQAAoKCEdgAAACgooR0AAAAKSmgHAACAgqrR0D569Oj07NkzTZo0SatWrXLEEUfkjTfeqNRnyZIlGTp0aLbaaqs0btw4AwcOzHvvvVepz9tvv51DDz00DRs2TKtWrXLeeedlxYoVG3MqAAAAUO1qNLRPmTIlQ4cOzdNPP51HHnkky5cvz8EHH5xFixaV+nzve9/Lb37zm9xzzz2ZMmVK3n333Xzzm98sHV+5cmUOPfTQLFu2LE899VRuvfXWTJgwIRdddFFNTAkAAACqTVlFRUVFTRex2vvvv59WrVplypQp2XfffTN//vy0bNkyd9xxR4488sgkyeuvv57OnTtn6tSp+drXvpbf/e53Oeyww/Luu++mdevWSZKbb745559/ft5///3UrVv3c8+7YMGCNGvWLPPnz0/Tpk036ByrW4/zbqvpEjYZ0678dk2XAAAAkGTtc2ih7mmfP39+kqRFixZJkmnTpmX58uU58MADS306deqU7bbbLlOnTk2STJ06Nd26dSsF9iTp27dvFixYkFdeeWWN51m6dGkWLFhQaQMAAICiKUxoX7VqVc4+++zss88+2XXXXZMkc+bMSd26ddO8efNKfVu3bp05c+aU+nwysK8+vvrYmowePTrNmjUrbe3atavm2QAAAMAXV5jQPnTo0Lz88sv5xS9+scHPdcEFF2T+/Pml7Z133tng5wQAAIB1VaemC0iSYcOG5cEHH8wf/vCHbLvttqX28vLyLFu2LPPmzau02v7ee++lvLy81OfZZ5+tNN7qp8uv7vOv6tWrl3r16lXzLAAAAKB61ehKe0VFRYYNG5b77rsvjz32WDp06FDpeI8ePbLFFltk0qRJpbY33ngjb7/9dnr16pUk6dWrV1566aXMnTu31OeRRx5J06ZN06VLl40zEQAAANgAanSlfejQobnjjjvy61//Ok2aNCndg96sWbM0aNAgzZo1y5AhQzJ8+PC0aNEiTZs2zZlnnplevXrla1/7WpLk4IMPTpcuXfKtb30rV1xxRebMmZMf/vCHGTp0qNV0AAAANmk1GtpvuummJEnv3r0rtd9yyy0ZPHhwkuSaa65JrVq1MnDgwCxdujR9+/bNjTfeWOpbu3btPPjggzn99NPTq1evNGrUKIMGDcqoUaM21jQAAABggyjU97TXFN/TvnnwPe0AAEBRbJLf0w4AAAD8H6EdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIKqU9MFAPDl9tNzflPTJWwShl01oKZLAAAKyEo7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAW1XqG9T58+mTdvXpX2BQsWpE+fPl+0JgAAACDrGdonT56cZcuWVWlfsmRJ/vjHP37hogAAAICkzrp0/vOf/1z6+dVXX82cOXNK+ytXrszEiROzzTbbVF91AAAAsBlbp9C+++67p6ysLGVlZWu8DL5Bgwa54YYbqq04AAAA2JytU2ifOXNmKioqssMOO+TZZ59Ny5YtS8fq1q2bVq1apXbt2tVeJAAAAGyO1im0b7/99kmSVatWbZBiAAAAgP+zTqH9k9588808/vjjmTt3bpUQf9FFF33hwgAAAGBzt15Pj//v//7vdO7cORdddFHuvffe3HfffaXt/vvvX+tx/vCHP2TAgAFp27ZtysrKqrx28ODBpXvoV2/9+vWr1OfDDz/MCSeckKZNm6Z58+YZMmRIFi5cuD7TAgAAgEJZr5X2H/3oR7nsssty/vnnf6GTL1q0KN27d89JJ52Ub37zm2vs069fv9xyyy2l/Xr16lU6fsIJJ2T27Nl55JFHsnz58nznO9/JqaeemjvuuOML1QYAAAA1bb1C+z/+8Y8cddRRX/jk/fv3T//+/T+zT7169VJeXr7GY6+99lomTpyY5557LnvuuWeS5IYbbsghhxySn/zkJ2nbtu0XrhEAAABqynpdHn/UUUfl4Ycfru5a1mjy5Mlp1apVdtlll5x++un54IMPSsemTp2a5s2blwJ7khx44IGpVatWnnnmmU8dc+nSpVmwYEGlDQAAAIpmvVbaO3bsmAsvvDBPP/10unXrli222KLS8e9+97vVUly/fv3yzW9+Mx06dMiMGTPy/e9/P/3798/UqVNTu3btzJkzJ61atar0mjp16qRFixaZM2fOp447evToXHLJJdVSIwAAAGwo6xXa/+u//iuNGzfOlClTMmXKlErHysrKqi20H3vssaWfu3Xrlt122y077rhjJk+enAMOOGC9x73gggsyfPjw0v6CBQvSrl27L1QrAAAAVLf1Cu0zZ86s7jrWyg477JCtt946b731Vg444ICUl5dn7ty5lfqsWLEiH3744afeB5/88z75f32gHQAAABTNet3TXlP+9re/5YMPPkibNm2SJL169cq8efMybdq0Up/HHnssq1atyl577VVTZQIAAEC1WK+V9pNOOukzj48fP36txlm4cGHeeuut0v7MmTMzffr0tGjRIi1atMgll1ySgQMHpry8PDNmzMiIESPSsWPH9O3bN0nSuXPn9OvXL6ecckpuvvnmLF++PMOGDcuxxx7ryfEAAABs8tb7K98+afny5Xn55Zczb9689OnTZ63Hef7557P//vuX9lffZz5o0KDcdNNN+fOf/5xbb7018+bNS9u2bXPwwQfn0ksvrXRp++23355hw4blgAMOSK1atTJw4MBcf/316zMtAAAAKJT1Cu333XdflbZVq1bl9NNPz4477rjW4/Tu3TsVFRWfevz3v//9547RokWL3HHHHWt9TgAAANhUVNs97bVq1crw4cNzzTXXVNeQAAAAsFmr1gfRzZgxIytWrKjOIQEAAGCztV6Xx3/yO86TpKKiIrNnz85DDz2UQYMGVUthAAAAsLlbr9D+pz/9qdJ+rVq10rJly1x11VWf+2R5AAAAYO2sV2h//PHHq7sOAAAA4F+sV2hf7f33388bb7yRJNlll13SsmXLaikKAAAAWM8H0S1atCgnnXRS2rRpk3333Tf77rtv2rZtmyFDhuTjjz+u7hoBAABgs7ReoX348OGZMmVKfvOb32TevHmZN29efv3rX2fKlCk555xzqrtGAAAA2Cyt1+Xxv/zlL3Pvvfemd+/epbZDDjkkDRo0yNFHH52bbrqpuuoDAACAzdZ6rbR//PHHad26dZX2Vq1auTweAAAAqsl6hfZevXrl4osvzpIlS0ptixcvziWXXJJevXpVW3EAAACwOVuvy+Ovvfba9OvXL9tuu226d++eJHnxxRdTr169PPzww9VaIAAAAGyu1iu0d+vWLW+++WZuv/32vP7660mS4447LieccEIaNGhQrQUCAADA5mq9Qvvo0aPTunXrnHLKKZXax48fn/fffz/nn39+tRQHAAAAm7P1uqf9//2//5dOnTpVae/atWtuvvnmL1wUAAAAsJ6hfc6cOWnTpk2V9pYtW2b27NlfuCgAAABgPUN7u3bt8uSTT1Zpf/LJJ9O2bdsvXBQAAACwnve0n3LKKTn77LOzfPny9OnTJ0kyadKkjBgxIuecc061FggAAACbq/UK7eedd14++OCDnHHGGVm2bFmSpH79+jn//PNzwQUXVGuBAAAAsLlar9BeVlaWH//4x7nwwgvz2muvpUGDBtlpp51Sr1696q4PAAAANlvrFdpXa9y4cXr27FldtQAAAACfsF4PogMAAAA2PKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACqpOTRcAFMs+N+xT0yVsEp4888maLgEAgM2AlXYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACqpGQ/sf/vCHDBgwIG3btk1ZWVnuv//+SscrKipy0UUXpU2bNmnQoEEOPPDAvPnmm5X6fPjhhznhhBPStGnTNG/ePEOGDMnChQs34iwAAABgw6jR0L5o0aJ07949Y8eOXePxK664Itdff31uvvnmPPPMM2nUqFH69u2bJUuWlPqccMIJeeWVV/LII4/kwQcfzB/+8IeceuqpG2sKAAAAsMHUqcmT9+/fP/3791/jsYqKilx77bX54Q9/mMMPPzxJctttt6V169a5//77c+yxx+a1117LxIkT89xzz2XPPfdMktxwww055JBD8pOf/CRt27bdaHMBAACA6lbYe9pnzpyZOXPm5MADDyy1NWvWLHvttVemTp2aJJk6dWqaN29eCuxJcuCBB6ZWrVp55plnPnXspUuXZsGCBZU2AAAAKJrChvY5c+YkSVq3bl2pvXXr1qVjc+bMSatWrSodr1OnTlq0aFHqsyajR49Os2bNSlu7du2quXoAAAD44gob2jekCy64IPPnzy9t77zzTk2XBAAAAFUUNrSXl5cnSd57771K7e+9917pWHl5eebOnVvp+IoVK/Lhhx+W+qxJvXr10rRp00obAAAAFE1hQ3uHDh1SXl6eSZMmldoWLFiQZ555Jr169UqS9OrVK/Pmzcu0adNKfR577LGsWrUqe+2110avGQAAAKpTjT49fuHChXnrrbdK+zNnzsz06dPTokWLbLfddjn77LPzox/9KDvttFM6dOiQCy+8MG3bts0RRxyRJOncuXP69euXU045JTfffHOWL1+eYcOG5dhjj/XkeAAAADZ5NRran3/++ey///6l/eHDhydJBg0alAkTJmTEiBFZtGhRTj311MybNy//9m//lokTJ6Z+/fql19x+++0ZNmxYDjjggNSqVSsDBw7M9ddfv9HnAgAAANWtRkN77969U1FR8anHy8rKMmrUqIwaNepT+7Ro0SJ33HHHhigPAAAAalRh72kHAACAzZ3QDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUEI7AAAAFJTQDgAAAAUltAMAAEBBCe0AAABQUHVqugDYWN4e1a2mS9g0bNm0pisAAAD+/6y0AwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABSW0AwAAQEEJ7QAAAFBQQjsAAAAUlNAOAAAABVXo0D5y5MiUlZVV2jp16lQ6vmTJkgwdOjRbbbVVGjdunIEDB+a9996rwYoBAACg+hQ6tCdJ165dM3v27NL2xBNPlI5973vfy29+85vcc889mTJlSt59991885vfrMFqAQAAoPrUqekCPk+dOnVSXl5epX3+/PkZN25c7rjjjvTp0ydJcsstt6Rz5855+umn87WvfW1jlwoAAADVqvAr7W+++Wbatm2bHXbYISeccELefvvtJMm0adOyfPnyHHjggaW+nTp1ynbbbZepU6d+5phLly7NggULKm0AAABQNIUO7XvttVcmTJiQiRMn5qabbsrMmTPz7//+7/noo48yZ86c1K1bN82bN6/0mtatW2fOnDmfOe7o0aPTrFmz0tauXbsNOAsAAABYP4W+PL5///6ln3fbbbfstdde2X777XP33XenQYMG6z3uBRdckOHDh5f2FyxYILgDAABQOIVeaf9XzZs3z84775y33nor5eXlWbZsWebNm1epz3vvvbfGe+A/qV69emnatGmlDQAAAIpmkwrtCxcuzIwZM9KmTZv06NEjW2yxRSZNmlQ6/sYbb+Ttt99Or169arBKAAAAqB6Fvjz+3HPPzYABA7L99tvn3XffzcUXX5zatWvnuOOOS7NmzTJkyJAMHz48LVq0SNOmTXPmmWemV69enhwPAADAl0KhQ/vf/va3HHfccfnggw/SsmXL/Nu//VuefvrptGzZMklyzTXXpFatWhk4cGCWLl2avn375sYbb6zhqgEAAKB6FDq0/+IXv/jM4/Xr18/YsWMzduzYjVQRAAAAbDyb1D3tAAAAsDkR2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoqDo1XQDApmjKvvvVdAmbjp7n1nQFAACbLCvtAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFVaemCwAAoNjeHtWtpkvYJBy3ZdOaLmGT8OSZT9Z0CbBJsdIOAAAABSW0AwAAQEF9aUL72LFj0759+9SvXz977bVXnn322ZouCQAAAL6QL0Vov+uuuzJ8+PBcfPHFeeGFF9K9e/f07ds3c+fOrenSAAAAYL19KUL71VdfnVNOOSXf+c530qVLl9x8881p2LBhxo8fX9OlAQAAwHrb5J8ev2zZskybNi0XXHBBqa1WrVo58MADM3Xq1DW+ZunSpVm6dGlpf/78+UmSBQsWbNhiN4CVSxfXdAmbjI+2WFnTJWwSVixeUdMlbBIWeZvW2uKlH9d0CZuETfEziM3HR0t8hq4Nn6Frx3/v4J9W/7tQUVHxmf02+dD+97//PStXrkzr1q0rtbdu3Tqvv/76Gl8zevToXHLJJVXa27Vrt0FqpBh2rekC+FI5tKYL2JRMfaqmK9gkjBhb0xUAbBzNzm9W0yVAoXz00Udp1uzT/73Y5EP7+rjgggsyfPjw0v6qVavy4YcfZquttkpZWVkNVgb8qwULFqRdu3Z555130rSp778FgLXlMxSKraKiIh999FHatm37mf02+dC+9dZbp3bt2nnvvfcqtb/33nspLy9f42vq1auXevXqVWpr3rz5hioRqAZNmzb1PxwAsB58hkJxfdYK+2qb/IPo6tatmx49emTSpEmltlWrVmXSpEnp1atXDVYGAAAAX8wmv9KeJMOHD8+gQYOy55575qtf/WquvfbaLFq0KN/5zndqujQAAABYb1+K0H7MMcfk/fffz0UXXZQ5c+Zk9913z8SJE6s8nA7Y9NSrVy8XX3xxlVtaAIDP5jMUvhzKKj7v+fIAAABAjdjk72kHAACALyuhHQAAAApKaAcAAICCEtqBL52RI0dm9913X6fXtG/fPtdee+0GqQcANrbBgwfniCOOWKfXlJWV5f77798g9QDrT2gHPtPaBuCRI0emrKysyvboo49u+CILqnfv3jn77LNrugwANqLBgwev8fPwrbfequnSaoQ/isMX96X4yjegGLp27VolpLdo0aJKv2XLlqVu3bobqywA2Kj69euXW265pVJby5YtK+37LATWlpV2+BLr3bt3vvvd72bEiBFp0aJFysvLM3LkyEp93n777Rx++OFp3LhxmjZtmqOPPjrvvfdekmTChAm55JJL8uKLL5ZWCiZMmPCp56tTp07Ky8srbXXr1i1donfZZZelbdu22WWXXZIkP//5z7PnnnumSZMmKS8vz/HHH5+5c+eWxpswYUKaN29e6Rz3339/ysrKKrWNGTMmrVu3TpMmTTJkyJAsWbKkyvvwryveRxxxRAYPHvypc5k3b15OPvnktGzZMk2bNk2fPn3y4osvlo6vvgLh5z//edq3b59mzZrl2GOPzUcffZTknystU6ZMyXXXXVd672bNmvWp5wPgy6NevXpVPg8POOCADBs2LGeffXa23nrr9O3bN0ly9dVXp1u3bmnUqFHatWuXM844IwsXLiyNtaYr3q699tq0b9++tL9y5coMHz48zZs3z1ZbbZURI0bkX7/VeU0r3rvvvnuV/y/4pHfeeSdHH310mjdvnhYtWuTwww+v9Fm2+vP9Jz/5Sdq0aZOtttoqQ4cOzfLly5P88/P3r3/9a773ve+VPguBdSe0w5fcrbfemkaNGuWZZ57JFVdckVGjRuWRRx5JkqxatSqHH354Pvzww0yZMiWPPPJI/vd//zfHHHNMkuSYY47JOeeck65du2b27NmZPXt26di6mjRpUt5444088sgjefDBB5Mky5cvz6WXXpoXX3wx999/f2bNmvWZQXpN7r777owcOTKXX355nn/++bRp0yY33njjetX4SUcddVTmzp2b3/3ud5k2bVr22GOPHHDAAfnwww9LfWbMmJH7778/Dz74YB588MFMmTIlY8aMSZJcd9116dWrV0455ZTSe9euXbsvXBcAm65bb701devWzZNPPpmbb745SVKrVq1cf/31eeWVV3Lrrbfmsccey4gRI9Zp3KuuuioTJkzI+PHj88QTT+TDDz/Mfffd94VqXb58efr27ZsmTZrkj3/8Y5588sk0btw4/fr1y7Jly0r9Hn/88cyYMSOPP/54br311kyYMKH0B/5f/epX2XbbbTNq1KjSZyGw7lweD19yu+22Wy6++OIkyU477ZSf/vSnmTRpUg466KBMmjQpL730UmbOnFkKlLfddlu6du2a5557Lj179kzjxo1LK+if56WXXkrjxo1L+126dMmzzz6bJGnUqFF+9rOfVboU8KSTTir9vMMOO+T6669Pz549s3DhwkrjfJZrr702Q4YMyZAhQ5IkP/rRj/Loo49WWW1fF0888USeffbZzJ07N/Xq1UuS/OQnP8n999+fe++9N6eeemqSf/7RY8KECWnSpEmS5Fvf+lYmTZqUyy67LM2aNUvdunXTsGHDtXrvAPjyePDBByt9jvXv3z/JPz+Hr7jiikp9P3klWPv27fOjH/0op5122jr9Afraa6/NBRdckG9+85tJkptvvjm///3vv8AMkrvuuiurVq3Kz372s9IK+S233JLmzZtn8uTJOfjgg5MkW265ZX7605+mdu3a6dSpUw499NBMmjQpp5xySlq0aJHatWuXrqgD1o/QDl9yu+22W6X9Nm3alC5Bf+2119KuXbtKK8BdunRJ8+bN89prr6Vnz57rdK5ddtklDzzwQGl/deBNkm7dulW5d2/atGkZOXJkXnzxxfzjH//IqlWrkvzzkv0uXbqs1Tlfe+21nHbaaZXaevXqlccff3ydav+kF198MQsXLsxWW21VqX3x4sWZMWNGab99+/alwJ5Ufm8B2Hztv//+uemmm0r7jRo1ynHHHZcePXpU6fvoo49m9OjRef3117NgwYKsWLEiS5Ysyccff5yGDRt+7rnmz5+f2bNnZ6+99iq11alTJ3vuuWeVS+TXxYsvvpi33nqr0udckixZsqTSZ2HXrl1Tu3bt0n6bNm3y0ksvrfd5gaqEdviS22KLLSrtl5WVlcJxdatbt246duy4xmONGjWqtL9o0aL07ds3ffv2ze23356WLVvm7bffTt++fUuX3dWqVavK/3Csvk9uXazrOAsXLkybNm0yefLkKsc+eY/9xnxvAdh0NGrUaI2fh//6WThr1qwcdthhOf3003PZZZelRYsWeeKJJzJkyJAsW7YsDRs2rNHPwh49euT222+vcuyTD9XzWQgbnnvaYTPWuXPnvPPOO3nnnXdKba+++mrmzZtXWumuW7duVq5cWe3nfv311/PBBx9kzJgx+fd///d06tSpyip1y5Yt89FHH2XRokWltunTp1eZwzPPPFOp7emnn64yzifvo1u5cmVefvnlT61tjz32yJw5c1KnTp107Nix0rb11luv9Rw31HsHwJfDtGnTsmrVqlx11VX52te+lp133jnvvvtupT4tW7bMnDlzKgXuT34WNmvWLG3atKn0WbhixYpMmzatyjif/CxcsGBBZs6c+am17bHHHnnzzTfTqlWrKp+FzZo1W+s5+iyEL05oh83YgQcemG7duuWEE07ICy+8kGeffTbf/va3s99++2XPPfdM8s9LwGfOnJnp06fn73//e5YuXVot595uu+1St27d3HDDDfnf//3fPPDAA7n00ksr9dlrr73SsGHDfP/738+MGTNyxx13VHl6/VlnnZXx48fnlltuyV/+8pdcfPHFeeWVVyr16dOnTx566KE89NBDef3113P66adn3rx5n/m+9OrVK0cccUQefvjhzJo1K0899VR+8IMf5Pnnn1/rObZv3z7PPPNMZs2alb///e9WHgCopGPHjlm+fHnps/DnP/956QF1q/Xu3Tvvv/9+rrjiisyYMSNjx47N7373u0p9zjrrrIwZMyb3339/Xn/99ZxxxhlVPuf69OmTn//85/njH/+Yl156KYMGDap0Wfu/OuGEE7L11lvn8MMPzx//+MfMnDkzkydPzne/+9387W9/W+s5tm/fPn/4wx/y//1//1/+/ve/r/XrgP8jtMNmrKysLL/+9a+z5ZZbZt99982BBx6YHXbYIXfddVepz8CBA9OvX7/sv//+admyZe68885qOXfLli0zYcKE3HPPPenSpUvGjBmTn/zkJ5X6tGjRIv/zP/+T3/72t+nWrVvuvPPOKl9Nc8wxx+TCCy/MiBEj0qNHj/z1r3/N6aefXqnPSSedlEGDBpX+ILHDDjtk//33/9TaysrK8tvf/jb77rtvvvOd72TnnXfOsccem7/+9a9p3br1Ws/x3HPPTe3atdOlS5fS5f8AsFr37t1z9dVX58c//nF23XXX3H777Rk9enSlPp07d86NN96YsWPHpnv37nn22Wdz7rnnVupzzjnn5Fvf+lYGDRqUXr16pUmTJvnGN75Rqc8FF1yQ/fbbL4cddlgOPfTQHHHEEdlxxx0/tbaGDRvmD3/4Q7bbbrt885vfTOfOnUtfq9q0adO1nuOoUaMya9as7LjjjlW+qx5YO2UVX+QJFQAAAMAGY6UdAAAACkpoBwAAgIIS2gEAAKCghHYAAAAoKKEdAAAACkpoBwAAgIIS2gEAAKCghHYANmsVFRU59dRT06JFi5SVlWX69Okb9fyDBw/OEUccsVHPWWSTJ09OWVlZ5s2bt1HON2vWrHX+vVfH72xjzxOATZfQDsBmbeLEiZkwYUIefPDBzJ49O7vuumtNl/SpevfunbPPPrumy6g2a5rP3nvvndmzZ6dZs2Y1UxQAFEydmi4AAGrSjBkz0qZNm+y9995rPL5s2bLUrVt3I1e1/ioqKrJy5crUqbNpfsTXrVs35eXlNV0GABSGlXYANluDBw/OmWeembfffjtlZWVp3759evfunWHDhuXss8/O1ltvnb59+yZJrr766nTr1i2NGjVKu3btcsYZZ2ThwoWlsUaOHJndd9+90vjXXntt2rdvX9pfuXJlhg8fnubNm2errbbKiBEjUlFRsda1TpkyJdddd13KyspSVlaWWbNmlS6z/t3vfpcePXqkXr16eeKJJzJjxowcfvjhad26dRo3bpyePXvm0UcfrTRm+/btc/nll+ekk05KkyZNst122+W//uu/SseXLVuWYcOGpU2bNqlfv3623377jB49unT8896TJHnyySfTu3fvNGzYMFtuuWX69u2bf/zjH587n09eNv7LX/4yXbt2Tb169dK+fftcddVV6zSPdbFy5coMGTIkHTp0SIMGDbLLLrvkuuuuW2PfSy65JC1btkzTpk1z2mmnZdmyZaVjq1atyujRo0vjdO/ePffee+961QTA5k1oB2Czdd1112XUqFHZdtttM3v27Dz33HNJkltvvTV169bNk08+mZtvvjlJUqtWrVx//fV55ZVXcuutt+axxx7LiBEj1ul8V111VSZMmJDx48fniSeeyIcffpj77rtvrWvt1atXTjnllMyePTuzZ89Ou3btSsf/8z//M2PGjMlrr72W3XbbLQsXLswhhxySSZMm5U9/+lP69euXAQMG5O23365S05577pk//elPOeOMM3L66afnjTfeSJJcf/31eeCBB3L33XfnjTfeyO23317pjxCf955Mnz49BxxwQLp06ZKpU6fmiSeeyIABA7Jy5crPnc9q06ZNy9FHH51jjz02L730UkaOHJkLL7wwEyZMWOt5rItVq1Zl2223zT333JNXX301F110Ub7//e/n7rvvrtRv0qRJee211zJ58uTceeed+dWvfpVLLrmkdHz06NG57bbbcvPNN+eVV17J9773vZx44omZMmXKOtcEwGauAgA2Y9dcc03F9ttvX9rfb7/9Kr7yla987uvuueeeiq222qq0f/HFF1d07979M8du06ZNxRVXXFHaX758ecW2225bcfjhh69Vrfvtt1/FWWedVant8ccfr0hScf/993/u67t27Vpxww03lPa33377ihNPPLG0v2rVqopWrVpV3HTTTRUVFRUVZ555ZkWfPn0qVq1atVb1/et7ctxxx1Xss88+6zWff/zjHxUVFRUVxx9/fMVBBx1Uqc95551X0aVLl7Wex2eZOXNmRZKKP/3pT5/aZ+jQoRUDBw4s7Q8aNKiiRYsWFYsWLSq13XTTTRWNGzeuWLlyZcWSJUsqGjZsWPHUU09VGmfIkCEVxx133BrnCQCfZtO84Q0ANqAePXpUaXv00UczevTovP7661mwYEFWrFiRJUuW5OOPP07Dhg0/d8z58+dn9uzZ2WuvvUptderUyZ577rnWl8h/lj333LPS/sKFCzNy5Mg89NBDmT17dlasWJHFixdXWWnfbbfdSj+XlZWlvLw8c+fOTfLPS/IPOuig7LLLLunXr18OO+ywHHzwwaX+n/eeTJ8+PUcdddQXmtdrr72Www8/vFLbPvvsk2uvvTYrV65M7dq1P3ce62rs2LEZP3583n777SxevDjLli2rcutD9+7dK/3ee/XqlYULF+add97JwoUL8/HHH+eggw6q9Jply5blK1/5ynrVBMDmy+XxAPAvGjVqVGl/1qxZOeyww7Lbbrvll7/8ZaZNm5axY8cmSek+5lq1alUJ38uXL984Badqzeeee27uu+++XH755fnjH/+Y6dOnp1u3bpXuu06SLbbYotJ+WVlZVq1alSTZY489MnPmzFx66aVZvHhxjj766Bx55JFJ1u49adCgwQaZ65p81jzWxS9+8Yuce+65GTJkSB5++OFMnz493/nOd6q8b59l9X39Dz30UKZPn17aXn31Vfe1A7DOrLQDwOeYNm1aVq1alauuuiq1av3z793/eo9zy5YtM2fOnFRUVKSsrCxJKn33d7NmzdKmTZs888wz2XfffZMkK1asyLRp07LHHnusVR1169bNypUr16rvk08+mcGDB+cb3/hGkn8GyVmzZq3Vaz+padOmOeaYY3LMMcfkyCOPTL9+/fLhhx+u1Xuy2267ZdKkSZXu9V7X+XTu3DlPPvlklbntvPPOpVX26vTkk09m7733zhlnnFFqmzFjRpV+L774YhYvXlz6w8TTTz+dxo0bp127dmnRokXq1auXt99+O/vtt1+11wjA5kVoB4DP0bFjxyxfvjw33HBDBgwYUOkBdav17t0777//fq644ooceeSRmThxYn73u9+ladOmpT5nnXVWxowZk5122imdOnXK1VdfXekp6Z+nffv2eeaZZzJr1qw0btw4LVq0+NS+O+20U371q19lwIABKSsry4UXXrjOK89XX3112rRpk6985SupVatW7rnnnpSXl6d58+Zr9Z5ccMEF6datW84444ycdtppqVu3bh5//PEcddRR2XrrrddqPuecc0569uyZSy+9NMccc0ymTp2an/70p7nxxhvXaS5ra6eddsptt92W3//+9+nQoUN+/vOf57nnnkuHDh0q9Vu2bFmGDBmSH/7wh5k1a1YuvvjiDBs2LLVq1UqTJk1y7rnn5nvf+15WrVqVf/u3f8v8+fPz5JNPpmnTphk0aNAGqR2ALyeXxwPA5+jevXuuvvrq/PjHP86uu+6a22+/vdJXnyX/XBG+8cYbM3bs2HTv3j3PPvtszj333Ep9zjnnnHzrW9/KoEGD0qtXrzRp0qS0Er42zj333NSuXTtdunRJy5Ytq9yf/klXX311ttxyy+y9994ZMGBA+vbtu9Yr+qs1adIkV1xxRfbcc8/07Nkzs2bNym9/+9vUqlVrrd6TnXfeOQ8//HBefPHFfPWrX02vXr3y61//uvQd8msznz322CN33313fvGLX2TXXXfNRRddlFGjRmXw4MHrNJe19R//8R/55je/mWOOOSZ77bVXPvjgg0qr7qsdcMAB2WmnnbLvvvvmmGOOyde//vWMHDmydPzSSy/NhRdemNGjR6dz587p169fHnrooSrhHwA+T1lFdTz9BgAAAKh2VtoBAACgoIR2ACiAt99+O40bN/7U7bMuhefTXX755Z/6nvbv37+mywOAz+XyeAAogBUrVnzm093bt29fuhectffhhx/mww8/XOOxBg0aZJttttnIFQHAuhHaAQAAoKBcHg8AAAAFJbQDAABAQQntAAAAUFBCOwAAABSU0A4AAAAFJbQDAABAQQntAAAAUFBCOwAAABTU/w/6H3ejLbW0QQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x= \"Transfer_type\", y= \"Transaction_id\", hue =\"fraud_transaction_label\", data= data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "4-Z2jnnRi6Do",
        "outputId": "22eb7656-0b2f-4a42-b0e7-29f89003e7fe"
      },
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Transfer_type', ylabel='Transaction_id'>"
            ]
          },
          "metadata": {},
          "execution_count": 264
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_features = data.select_dtypes(include=['number']).columns\n",
        "core = data[num_features].corr()\n",
        "sns.heatmap(core, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "id": "Pa4_w0UJlFdN",
        "outputId": "029b6e86-b52b-4879-8b78-1b8d43c7fe41"
      },
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 265
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical = ['Transfer_type']\n",
        "\n",
        "categories_dummies = pd.get_dummies(data[categorical])\n",
        "categories_dummies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GTTLgBB9lFTk",
        "outputId": "13b87dfd-291f-4d25-8b07-63876dcd7458"
      },
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Transfer_type_CASH_IN  Transfer_type_CASH_OUT  Transfer_type_DEBIT  \\\n",
              "0                  False                   False                False   \n",
              "1                  False                   False                False   \n",
              "2                  False                   False                False   \n",
              "3                  False                    True                False   \n",
              "4                  False                   False                False   \n",
              "\n",
              "   Transfer_type_PAYMENT  Transfer_type_TRANSFER  \n",
              "0                   True                   False  \n",
              "1                   True                   False  \n",
              "2                  False                    True  \n",
              "3                  False                   False  \n",
              "4                   True                   False  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-77862c60-d179-416d-a889-a16b9bbced24\" class=\"colab-df-container\">\n",
              "    <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>Transfer_type_CASH_IN</th>\n",
              "      <th>Transfer_type_CASH_OUT</th>\n",
              "      <th>Transfer_type_DEBIT</th>\n",
              "      <th>Transfer_type_PAYMENT</th>\n",
              "      <th>Transfer_type_TRANSFER</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-77862c60-d179-416d-a889-a16b9bbced24');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  }\n",
              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
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              "  }\n",
              "\n",
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              "  }\n",
              "\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "categories_dummies",
              "summary": "{\n  \"name\": \"categories_dummies\",\n  \"rows\": 699,\n  \"fields\": [\n    {\n      \"column\": \"Transfer_type_CASH_IN\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type_CASH_OUT\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type_DEBIT\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type_PAYMENT\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type_TRANSFER\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data= pd.concat([data,categories_dummies], axis=1)\n",
        "\n",
        "print(data.shape)\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "id": "gDslrzZJlFLl",
        "outputId": "9b713c94-31a6-4353-ee7a-94c6155a67d2"
      },
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(699, 21)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Transaction_id Transfer_type    Amount Sender_account  Initial_balance_p1  \\\n",
              "0               1       PAYMENT   9839.64    ACC10204397            170136.0   \n",
              "1               2       PAYMENT   1864.28    ACC36787793             21249.0   \n",
              "2               3      TRANSFER    181.00    ACC69442839               181.0   \n",
              "3               4      CASH_OUT    181.00    ACC13673580               181.0   \n",
              "4               5       PAYMENT  11668.14    ACC46873273             41554.0   \n",
              "\n",
              "   new_balance_1 Recipient_account  Initial_balance_p2  new_balance_2  \\\n",
              "0      160296.36       ACC18330495                 0.0            0.0   \n",
              "1       19384.72       ACC43829436                 0.0            0.0   \n",
              "2           0.00       ACC19495987                 0.0            0.0   \n",
              "3           0.00       ACC94944418             21182.0            0.0   \n",
              "4       29885.86       ACC17787237                 0.0            0.0   \n",
              "\n",
              "   isFraud  ...  Transaction_Date Transaction_Time  \\\n",
              "0        0  ...          06-06-24         00:34:36   \n",
              "1        0  ...          16-07-24         19:13:41   \n",
              "2        1  ...          24-02-24         07:29:34   \n",
              "3        1  ...          30-12-24         04:48:05   \n",
              "4        0  ...          17-04-24         05:54:56   \n",
              "\n",
              "                                 Coordinates    ip_addresses  \\\n",
              "0  (43.550740883275296, -141.72974723173664)   15.246.64.212   \n",
              "1   (78.74630788793377, -2.8864833917104136)   29.13.180.155   \n",
              "2    (-48.70562123207855, 3.433940326483139)    132.7.36.190   \n",
              "3   (-54.63748785685925, 60.509022769098294)   153.105.24.95   \n",
              "4   (-57.96291770179832, -99.23770861640779)  161.203.207.51   \n",
              "\n",
              "  fraud_transaction_label Transfer_type_CASH_IN  Transfer_type_CASH_OUT  \\\n",
              "0          not Fraudulent                 False                   False   \n",
              "1          not Fraudulent                 False                   False   \n",
              "2              Fraudulent                 False                   False   \n",
              "3              Fraudulent                 False                    True   \n",
              "4          not Fraudulent                 False                   False   \n",
              "\n",
              "   Transfer_type_DEBIT  Transfer_type_PAYMENT  Transfer_type_TRANSFER  \n",
              "0                False                   True                   False  \n",
              "1                False                   True                   False  \n",
              "2                False                  False                    True  \n",
              "3                False                  False                   False  \n",
              "4                False                   True                   False  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
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              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        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>Transaction_id</th>\n",
              "      <th>Transfer_type</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Sender_account</th>\n",
              "      <th>Initial_balance_p1</th>\n",
              "      <th>new_balance_1</th>\n",
              "      <th>Recipient_account</th>\n",
              "      <th>Initial_balance_p2</th>\n",
              "      <th>new_balance_2</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>...</th>\n",
              "      <th>Transaction_Date</th>\n",
              "      <th>Transaction_Time</th>\n",
              "      <th>Coordinates</th>\n",
              "      <th>ip_addresses</th>\n",
              "      <th>fraud_transaction_label</th>\n",
              "      <th>Transfer_type_CASH_IN</th>\n",
              "      <th>Transfer_type_CASH_OUT</th>\n",
              "      <th>Transfer_type_DEBIT</th>\n",
              "      <th>Transfer_type_PAYMENT</th>\n",
              "      <th>Transfer_type_TRANSFER</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>9839.64</td>\n",
              "      <td>ACC10204397</td>\n",
              "      <td>170136.0</td>\n",
              "      <td>160296.36</td>\n",
              "      <td>ACC18330495</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>06-06-24</td>\n",
              "      <td>00:34:36</td>\n",
              "      <td>(43.550740883275296, -141.72974723173664)</td>\n",
              "      <td>15.246.64.212</td>\n",
              "      <td>not Fraudulent</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>ACC36787793</td>\n",
              "      <td>21249.0</td>\n",
              "      <td>19384.72</td>\n",
              "      <td>ACC43829436</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>16-07-24</td>\n",
              "      <td>19:13:41</td>\n",
              "      <td>(78.74630788793377, -2.8864833917104136)</td>\n",
              "      <td>29.13.180.155</td>\n",
              "      <td>not Fraudulent</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>181.00</td>\n",
              "      <td>ACC69442839</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>ACC19495987</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>24-02-24</td>\n",
              "      <td>07:29:34</td>\n",
              "      <td>(-48.70562123207855, 3.433940326483139)</td>\n",
              "      <td>132.7.36.190</td>\n",
              "      <td>Fraudulent</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>181.00</td>\n",
              "      <td>ACC13673580</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>ACC94944418</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>30-12-24</td>\n",
              "      <td>04:48:05</td>\n",
              "      <td>(-54.63748785685925, 60.509022769098294)</td>\n",
              "      <td>153.105.24.95</td>\n",
              "      <td>Fraudulent</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>11668.14</td>\n",
              "      <td>ACC46873273</td>\n",
              "      <td>41554.0</td>\n",
              "      <td>29885.86</td>\n",
              "      <td>ACC17787237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>17-04-24</td>\n",
              "      <td>05:54:56</td>\n",
              "      <td>(-57.96291770179832, -99.23770861640779)</td>\n",
              "      <td>161.203.207.51</td>\n",
              "      <td>not Fraudulent</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#remove the initial categorical columns now that we have encoded them\n",
        "data.drop(columns=['fraud_transaction_label'], inplace=True)"
      ],
      "metadata": {
        "id": "sTs8y7IqmM0e"
      },
      "execution_count": 268,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "Mp5qjftnmMyO",
        "outputId": "48e32d41-0835-4819-a369-07d4fc7e2acf"
      },
      "execution_count": 269,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Transaction_id Transfer_type    Amount Sender_account  Initial_balance_p1  \\\n",
              "0               1       PAYMENT   9839.64    ACC10204397            170136.0   \n",
              "1               2       PAYMENT   1864.28    ACC36787793             21249.0   \n",
              "2               3      TRANSFER    181.00    ACC69442839               181.0   \n",
              "3               4      CASH_OUT    181.00    ACC13673580               181.0   \n",
              "4               5       PAYMENT  11668.14    ACC46873273             41554.0   \n",
              "\n",
              "   new_balance_1 Recipient_account  Initial_balance_p2  new_balance_2  \\\n",
              "0      160296.36       ACC18330495                 0.0            0.0   \n",
              "1       19384.72       ACC43829436                 0.0            0.0   \n",
              "2           0.00       ACC19495987                 0.0            0.0   \n",
              "3           0.00       ACC94944418             21182.0            0.0   \n",
              "4       29885.86       ACC17787237                 0.0            0.0   \n",
              "\n",
              "   isFraud  isFlaggedFraud Transaction_Date Transaction_Time  \\\n",
              "0        0               0         06-06-24         00:34:36   \n",
              "1        0               0         16-07-24         19:13:41   \n",
              "2        1               0         24-02-24         07:29:34   \n",
              "3        1               0         30-12-24         04:48:05   \n",
              "4        0               0         17-04-24         05:54:56   \n",
              "\n",
              "                                 Coordinates    ip_addresses  \\\n",
              "0  (43.550740883275296, -141.72974723173664)   15.246.64.212   \n",
              "1   (78.74630788793377, -2.8864833917104136)   29.13.180.155   \n",
              "2    (-48.70562123207855, 3.433940326483139)    132.7.36.190   \n",
              "3   (-54.63748785685925, 60.509022769098294)   153.105.24.95   \n",
              "4   (-57.96291770179832, -99.23770861640779)  161.203.207.51   \n",
              "\n",
              "   Transfer_type_CASH_IN  Transfer_type_CASH_OUT  Transfer_type_DEBIT  \\\n",
              "0                  False                   False                False   \n",
              "1                  False                   False                False   \n",
              "2                  False                   False                False   \n",
              "3                  False                    True                False   \n",
              "4                  False                   False                False   \n",
              "\n",
              "   Transfer_type_PAYMENT  Transfer_type_TRANSFER  \n",
              "0                   True                   False  \n",
              "1                   True                   False  \n",
              "2                  False                    True  \n",
              "3                  False                   False  \n",
              "4                   True                   False  "
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              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>ACC36787793</td>\n",
              "      <td>21249.0</td>\n",
              "      <td>19384.72</td>\n",
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              "      <td>181.00</td>\n",
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              "type": "dataframe",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 699,\n  \"fields\": [\n    {\n      \"column\": \"Transaction_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 201,\n        \"min\": 1,\n        \"max\": 699,\n        \"num_unique_values\": 699,\n        \"samples\": [\n          159,\n          500,\n          397\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"TRANSFER\",\n          \"CASH_IN\",\n          \"CASH_OUT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260036.97996259067,\n        \"min\": 8.73,\n        \"max\": 2545478.01,\n        \"num_unique_values\": 696,\n        \"samples\": [\n          10648.06,\n          9686.85,\n          188238.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sender_account\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 699,\n        \"samples\": [\n          \"ACC71151265\",\n          \"ACC50520744\",\n          \"ACC41019414\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Initial_balance_p1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2399672.6713369135,\n        \"min\": 0.0,\n        \"max\": 9716462.14,\n        \"num_unique_values\": 493,\n        \"samples\": [\n          7243810.89,\n          71636.0,\n          6185786.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 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608612.15,\n          2441173.94,\n          402063.39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"new_balance_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3707628.9148058435,\n        \"min\": 0.0,\n        \"max\": 19169204.93,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          54985.05,\n          9940339.29,\n          10588885.27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isFraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isFlaggedFraud\",\n      \"properties\": {\n        \"dtype\": 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\"(64.18058489539649, -124.96356708496765)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ip_addresses\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 699,\n        \"samples\": [\n          \"34.210.92.96\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type_CASH_IN\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type_CASH_OUT\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      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            }
          },
          "metadata": {},
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "25UzX0jdmMv_"
      },
      "execution_count": 269,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 270,
      "metadata": {
        "id": "GLyoe4koNIwK"
      },
      "outputs": [],
      "source": [
        "data['Transaction_Date'] = pd.to_datetime(data['Transaction_Date'], format='%d-%m-%y')\n",
        "data['Transaction_Time'] = pd.to_datetime(data['Transaction_Time'], format='%H:%M:%S').dt.time\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 271,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "SoFT5f9QOVSd",
        "outputId": "298ffe21-f997-4343-8412-a40c78838d8a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Transaction_id Transfer_type    Amount Sender_account  Initial_balance_p1  \\\n",
              "0               1       PAYMENT   9839.64    ACC10204397            170136.0   \n",
              "1               2       PAYMENT   1864.28    ACC36787793             21249.0   \n",
              "2               3      TRANSFER    181.00    ACC69442839               181.0   \n",
              "3               4      CASH_OUT    181.00    ACC13673580               181.0   \n",
              "4               5       PAYMENT  11668.14    ACC46873273             41554.0   \n",
              "\n",
              "   new_balance_1 Recipient_account  Initial_balance_p2  new_balance_2  \\\n",
              "0      160296.36       ACC18330495                 0.0            0.0   \n",
              "1       19384.72       ACC43829436                 0.0            0.0   \n",
              "2           0.00       ACC19495987                 0.0            0.0   \n",
              "3           0.00       ACC94944418             21182.0            0.0   \n",
              "4       29885.86       ACC17787237                 0.0            0.0   \n",
              "\n",
              "   isFraud  isFlaggedFraud Transaction_Date    ip_addresses  \\\n",
              "0        0               0       2024-06-06   15.246.64.212   \n",
              "1        0               0       2024-07-16   29.13.180.155   \n",
              "2        1               0       2024-02-24    132.7.36.190   \n",
              "3        1               0       2024-12-30   153.105.24.95   \n",
              "4        0               0       2024-04-17  161.203.207.51   \n",
              "\n",
              "   Transfer_type_CASH_IN  Transfer_type_CASH_OUT  Transfer_type_DEBIT  \\\n",
              "0                  False                   False                False   \n",
              "1                  False                   False                False   \n",
              "2                  False                   False                False   \n",
              "3                  False                    True                False   \n",
              "4                  False                   False                False   \n",
              "\n",
              "   Transfer_type_PAYMENT  Transfer_type_TRANSFER   Latitude   Longitude  \n",
              "0                   True                   False  43.550741 -141.729747  \n",
              "1                   True                   False  78.746308   -2.886483  \n",
              "2                  False                    True -48.705621    3.433940  \n",
              "3                  False                   False -54.637488   60.509023  \n",
              "4                   True                   False -57.962918  -99.237709  "
            ],
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              "      <th>Transaction_id</th>\n",
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              "      <th>ip_addresses</th>\n",
              "      <th>Transfer_type_CASH_IN</th>\n",
              "      <th>Transfer_type_CASH_OUT</th>\n",
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              "      <th>Transfer_type_PAYMENT</th>\n",
              "      <th>Transfer_type_TRANSFER</th>\n",
              "      <th>Latitude</th>\n",
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              "      <th>0</th>\n",
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              "      <td>PAYMENT</td>\n",
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              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>43.550741</td>\n",
              "      <td>-141.729747</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>ACC36787793</td>\n",
              "      <td>21249.0</td>\n",
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              "      <td>2024-07-16</td>\n",
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              "      <td>78.746308</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>181.00</td>\n",
              "      <td>ACC69442839</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>ACC19495987</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2024-02-24</td>\n",
              "      <td>132.7.36.190</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>-48.705621</td>\n",
              "      <td>3.433940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>181.00</td>\n",
              "      <td>ACC13673580</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>ACC94944418</td>\n",
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              "      <td>False</td>\n",
              "      <td>-54.637488</td>\n",
              "      <td>60.509023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>11668.14</td>\n",
              "      <td>ACC46873273</td>\n",
              "      <td>41554.0</td>\n",
              "      <td>29885.86</td>\n",
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              "      <td>0</td>\n",
              "      <td>2024-04-17</td>\n",
              "      <td>161.203.207.51</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>-57.962918</td>\n",
              "      <td>-99.237709</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ab9727c5-7b54-4934-ba65-2bf039eac4ec')\"\n",
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              "\n",
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              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #D2E3FC;\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "\n",
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              "        document.querySelector('#df-ab9727c5-7b54-4934-ba65-2bf039eac4ec 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-ab9727c5-7b54-4934-ba65-2bf039eac4ec');\n",
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              "          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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 699,\n  \"fields\": [\n    {\n      \"column\": \"Transaction_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 201,\n        \"min\": 1,\n        \"max\": 699,\n        \"num_unique_values\": 699,\n        \"samples\": [\n          159,\n          500,\n          397\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"TRANSFER\",\n          \"CASH_IN\",\n          \"CASH_OUT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260036.97996259067,\n        \"min\": 8.73,\n        \"max\": 2545478.01,\n        \"num_unique_values\": 696,\n        \"samples\": [\n          10648.06,\n          9686.85,\n          188238.54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sender_account\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 699,\n        \"samples\": [\n          \"ACC71151265\",\n          \"ACC50520744\",\n          \"ACC41019414\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Initial_balance_p1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2399672.6713369135,\n        \"min\": 0.0,\n        \"max\": 9716462.14,\n        \"num_unique_values\": 493,\n        \"samples\": [\n          7243810.89,\n          71636.0,\n          6185786.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"new_balance_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2455110.700869648,\n        \"min\": 0.0,\n        \"max\": 9987286.56,\n        \"num_unique_values\": 409,\n        \"samples\": [\n          3401668.32,\n          1056.3,\n          9446.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recipient_account\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 699,\n        \"samples\": [\n          \"ACC81959731\",\n          \"ACC16212824\",\n          \"ACC94200769\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Initial_balance_p2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2166217.2851094683,\n        \"min\": 0.0,\n        \"max\": 17000998.23,\n        \"num_unique_values\": 416,\n        \"samples\": [\n          608612.15,\n          2441173.94,\n          402063.39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"new_balance_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3707628.9148058435,\n        \"min\": 0.0,\n        \"max\": 19169204.93,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          54985.05,\n          9940339.29,\n          10588885.27\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isFraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isFlaggedFraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transaction_Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2024-01-02 00:00:00\",\n        \"max\": \"2024-12-30 00:00:00\",\n        \"num_unique_values\": 316,\n        \"samples\": [\n          \"2024-12-17 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ip_addresses\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 699,\n        \"samples\": [\n          \"34.210.92.96\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type_CASH_IN\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type_CASH_OUT\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type_DEBIT\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type_PAYMENT\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transfer_type_TRANSFER\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.12801525805465,\n        \"min\": -89.5698553762385,\n        \"max\": 89.94220760283875,\n        \"num_unique_values\": 699,\n        \"samples\": [\n          64.18058489539649\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103.3778593293296,\n        \"min\": -179.46747073881204,\n        \"max\": 179.18739770829205,\n        \"num_unique_values\": 699,\n        \"samples\": [\n          -124.96356708496765\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 271
        }
      ],
      "source": [
        "data[['Latitude', 'Longitude']] = data['Coordinates'].str.extract(r'\\(([^,]+), ([^,]+)\\)')\n",
        "data['Latitude'] = data['Latitude'].astype(float)\n",
        "data['Longitude'] = data['Longitude'].astype(float)\n",
        "\n",
        "\n",
        "data.drop(columns=['Coordinates', 'Transaction_Time'], inplace=True)\n",
        "data.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 271,
      "metadata": {
        "id": "F6BgM6Lj5geq"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 272,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hhoLSebVPmGk",
        "outputId": "5e30fe15-3c6a-4ec9-c8c5-b7f085673062"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 699 entries, 0 to 698\n",
            "Data columns (total 20 columns):\n",
            " #   Column                  Non-Null Count  Dtype         \n",
            "---  ------                  --------------  -----         \n",
            " 0   Transaction_id          699 non-null    int64         \n",
            " 1   Transfer_type           699 non-null    object        \n",
            " 2   Amount                  699 non-null    float64       \n",
            " 3   Sender_account          699 non-null    object        \n",
            " 4   Initial_balance_p1      699 non-null    float64       \n",
            " 5   new_balance_1           699 non-null    float64       \n",
            " 6   Recipient_account       699 non-null    object        \n",
            " 7   Initial_balance_p2      699 non-null    float64       \n",
            " 8   new_balance_2           699 non-null    float64       \n",
            " 9   isFraud                 699 non-null    int64         \n",
            " 10  isFlaggedFraud          699 non-null    int64         \n",
            " 11  Transaction_Date        699 non-null    datetime64[ns]\n",
            " 12  ip_addresses            699 non-null    object        \n",
            " 13  Transfer_type_CASH_IN   699 non-null    bool          \n",
            " 14  Transfer_type_CASH_OUT  699 non-null    bool          \n",
            " 15  Transfer_type_DEBIT     699 non-null    bool          \n",
            " 16  Transfer_type_PAYMENT   699 non-null    bool          \n",
            " 17  Transfer_type_TRANSFER  699 non-null    bool          \n",
            " 18  Latitude                699 non-null    float64       \n",
            " 19  Longitude               699 non-null    float64       \n",
            "dtypes: bool(5), datetime64[ns](1), float64(7), int64(3), object(4)\n",
            "memory usage: 85.5+ KB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 273,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "nYsQsb3yQ1-I",
        "outputId": "98e5d544-52d8-4b83-d58b-ce2ec8fa3585"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Transaction_id        Amount  Initial_balance_p1  new_balance_1  \\\n",
              "count      699.000000  6.990000e+02        6.990000e+02   6.990000e+02   \n",
              "mean       350.000000  1.269811e+05        1.217855e+06   1.252809e+06   \n",
              "min          1.000000  8.730000e+00        0.000000e+00   0.000000e+00   \n",
              "25%        175.500000  4.859210e+03        0.000000e+00   0.000000e+00   \n",
              "50%        350.000000  2.153096e+04        1.986900e+04   1.076672e+04   \n",
              "75%        524.500000  1.588854e+05        6.697019e+05   7.150038e+05   \n",
              "max        699.000000  2.545478e+06        9.716462e+06   9.987287e+06   \n",
              "std        201.928205  2.600370e+05        2.399673e+06   2.455111e+06   \n",
              "\n",
              "       Initial_balance_p2  new_balance_2     isFraud  isFlaggedFraud  \\\n",
              "count        6.990000e+02   6.990000e+02  699.000000           699.0   \n",
              "mean         7.175774e+05   1.299207e+06    0.008584             0.0   \n",
              "min          0.000000e+00   0.000000e+00    0.000000             0.0   \n",
              "25%          0.000000e+00   0.000000e+00    0.000000             0.0   \n",
              "50%          5.790166e+04   0.000000e+00    0.000000             0.0   \n",
              "75%          5.076523e+05   9.714189e+05    0.000000             0.0   \n",
              "max          1.700100e+07   1.916920e+07    1.000000             0.0   \n",
              "std          2.166217e+06   3.707629e+06    0.092316             0.0   \n",
              "\n",
              "                    Transaction_Date    Latitude   Longitude  \n",
              "count                            699  699.000000  699.000000  \n",
              "mean   2024-06-30 07:45:34.763948800    0.780455   -1.929715  \n",
              "min              2024-01-02 00:00:00  -89.569855 -179.467471  \n",
              "25%              2024-03-30 00:00:00  -43.278919  -88.906294  \n",
              "50%              2024-06-26 00:00:00   -0.944188   -2.886483  \n",
              "75%              2024-09-27 00:00:00   43.502462   86.278912  \n",
              "max              2024-12-30 00:00:00   89.942208  179.187398  \n",
              "std                              NaN   51.128015  103.377859  "
            ],
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Transaction_id</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Initial_balance_p1</th>\n",
              "      <th>new_balance_1</th>\n",
              "      <th>Initial_balance_p2</th>\n",
              "      <th>new_balance_2</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>isFlaggedFraud</th>\n",
              "      <th>Transaction_Date</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>699.000000</td>\n",
              "      <td>6.990000e+02</td>\n",
              "      <td>6.990000e+02</td>\n",
              "      <td>6.990000e+02</td>\n",
              "      <td>6.990000e+02</td>\n",
              "      <td>6.990000e+02</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.0</td>\n",
              "      <td>699</td>\n",
              "      <td>699.000000</td>\n",
              "      <td>699.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>350.000000</td>\n",
              "      <td>1.269811e+05</td>\n",
              "      <td>1.217855e+06</td>\n",
              "      <td>1.252809e+06</td>\n",
              "      <td>7.175774e+05</td>\n",
              "      <td>1.299207e+06</td>\n",
              "      <td>0.008584</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2024-06-30 07:45:34.763948800</td>\n",
              "      <td>0.780455</td>\n",
              "      <td>-1.929715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.730000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2024-01-02 00:00:00</td>\n",
              "      <td>-89.569855</td>\n",
              "      <td>-179.467471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>175.500000</td>\n",
              "      <td>4.859210e+03</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2024-03-30 00:00:00</td>\n",
              "      <td>-43.278919</td>\n",
              "      <td>-88.906294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>350.000000</td>\n",
              "      <td>2.153096e+04</td>\n",
              "      <td>1.986900e+04</td>\n",
              "      <td>1.076672e+04</td>\n",
              "      <td>5.790166e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2024-06-26 00:00:00</td>\n",
              "      <td>-0.944188</td>\n",
              "      <td>-2.886483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>524.500000</td>\n",
              "      <td>1.588854e+05</td>\n",
              "      <td>6.697019e+05</td>\n",
              "      <td>7.150038e+05</td>\n",
              "      <td>5.076523e+05</td>\n",
              "      <td>9.714189e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2024-09-27 00:00:00</td>\n",
              "      <td>43.502462</td>\n",
              "      <td>86.278912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>699.000000</td>\n",
              "      <td>2.545478e+06</td>\n",
              "      <td>9.716462e+06</td>\n",
              "      <td>9.987287e+06</td>\n",
              "      <td>1.700100e+07</td>\n",
              "      <td>1.916920e+07</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2024-12-30 00:00:00</td>\n",
              "      <td>89.942208</td>\n",
              "      <td>179.187398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>201.928205</td>\n",
              "      <td>2.600370e+05</td>\n",
              "      <td>2.399673e+06</td>\n",
              "      <td>2.455111e+06</td>\n",
              "      <td>2.166217e+06</td>\n",
              "      <td>3.707629e+06</td>\n",
              "      <td>0.092316</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>51.128015</td>\n",
              "      <td>103.377859</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9b1d4f59-ea33-453c-9265-4d4552c57c15 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Transaction_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 251.61825919069915,\n        \"min\": 1.0,\n        \"max\": 699.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          699.0,\n          350.0,\n          201.92820506308672\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 876137.6094239581,\n        \"min\": 8.73,\n        \"max\": 2545478.01,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          126981.06075822604,\n          158885.435,\n          699.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Initial_balance_p1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3327700.8377106744,\n        \"min\": 0.0,\n        \"max\": 9716462.14,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          699.0,\n          1217854.647267525,\n          9716462.14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"new_balance_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3419612.500845059,\n        \"min\": 0.0,\n        \"max\": 9987286.56,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          699.0,\n          1252808.6134191703,\n          9987286.56\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Initial_balance_p2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5882440.695576243,\n        \"min\": 0.0,\n        \"max\": 17000998.23,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          699.0,\n          717577.4124320459,\n          17000998.23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"new_balance_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6598514.861894033,\n        \"min\": 0.0,\n        \"max\": 19169204.93,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          699.0,\n          1299206.7455221748,\n          3707628.9148058435\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isFraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 247.07845772891068,\n        \"min\": 0.0,\n        \"max\": 699.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.008583690987124463,\n          0.09231577995845096,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"isFlaggedFraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 247.13382002469837,\n        \"min\": 0.0,\n        \"max\": 699.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          699.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Transaction_Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000699\",\n        \"max\": \"2024-12-30 00:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"699\",\n          \"2024-06-30 07:45:34.763948800\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 250.90604125018848,\n        \"min\": -89.5698553762385,\n        \"max\": 699.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.780454527390544,\n          43.50246158834909\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 267.26944677953645,\n        \"min\": -179.46747073881204,\n        \"max\": 699.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -1.929714990011802,\n          86.27891231066585\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 273
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 274,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "OB4SLKIxSdC_",
        "outputId": "dc3e9858-c7e4-45b5-8bfd-690435809d1e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.countplot(x='isFraud', data=data, color='blue')\n",
        "plt.title('Distribution of Fraudulent Transactions')\n",
        "plt.xlabel('isFraud')\n",
        "plt.ylabel('Count')\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 275,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "id": "UlrMDWL9SttE",
        "outputId": "57e9520a-7948-48f1-80d0-374280440d3f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "num_features = data.select_dtypes(include=['number']).columns\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(data[num_features].corr(), annot=True, fmt='.2f', cmap='coolwarm')\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 276,
      "metadata": {
        "id": "tYt1vAPJTMie"
      },
      "outputs": [],
      "source": [
        "data['balance_change'] = data['Initial_balance_p1'] - data['new_balance_1']\n",
        "\n",
        "\n",
        "data['balance_1_ratio'] = data['Initial_balance_p1'] / (data['Initial_balance_p1'] + 1)\n",
        "data['balance_2_ratio'] = data['Initial_balance_p2'] / (data['Initial_balance_p2'] + 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "tbnyCvTKBWLx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data['amount_to_avg_ratio'] = data['Amount'] / (data['average_transaction_amount'] + 1)\n",
        "data = pd.get_dummies(data, columns=['ip_addresses'], drop_first=True)\n",
        ""
      ],
      "metadata": {
        "id": "f7DNbziMCXDB"
      },
      "execution_count": 305,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(data.columns)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZiF2ZS57-n68",
        "outputId": "58513d16-ab5c-417c-bc32-412af031e340"
      },
      "execution_count": 306,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Transaction_id', 'Transfer_type', 'Amount', 'Sender_account',\n",
            "       'Initial_balance_p1', 'new_balance_1', 'Recipient_account',\n",
            "       'Initial_balance_p2', 'new_balance_2', 'isFraud',\n",
            "       ...\n",
            "       'ip_addresses_98.131.248.68', 'ip_addresses_98.145.54.164',\n",
            "       'ip_addresses_98.183.239.247', 'ip_addresses_98.196.248.83',\n",
            "       'ip_addresses_98.199.132.55', 'ip_addresses_98.76.69.193',\n",
            "       'ip_addresses_99.167.174.194', 'ip_addresses_99.21.227.221',\n",
            "       'ip_addresses_99.229.27.71', 'ip_addresses_99.238.189.2'],\n",
            "      dtype='object', length=728)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 307,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "V2_XYCvUWql5",
        "outputId": "9a05dce1-19e0-40b4-eab1-a672ccbb2c09"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Transaction_id Transfer_type    Amount  Sender_account  Initial_balance_p1  \\\n",
              "0               1       PAYMENT   9839.64               3            170136.0   \n",
              "1               2       PAYMENT   1864.28             202             21249.0   \n",
              "2               3      TRANSFER    181.00             449               181.0   \n",
              "3               4      CASH_OUT    181.00              37               181.0   \n",
              "4               5       PAYMENT  11668.14             269             41554.0   \n",
              "\n",
              "   new_balance_1 Recipient_account  Initial_balance_p2  new_balance_2  \\\n",
              "0      160296.36       ACC18330495                 0.0            0.0   \n",
              "1       19384.72       ACC43829436                 0.0            0.0   \n",
              "2           0.00       ACC19495987                 0.0            0.0   \n",
              "3           0.00       ACC94944418             21182.0            0.0   \n",
              "4       29885.86       ACC17787237                 0.0            0.0   \n",
              "\n",
              "   isFraud  ...  ip_addresses_98.131.248.68 ip_addresses_98.145.54.164  \\\n",
              "0        0  ...                       False                      False   \n",
              "1        0  ...                       False                      False   \n",
              "2        1  ...                       False                      False   \n",
              "3        1  ...                       False                      False   \n",
              "4        0  ...                       False                      False   \n",
              "\n",
              "   ip_addresses_98.183.239.247  ip_addresses_98.196.248.83  \\\n",
              "0                        False                       False   \n",
              "1                        False                       False   \n",
              "2                        False                       False   \n",
              "3                        False                       False   \n",
              "4                        False                       False   \n",
              "\n",
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              "      <th>isFraud</th>\n",
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              "      <th>ip_addresses_98.145.54.164</th>\n",
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              "      <th>ip_addresses_98.196.248.83</th>\n",
              "      <th>ip_addresses_98.199.132.55</th>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
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              "      <td>181.00</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>181.00</td>\n",
              "      <td>37</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>ACC94944418</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 728 columns</p>\n",
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              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "      try {\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f42dd1c3-42ac-408b-9d2a-c07b20f6d3dc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 307
        }
      ],
      "source": [
        "data.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 308,
      "metadata": {
        "id": "RKe8aWB2WtIr"
      },
      "outputs": [],
      "source": [
        "features = ['Amount', 'Initial_balance_p1', 'new_balance_1', 'Initial_balance_p2', 'new_balance_2', 'balance_change', 'balance_1_ratio', 'balance_2_ratio']\n",
        "x = data[features]\n",
        "y = data['isFraud']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 308,
      "metadata": {
        "id": "gB2NGnZEYM3L"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 309,
      "metadata": {
        "id": "YuGDHcJdgZ2Y"
      },
      "outputs": [],
      "source": [
        "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": 310,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "YQXb29nhgiZ2",
        "outputId": "aa9c84a9-2add-461b-d802-0107a4512b24"
      },
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        {
          "output_type": "execute_result",
          "data": {
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              "LogisticRegression()"
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              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-10 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-10 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-10 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-10 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-10 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-10 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-10 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-10 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 310
        }
      ],
      "source": [
        "lor= LogisticRegression()\n",
        "lor.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 311,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "snmXdAtDgtJU",
        "outputId": "3cd0380e-d631-408a-e7b6-943e862cd5b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-11 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-11 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-11 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-11 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-11 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-11 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-11 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-11 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-11 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-11 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-11 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 311
        }
      ],
      "source": [
        "rf= RandomForestClassifier()\n",
        "rf.fit(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 312,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "qqtvjINdhjvc",
        "outputId": "e6a3f2e5-7e91-47b6-91a0-ab7ba503ed74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric='logloss',\n",
              "              feature_types=None, gamma=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
              "              max_leaves=None, min_child_weight=None, missing=nan,\n",
              "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
              "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-12 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-12 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-12 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-12 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-12 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-12 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-12 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-12 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-12 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-12 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-12 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-12 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-12 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-12 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
              "              feature_types=None, gamma=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
              "              max_leaves=None, min_child_weight=None, missing=nan,\n",
              "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
              "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=&#x27;logloss&#x27;,\n",
              "              feature_types=None, gamma=None, grow_policy=None,\n",
              "              importance_type=None, interaction_constraints=None,\n",
              "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
              "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
              "              max_leaves=None, min_child_weight=None, missing=nan,\n",
              "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
              "              n_jobs=None, num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 312
        }
      ],
      "source": [
        "xgb= XGBClassifier(use_label_encoder= False, eval_metric='logloss')\n",
        "xgb.fit(x_train,y_train)\n",
        "\n",
        "#log_loss = - (y * log(p) + (1 - y) * log(1 - p))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(y_test,y_pred):\n",
        "    cm_ = confusion_matrix(y_test,y_pred)\n",
        "    plt.figure(figsize = (6,4))\n",
        "    sns.heatmap(cm_, cmap ='coolwarm', linecolor = 'white', linewidths = 1, annot = True, fmt = 'd')\n",
        "    plt.title('Confusion Matrix')\n",
        "    plt.ylabel('True Label')\n",
        "    plt.xlabel('Predicted Label')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "NBlAQ3lb9UjD"
      },
      "execution_count": 313,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 314,
      "metadata": {
        "id": "0mixqgsCht_z"
      },
      "outputs": [],
      "source": [
        "def evaluation(model, x_test, y_test):\n",
        "  y_pred = model.predict(x_test)\n",
        "  y_prob = model.predict_proba(x_test)[:, 1]\n",
        "\n",
        "  score = model.score(x_test, y_test)\n",
        "  accuracy = accuracy_score(y_test, y_pred)\n",
        "\n",
        "  precision = precision_score(y_test, y_pred)\n",
        "  recall = recall_score(y_test, y_pred)\n",
        "  f1 = f1_score(y_test, y_pred)\n",
        "\n",
        "\n",
        "\n",
        "  precision_recall_curve(y_test, y_prob)\n",
        "\n",
        "\n",
        "  roc_auc = roc_auc_score(y_test, y_prob)\n",
        "\n",
        "\n",
        "\n",
        "  print(f'Precision: {precision}')\n",
        "  print(f'Recall: {recall}')\n",
        "  print(f'F1 Score: {f1}')\n",
        "\n",
        "  print(f'Accuracy: {accuracy}')\n",
        "  print(f'Score: {score}')\n",
        "  print(f'ROC AUC: {roc_auc}')\n",
        "\n",
        "  fpr, tpr, _ = roc_curve(y_test, y_prob)\n",
        "\n",
        "\n",
        "  plot_confusion_matrix(y_test,y_pred)\n",
        "  print(classification_report(y_test, y_pred))\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "  plt.plot(fpr, tpr, label=f'ROC Curve (area = {roc_auc:.2f})')\n",
        "\n",
        "  plt.plot([0, 1], [0, 1], color='red', linewidth=2)\n",
        "  plt.scatter(fpr, tpr, color='blue', marker='o')\n",
        "\n",
        "\n",
        "  plt.xlabel('False Positive Rate')\n",
        "  plt.ylabel('True Positive Rate')\n",
        "\n",
        "  plt.title('ROC Curve')\n",
        "  plt.legend(loc='lower right')\n",
        "  plt.show()\n",
        "\n",
        "  plt.grid()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 315,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AUKoGgiEk7oy",
        "outputId": "2db15203-e3b1-4da3-c295-fd2ca157820e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression: \n",
            "Precision: 1.0\n",
            "Recall: 1.0\n",
            "F1 Score: 1.0\n",
            "Accuracy: 1.0\n",
            "Score: 1.0\n",
            "ROC AUC: 1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       137\n",
            "           1       1.00      1.00      1.00         3\n",
            "\n",
            "    accuracy                           1.00       140\n",
            "   macro avg       1.00      1.00      1.00       140\n",
            "weighted avg       1.00      1.00      1.00       140\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "print(\"Logistic Regression: \")\n",
        "evaluation(lor, x_test, y_test)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 316,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "eMAnVH8Gle33",
        "outputId": "88e91acd-962a-4573-b8c4-1655916ff0a5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest: \n",
            "Precision: 0.0\n",
            "Recall: 0.0\n",
            "F1 Score: 0.0\n",
            "Accuracy: 0.9785714285714285\n",
            "Score: 0.9785714285714285\n",
            "ROC AUC: 0.6350364963503649\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99       137\n",
            "           1       0.00      0.00      0.00         3\n",
            "\n",
            "    accuracy                           0.98       140\n",
            "   macro avg       0.49      0.50      0.49       140\n",
            "weighted avg       0.96      0.98      0.97       140\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ZVDaT4kYIIYQwU8evPGTylkBOXH0MgJujnqF+FehWxxvr3/eBX0+4fVs72MJCa7355BPTrLyXjaS4EUIIIczMpXuRTNsezPZ/tMLFztqS/o1LM6BpWRytdDDpc/jiC5I3gipeXBs03KyZ6ZLORlLcCCGEEGbifmQc3+y+wOqjV0k0Kix00LW2N8NaVaCYs602WLhnT9i3L+Wk1q1hxQptnI2ZkOJGCCGEyOdi4g0sPniJBb9fIjJO29Cyha87o9v6UrG4k3bQb7/BW2/BPW09GywstNabMWO0P5sRKW6EEEKIfMpgVPx4/DozdgZzJzwOgJdLODOufSUalHXTDkpM1MbSBASkdEOVKKF1QzVubKLMc5YUN0IIIUQ+o5Ri3/l7TN0aRPCdCABKuNoxqm1FOlT1xMJCpx14/Tp07w4HD6ac3L69tumlm5sJMs8dUtwIIYQQ+cjZG2FM2RrI4YsPAHC2tWJIi/L0ql8KW+snZjlt3Qq9e8MD7TisrGDKFPj4Y7Prhvo3KW6EEEKIfOD6o2hm/Haen07eAEBvaUGfBqX4oHk5XO31KQcmJGjTuadPT4mVLKnt5F2/fi5nbRpS3AghhBB5WFh0AvP3hbDsUCjxBm0F4derezKidUW8C9unPvjKFejWDf74IyXWsSMsXQqFC+di1qYlxY0QQgiRB8UlGlhx5Apz94QQFpMAQP0yRRjXvhJVvFyePuGXX6BfP3j0SHtuba213nz4obZPVAEixY0QQgiRhxiNiv87c5PpO4K5/igGgPLujoxt70vziu7o/l2oxMfDqFEwZ05KrHRpWLcO6tTJxczzDiluhBBCiDziyMUHBGwL5Mz1MADcnWz4uHUF3qjphZVlGoOAL10Cf3/466+U2BtvwKJF4OqaO0nnQVLcCCGEECZ24U4EU7cFsTvoLgAOeksGNi3Lu41LY69P56v6xx/h7bchPFx7rtfDzJkwaFCB64b6NyluhBBCCBO5Gx7LrF3nWffnNYwKLC109Khbkg9blqeok03aJ8XGwogRMH9+SqxcOVi/HmrUyJ3E8zgpboQQQohcFhmXyA/7L7Fw/yViEgwAtK5cjNHtfClb1DH9Ey9c0LqhTp5MiXXrBt9/D87OOZx1/iHFjRBCCJFLEg1G1v55jdm7LnA/UtsuoUZJV8a1r0Qdn+dM1V67FgYMgAhtRWJsbbVBxP37F/huqH+T4kYIIYTIYUopdp67w9TtQVy6FwVAqSL2jG7rS7uXiz89A+pJMTHw0Ufwww8psYoVtW6oqlVzNvF8SoobIYQQIgedvPqIgK1BHAt9CEAhe2uGtixPj3ql0Fs9ZxuEoCDo2hX+/jsl1qsXfPstOD6j+6qAk+JGCCGEyAFXHkQxbXswW/6+BYCNlQXvNCrNe83K4mxr/fwLrFgB778PUVpLD3Z22iDivn2lG+o5pLgRQgghstHDqHi+2X2BVUevkGBQ6HTwRk0vPm5dAQ8Xu+dfICoKhgzRtkxIUrkybNig/Vc8lxQ3QgghRDaITTCw9FAo3+4NISIuEYAmFYoypq0vlT0zOJPpn3+0bqhz51Jib78Nc+eCvX3654lUpLgRQgghXoDBqPj55A1m/BbMzbBYACp7ODO2vS+NyxfN2EWU0lpqBg/WBhADODjAggXw1ls5lLn5kuJGCCGEyKIDF+4xZWsQgbe0VYI9XWwZ0aYinaqXwMIig+NiIiO1sTUrV6bEqlbV9oby9c2BrM2fFDdCCCFEJp27GU7AtkAOXLgPgJOtFR80L0ffBj7YWltm/EJnzsCbb8L58ymxgQNh1ixtALHIEiluhBBCiAy6+TiGGb+dZ9PJ6ygF1pY63nqlFENalKewgz7jF1JKW7dm6FCI0xbzw8lJi3XrljPJFyBS3AghhBDPER6bwHf7LrLk4GXiEo0AvFrVg1FtKlKqiEMmLxaurTS8bl1KrEYNbVG+cuWyMeuCS4obIYQQIh3xiUZWHb3CN7sv8Cg6AYC6PoUZ296XGiULZf6CJ05oe0OFhKTEBg+G6dO17RREtpDiRgghhPgXpRRb/77NtB1BXHkQDUDZog6MaVcJv0ruz94uIe0LagvwffwxxMdrMRcXWLwY3ngjm7MXUtwIIYQQT/gz9CGTtwRy6tpjANwcbRjWqjz+tb2xsnzOdglpefwY3nkHNm1KidWpo22EWaZMtuQsUpPiRgghhAAu3otk6rYgdp67A4CdtSUDmpShf5MyONpk8evy2DGtGyo0NCU2bBhMnQr6TAxAFpkixY0QQogC7V5EHHN2n2fNsWsYjAoLHfjXKckwv/K4O2dxHIxSMHs2jB4NCdpYHQoVgmXLoGPH7EpdpEOKGyGEEAVSdHwiC/df5of9F4mKNwDgV8md0W19KV/MKesXfvgQ+vWDzZtTYvXrw5o1UKrUC2YtMkKKGyGEEAVKosHIxuPXmbnzPHcjtDVmqnq5MLZdJeqXLfJiFz9yROuGunYtJTZqFHz5JVhnYCdwkS2kuBFCCFEgKKXYE3SXqduCuHA3EgDvwnaMbOPLa1U8Mr5dQlqMRvj6axg3DgxaKxBFisDy5dC+fTZkLzJDihshhBBm78z1x0zZGsgflx4C4GJnzZAW5ehVvxQ2VpnYLiEt9+9D796wbVtKrFEjrRvKy+vFri2yRIobIYQQZuvaw2im7whm8+mbAOitLOjXwIdBzcrhYp8N3UQHDkD37nDjhvZcp9Nabz77DKzkK9ZU5M4LIYQwO4+j45m3J4TlR64Qb9C2S+hcowTDW1fAq5D9i7+B0QgBATBhgvZngKJFYdUqaNXqxa8vXogUN0IIIcxGbIKB5UdCmbcnhPDYRAAalivC2HaVeLmES/a8yZ070KsX7NyZEmveXCtsPDyy5z3EC5HiRgghRL5nNCo2n77J9B3B3HgcA0DFYk6Mbe9L0wpFM79dQnr27IGePeH2be25TgcTJ8Knn4LlC47dEdlGihshhBD52uGQ+0zZFsjZG+EAFHO24ePWFXmjpheWLzID6kkGA3zxBUyapC3QB1C8OKxerbXaiDxFihshhBD5UvDtCAK2BbIv+B4AjjZWvN+sLG83LI2dPhtbUW7d0lpr9u5NibVqBStWQLFi2fc+IttIcSOEECJfuR0Wy6yd59lw/BpGBVYWOnrWK8mQluVxc7TJ3jfbuRPeegvu3tWeW1hoLThjxmh/FnmSFDdCCCHyhYjYBH7Yf4mFBy4Rm6DNUGr3cnFGtqlImaKO2ftmiYnadO4pU1K6oUqU0Nauadw4e99LZDspboQQQuRpCQYja49dZfauCzyIigegVqlCjGvvS61ShbP/Da9fhx49tDVskrRrp6027OaW/e8nsp0UN0IIIfIkpRQ7/rnDtO1BXLofBUBpNwdGt61Im5eKZ98MqCdt3aqtNvzggfbc0lJbz+bjj6UbKh+R4kYIIUSec/zKIwK2BvLXlUcAFHHQM9SvPN3rlsTaMgeKjIQE+OQTmD49JebtDevWaTt6i3xFihshhBB5xuX7UUzbHsS2s9o6MrbWFrzbqAwDm5bByTaHdtW+ehW6ddN29E7SsSMsXQqFc6DbS+Q4KW6EEEKY3IPIOL7ZfYFVR6+SaFTodPBmLS+Gt6pIcRfbnHvjzZuhb194pLUQYW0NX30FH32kLdAn8iUpboQQQphMTLyBJYcu892+i0TGadslNKtYlDHtfPEt7pxzbxwfD6NHw+zZKTEfH60bqm7dnHtfkStMPjpq/vz5+Pj4YGtrS7169Th27Ngzj589ezYVK1bEzs4Ob29vhg0bRmxsbC5lK4QQIjsYjIr1f12j+df7mL4jmMi4RF7ydGbVu/VY1q9uzhY2ly9Do0apC5vOneHkSSlszIRJW27WrVvH8OHDWbBgAfXq1WP27Nm0adOG4OBg3N3dnzp+9erVjBkzhiVLltCgQQPOnz9P37590el0zJw50wSfQAghRGYopfj9/D2mbgsi6HYEACVc7RjZpiIdq3likV3bJaRn0yZ4+20IC9Oe6/UwcyYMGiTdUGZEp1TS6kS5r169etSpU4d58+YBYDQa8fb2ZsiQIYwZM+ap4wcPHkxgYCC7d+9Ojn388cccPXqUgwcPZug9w8PDcXFxISwsDGfnHPzNQAghRCpnb4QxdVsQB0PuA+Bsa8XgFuXoXd8HW+sc3nQyNhZGjoT/fd8AULYsrF8PNWvm7HuLbJGZ72+TtdzEx8dz/Phxxo4dmxyzsLDAz8+PI0+OWH9CgwYNWLlyJceOHaNu3bpcunSJrVu30qtXr3TfJy4ujri4uOTn4eHh2fchhBBCPNeNxzHM2BHMT6duoBRYW+roXd+Hwc3LUchBn/MJhIRA165at1MSf3/44QeQX3LNksmKm/v372MwGCj2r03HihUrRlBQUJrn9OjRg/v379OoUSOUUiQmJvLee+8xbty4dN8nICCAzz//PFtzF0II8XxhMQl8uy+EpYdCiU/UtkvoWM2TkW0q4l3YPneSWLcO+veHCK0LDBsb+OYbLSbdUGbL5AOKM2Pfvn1MmTKFb7/9lhMnTrBp0ya2bNnCF198ke45Y8eOJSwsLPlx7dq1XMxYCCEKnrhEA4sPXqbp9L18//sl4hON1CtdmF8+aMg33WvkTmETEwMDB2rr1yQVNhUqwLFjMGCAFDZmzmQtN25ublhaWnLnzp1U8Tt37lC8ePE0zxk/fjy9evXi3XffBaBKlSpERUUxYMAAPvnkEyzSWBrbxsYGG5ts3iVWCCHEU5RS/HrmFtN2BHHtYQwA5dwdGdvOlxa+7jmzXUJagoO1bqgzZ1Jib70F330Hjtm8wabIk0xW3Oj1emrVqsXu3bvp1KkToA0o3r17N4MHD07znOjo6KcKGEtLbRCaCcdFCyFEgXf00gOmbA3k9HVtFlJRJxuGt6rAm7W8sMqJ7RLSs3IlvPceRGl7UWFnpw0i7tdPWmsKEJNOBR8+fDh9+vShdu3a1K1bl9mzZxMVFUW/fv0A6N27NyVKlCAgIACADh06MHPmTGrUqEG9evUICQlh/PjxdOjQIbnIEUIIkXtC7kYwdVsQuwLvAmCvt2Rgk7L0b1Iae30ufsVER8PgwdqWCUkqV9ZmQ730Uu7lIfIEkxY3/v7+3Lt3jwkTJnD79m2qV6/O9u3bkwcZX716NVVLzaeffopOp+PTTz/lxo0bFC1alA4dOjB58mRTfQQhhCiQ7kbEMmvnBdb9eRWjAksLHd3qePORXwWKOuXyUIB//tG6oc6dS4n16wdz54KDQ+7mIvIEk65zYwqyzo0QQmRdVFwiP+y/xMIDl4iONwDQqnIxRrf1pZx7Lo9nUQqWLYMPPtAGEINWzHz3HTxjiRCRP+WLdW6EEELkH4kGI+v+usasnRe4H6mtHVbN25Vx7XypV6ZI7icUGamtKrxiRUqsShWtG8rXN/fzEXmKFDdCCCHSpZRiV+Bdpm4L5OI9bZBuycL2jGpbkVereOTeDKgnnTmjdUMFB6fEBgzQ9oqys8v9fESeI8WNEEKINJ2+9pjJWwM5dvkhAIXsrfmwZXl61iuF3soEy6QpBQsXwtCh2nYKoE3tXrhQW89GiP+R4kYIIUQqVx9EM21HEL+euQWA3sqCtxuW5v1mZXGxszZNUuHh2qJ8a9emxGrU0FYgLl/eNDmJPEuKGyGEEAA8iopn7p4QVvwRSoJBodNB5xpeDG9dgRKuJuzuOXlS64YKCUmJffABfP012NqaLi+RZ0lxI4QQBVxsgoFlh0OZvzeEiNhEABqXd2NMO19e8nQxXWJKwbffwvDhEB+vxZydYfFi6NLFdHmJPE+KGyGEKKCMRsXPp27w9Y5gboZpY1h8izsxrn0lmlQoatrkHj+Gd9+FH39MidWurXVDlSljsrRE/iDFjRBCFEAHL9xnytZAzt0KB8DDxZaPW1fkPzVKYGlh4m0K/vwT/P3h8uWU2EcfwVdfgV5vsrRE/iHFjRBCFCCBt8IJ2BbE/vP3AHCyseL95mV5u2FpbK1NvI2NUjBnDowaBQkJWszVVVuo7/XXTZmZyGekuBFCiALgVlgMM387z8YT11EKrCx0vPVKKYa0KEcRx1zeLiEtDx9qWyZs3pwSe+UVbXZUqVKmy0vkS1LcCCGEGQuPTeD73y+y+OBlYhOMALxaxYORbSri45ZH9l06ckRbp+bq1ZTYyJEweTJYm2jqucjXpLgRQggzFJ9oZM2xq8zZfYGHUdpMozo+hRjbvhI1SxYycXb/YzTCjBkwbhwkarO0KFIE/vtfePVV0+Ym8jUpboQQwowopdh+9jZfbQ8i9EE0AGWKOjCmrS+tKhczzXYJabl/H/r0ga1bU2KNGsGaNeDlZbq8hFmQ4kYIIczEX6EPmbI1kBNXHwPg5qhnqF8FutXxxtrSBNslpOfAAejeHW7c0J7rdDB2LHz+OVjJ15J4cfK3SAgh8rmL9yKZtj2IHf/cAcDO2pL+jUszoGlZHG3y0I95oxGmToUJE8Bg0GJFi8LKldC6tWlzE2YlD/2tF0IIkRn3I+OYs+sCq49dxWBUWOiga21vhrWqQDHnPLYtwd278NZbsHNnSqxZM1i1Cjw9TZaWME9S3AghRD4TE29g0YFLLPj9IlHxWgtIS193RrfzpUIxJxNnl4a9e6FHD7h9W3uu02mtN+PHg6WJ19YRZkmKGyGEyCcMRsXG49eYufM8d8LjAKhSwoWx7X1pUNbNxNmlwWCAL7+ESZO0LimA4sW11poWLUybmzBrUtwIIUQep5RiX/A9ArYFcv5OJABehewY2aYiHap6YmHq7RLScuuW1g21Z09KzM9PG19TrJjp8hIFghQ3QgiRh/19PYyAbYEcvvgAABc7a4a0KEev+qWwscqjXTo7d2qFzd272nMLC631ZswY6YYSuUKKGyGEyIOuPYzm69+C+eXUTQD0lhb0bejDB83K4WKfR1ftTUyEzz6DKVO0faJAGyy8Zg00aWLS1ETBIsWNEELkIWHRCczfF8KyQ6HEG7RxKp2qe/Jx64p4F7Y3cXbPcOOGtnbNgQMpsbZtYflybbq3ELlIihshhMgD4hINLD98hXl7QwiL0XbErl+mCOPaV6KKl4uJs3uObdugd29t1WHQup6mTIERI7QuKSFymRQ3QghhQkaj4v/O3GT6jmCuP4oBoEIxR8a2q0SzikXzznYJaUlIgE8/hWnTUmLe3tpO3g0amC4vUeBJcSOEECZy5OIDArYFcuZ6GADuTjZ83LoCXWp5Y5kXZ0A96epVbSfvI0dSYh06wNKl2uaXQpiQFDdCCJHLzt+JYOq2IPYEabOJHPSWvNe0LO80Lo29Ph/8WN68Gfr2hUePtOdWVlrrzUcfaQv0CWFi+eBfkRBCmIc74bHM2nme9X9dw6jA0kJHj7olGepXHjdHG1On93zx8dp07lmzUmI+PrBuHdSta7K0hPg3KW6EECKHRcYl8sPvF1l44DIxCdp2CW1eKsaotr6ULepo4uwy6PJlrRvq2LGU2H/+A0uWgKurydISIi1S3AghRA5JMBhZ++c15uw6z/3IeABqlnRlXPtK1PYpbOLsMmHTJnj7bQjTxgah18OMGfDBB9INJfIkKW6EECKbKaX47dwdvtoexKV7UQD4FLFndFtf2r5cPG/PgHpSXJw2nXvevJRY2bJaN1StWqbLS4jnkOJGCCGy0YmrjwjYGsifodpg28IOeoa2LE/3uiXRW+WjNV9CQsDfH06cSIl17QoLF4Kzs+nyEiIDpLgRQohsEHo/iuk7gtny9y0AbKwseLdxaQY2LYuzbR7dLiE969fDu+9CRIT23MYG5syBAQOkG0rkC1LcCCHEC3gYFc83uy+w6ugVEgwKnQ661PRieOsKeLjYmTq9zImJgWHD4PvvU2IVKmjFTrVqpstLiEx6oeImNjYWW1vb7MpFCCHyjdgEA0sOXea7vReJiEsEoGmFooxp50slj3zYbRMcrHU7nTmTEuvZE777DpycTJeXEFmQ6Q5go9HIF198QYkSJXB0dOTSpUsAjB8/nsWLF2d7gkIIkZcYjIqNx6/T/Ot9TNseTERcIpU9nFn5Tj3++3bd/FnYrFypDRBOKmzs7GDxYlixQgobkS9lurj58ssvWbZsGdOmTUOv1yfHX375ZRYtWpStyQkhRF6y//w9Xpt7kBEbTnMrLBZPF1tmdq3Gr0Ma0ai8m6nTy7zoaHjnHejVC6K0WV1UqqStZfP22zK+RuRbme6WWr58OT/88AMtW7bkvffeS45Xq1aNoKCgbE1OCCHygnM3wwnYFsiBC9qu1062VnzQvBx9G/hga21p4uyy6Nw5rRvqn39SYn37atO+HRxMlpYQ2SHTxc2NGzcoV67cU3Gj0UhCQkK2JCWEEHnBzccxfP1bMD+dvIFSYG2po9crPgxpUY5CDvrnXyCvWrYMBg3SBhAD2NtrY2t69zZpWkJkl0wXN5UrV+bAgQOUKlUqVXzjxo3UqFEj2xITQghTCY9N4Nu9F1l66DJxiUYAXqvqwag2vpQsYm/i7F5AZKS2qvDy5Smxl1+GDRvA19d0eQmRzTJd3EyYMIE+ffpw48YNjEYjmzZtIjg4mOXLl/Prr7/mRI5CCJEr4hONrPzjCnP3XOBRtNYSXbd0Yca1r0R1b1fTJvei/v5b64Z6cvhA//7a+jV2+WzKuhDPoVNKqcyedODAASZNmsTp06eJjIykZs2aTJgwgdatW+dEjtkqPDwcFxcXwsLCcJZVNoUQaNslbPn7FtO2B3P1YTQAZYs6MLZdJVpWcs8/2yWkRSlYtAg+/BBiY7WYoyP88AN0727a3ITIhMx8f2epuMnPpLgRQjzp2OWHTN4ayOlrjwFwc7RhWKvy+Nf2xsoyH22XkJbwcBg4ENauTYlVr64tyle+vMnSEiIrMvP9neluqTJlyvDnn39SpEiRVPHHjx9Ts2bN5HVvhBAiLwu5G8lX24PYee4OAPZ6SwY0KUP/xmVwsDGDxdtPntS6oUJCUmKDBmm7ecviq8LMZfpfcGhoKAaD4al4XFwcN27cyJakhBAip9yNiGXOrgus/fMaBqPCQgf+dUoyzK887s5m8KWvlDbzadgwiI/XYs7O2qJ8XbqYNjchckmGi5vNmzcn/3nHjh24uLgkPzcYDOzevRsfH59sTU4IIbJLdHwiC/df5vv9F4mO135B86tUjDHtKlLO3UxW4Q0L0za83LgxJVa7NqxbB2XKmC4vIXJZhoubTp06AaDT6ejTp0+q16ytrfHx8WHGjBnZmpwQQryoRIORDcevM3Pnee5FxAFQzcuFse0r8UqZIs85Ox/580/w94fLl1NiQ4fCV19pu3oLUYBkuLgxGrW1HkqXLs2ff/6Jm1s+XGpcCFFgKKXYE3SXqduCuHA3EgDvwnaMauPLa1U98vcMqCcpBd98AyNHQtJCqq6usHQp/O+XUiEKmkyPubn85G8FQgiRB525/pgpWwP549JDAFztrRnSojxvvVISG6t8ul1CWh4+1PaA+uWXlFi9elo31L8WWhWiIMnSlICoqCh+//13rl69SnzSgLX/+fDDD7MlMSGEyKxrD6OZtiOY/zt9EwC9lQX9GvowqFk5XOysTZxdNvvjD60b6urVlNiIETBlClib2WcVIpMyXdycPHmS9u3bEx0dTVRUFIULF+b+/fvY29vj7u4uxY0QItc9jo5n7p4QVhy5QrzBiE4H/6leguGtK+BVKB9vl5AWoxFmzoSxYyExUYsVKQL//S+8+qppcxMij8h0cTNs2DA6dOjAggULcHFx4Y8//sDa2pq33nqLoUOH5kSOQgiRptgEA/89HMr8vSGEx2pf9A3LFWFsu0q8XMLlOWfnQ/fvazt3b9mSEmvYUFukz8vLZGkJkddkurg5deoU33//PRYWFlhaWhIXF0eZMmWYNm0affr0oXPnzjmRpxBCJDMaFZtP32T6jmBuPNZ2tvYt7sTY9pVoUt7NfAYLP+ngQejWDZ5cT2zsWJg0CazMYNFBIbJRpv9FWFtbY2GhLUnu7u7O1atXqVSpEi4uLly7di3bExRCiCcdDrnPlG2BnL0RDkBxZ1uGt67AGzW9sLQww6LGaNSmc48fD0kLqBYtCitWQJs2ps1NiDwq08VNjRo1+PPPPylfvjxNmzZlwoQJ3L9/nxUrVvDyyy/nRI5CCEHw7QgCtgWyL/geAI42VrzfrCxvNyyNnd6MZkA96e5d6NULfvstJdasGaxaBZ6eJktLiLwu08XNlClTiIiIAGDy5Mn07t2b999/n/Lly7N48eJsT1AIUbDdDotl5s5gNh6/jlGBlYWOt14pxZAW5SjiaMaL0+3bBz16wK1b2nOdTmu9mTABLM20mBMim8iu4EKIPCkiNoHvf7/EooOXiE3QFhFt93JxRrX1pbSbg4mzy0EGA0yeDJ9/rnVJARQrprXWtGxp2tyEMKHMfH9bZNebnjhxgtdeey3T582fPx8fHx9sbW2pV68ex44de+bxjx8/5oMPPsDDwwMbGxsqVKjA1q1bs5q2ECKPSTAYWX4klGbT9zFvbwixCUZqlSrEj+834Lu3apl3YXP7NrRuDRMnphQ2LVvCqVNS2AiRCZnqltqxYwc7d+5Er9fz7rvvUqZMGYKCghgzZgz/93//R5tMDm5bt24dw4cPZ8GCBdSrV4/Zs2fTpk0bgoODcXd3f+r4+Ph4WrVqhbu7Oxs3bqREiRJcuXIFV1fXTL2vECLvUUqx45/bfLU9mMv3owAo7ebA6La+tHmpmHnOgHrSrl3Qs6c2zgbAwkJrvRk7VrqhhMikDHdLLV68mP79+1O4cGEePXpEkSJFmDlzJkOGDMHf35+hQ4dSqVKlTL15vXr1qFOnDvPmzQO0/au8vb0ZMmQIY8aMeer4BQsWMH36dIKCgrDO4gqc0i0lRN5z/MpDpmwN4viVRwAUcdDzkV95utUtibVltjUw502JiVoRM3mytk8UaIOFV6+Gpk1Nm5sQeUhmvr8zXNxUrVqVXr16MXLkSH788UfefPNNXnnlFdavX49XFhaPio+Px97eno0bNybvOA7Qp08fHj9+zC9P7pXyP+3bt6dw4cLY29vzyy+/ULRoUXr06MHo0aOxTOc3m7i4OOLi4pKfh4eH4+3tLcWNEHnA5ftRTNsexLaztwGwtbagf+MyDGhSBifbArCFwI0b2qDh/ftTYm3bwvLl2nRvIUSyzBQ3Ge6WunjxIm+++SYAnTt3xsrKiunTp2epsAG4f/8+BoOBYsWKpYoXK1aMoKCgNM+5dOkSe/bsoWfPnmzdupWQkBAGDRpEQkICEydOTPOcgIAAPv/88yzlKITIGQ8i4/hm9wVWHb1KolFhoYM3a3kzrFUFirvYmjq93LF9uzbN+/597bmlpdZ6M3Kk1iUlhMiyDBc3MTEx2Ntre7TodDpsbGzw8PDIscTSYjQacXd354cffsDS0pJatWpx48YNpk+fnm5xM3bsWIYPH578PKnlRgiR+2LiDSw5dJnv9l0kMk7bLqF5xaKMaVeJisWdTJxdLklI0KZ0f/VVSszLS9tCoWFD0+UlhBnJ1IDiRYsW4ejoCEBiYiLLli3Dzc0t1TEZ3TjTzc0NS0tL7ty5kyp+584dihcvnuY5Hh4eWFtbp+qCqlSpErdv3yY+Ph69Xv/UOTY2NtjYmPFaGELkAwaj4sfj15mxM5g74Vo38cslnBnXrhINyrk952wzcvUqdO8Ohw+nxF57DZYt0za/FEJkiwwXNyVLlmThwoXJz4sXL86KFStSHaPT6TJc3Oj1emrVqsXu3buTx9wYjUZ2797N4MGD0zynYcOGrF69GqPRmLwFxPnz5/Hw8EizsBFCmJZSit/P32PqtiCCbmuLf5ZwtWNkm4p0rOaJhTlul5Ce//s/bdPLhw+151ZWWuvNsGHaAn1CiGyT4eImNDQ02998+PDh9OnTh9q1a1O3bl1mz55NVFQU/fr1A6B3796UKFGCgIAAAN5//33mzZvH0KFDGTJkCBcuXGDKlCkZLqiEELnn7I0wArYFcijkAQDOtlYMblGO3vV9sLUuQFOb4+O16dwzZ6bESpWCdeugXj3T5SWEGTPpVrL+/v7cu3ePCRMmcPv2bapXr8727duTBxlfvXo1uYUGwNvbmx07djBs2DCqVq1KiRIlGDp0KKNHjzbVRxBC/Mv1R9HM+O08P53Udq/WW1rQu34pBrcoh6t9AWthvXxZ28n7ycVJO3WCJUugUCGTpSWEuZPtF4QQ2SIsJoFv94aw9HAo8Yna6rodq3kysk1FvAvbmzg7E/jpJ+jXD8LCtOd6PXz9NQweLN1QQmRBjkwFF0KItMQlGlhx5Arz9obwODoBgFfKFGZc+0pU9XI1bXKmEBenTeeeOzclVqYMrF8PtWqZLi8hChApboQQWWI0Kn79+xbTdwRx7WEMAOXdHRnb3pfmFd3Nf7uEtFy8CP7+cPx4SuzNN2HhQnBxMV1eQhQwUtwIITLtj0sPCNgayOnrWpeLu5MNw1tVoEstL6zMfbuE9KxfD+++CxHarDBsbGD2bBg4ULqhhMhlWSpuLl68yNKlS7l48SJz5szB3d2dbdu2UbJkSV566aXszlEIkUdcuBPBV9uD2BWobe7ooLdkYNOyvNu4NPb6Avq7UkwMDB8OCxakxMqX14qd6tVNlpYQBVmmf8X6/fffqVKlCkePHmXTpk1ERkYCcPr06XRXCRZC5G93w2MZu+kMbWbvZ1fgXSwtdLz1Skn2jWzOhy3LF9zCJjgYXnkldWHTo4fWLSWFjRAmk+mfSGPGjOHLL79k+PDhODmlLJfeokWL5N29hRDmISoukR/2X2LhgUtExxsAaF25GKPa+lLO3dHE2ZnYqlVal1NUlPbc1hbmzYO335ZuKCFMLNPFzd9//83q1aufiru7u3M/aQM4IUS+lmgwsu6va8zaeYH7kdp2CdW9XRnXvhJ1Sxc2cXYmFh0NH34IixenxCpV0rqhXn7ZdHkJIZJlurhxdXXl1q1blC5dOlX85MmTlChRItsSE0LkPqUUuwLvMnVbIBfvaS0SpYrYM6qNL+2rFC+YM6CedO4cdO0K//yTEuvTB+bPBwcH0+UlhEgl08VNt27dGD16NBs2bECn02E0Gjl06BAjRoygd+/eOZGjECIXnLr2mClbAzl2Wdv7qJC9NR+2LE/PeqXQWxXQGVBPWrYMPvhAa7kBsLeHb7/VihshRJ6S6eJmypQpfPDBB3h7e2MwGKhcuTIGg4EePXrw6aef5kSOQogcdOVBFNN2BLPlzC0AbKwseLtRad5vVhZnW2sTZ5cHREZqRc3y5Smxl1/WuqEqVTJdXkKIdGV5+4WrV69y9uxZIiMjqVGjBuXLl8/u3HKEbL8ghOZRVDzf7LnAyj+ukGBQ6HTQuYYXH7eugKernanTyxv+/lvrhgoKSom9+y7MmaO13Aghck2Obr9w8OBBGjVqRMmSJSlZsmSWkxRCmEZsgoGlh0L5dl8IEbGJADQu78bYdpWo7CkFPwBKaQOGhwyB2Fgt5ugI33+vTfUWQuRpmS5uWrRoQYkSJejevTtvvfUWlStXzom8hBDZzGhU/HTyBjN+C+ZmmPaFXcnDmbHtfGlSoaiJs8tDIiK0Kd5r1qTEqlXTuqEqVDBdXkKIDMt0cXPz5k3Wrl3LmjVrmDp1KlWrVqVnz550794dLy+vnMhRCPGCDly4R8DWIM7dCgfAw8WWEa0r0qlGCSwtCvgMqCedPKl1Q4WEpMQGDYIZM7R1bIQQ+UKWx9wAXL58mdWrV7NmzRqCgoJo0qQJe/bsyc78sp2MuREFSeCtcAK2BbH//D0AnGysGNS8HP0a+mBrbWni7PIQpeC777RtFOK0dX1wdoZFi7SNL4UQJpeZ7+8XKm4ADAYD27ZtY/z48Zw5cwaDwfAil8txUtyIguBWWAwzfjvPjyeuoxRYW+p465VSDGlRnsIOelOnl7eEhWmDhDduTInVqgXr1kHZsqbLSwiRSo4OKE5y6NAhVq1axcaNG4mNjeX1118nICAgq5cTQmSD8NgEFuy7yOKDl4lLNALwahUPRrWtSKkissjcU/76C/z94dKllNiHH8K0adqu3kKIfCnTxc3YsWNZu3YtN2/epFWrVsyZM4fXX38de5kWKYTJxCcaWX30Ct/sCeFhVDwAdXwKMa59JWqULGTi7PIgpeCbb2DkSEhI0GKurrB0KXTqZMrMhBDZINPFzf79+xk5ciRdu3bFzc0tJ3ISQmSQUoptZ28zbXsQoQ+0lXPLFHVgTFtfWlUuJtslpOXRI21zy59/TonVqwdr14KPj6myEkJko0wXN4cOHcqJPIQQmfRX6EMmbw3k5NXHALg56vnIrwLd6nhjZSnbJaTp6FGtG+rKlZTYxx/DlCmgl7FIQpiLDBU3mzdvpl27dlhbW7N58+ZnHtuxY8dsSUwIkbaL9yKZtj2IHf/cAcDO2pL+TcowoEkZHG2yPIzOvBmNMGsWjBkDidrChRQuDP/9L7z2mmlzE0JkuwzNlrKwsOD27du4u7tjYZH+b4Q6nU5mSwmRQ+5FxDFn93nWHLuGwaiw0IF/HW+G+VXA3VnWYEnXgwfa5pZbtqTEGjbUFunz9jZdXkKITMn22VJGozHNPwshcl50fCKLDlzm+98vEhWv/fLQ0tedMe18KV/MycTZ5XEHD0L37nD9ekps7Fj4/HOwlk1BhTBXme6YX758OXFJi1w9IT4+nuVP7porhHghBqNi7bGrNJu+j5k7zxMVb6BKCRfW9H+FxX3rSGHzLEYjBARAs2YphY2bG2zfro2vkcJGCLOW6UX8LC0tuXXrFu7u7qniDx48wN3dXbqlhHhBSin2Bt9l6rYgzt+JBMCrkB2j2vryWhUPLGS7hGe7exd694YdO1JiTZvC6tXg6Wm6vIQQLyRHF/FTSqU5vfT69eu4uLhk9nJCiCf8fT2MKVsDOXLpAQAudtYMaVGOXvVLYWMl2yU81++/a91Qt25pz3U6+PRTmDABrGSwtRAFRYb/tdeoUQOdTodOp6Nly5ZYPfGDwmAwcPnyZdq2bZsjSQph7q49jObr34L55dRNAPRWFvRr4MOgZuVwsZculOcyGGDyZG0sTdK4wGLFYOVK8PMzbW5CiFyX4eKm0/9W7Tx16hRt2rTB0dEx+TW9Xo+Pjw9vvPFGticohDl7HB3P/L0h/PfwFeIN2pdyp+qejGhTEa9Csup3hty+DW+9Bbt3p8RattQKm+LFTZeXEMJkMlzcTJw4EQAfHx/8/f2xtZWpp0JkVVyigeWHrzBvbwhhMdry/w3KFmFc+0q8XEK6dzNs927o2RPuaGv+YGEBn30G48aBpXTjCVFQZboTuk+fPjmRhxAFgtGo+L8zN5m+I5jrj2IAqFjMiTHtfWlWoahsl5BRiYkwaRJ8+aW2TxSAh4e2dk3TpqbNTQhhchkqbgoXLsz58+dxc3OjUKFCz/wB/PDhw2xLTghzcvjifQK2BvH3jTAAijnb8HGrirxRywtLmQGVcTduQI8esH9/SqxNG1i+HP41i1MIUTBlqLiZNWsWTk5OyX+W3y6FyLjzdyKYui2IPUF3AXC0seK9pmV4u1Fp7PUygydTtm+HXr3g/n3tuaWl1nozapTWJSWEEGRhnZv8Tta5EbnlTngsM387z4bj1zAqsLLQ0aNeST5sWR43RxtTp5e/JCRo07mnTk2JeXlp3VCNGpkuLyFErsnRdW5OnDiBtbU1VapUAeCXX35h6dKlVK5cmc8++wy97KwrCrjIuER++P0iCw9cJiZBW9Sy7UvFGdW2ImWKOj7nbPGUa9egWzc4fDgl9tprsGwZFClisrSEEHlXpttxBw4cyPnz5wG4dOkS/v7+2Nvbs2HDBkaNGpXtCQqRXyQYjKz44wrNpu/lmz0hxCQYqFnSlY3v1WdBr1pS2GTFr79C9eophY2VFXz9NWzeLIWNECJdmW65OX/+PNWrVwdgw4YNNG3alNWrV3Po0CG6devG7NmzszlFIfI2pRS/nbvDV9uCuHQ/CgCfIvaMbutL25eLyxi1rIiP1za4nDkzJVaqFKxdC6+8Yrq8hBD5Qpa2X0jaGXzXrl289tprAHh7e3M/aZCfEAXEiauPCNgayJ+hjwAo7KBnaMvy9KhXEmtLGeCaJaGhWjfU0aMpsU6dYMkSKFTIVFkJIfKRTBc3tWvX5ssvv8TPz4/ff/+d7777DoDLly9TrFixbE9QiLwo9H4U03YEsfXv2wDYWFnwbuPSvNe0LE62sl1Clv38M/TrB48fa8+trbVuqCFDtH2ihBAiAzJd3MyePZuePXvy888/88knn1CuXDkANm7cSIMGDbI9QSHykgeRcczdE8LKP66QaFTodNClphfDW1fAw8XO1OnlX3Fx2nTub75JiZUpA+vWQe3apstLCJEvZdtU8NjYWCwtLbG2ztu/tcpUcJEVsQkGFh+8zIJ9F4mISwSgaYWijG3vi29x+Xv0Qi5eBH9/OH48JdalCyxaBC6yFYUQQpOjU8GTHD9+nMDAQAAqV65MzZo1s3opIfIsg1Gx6cR1Zu48z62wWABe8nRmbLtKNCrvZuLszMCGDfDuuxAerj23sYFZs+C996QbSgiRZZkubu7evYu/vz+///47rq6uADx+/JjmzZuzdu1aihYtmt05CmESv5+/R8DWQIJuRwBQwtWOEW0q8Hq1EljIdgkvJjYWhg+H/43ZA6B8eVi/Xpv6LYQQLyDT0zmGDBlCZGQk//zzDw8fPuThw4ecPXuW8PBwPvzww5zIUYhc9c/NMHotPkqfJccIuh2Bk60VY9v5svvjpvynhpcUNi/q/HltOveThU337lq3lBQ2QohskOkxNy4uLuzatYs6deqkih87dozWrVvzOGmWQx4lY25Eem48jmHGb8H8dPIGSoG1pY7e9X0Y3LwchRxk5e1ssXo1DBwIkZHac1tbmDsX3nlHuqGEEM+Uo2NujEZjmoOGra2tk9e/ESI/CYtJ4Nt9ISw9FEp8ovZ3uEM1T0a2rkjJIvYmzs5MREfD0KHaIOEkvr5aN9T/tnIRQojskunipkWLFgwdOpQ1a9bg6ekJwI0bNxg2bBgtW7bM9gSFyCnxiUZW/nGFuXsu8Cg6AYC6pQszrn0lqnu7mjY5cxIYCF27wtmzKbE+fWD+fHBwMF1eQgizleniZt68eXTs2BEfHx+8vb0BuHbtGi+//DIrV67M9gSFyG5KKX49c4vpO4K5+jAagHLujoxp60vLSu6yXUJ2+u9/YdAgreUGwN4evv1WK26EECKHZLq48fb25sSJE+zevTt5KnilSpXw8/PL9uSEyG7HLj9k8tZATl97DEBRJxuG+VWga20vrGS7hOwTFQUffKAVN0leeknrhqpc2XR5CSEKhEwVN+vWrWPz5s3Ex8fTsmVLhgwZklN5CZGtQu5GMnVbELsC7wBgr7dkQJMy9G9cBgebLC/3JNJy9iy8+SYEBaXE3n0X5szRWm6EECKHZfin+nfffccHH3xA+fLlsbOzY9OmTVy8eJHp06fnZH5CvJC7EbHM3nWBdX9ew2BUWFro8K/jzUd+5XF3sjV1euZFKVi8WNsHKlZb8BBHR/j+e+jRw7S5CSEKlAxPBX/ppZfo2rUrEydOBGDlypUMHDiQqKioHE0wu8lU8IIhKi6RhQcu8cP+S0THGwDwq1SMMe0qUs7dycTZmaGICG1V4dWrU2LVqmndUBUqmC4vIYTZyMz3d4aLGzs7OwIDA/Hx8QG0KeF2dnaEhobi4eHxwknnFiluzFuiwcj6v64za9d57kXEAVDN25Vx7XypV6aIibMzU6dOabOhLlxIib33nraNgq20jgkhskeOrHMTFxeHwxPTNi0sLNDr9cTExGQ9UyGyiVKK3YF3mbo9iJC72gJxJQvbM6ptRV6t4iEzoHKCUrBgAQwbpu3qDeDkpK1l07WraXMTQhRomRpJOX78eOyfGBAYHx/P5MmTcXli596ZM2dmX3ZCZMDpa4+ZsjWQo5cfAuBqb82HLcrT85WS2FhZmjg7MxUWBv37axtfJqlVC9atg7JlTZeXEEKQieKmSZMmBAcHp4o1aNCAS5cuJT+X345Fbrr6IJrpvwXzf6dvAqC3suDthqV5v1lZXOyeXkVbZJO//gJ/f3ji3z5DhsD06dqu3kIIYWIZLm727duXg2kIkXGPouKZtzeE5UdCSTAodDr4T40SfNy6IiVc7UydnvlSStsHasQISNBWdMbVFZYsgf/8x6SpCSHEk/LEqmXz58/Hx8cHW1tb6tWrx7FjxzJ03tq1a9HpdHTq1ClnExR5QmyCge9/v0iT6XtZfPAyCQZFo3Ju/N/gRszsWl0Km5z06BG88Ya2P1RSYVO3Lpw8KYWNECLPMfnqZevWrWP48OEsWLCAevXqMXv2bNq0aUNwcDDu7u7pnhcaGsqIESNo3LhxLmYrTMFoVPxy+gZf7zjPjcfaAHbf4k6MbV+JJuXdpDs0px09qnVDXbmSEvv4Y5gyBfSyW7oQIu/J8FTwnFKvXj3q1KnDvHnzAG2Kube3N0OGDGHMmDFpnmMwGGjSpAlvv/02Bw4c4PHjx/z8888Zej+ZCp6/HAq5z5StgfxzMxyA4s62fNy6Ap1remFpIUVNjlIKZs6EMWMgMVGLFS4My5ZBhw4mTU0IUfDkyFTwnBAfH8/x48cZO3ZscszCwgI/Pz+OHDmS7nmTJk3C3d2dd955hwMHDuRGqiKXBd0OJ2BrEL+fvweAo40V7zcry9sNS2OnlxlQOe7BA+jbF379NSXWoAGsWQMlS5osLSGEyAiTFjf379/HYDBQrFixVPFixYoR9OS+NE84ePAgixcv5tSpUxl6j7i4OOKS1uBAq/xE3nU7LJYZvwWz8cR1lAIrCx1vvVKKIS3KUcRRZuLkikOHoFs3uH49JTZ6NHzxBVjLLDQhRN6XpQHFBw4c4K233qJ+/frcuHEDgBUrVnDw4MFsTe7fIiIi6NWrFwsXLsTNzS1D5wQEBODi4pL88Pb2ztEcRdZExCYwfUcQzb7ey4bjWmHTvkpxdg1vymcdX5LCJjcYjTB1KjRtmlLYuLnBtm1aXAobIUQ+kemWmx9//JFevXrRs2dPTp48mdwqEhYWxpQpU9i6dWuGr+Xm5oalpSV37txJFb9z5w7Fixd/6viLFy8SGhpKhyf6+41Go/ZBrKwIDg6m7L8WEBs7dizDhw9Pfh4eHi4FTh6SYDCy+uhV5uy+wMOoeABqlyrEuFcrUbNkIRNnV4Dcuwe9e8P27SmxJk20vaJKlDBdXkIIkQWZbrn58ssvWbBgAQsXLsT6id/kGjZsyIkTJzJ1Lb1eT61atdi9e3dyzGg0snv3burXr//U8b6+vvz999+cOnUq+dGxY0eaN2/OqVOn0ixabGxscHZ2TvUQpqeUYtvft2g9az8TN//Dw6h4yrg5sOCtWmx4r74UNrnp99+hevWUwkang08/hd27pbARQuRLmW65CQ4OpkmTJk/FXVxcePz4caYTGD58OH369KF27drUrVuX2bNnExUVRb9+/QDo3bs3JUqUICAgAFtbW15++eVU57u6ugI8FRd51/ErD5m8JZATVx8D4OaoZ6hfBbrV8cbaMk8svVQwGAzadO7PPtO6pACKFYOVK8HPz6SpCSHEi8h0cVO8eHFCQkKSdwdPcvDgQcqUKZPpBPz9/bl37x4TJkzg9u3bVK9ene3btycPMr569SoWFvKFZw4u3Ytk2vZgtv9zGwBbawv6Ny7DwKZlcbQx+ZJLBcvt2/DWW1rrTJIWLWDVKkijS1gIIfKTTK9zExAQwMqVK1myZAmtWrVi69atXLlyhWHDhjF+/HiGDBmSU7lmC1nnJvfdj4zjm90XWH30KolGhYUOutb2ZlirChRztjV1egXP7t3QsyckjXWzsICJE+GTT8BSptkLIfKmHF3nZsyYMRiNRlq2bEl0dDRNmjTBxsaGESNG5PnCRuSumHgDiw9eYsHvl4iM0xaBa16xKGPaVaJicScTZ1cAGQwwaZI2pTvpdxoPD23QcLNmJk1NCCGyU5ZXKI6PjyckJITIyEgqV66Mo6NjdueWI6TlJucZjIofj19nxs5g7oRrs+leLuHMuHaVaFAuY1P4RTa7eRN69NAGDydp3RpWrIBnbHMihBB5Ra6sUKzX66lcuXJWTxdmSCnFvvP3mLo1iOA7EQCUcLVjVNuKdKjqiYVsl2AaO3Zo42vu39eeW1pqrTejR2tdUkIIYWYyXdw0b978mRsV7tmz54USEvnT2RthTNkayOGLDwBwtrViSIvy9KpfCltrGcdhEomJMH68tgBfkhIlYO1aaNTIdHkJIUQOy3RxU7169VTPExISOHXqFGfPnqVPnz7ZlZfIJ64/imbGb+f56aS2UrXe0oI+DUrxQfNyuNrLjtEmc+0adO+ubaWQ5NVXtU0vM7i6txBC5FeZLm5mzZqVZvyzzz4jMjLyhRMS+UNYdALz94Ww7FAo8QZtjZTXq3syonVFvAvbmzi7Am7LFm214YcPtedWVhAQAMOHSzeUEKJAyPKA4n8LCQmhbt26PEz6gZpHyYDiFxOXaGDFkSvM3RNCWEwCAK+UKcy49pWo6uVq2uQKuoQEGDsWZsxIiZUqpXVDvfKK6fISQohskCsDiv/tyJEj2NrKmiXmymhU/N+Zm0zfEcz1RzEAlHd3ZGx7X5pXdH/mOCyRC0JDtZ28jx5Nib3+OixdCoVkKwshRMGS6eKmc+fOqZ4rpbh16xZ//fUX48ePz7bERN7xx6UHTNkayJnrYQC4O9nwcesKvFHTCyvZLsH0fv4Z+vWDpO1PrK1h+nT48ENtnyghhChgMl3cuLi4pHpuYWFBxYoVmTRpEq1bt862xITpXbgTwdRtQewOuguAg96SgU3L8m7j0tjrZbsEk4uL06Zzz5mTEitdGtatgzp1TJeXEEKYWKa+oQwGA/369aNKlSoUkqZus3U3PJZZu86z7s9rGBVYWujoXteboS0rUNTJxtTpCYCLF8HfH44fT4l16QKLFsG/fgERQoiCJlPFjaWlJa1btyYwMFCKm38xGODAAbh1S1vRvnHjtLfpye7jsjPXyLhEfth/iYX7LxGTYACgdeVijG7nS9mi+WMF6gJhwwZ4910ID9ee6/Uwaxa8/750QwkhBFnolnr55Ze5dOkSpUuXzol88qVNm2DoULh+PSXm5aX1Fjw5RCm7j8u2XL2N+I+7xr77F7gfqW2XUKOkK+PaV6KOT+EXe0ORfWJjtenc332XEitXDtavhxo1TJeXEELkMZmeCr59+3bGjh3LF198Qa1atXBwcEj1el6fXp3dU8E3bdJ6A/59F5N+gd64UStIsvu47MlVYVfuDoWaBWFdJAqAUkXsGd3Wl3YvF5cZUHnJhQvQtSucOpUS694dvv8enGQTUiGE+cvM93eGi5tJkybx8ccf4/TED9Inv/yUUuh0OgwGQxbTzh3ZWdwYDODjk9IKorMyYGEfl/y6TgcexeH3/dCkidYNlJbMHnfwIFhksovKaICGjVKubeUcg2uT89h6a+sSGaKt0Z0rT/CWUtjZyAyoPGXNGhgwAJIWybS1hW++0bqmpAAVQhQQOVLcWFpacuvWLQIDA595XNOmTTOeqQlkZ3Gzbx80b6792cIuHs8Be7G0TXzxJHORMcGCiL9KE/ZHWVS8NXv3QrNmps5KABATo03nXrQoJVaxotYNVbWq6fISQggTyJFF/JJqoLxevOSmJ1tYrApFJRc2xoTULR9WVtoehs+T0eP0+swPLjYYID7+iYDRgujzxXl8oAKGCLvkcHqtRiKXBQZq3VBnz6bEevWCb78FRxncLYQQz5KpAcUyBiM1D4+nYwmP7Ln5Q/NUsVmzYNiw518vo8dlpXXlyVamZ0nrM4lctny5NvMpOlp7bm8P8+dD374mTUsIIfKLDHdLWVhY4OLi8twCpyDtLZU05ubGDbD2eIRHr8OpihudTpvlFBICZctqx6V1tzN73OXLWWu5Sco1u68tsklUFAwerO3cneSll7RuqMqVTZaWEELkBTm2t9Tnn3/+1ArFBZmlpTY9u0sX+HfJl1QDzp6tdSMlH6dLXVxk5bisFB+pcs3ma4tscPas1g315Ji2d97RBg7byy7rQgiRKSqDdDqdunPnTkYPz7PCwsIUoMLCwrLtmj/+qJRXtYeq1OhfleeAPQqU8vbW4k8d56WUVlqoFz4uy7nm0LVFFhiNSi1apJStbcr/EAcHpVauNHVmQgiRp2Tm+zvTs6Xc3d1zttrKYdm9zk2Svy4/osv3hymst2dmi+b5coVikcsiIrSxNatWpcSqVtW6oSpWNF1eQgiRB+XobCmRtqR1Zxwdnz3Y19IyY4OBM3pcVuTktUUGnT6tdUOdP58Se+89mDkT7OzSP08IIcRzZbi4MRqNOZmHEAWDUtqqwh99pO3qDdoKwwsXahthCiGEeGGZ3ltKCJFFYWHaSsPr16fEataEdeu0PaKEEEJkC1lnX4jccPw41KqVurAZMgQOH5bCRgghspkUN0LkJKVg7lxo0AAuXtRiLi7w44/aNG8bG9PmJ4QQZki6pYTIKY8eaWvV/PRTSqxOHa0bqnRp0+UlhBBmTlpuhMgJR49q42meLGyGD9e2dJfCRgghcpQUN0JkJ6W06dyNGkFoqBYrVAg2b4YZM7RlqIUQQuQo6ZYSIrs8eKBtbvnrrymx+vVh7VooWdJkaQkhREEjLTdCZIfDh6FGjdSFzahR8PvvUtgIIUQuk+JGiBdhNMJXX0GTJnDtmhZzc4OtW7W4tbVp8xNCiAJIuqWEyKp796B3b9i+PSXWuDGsWQMlSpguLyGEKOCk5UaIrNi/H6pXTylsdDr49FPYs0cKGyGEMDFpuREiMwwGCAiAiRO1LikAd3dYuRJatTJtbkIIIQApboTIuDt34K23YNeulFjz5rBqFXh4mC4vIYQQqUi3lBAZsWcPVKuWUthYWMDnn8POnVLYCCFEHiMtN0I8i8EAkybBF19oC/QBFC+uDRpu1sykqQkhhEibFDdCpOfmTejZE/btS4m1bg0rVmjjbIQQQuRJ0i0lRFp27NBmQyUVNhYWMHkybNsmhY0QQuRx0nIjxJMSE2HCBG1GVJISJbRuqMaNTZeXEEKIDJPiRogk169D9+7azt1J2reH//5XW3VYCCFEviDdUkIAbNmidUMlFTZWVjBtGvzf/0lhI4QQ+Yy03IiCLSEBxo2Dr79OiZUsqe3kXb++6fISQgiRZVLciILryhXo1g3++CMl1rEjLF0KhQubLi8hhBAvRLqlRMH0yy9aN1RSYWNtDbNnw88/S2EjhBD5nLTciIIlPh5GjYI5c1JipUvDunVQp47p8hJCCJFtpLgRBcelS+DvD3/9lRJ74w1YtAhcXU2WlhBCiOwl3VKiYNi4EWrUSCls9HqYNw82bJDCRgghzIy03AjzFhsLH38M336bEitXDtav14odIYQQZkeKG2G+LlyArl3h1KmUWLdu8P334OxssrSEEELkLOmWEuZpzRqoWTOlsLG11Yqa1aulsBFCCDMnLTfCvMTEwNChsHBhSqxiRa0bqmpV0+UlhBAi10hxI8xHUJDWDfX33ymxXr208TaOjqbLSwghRK6SbilhHpYvh1q1UgobOztYskTb9FIKGyGEKFDyRHEzf/58fHx8sLW1pV69ehw7dizdYxcuXEjjxo0pVKgQhQoVws/P75nHCzMXFQX9+kGfPhAdrcUqV4Y//9TiOp1p8xNCCJHrTF7crFu3juHDhzNx4kROnDhBtWrVaNOmDXfv3k3z+H379tG9e3f27t3LkSNH8Pb2pnXr1ty4cSOXMxcm988/ULcuLFuWEnv7ba2weeklk6UlhBDCtExe3MycOZP+/fvTr18/KleuzIIFC7C3t2fJkiVpHr9q1SoGDRpE9erV8fX1ZdGiRRiNRnbv3p3LmQuTUQoWL9a2Szh3Tos5OMCKFVrc3t60+QkhhDApkxY38fHxHD9+HD8/v+SYhYUFfn5+HDlyJEPXiI6OJiEhgcKy2WHBEBGhDRJ+911tZhRos6D++gveesu0uQkhhMgTTDpb6v79+xgMBooVK5YqXqxYMYKCgjJ0jdGjR+Pp6ZmqQHpSXFwccXFxyc/Dw8OznrAwrdOntdlQ58+nxAYOhFmztAHEQgghBHmgW+pFTJ06lbVr1/LTTz9ha2ub5jEBAQG4uLgkP7y9vXM5S/HClNIW4KtXL6WwcXLSFupbsEAKGyGEEKmYtLhxc3PD0tKSO3fupIrfuXOH4sWLP/Pcr7/+mqlTp/Lbb79R9RmLs40dO5awsLDkx7Vr17Ild5FLwsO1LRPeew+SWuBq1IATJ7S4EEII8S8mLW70ej21atVKNRg4aXBw/fr10z1v2rRpfPHFF2zfvp3atWs/8z1sbGxwdnZO9RD5xIkT2hYK69enxAYPhsOHtc0vhRBCiDSYfIXi4cOH06dPH2rXrk3dunWZPXs2UVFR9OvXD4DevXtTokQJAgICAPjqq6+YMGECq1evxsfHh9u3bwPg6OiIoyzWZh6Ugvnztd284+O1mIuLNhPqjTdMm5sQQog8z+TFjb+/P/fu3WPChAncvn2b6tWrs3379uRBxlevXsXCIqWB6bvvviM+Pp4uXbqkus7EiRP57LPPcjN1kRMeP4Z33oFNm1JiderA2rVQpozJ0hJCCJF/mLy4ARg8eDCDBw9O87V9+/aleh4aGprzCQnTOHYM/P3hyf/Hw4bB1Kmg15ssLSGEEPlLvp4tJcyEUjBzJjRsmFLYFCoEv/yixaWwEUIIkQl5ouVGFGAPH0LfvvB//5cSq19fm+ZdqpTJ0hJCCJF/ScuNMJ3Dh6F69dSFzahR8PvvUtgIIYTIMiluRO4zGmHaNGjSBJLWHSpSBLZsga++Amtr0+YnhBAiX5NuKZG77t2DPn1g27aUWKNGWjeUl5fp8hJCCGE2pOVG5J79+7VuqKTCRqeDTz6BvXulsBFCCJFtpOVG5DyjEQICYMIE7c8ARYvCqlXQqpVpcxNCCGF2pLgROevOHejVC3buTIk1b64VNh4epstLCCGE2ZJuKZFz9uzRuqGSChudDiZO1J5LYSOEECKHSMuNyH4GA0yaBF98oS3QB1C8uNZa06KFaXMTQghh9qS4Ednr5k3o2ROe3DajVStYsQL+t1+YEEIIkZOkW0pkn99+07qhkgobCwuYPBm2b5fCRgghRK6Rlhvx4hITtbE0AQEp3VAlSmhr1zRubNrchBBCFDhS3IgXc/06dO8OBw+mxNq1g+XLwc3NdHkJIYQosKRbSmTd1q1aN1RSYWNpqW2r8OuvUtgIIYQwGWm5EZmXkKCtLDx9ekrM2xvWrdN29BZCCCFMSIobkTlXrkC3bvDHHymxjh1h6VIoXNh0eQkhhBD/I91SIuN++QVq1EgpbKytYeZM+PlnKWyEEELkGdJyI54vPh5GjYI5c1JiPj5aN1TduiZLSwghhEiLFDfi2S5dAn9/+OuvlFjnzrB4Mbi6miwtIYQQIj3SLSXS9+OPWjdUUmGj18PcubBxoxQ2Qggh8ixpuRFPi42FESNg/vyUWNmysH491KxpuryEEEKIDJDiRqR24YLWDXXyZErM3x9++AGcnU2XlxBCCJFB0i0lUqxdC7VqpRQ2Njbw/ffaNgpS2AghhMgnpOVGQEwMfPSR1jqTpEIF2LABqlY1WVpCCCFEVkhxU9AFBUHXrvD33ymxt96C774DR0fT5SWEEEJkkXRLFWQrVkDt2imFjZ2dNsV7+XIpbIQQQuRb0nJTEEVFwZAh2pYJSSpX1mZDvfSS6fISQgghsoG03BQ0//yjrSr8ZGHTrx8cOyaFjRBCCLMgxU1BoRQsWQJ16sC5c1rMwUHrglqyRPuzEEIIYQakW6ogiIyE99+HlStTYlWqaN1Qvr6my0sIIYTIAdJyY+7OnNHWrnmysBkwAI4elcJGCCGEWZLixlwppS3AV7cunD+vxRwdtQX5vv9emxklhBBCmCHpljJH4eFa68y6dSmxGjW05+XLmy4vIYQQIhdIy425OXFC64Z6srD54AM4fFgKGyGEEAWCFDfmQimYNw/q14eQEC3m7KxtoTBvHtjamjY/IYQQIpdIt5Q5ePwY3n0XfvwxJVa7ttZ6U6aMydISQgghTEFabvK7Y8e08TRPFjYffQSHDklhI4QQokCS4ia/UgpmzYJGjSA0VIu5usLPP2txvd6EyQkhhBCmI91S+dHDh9qWCZs3p8ReeQXWroVSpUyXlxBCCJEHSMtNfnPkCFSvnrqwGTkS9u+XwkYIIYRAipv8w2iEadOgcWO4dk2LFSkCv/6qxa2tTZufEEIIkUdIt1R+cP8+9O4N27alxBo10lYb9vIyXV5CCCFEHiQtN3ndgQNaN1RSYaPTwbhxsHevFDZCCCFEGqTlJq8yGiEgACZM0P4MULSotgFm69amzU0IIYTIw6S4yYvu3IFevWDnzpRYs2awahV4eposLSEKGqUUiYmJGAwGU6ciRIFgbW2NpaXlC19Hipu8Zs8e6NkTbt/Wnut0WuvN+PGQDf/DhRAZEx8fz61bt4iOjjZ1KkIUGDqdDi8vLxwdHV/oOlLc5BUGA3zxBUyapC3QB1C8uNZa06KFaXMTooAxGo1cvnwZS0tLPD090ev16HQ6U6clhFlTSnHv3j2uX79O+fLlX6gFR4qbvODWLa21Zu/elJifnza+plgx0+UlRAEVHx+P0WjE29sbe3t7U6cjRIFRtGhRQkNDSUhIeKHiRmZLmdrOndpsqKTCxsICvvwStm+XwkYIE7OwkB+RQuSm7GohlZYbU0lMhM8+gylTUrqhPD21tWuaNDFpakIIIUR+JsWNKVy/Dj16aGvYJGnbFpYv16Z7CyGEECLLpM01t23dqnVDJRU2lpbw1VewZYsUNkIIkQc8ePAAd3d3QkNDTZ2K2XnllVf48ccfc/x9pLjJLQkJMGoUvPoqPHigxby9tQ0vR43SxtoIIcQL6tu3LzqdDp1Oh7W1NaVLl2bUqFHExsY+deyvv/5K06ZNcXJywt7enjp16rBs2bI0r/vjjz/SrFkzXFxccHR0pGrVqkyaNImHDx8+M5+9e/fSvn17ihQpgr29PZUrV+bjjz/mxo0b2fFxc8TkyZN5/fXX8fHxMXUqOWbDhg34+vpia2tLlSpV2Lp163PPiYuL45NPPqFUqVLY2Njg4+PDkiVL0jx27dq16HQ6OnXqlCr+6aefMmbMGIxJi9PmEPlGzQ1Xr0LTpjB9ekqsQwc4eRIaNDBdXkIIs9S2bVtu3brFpUuXmDVrFt9//z0TJ05MdczcuXN5/fXXadiwIUePHuXMmTN069aN9957jxEjRqQ69pNPPsHf3586deqwbds2zp49y4wZMzh9+jQrVqxIN4/vv/8ePz8/ihcvzo8//si5c+dYsGABYWFhzJgxI8ufLz4+PsvnPk90dDSLFy/mnXfeeaHr5GSOL+rw4cN0796dd955h5MnT9KpUyc6derE2bNnn3le165d2b17N4sXLyY4OJg1a9ZQsWLFp44LDQ1lxIgRNG7c+KnX2rVrR0REBNue3CsxJ6gCJiwsTAEqLCwsW697/MpDVWr0r6rxV3tSv/DLL0oVKqSUNmxYKSsrpWbOVMpozNb3F0Jkn5iYGHXu3DkVExOTHDMajSoqLiHXH8ZM/qzo06ePev3111PFOnfurGrUqJH8/OrVq8ra2loNHz78qfO/+eYbBag//vhDKaXU0aNHFaBmz56d5vs9evQozfi1a9eUXq9XH3300TPPmzhxoqpWrVqq12bNmqVKlSr11Gf68ssvlYeHh/Lx8VFjx45VdevWfeq6VatWVZ9//nny84ULFypfX19lY2OjKlasqObPn59mPkk2bNigihYtmiqWmJio3n77beXj46NsbW1VhQoVnrofaeWolHav33zzTeXi4qIKFSqkOnbsqC5fvpx83rFjx5Sfn58qUqSIcnZ2Vk2aNFHHjx9/Zo4vqmvXrurVV19NFatXr54aOHBguuds27ZNubi4qAcPHjzz2omJiapBgwZq0aJFaf5dVEqpfv36qbfeeivN89P6t5ckM9/fMqA4mxj/tzp7ZCTs2weN68VjOW40zJ6dcpCPD6xbB3XrmiBDIcSLiEkwUHnCjlx/33OT2mCvz/qP6rNnz3L48GFKlSqVHNu4cSMJCQlPtdAADBw4kHHjxrFmzRrq1avHqlWrcHR0ZNCgQWle39XVNc34hg0biI+PZ9SoUZk6Lz27d+/G2dmZnU9sSxMQEMDFixcpW7YsAP/88w9nzpxJHtOxatUqJkyYwLx586hRowYnT56kf//+ODg40KdPnzTf58CBA9SqVStVzGg04uXlxYYNGyhSpAiHDx9mwIABeHh40LVr13RzTEhIoE2bNtSvX58DBw5gZWXFl19+Sdu2bTlz5gx6vZ6IiAj69OnD3LlzUUoxY8YM2rdvz4ULF3Byckozx1WrVjFw4MBn3q9t27al2XICcOTIEYYPH54q1qZNG37++ed0r7d582Zq167NtGnTWLFiBQ4ODnTs2JEvvvgCOzu75OMmTZqEu7s777zzDgeenDTzhLp16zJ16tRn5v+i8kRxM3/+fKZPn87t27epVq0ac+fOpe4zCoANGzYwfvx4QkNDKV++PF999RXt27fPxYxT27QJhk4Cy7batlD9ml/mJ2t/qif8mXLQf/4DS5ZAJv9BCyFEZv366684OjqSmJhIXFwcFhYWzJs3L/n18+fP4+LigoeHx1Pn6vV6ypQpw/nz5wG4cOECZcqUwdraOlM5XLhwAWdn5zTfIyscHBxYtGgRer0+OVatWjVWr17N+PHjAe1Lv169epQrVw6AiRMnMmPGDDp37gxA6dKlOXfuHN9//326xc2VK1fw/NceftbW1nz++efJz0uXLs2RI0dYv359quLm3zmuXLkSo9HIokWLktdvWbp0Ka6uruzbt4/WrVvT4l8r0P/www+4urry+++/89prr6WZY8eOHalXr94z71eJEiXSfe327dsU+9c6asWKFeN20rY/abh06RIHDx7E1taWn376ifv37zNo0CAePHjA0qVLATh48CCLFy/m1KlTz8zN09OTa9euYTQac2wtKZMXN+vWrWP48OEsWLCAevXqMXv2bNq0aUNwcDDu7u5PHZ/UVxgQEMBrr73G6tWr6dSpEydOnODll1/O9fw3bYIuXcDaAzyAdpf38zX/wTUhDACDlR7LWTPggw+0faKEEPmSnbUl5ya1Mcn7Zlbz5s357rvviIqKYtasWVhZWfHGG29k6f1V0jpcWTgvO7esqFKlSqrCBqBnz54sWbKE8ePHo5RizZo1yS0SUVFRXLx4kXfeeYf+/fsnn5OYmIiLi0u67xMTE4Otre1T8fnz57NkyRKuXr1KTEwM8fHxVK9e/Zk5nj59mpCQkKdaYGJjY7l48SIAd+7c4dNPP2Xfvn3cvXsXg8FAdHQ0V69eTTdHJyendFt1corRaESn07Fq1ark+zdz5ky6dOnCt99+S2JiIr169WLhwoW4ubk981p2dnYYjUbi4uJStfpkJ5MXNzNnzqR///7069cPgAULFrBlyxaWLFnCmDFjnjp+zpw5tG3blpEjRwLwxRdfsHPnTubNm8eCBQtyNXeDAYYO1QbT2KhYPtu5gL4nfk1+PYSyDC2yjs3v18JS6hoh8jWdTvdC3UO5ycHBIbn1YsmSJVSrVi3VINkKFSoQFhbGzZs3n2qliI+P5+LFizRv3jz52IMHD5KQkJCp1puk97h169YzW28sLCyeKqASEhLS/Ez/1r17d0aPHs2JEyeIiYnh2rVr+Pv7AxAZGQnAwoULn2rleNay/m5ubjx69ChVbO3atYwYMYIZM2ZQv359nJycmD59OkePHn1mjpGRkdSqVYtVq1Y99T5F/7f0R58+fXjw4AFz5sxJnoVUv379Zw5IftFuqeLFi3Pnzp1UsTt37lC8ePF0r+fh4UGJEiVSFYaVKlVCKcX169eJiooiNDSUDh06JL+eNCPKysqK4ODg5O7Dhw8f4uDgkGOFDZh4tlR8fDzHjx/Hz88vOWZhYYGfnx9HjhxJ85wjR46kOh60vsL0jo+LiyM8PDzVI7scOKCtx2dJIrvvv5aqsFlHV2pxnK13apFOt6MQQuQ4CwsLxo0bx6effkpMTAwAb7zxBtbW1mnOWFqwYAFRUVF0794dgB49ehAZGcm3336b5vUfP36cZrxLly7o9XqmTZv2zPOKFi3K7du3UxU4z+vWSOLl5UXTpk1ZtWoVq1atolWrVskt/sWKFcPT05NLly5Rrly5VI/SpUune80aNWpw7ty5VLFDhw7RoEEDBg0aRI0aNShXrlxyy8uz1KxZkwsXLuDu7v5UDklFwqFDh/jwww9p3749L730EjY2Nty/f/+Z1+3YsSOnTp165qN27drpnl+/fn12796dKrZz507q16+f7jkNGzbk5s2byUUjaN2bFhYWeHl54evry99//50qh44dO9K8eXNOnTqFt7d38nlnz56lRo0az/yML+y5Q45z0I0bNxSgDh8+nCo+cuTINEfBK6WUtbW1Wr16darY/Pnzlbu7e5rHT5w4UQFPPbJjttTq1SmToMY7fqIUqBhLazWABQqMya/9K10hRB73rBkbeV1aM1QSEhJUiRIl1PTp05Njs2bNUhYWFmrcuHEqMDBQhYSEqBkzZigbGxv18ccfpzp/1KhRytLSUo0cOVIdPnxYhYaGql27dqkuXbqkO4tKKe1ns06nU2+//bbat2+fCg0NVQcPHlQDBgxInql17tw5pdPp1NSpU1VISIiaN2+eKlSoUJqzpdKycOFC5enpqdzc3NSKFSuees3Ozk7NmTNHBQcHqzNnzqglS5aoGTNmpJvzmTNnlJWVlXr48GFybM6cOcrZ2Vlt375dBQcHq08//VQ5OzunmuWVVo5RUVGqfPnyqlmzZmr//v3q0qVLau/evWrIkCHq2rVrSimlatSooVq1aqXOnTun/vjjD9W4cWNlZ2enZs2alW6OL+rQoUPKyspKff311yowMFBNnDhRWVtbq7///jv5mDFjxqhevXolP4+IiFBeXl6qS5cu6p9//lG///67Kl++vHr33XfTfZ/0/r81bdpUTZo0Kc1zsmu2lNkXN7GxsSosLCz5ce3atWwrbvbuTSluLEhU3zFQVeVUcizpsXfvC7+VECIXmVtxo5RSAQEBqmjRoioyMjI59ssvv6jGjRsrBwcHZWtrq2rVqqWWLFmS5nXXrVunmjRpopycnJSDg4OqWrWqmjRpUrpTwZPs3LlTtWnTRhUqVEjZ2toqX19fNWLECHXz5s3kY7777jvl7e2tHBwcVO/evdXkyZMzXNw8evRI2djYKHt7exUREfHU66tWrVLVq1dXer1eFSpUSDVp0kRt2rTpmTnXrVtXLViwIPl5bGys6tu3r3JxcVGurq7q/fffV2PGjHlucaOUUrdu3VK9e/dWbm5uysbGRpUpU0b1798/+TvoxIkTqnbt2srW1laVL19ebdiwQZUqVSpHixullFq/fr2qUKGC0uv16qWXXlJbtmxJ9XqfPn1U06ZNU8UCAwOVn5+fsrOzU15eXmr48OEqOjo63fdI655cv35dWVtbJxd3/5ZdxY1OqSyOFssG8fHx2Nvbs3HjxlSrGPbp04fHjx/zyy+/PHVOyZIlGT58OB999FFybOLEifz888+cPn36ue8ZHh6Oi4sLYWFhODs7v1D+BoM2u/vGjZS9L5+k04GXF1y+rO2yIITIH2JjY7l8+TKlS5dOc3CpMG9btmxh5MiRnD17VnaGz2ajR4/m0aNH/PDDD2m+/qx/e5n5/jbp/zW9Xk+tWrVS9f0ZjUZ2796dbt9fVvoKc4qlJcyZo/3535MCkp7Pni2FjRBC5CevvvoqAwYMyNNbRORX7u7ufPHFFzn+PiYvSYcPH87ChQv573//S2BgIO+//z5RUVHJs6d69+7N2LFjk48fOnQo27dvZ8aMGQQFBfHZZ5/x119/MXjwYJPk37kzbNwI/15SwMtLi/9veQUhhBD5yEcffZRqEKzIHh9//PFTa+zkBJPPa/T39+fevXtMmDCB27dvU716dbZv35784a9evZqqWbBBgwasXr2aTz/9lHHjxlG+fHl+/vlnk6xxk6RzZ3j9dW321K1b4OEBjRtLi40QQghhCiYdc2MK2TnmRghhnmTMjRCmYRZjboQQIi8rYL/7CWFy2fVvToobIYT4l6SVeKOjo02ciRAFS9LKzM9aRTojTD7mRggh8hpLS0tcXV25e/cuAPb29tm6T5IQ4mlGo5F79+5hb2+PldWLlSdS3AghRBqS9tlJKnCEEDnPwsKCkiVLvvAvE1LcCCFEGnQ6HR4eHri7u6e5kaMQIvvp9fpsWThRihshhHgGS0vLF+7/F0LkLhlQLIQQQgizIsWNEEIIIcyKFDdCCCGEMCsFbsxN0gJB4eHhJs5ECCGEEBmV9L2dkYX+ClxxExERASAbogkhhBD5UEREBC4uLs88psDtLWU0Grl58yZOTk7ZvihXeHg43t7eXLt2TfatykFyn3OH3OfcIfc598i9zh05dZ+VUkRERODp6fnc6eIFruXGwsICLy+vHH0PZ2dn+YeTC+Q+5w65z7lD7nPukXudO3LiPj+vxSaJDCgWQgghhFmR4kYIIYQQZkWKm2xkY2PDxIkTsbGxMXUqZk3uc+6Q+5w75D7nHrnXuSMv3OcCN6BYCCGEEOZNWm6EEEIIYVakuBFCCCGEWZHiRgghhBBmRYobIYQQQpgVKW4yaf78+fj4+GBra0u9evU4duzYM4/fsGEDvr6+2NraUqVKFbZu3ZpLmeZvmbnPCxcupHHjxhQqVIhChQrh5+f33P8vQpPZv89J1q5di06no1OnTjmboJnI7H1+/PgxH3zwAR4eHtjY2FChQgX52ZEBmb3Ps2fPpmLFitjZ2eHt7c2wYcOIjY3NpWzzp/3799OhQwc8PT3R6XT8/PPPzz1n37591KxZExsbG8qVK8eyZctyPE+UyLC1a9cqvV6vlixZov755x/Vv39/5erqqu7cuZPm8YcOHVKWlpZq2rRp6ty5c+rTTz9V1tbW6u+//87lzPOXzN7nHj16qPnz56uTJ0+qwMBA1bdvX+Xi4qKuX7+ey5nnL5m9z0kuX76sSpQooRo3bqxef/313Ek2H8vsfY6Li1O1a9dW7du3VwcPHlSXL19W+/btU6dOncrlzPOXzN7nVatWKRsbG7Vq1Sp1+fJltWPHDuXh4aGGDRuWy5nnL1u3blWffPKJ2rRpkwLUTz/99MzjL126pOzt7dXw4cPVuXPn1Ny5c5WlpaXavn17juYpxU0m1K1bV33wwQfJzw0Gg/L09FQBAQFpHt+1a1f16quvporVq1dPDRw4MEfzzO8ye5//LTExUTk5Oan//ve/OZWiWcjKfU5MTFQNGjRQixYtUn369JHiJgMye5+/++47VaZMGRUfH59bKZqFzN7nDz74QLVo0SJVbPjw4aphw4Y5mqc5yUhxM2rUKPXSSy+livn7+6s2bdrkYGZKSbdUBsXHx3P8+HH8/PySYxYWFvj5+XHkyJE0zzly5Eiq4wHatGmT7vEia/f536Kjo0lISKBw4cI5lWa+l9X7PGnSJNzd3XnnnXdyI818Lyv3efPmzdSvX58PPviAYsWK8fLLLzNlyhQMBkNupZ3vZOU+N2jQgOPHjyd3XV26dImtW7fSvn37XMm5oDDV92CB2zgzq+7fv4/BYKBYsWKp4sWKFSMoKCjNc27fvp3m8bdv386xPPO7rNznfxs9ejSenp5P/YMSKbJynw8ePMjixYs5depULmRoHrJyny9dusSePXvo2bMnW7duJSQkhEGDBpGQkMDEiRNzI+18Jyv3uUePHty/f59GjRqhlCIxMZH33nuPcePG5UbKBUZ634Ph4eHExMRgZ2eXI+8rLTfCrEydOpW1a9fy008/YWtra+p0zEZERAS9evVi4cKFuLm5mTods2Y0GnF3d+eHH36gVq1a+Pv788knn7BgwQJTp2ZW9u3bx5QpU/j22285ceIEmzZtYsuWLXzxxRemTk1kA2m5ySA3NzcsLS25c+dOqvidO3coXrx4mucUL148U8eLrN3nJF9//TVTp05l165dVK1aNSfTzPcye58vXrxIaGgoHTp0SI4ZjUYArKysCA4OpmzZsjmbdD6Ulb/PHh4eWFtbY2lpmRyrVKkSt2/fJj4+Hr1en6M550dZuc/jx4+nV69evPvuuwBUqVKFqKgoBgwYwCeffIKFhfzunx3S+x50dnbOsVYbkJabDNPr9dSqVYvdu3cnx4xGI7t376Z+/fppnlO/fv1UxwPs3Lkz3eNF1u4zwLRp0/jiiy/Yvn07tWvXzo1U87XM3mdfX1/+/vtvTp06lfzo2LEjzZs359SpU3h7e+dm+vlGVv4+N2zYkJCQkOTiEeD8+fN4eHhIYZOOrNzn6OjopwqYpIJSyZaL2cZk34M5OlzZzKxdu1bZ2NioZcuWqXPnzqkBAwYoV1dXdfv2baWUUr169VJjxoxJPv7QoUPKyspKff311yowMFBNnDhRpoJnQGbv89SpU5Ver1cbN25Ut27dSn5ERESY6iPkC5m9z/8ms6UyJrP3+erVq8rJyUkNHjxYBQcHq19//VW5u7urL7/80lQfIV/I7H2eOHGicnJyUmvWrFGXLl1Sv/32mypbtqzq2rWrqT5CvhAREaFOnjypTp48qQA1c+ZMdfLkSXXlyhWllFJjxoxRvXr1Sj4+aSr4yJEjVWBgoJo/f75MBc+L5s6dq0qWLKn0er2qW7eu+uOPP5Jfa9q0qerTp0+q49evX68qVKig9Hq9eumll9SWLVtyOeP8KTP3uVSpUgp46jFx4sTcTzyfyezf5ydJcZNxmb3Phw8fVvXq1VM2NjaqTJkyavLkySoxMTGXs85/MnOfExIS1GeffabKli2rbG1tlbe3txo0aJB69OhR7ieej+zduzfNn7dJ97ZPnz6qadOmT51TvXp1pdfrVZkyZdTSpUtzPE+dUtL+JoQQQgjzIWNuhBBCCGFWpLgRQgghhFmR4kYIIYQQZkWKGyGEEEKYFSluhBBCCGFWpLgRQgghhFmR4kYIIYQQZkWKGyFEKsuWLcPV1dXUaWSZTqfj559/fuYxffv2pVOnTrmSjxAi90lxI4QZ6tu3Lzqd7qlHSEiIqVNj2bJlyflYWFjg5eVFv379uHv3brZc/9atW7Rr1w6A0NBQdDodp06dSnXMnDlzWLZsWba8X3o+++yz5M9paWmJt7c3AwYM4OHDh5m6jhRiQmSe7AouhJlq27YtS5cuTRUrWrSoibJJzdnZmeDgYIxGI6dPn6Zfv37cvHmTHTt2vPC1n7d7PICLi8sLv09GvPTSS+zatQuDwUBgYCBvv/02YWFhrFu3LlfeX4iCSlpuhDBTNjY2FC9ePNXD0tKSmTNnUqVKFRwcHPD29mbQoEFERkame53Tp0/TvHlznJyccHZ2platWvz111/Jrx88eJDGjRtjZ2eHt7c3H374IVFRUc/MTafTUbx4cTw9PWnXrh0ffvghu3btIiYmBqPRyKRJk/Dy8sLGxobq1auzffv25HPj4+MZPHgwHh4e2NraUqpUKQICAlJdO6lbqnTp0gDUqFEDnU5Hs2bNgNStIT/88AOenp6pduEGeP3113n77beTn//yyy/UrFkTW1tbypQpw+eff05iYuIzP6eVlRXFixenRIkS+Pn58eabb7Jz587k1w0GA++88w6lS5fGzs6OihUrMmfOnOTXP/vsM/773//yyy+/JLcC7du3D4Br167RtWtXXF1dKVy4MK+//jqhoaHPzEeIgkKKGyEKGAsLC7755hv++ecf/vvf/7Jnzx5GjRqV7vE9e/bEy8uLP//8k+PHjzNmzBisra0BuHjxIm3btuWNN97gzJkzrFu3joMHDzJ48OBM5WRnZ4fRaCQxMZE5c+YwY8YMvv76a86cOUObNm3o2LEjFy5cAOCbb75h8+bNrF+/nuDgYFatWoWPj0+a1z127BgAu3bt4tatW2zatOmpY958800ePHjA3r17k2MPHz5k+/bt9OzZE4ADBw7Qu3dvhg4dyrlz5/j+++9ZtmwZkydPzvBnDA0NZceOHej1+uSY0WjEy8uLDRs2cO7cOSZMmMC4ceNYv349ACNGjKBr1660bduWW7ducevWLRo0aEBCQgJt2rTBycmJAwcOcOjQIRwdHWnbti3x8fEZzkkIs5XjW3MKIXJdnz59lKWlpXJwcEh+dOnSJc1jN2zYoIoUKZL8fOnSpcrFxSX5uZOTk1q2bFma577zzjtqwIABqWIHDhxQFhYWKiYmJs1z/n398+fPqwoVKqjatWsrpZTy9PRUkydPTnVOnTp11KBBg5RSSg0ZMkS1aNFCGY3GNK8PqJ9++kkppdTly5cVoE6ePJnqmH/vaP7666+rt99+O/n5999/rzw9PZXBYFBKKdWyZUs1ZcqUVNdYsWKF8vDwSDMHpZSaOHGisrCwUA4ODsrW1jZ59+SZM2eme45SSn3wwQfqjTfeSDfXpPeuWLFiqnsQFxen7Ozs1I4dO555fSEKAhlzI4SZat68Od99913ycwcHB0BrxQgICCAoKIjw8HASExOJjY0lOjoae3v7p64zfPhw3n33XVasWJHctVK2bFlA67I6c+YMq1atSj5eKYXRaOTy5ctUqlQpzdzCwsJwdHTEaDQSGxtLo0aNWLRoEeHh4dy8eZOGDRumOr5hw4acPn0a0LqUWrVqRcWKFWnbti2vvfYarVu3fqF71bNnT/r378+3336LjY0Nq1atolu3blhYWCR/zkOHDqVqqTEYDM+8bwAVK1Zk8+bNxMbGsnLlSk6dOsWQIUNSHTN//nyWLFnC1atXiYmJIT4+nurVqz8z39OnTxMSEoKTk1OqeGxsLBcvXszCHRDCvEhxI4SZcnBwoFy5cqlioaGhvPbaa7z//vtMnjyZwoULc/DgQd555x3i4+PT/JL+7LPP6NGjB1u2bGHbtm1MnDiRtWvX8p///IfIyEgGDhzIhx9++NR5JUuWTDc3JycnTpw4gYWFBR4eHtjZ2QEQHh7+3M9Vs2ZNLl++zLZt29i1axddu3bFz8+PjRs3Pvfc9HTo0AGlFFu2bKFOnTocOHCAWbNmJb8eGRnJ559/TufOnZ8619bWNt3r6vX65P8HU6dO5dVXX+Xzzz/niy++AGDt2rWMGDGCGTNmUL9+fZycnJg+fTpHjx59Zr6RkZHUqlUrVVGZJK8MGhfClKS4EaIAOX78OEajkRkzZiS3SiSN73iWChUqUKFCBYYNG0b37t1ZunQp//nPf6hZsybnzp17qoh6HgsLizTPcXZ2xtPTk0OHDtG0adPk+KFDh6hbt26q4/z9/fH396dLly60bduWhw8fUrhw4VTXSxrfYjAYnpmPra0tnTt3ZtWqVYSEhFCxYkVq1qyZ/HrNmjUJDg7O9Of8t08//ZQWLVrw/vvvJ3/OBg0aMGjQoORj/t3yotfrn8q/Zs2arFu3Dnd3d5ydnV8oJyHMkQwoFqIAKVeuHAkJCcydO5dLly6xYsUKFixYkO7xMTExDB48mH379nHlyhUOHTrEn3/+mdzdNHr0aA4fPszgwYM5deoUFy5c4Jdffsn0gOInjRw5kq+++op169YRHBzMmDFjOHXqFEOHDgVg5syZrFmzhqCgIM6fP8+GDRsoXrx4mgsPuru7Y2dnx/bt27lz5w5hYWHpvm/Pnj3ZsmULS5YsSR5InGTChAksX76czz//nH/++YfAwEDWrl3Lp59+mqnPVr9+fapWrcqUKVMAKF++PH/99Rc7duzg/PnzjB8/nj///DPVOT4+Ppw5c4bg4GDu379PQkICPXv2xM3Njddff50DBw5w+fJl9u3bx4cffsj169czlZMQZsnUg36EENkvrUGoSWbOnKk8PDyUnZ2datOmjVq+fLkC1KNHj5RSqQf8xsXFqW7duilvb2+l1+uVp6enGjx4cKrBwseOHVOtWrVSjo6OysHBQVWtWvWpAcFP+veA4n8zGAzqs88+UyVKlFDW1taqWrVqatu2bcmv//DDD6p69erKwcFBOTs7q5YtW6oTJ04kv84TA4qVUmrhwoXK29tbWVhYqKZNm6Z7fwwGg/Lw8FCAunjx4lN5bd++XTVo0EDZ2dkpZ2dnVbduXfXDDz+k+zkmTpyoqlWr9lR8zZo1ysbGRl29elXFxsaqvn37KhcXF+Xq6qref/99NWbMmFTn3b17N/n+Amrv3r1KKaVu3bqlevfurdzc3JSNjY0qU6aM6t+/vwoLC0s3JyEKCp1SSpm2vBJCCCGEyD7SLSWEEEIIsyLFjRBCCCHMihQ3QgghhDArUtwIIYQQwqxIcSOEEEIIsyLFjRBCCCHMihQ3QgghhDArUtwIIYQQwqxIcSOEEEIIsyLFjRBCCCHMihQ3QgghhDArUtwIIYQQwqz8PzxarnN9Qp15AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\"Random Forest: \")\n",
        "evaluation(rf, x_test, y_test)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 317,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "LGZv-XKbrDHh",
        "outputId": "fb4f5dc8-aef8-4d2c-9338-5454a3d642e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost: \n",
            "Precision: 0.0\n",
            "Recall: 0.0\n",
            "F1 Score: 0.0\n",
            "Accuracy: 0.9785714285714285\n",
            "Score: 0.9785714285714285\n",
            "ROC AUC: 0.8138686131386861\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      1.00      0.99       137\n",
            "           1       0.00      0.00      0.00         3\n",
            "\n",
            "    accuracy                           0.98       140\n",
            "   macro avg       0.49      0.50      0.49       140\n",
            "weighted avg       0.96      0.98      0.97       140\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\"XGBoost: \")\n",
        "evaluation(xgb, x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 318,
      "metadata": {
        "id": "q36x-lkOBSfG"
      },
      "outputs": [],
      "source": [
        "data['Transaction_Hour'] = data['Transaction_Date'].dt.hour\n",
        "data['Transaction_Day'] = data['Transaction_Date'].dt.dayofweek\n",
        "data['Transaction_Month'] = data['Transaction_Date'].dt.month"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 319,
      "metadata": {
        "id": "JPnE5E7sDQoc"
      },
      "outputs": [],
      "source": [
        "data['transaction_count'] = data.groupby('Sender_account')['Transaction_Date'].transform('count')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 320,
      "metadata": {
        "id": "ic65PEroPTq0"
      },
      "outputs": [],
      "source": [
        "data['Suspicion_level'] = data['transaction_count'] * data['Amount']\n",
        "data['average_transaction_amount'] = data.groupby('Sender_account')['Amount'].transform('mean')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 321,
      "metadata": {
        "id": "jYfUggYkdMNx"
      },
      "outputs": [],
      "source": [
        "data['amount_to_avg_ratio'] = data['Amount'] / (data['average_transaction_amount'] + 1)\n",
        "# data = pd.get_dummies(data, columns=['ip_addresses'], drop_first=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n"
      ],
      "metadata": {
        "id": "TKjasKyc-bHe"
      },
      "execution_count": 321,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 322,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "L3KRcloEPXwa",
        "outputId": "246a4c66-9afb-45f5-a36c-6b2fa63024ad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Transaction_id Transfer_type    Amount  Sender_account  Initial_balance_p1  \\\n",
              "0               1       PAYMENT   9839.64               3            170136.0   \n",
              "1               2       PAYMENT   1864.28             202             21249.0   \n",
              "2               3      TRANSFER    181.00             449               181.0   \n",
              "3               4      CASH_OUT    181.00              37               181.0   \n",
              "4               5       PAYMENT  11668.14             269             41554.0   \n",
              "\n",
              "   new_balance_1 Recipient_account  Initial_balance_p2  new_balance_2  \\\n",
              "0      160296.36       ACC18330495                 0.0            0.0   \n",
              "1       19384.72       ACC43829436                 0.0            0.0   \n",
              "2           0.00       ACC19495987                 0.0            0.0   \n",
              "3           0.00       ACC94944418             21182.0            0.0   \n",
              "4       29885.86       ACC17787237                 0.0            0.0   \n",
              "\n",
              "   isFraud  ...  ip_addresses_98.131.248.68 ip_addresses_98.145.54.164  \\\n",
              "0        0  ...                       False                      False   \n",
              "1        0  ...                       False                      False   \n",
              "2        1  ...                       False                      False   \n",
              "3        1  ...                       False                      False   \n",
              "4        0  ...                       False                      False   \n",
              "\n",
              "   ip_addresses_98.183.239.247  ip_addresses_98.196.248.83  \\\n",
              "0                        False                       False   \n",
              "1                        False                       False   \n",
              "2                        False                       False   \n",
              "3                        False                       False   \n",
              "4                        False                       False   \n",
              "\n",
              "   ip_addresses_98.199.132.55  ip_addresses_98.76.69.193  \\\n",
              "0                       False                      False   \n",
              "1                       False                      False   \n",
              "2                       False                      False   \n",
              "3                       False                      False   \n",
              "4                       False                      False   \n",
              "\n",
              "   ip_addresses_99.167.174.194  ip_addresses_99.21.227.221  \\\n",
              "0                        False                       False   \n",
              "1                        False                       False   \n",
              "2                        False                       False   \n",
              "3                        False                       False   \n",
              "4                        False                       False   \n",
              "\n",
              "   ip_addresses_99.229.27.71  ip_addresses_99.238.189.2  \n",
              "0                      False                      False  \n",
              "1                      False                      False  \n",
              "2                      False                      False  \n",
              "3                      False                      False  \n",
              "4                      False                      False  \n",
              "\n",
              "[5 rows x 728 columns]"
            ],
            "text/html": [
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              "  <div id=\"df-5abac52a-18ac-4998-a323-fd9359dc4159\" class=\"colab-df-container\">\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Transaction_id</th>\n",
              "      <th>Transfer_type</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Sender_account</th>\n",
              "      <th>Initial_balance_p1</th>\n",
              "      <th>new_balance_1</th>\n",
              "      <th>Recipient_account</th>\n",
              "      <th>Initial_balance_p2</th>\n",
              "      <th>new_balance_2</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>...</th>\n",
              "      <th>ip_addresses_98.131.248.68</th>\n",
              "      <th>ip_addresses_98.145.54.164</th>\n",
              "      <th>ip_addresses_98.183.239.247</th>\n",
              "      <th>ip_addresses_98.196.248.83</th>\n",
              "      <th>ip_addresses_98.199.132.55</th>\n",
              "      <th>ip_addresses_98.76.69.193</th>\n",
              "      <th>ip_addresses_99.167.174.194</th>\n",
              "      <th>ip_addresses_99.21.227.221</th>\n",
              "      <th>ip_addresses_99.229.27.71</th>\n",
              "      <th>ip_addresses_99.238.189.2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>9839.64</td>\n",
              "      <td>3</td>\n",
              "      <td>170136.0</td>\n",
              "      <td>160296.36</td>\n",
              "      <td>ACC18330495</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>202</td>\n",
              "      <td>21249.0</td>\n",
              "      <td>19384.72</td>\n",
              "      <td>ACC43829436</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>181.00</td>\n",
              "      <td>449</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>ACC19495987</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>181.00</td>\n",
              "      <td>37</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>ACC94944418</td>\n",
              "      <td>21182.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>11668.14</td>\n",
              "      <td>269</td>\n",
              "      <td>41554.0</td>\n",
              "      <td>29885.86</td>\n",
              "      <td>ACC17787237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 728 columns</p>\n",
              "</div>\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-5abac52a-18ac-4998-a323-fd9359dc4159 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-53079987-862c-4922-90f2-2eee24ae7c09 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 322
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 323,
      "metadata": {
        "id": "qZOmcNYxc8Qi"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "data['Sender_account'] = le.fit_transform(data['Sender_account'])\n",
        "\n",
        "# data['Receiver_account'] = le.fit_transform(data['Receiver_account'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 324,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhTqa0LBhSOB",
        "outputId": "3d4d8130-dc48-4166-f116-6961c45a713a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Available columns: Index(['Transaction_id', 'Transfer_type', 'Amount', 'Sender_account',\n",
            "       'Initial_balance_p1', 'new_balance_1', 'Recipient_account',\n",
            "       'Initial_balance_p2', 'new_balance_2', 'isFraud',\n",
            "       ...\n",
            "       'ip_addresses_98.131.248.68', 'ip_addresses_98.145.54.164',\n",
            "       'ip_addresses_98.183.239.247', 'ip_addresses_98.196.248.83',\n",
            "       'ip_addresses_98.199.132.55', 'ip_addresses_98.76.69.193',\n",
            "       'ip_addresses_99.167.174.194', 'ip_addresses_99.21.227.221',\n",
            "       'ip_addresses_99.229.27.71', 'ip_addresses_99.238.189.2'],\n",
            "      dtype='object', length=728)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "if 'Receiver_account' in data.columns:\n",
        "    data['Receiver_account'] = le.fit_transform(data['Receiver_account'])\n",
        "else:\n",
        "\n",
        "    print(f\"Available columns: {data.columns}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 325,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PL9dyYLyhIWG",
        "outputId": "c9bfdcac-0c67-4451-970a-5f9e46c8de83"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression: Average Accuracy = 0.9971 (std = 0.0057)\n",
            "Random Forest: Average Accuracy = 0.9914 (std = 0.0029)\n",
            "XGBoost: Average Accuracy = 0.9929 (std = 0.0045)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "models = [lor, rf, xgb]\n",
        "model_names = ['Logistic Regression', 'Random Forest', 'XGBoost']\n",
        "cvz = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "for model, name in zip(models, model_names):\n",
        "    scores = cross_val_score(model, x, y, cv=cvz, scoring='accuracy')\n",
        "    print(f'{name}: Average Accuracy = {scores.mean():.4f} (std = {scores.std():.4f})')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 326,
      "metadata": {
        "id": "gajUp_NquHVf"
      },
      "outputs": [],
      "source": [
        "\n",
        "param_dist_xgb = {\n",
        "    'n_estimators': [100, 200, 300],\n",
        "    'learning_rate': [0.01, 0.1, 0.2],\n",
        "    'max_depth': [3, 4, 5],\n",
        "    'subsample': [0.8, 0.9, 1.0],\n",
        "    'colsample_bytree': [0.8, 0.9, 1.0],\n",
        "}\n",
        "\n",
        "random_search_xgb = RandomizedSearchCV(\n",
        "    estimator=XGBClassifier(use_label_encoder=False, eval_metric='logloss'),\n",
        "    param_distributions=param_dist_xgb,\n",
        "    n_iter=50,\n",
        "    cv=5,\n",
        "    scoring='roc_auc',\n",
        "    verbose=1,\n",
        "    n_jobs=-1,\n",
        "    random_state=42\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 327,
      "metadata": {
        "id": "JoOfDyqis2dz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f508f8af-c6d8-417d-91e9-ad644689169a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
            "XGBoost Test ROC AUC: 0.5000\n"
          ]
        }
      ],
      "source": [
        "\n",
        "random_search_xgb.fit(x_train, y_train)\n",
        "best_xgb_model = random_search_xgb.best_estimator_\n",
        "xgb_pred = best_xgb_model.predict(x_test)\n",
        "xgb_roc_auc = roc_auc_score(y_test, xgb_pred)\n",
        "print(f\"XGBoost Test ROC AUC: {xgb_roc_auc:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 328,
      "metadata": {
        "id": "Bk5uBcMwhxMh"
      },
      "outputs": [],
      "source": [
        "# high-risk transaction hours (range I have given 00:00 - 06:00)\n",
        "data['high_risk_hour(flag)'] = data['Transaction_Hour'].apply(lambda x: 1 if x < 6 else 0)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 329,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "5DTODP2crJgU",
        "outputId": "1b19a964-b981-4757-b53e-3d23d9e2376f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Transaction_id Transfer_type    Amount  Sender_account  Initial_balance_p1  \\\n",
              "0               1       PAYMENT   9839.64               3            170136.0   \n",
              "1               2       PAYMENT   1864.28             202             21249.0   \n",
              "2               3      TRANSFER    181.00             449               181.0   \n",
              "3               4      CASH_OUT    181.00              37               181.0   \n",
              "4               5       PAYMENT  11668.14             269             41554.0   \n",
              "\n",
              "   new_balance_1 Recipient_account  Initial_balance_p2  new_balance_2  \\\n",
              "0      160296.36       ACC18330495                 0.0            0.0   \n",
              "1       19384.72       ACC43829436                 0.0            0.0   \n",
              "2           0.00       ACC19495987                 0.0            0.0   \n",
              "3           0.00       ACC94944418             21182.0            0.0   \n",
              "4       29885.86       ACC17787237                 0.0            0.0   \n",
              "\n",
              "   isFraud  ...  ip_addresses_98.131.248.68 ip_addresses_98.145.54.164  \\\n",
              "0        0  ...                       False                      False   \n",
              "1        0  ...                       False                      False   \n",
              "2        1  ...                       False                      False   \n",
              "3        1  ...                       False                      False   \n",
              "4        0  ...                       False                      False   \n",
              "\n",
              "   ip_addresses_98.183.239.247  ip_addresses_98.196.248.83  \\\n",
              "0                        False                       False   \n",
              "1                        False                       False   \n",
              "2                        False                       False   \n",
              "3                        False                       False   \n",
              "4                        False                       False   \n",
              "\n",
              "   ip_addresses_98.199.132.55  ip_addresses_98.76.69.193  \\\n",
              "0                       False                      False   \n",
              "1                       False                      False   \n",
              "2                       False                      False   \n",
              "3                       False                      False   \n",
              "4                       False                      False   \n",
              "\n",
              "   ip_addresses_99.167.174.194  ip_addresses_99.21.227.221  \\\n",
              "0                        False                       False   \n",
              "1                        False                       False   \n",
              "2                        False                       False   \n",
              "3                        False                       False   \n",
              "4                        False                       False   \n",
              "\n",
              "   ip_addresses_99.229.27.71  ip_addresses_99.238.189.2  \n",
              "0                      False                      False  \n",
              "1                      False                      False  \n",
              "2                      False                      False  \n",
              "3                      False                      False  \n",
              "4                      False                      False  \n",
              "\n",
              "[5 rows x 728 columns]"
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              "      <th></th>\n",
              "      <th>Transaction_id</th>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>2</td>\n",
              "      <td>PAYMENT</td>\n",
              "      <td>1864.28</td>\n",
              "      <td>202</td>\n",
              "      <td>21249.0</td>\n",
              "      <td>19384.72</td>\n",
              "      <td>ACC43829436</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>TRANSFER</td>\n",
              "      <td>181.00</td>\n",
              "      <td>449</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>ACC19495987</td>\n",
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              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>CASH_OUT</td>\n",
              "      <td>181.00</td>\n",
              "      <td>37</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>ACC94944418</td>\n",
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              "<p>5 rows × 728 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "\n",
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              "\n",
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              "        document.querySelector('#df-8ecabcc7-9ab1-47d5-9a86-db4c63e9cdff button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b1bb8d44-c5f6-4ea1-8a49-b8d46b30cfa9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 329
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.model_selection import cross_validate\n",
        "\n",
        "s1 = cross_validate(lor, x_test, y_test, scoring='recall_macro')\n",
        "s2 = cross_validate(rf, x_test, y_test, scoring='recall_macro')\n",
        "s3 = cross_validate(xgb, x_test, y_test, scoring='recall_macro')\n",
        "\n",
        "print('Logistic Regression Recall Cross-Validation:', np.mean(s1['test_score']))\n",
        "print('Random Forest Recall Cross-Validation:', np.mean(s2['test_score']))\n",
        "print('XGBoost Recall Cross-Validation:', np.mean(s3['test_score']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJzQKSgFAVDF",
        "outputId": "f99b74ec-4566-4840-b743-6d3f28588c63"
      },
      "execution_count": 330,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Recall Cross-Validation: 0.7891534391534392\n",
            "Random Forest Recall Cross-Validation: 0.7\n",
            "XGBoost Recall Cross-Validation: 0.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "param_dist_xgb = {\n",
        "    'learning_rate': [0.01, 0.1, 0.2],\n",
        "    'max_depth': [3, 5, 7],\n",
        "    'n_estimators': [100, 200],\n",
        "    'subsample': [0.8, 1.0],\n",
        "    'colsample_bytree': [0.8, 1.0],\n",
        "    'gamma': [0, 0.1, 0.2]\n",
        "}\n",
        "\n",
        "\n",
        "random_search_xgb = RandomizedSearchCV(estimator=XGBClassifier(use_label_encoder=False, eval_metric='logloss'),\n",
        "                                        param_distributions=param_dist_xgb,\n",
        "                                        n_iter=50,\n",
        "                                        cv=5,\n",
        "                                        scoring='roc_auc',\n",
        "                                        verbose=1,\n",
        "                                        n_jobs=-1,\n",
        "                                        random_state=42)\n",
        "\n",
        "\n",
        "random_search_xgb.fit(x_train, y_train)\n",
        "\n",
        "print(f\"Best parameters for XGBoost: {random_search_xgb.best_params_}\")\n",
        "print(f\"Best ROC AUC: {random_search_xgb.best_score_:.4f}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V0HO2mbdBD9A",
        "outputId": "a63cdfc3-d5dd-4af3-d3f5-fc1f297f26c2"
      },
      "execution_count": 331,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
            "Best parameters for XGBoost: {'subsample': 1.0, 'n_estimators': 100, 'max_depth': 7, 'learning_rate': 0.1, 'gamma': 0.2, 'colsample_bytree': 1.0}\n",
            "Best ROC AUC: nan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 332,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vsXN1zLcrmjG",
        "outputId": "8c09b5ee-515b-414b-cb6f-d17b708a8a88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['xgb_model.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 332
        }
      ],
      "source": [
        "import joblib\n",
        "# joblib.dump(lor, 'lor_model.pkl')\n",
        "# joblib.dump(rf, 'rf_model.pkl')\n",
        "joblib.dump(xgb, 'xgb_model.pkl')\n",
        "\n",
        "#sinc emy accuracy score in xgb greater\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 333,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "xE7TnHbnrirF",
        "outputId": "e72d3574-1065-4fd4-af3a-af23bf8ffcd6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_be28b100-9948-41be-ba78-08d62b368e9b\", \"xgb_model.pkl\", 78141)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from  google.colab import files\n",
        "files.download('xgb_model.pkl')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 333,
      "metadata": {
        "id": "kQiT4sbetWCH"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 333,
      "metadata": {
        "id": "uGuscy0xtr85"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNX7pIfpRlr4CCiqVsXV//i",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}