baseline_models.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 91,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "QegnvvOqFh1L",
        "outputId": "e9d4003b-e7b7-4249-d12c-1f41bf9a44a0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7a6da2b7-2929-460c-b420-b45087847ec3\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7a6da2b7-2929-460c-b420-b45087847ec3\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"adilguptagwl\",\"key\":\"8d9b84d8f53905aa1d70492366b4f10f\"}'}"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RhEmPLclFvAD",
        "outputId": "0054798b-6591-49b6-9f50-7f991ce83190"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "rm: cannot remove '/root/.kaggle': No such file or directory\n",
            "ref                                                       title                                               size  lastUpdated          downloadCount  voteCount  usabilityRating  \n",
            "--------------------------------------------------------  -------------------------------------------------  -----  -------------------  -------------  ---------  ---------------  \n",
            "ruchi798/data-science-job-salaries                        Data Science Job Salaries                            7KB  2022-06-15 08:59:12           6773        226  1.0              \n",
            "surajjha101/bigbasket-entire-product-list-28k-datapoints  BigBasket Entire Product List (~28K datapoints)      6MB  2022-06-22 12:51:18           2244         97  1.0              \n",
            "victorsoeiro/netflix-tv-shows-and-movies                  Netflix TV Shows and Movies                          2MB  2022-05-15 00:01:23          17011        492  1.0              \n",
            "sameepvani/nasa-nearest-earth-objects                     NASA - Nearest Earth Objects                         7MB  2022-06-17 02:32:18           2447        112  1.0              \n",
            "zusmani/petrolgas-prices-worldwide                        Petrol/Gas Prices Worldwide                         10KB  2022-06-24 01:25:33           2341        114  1.0              \n",
            "mukuldeshantri/ecommerce-fashion-dataset                  E-commerce Dataset with 30K Products               546KB  2022-07-08 12:28:18            687         28  1.0              \n",
            "jimschacko/airlines-dataset-to-predict-a-delay            Airlines Dataset to predict a delay                  6MB  2022-06-21 05:45:44           2179         61  1.0              \n",
            "iamsouravbanerjee/nifty500-stocks-dataset                 Stock Market Dataset (NIFTY-500)                    35KB  2022-07-17 11:46:10            716         55  1.0              \n",
            "zusmani/pakistans-national-assembly-attendance-20182022   Pakistan's National Assembly Attendance 2018-2022    2MB  2022-07-15 03:33:32            251         30  0.88235295       \n",
            "dansbecker/melbourne-housing-snapshot                     Melbourne Housing Snapshot                         451KB  2018-06-05 12:52:24          91230       1109  0.7058824        \n",
            "ramjasmaurya/1-gb-internet-price                          Internet Prices around 200+ countries in 2022.      22KB  2022-07-17 02:56:12           1206         43  0.9705882        \n",
            "datasnaek/youtube-new                                     Trending YouTube Video Statistics                  201MB  2019-06-03 00:56:47         179346       4602  0.7941176        \n",
            "zynicide/wine-reviews                                     Wine Reviews                                        51MB  2017-11-27 17:08:04         163171       3335  0.7941176        \n",
            "deepcontractor/car-price-prediction-challenge             Car Price Prediction Challenge                     429KB  2022-07-06 11:38:32            573         42  0.9411765        \n",
            "devansodariya/student-performance-data                    Student Performance Dataset                          7KB  2022-05-26 13:55:09           9928        258  0.9705882        \n",
            "datasnaek/chess                                           Chess Game Dataset (Lichess)                         3MB  2017-09-04 03:09:09          30494       1019  0.8235294        \n",
            "rtatman/188-million-us-wildfires                          1.88 Million US Wildfires                          168MB  2020-05-12 21:03:49          20718       1020  0.8235294        \n",
            "residentmario/ramen-ratings                               Ramen Ratings                                       40KB  2018-01-11 16:04:39          34830        792  0.7058824        \n",
            "dansbecker/powerlifting-database                          powerlifting-database                                9MB  2019-04-30 21:07:41           4931         59  0.5882353        \n",
            "jpmiller/publicassistance                                 US Public Food Assistance                          703KB  2020-08-21 16:51:18          16632        396  0.9117647        \n"
          ]
        }
      ],
      "source": [
        "!rm -r ~/.kaggle\n",
        "!mkdir ~/.kaggle\n",
        "!mv ./kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle datasets list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vkhJlCOzFzD0",
        "outputId": "76ea2226-749b-4be2-f510-58f791217607"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading give-me-some-credit-dataset.zip to /content\n",
            "\r  0% 0.00/5.16M [00:00<?, ?B/s]\n",
            "\r100% 5.16M/5.16M [00:00<00:00, 168MB/s]\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d brycecf/give-me-some-credit-dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "opC06shmF3Kj",
        "outputId": "5ecccf72-b809-4056-8876-c1486b6ef7da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  give-me-some-credit-dataset.zip\n",
            "replace Data Dictionary.xls? [y]es, [n]o, [A]ll, [N]one, [r]ename: "
          ]
        }
      ],
      "source": [
        "!unzip give-me-some-credit-dataset.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "execution": {
          "iopub.execute_input": "2022-07-20T18:41:47.840911Z",
          "iopub.status.busy": "2022-07-20T18:41:47.840257Z",
          "iopub.status.idle": "2022-07-20T18:41:48.089898Z",
          "shell.execute_reply": "2022-07-20T18:41:48.088730Z",
          "shell.execute_reply.started": "2022-07-20T18:41:47.840879Z"
        },
        "id": "KGSiclli9Jrl",
        "outputId": "f03da662-f4e0-4fb8-f0c3-c5acc6e6b3cd",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>SeriousDlqin2yrs</th>\n",
              "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
              "      <th>age</th>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "      <th>DebtRatio</th>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
              "      <th>NumberOfTimes90DaysLate</th>\n",
              "      <th>NumberRealEstateLoansOrLines</th>\n",
              "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
              "      <th>NumberOfDependents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.766127</td>\n",
              "      <td>45</td>\n",
              "      <td>2</td>\n",
              "      <td>0.802982</td>\n",
              "      <td>9120.0</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.957151</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>0.121876</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0.658180</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>0.085113</td>\n",
              "      <td>3042.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0.233810</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0.036050</td>\n",
              "      <td>3300.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0.907239</td>\n",
              "      <td>49</td>\n",
              "      <td>1</td>\n",
              "      <td>0.024926</td>\n",
              "      <td>63588.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "        \n",
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              "      \n",
              "  <style>\n",
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              "      gap: 12px;\n",
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              "\n",
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              "\n",
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              "\n",
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              "  "
            ],
            "text/plain": [
              "   Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
              "0           1                 1                              0.766127   45   \n",
              "1           2                 0                              0.957151   40   \n",
              "2           3                 0                              0.658180   38   \n",
              "3           4                 0                              0.233810   30   \n",
              "4           5                 0                              0.907239   49   \n",
              "\n",
              "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
              "0                                     2   0.802982         9120.0   \n",
              "1                                     0   0.121876         2600.0   \n",
              "2                                     1   0.085113         3042.0   \n",
              "3                                     0   0.036050         3300.0   \n",
              "4                                     1   0.024926        63588.0   \n",
              "\n",
              "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
              "0                               13                        0   \n",
              "1                                4                        0   \n",
              "2                                2                        1   \n",
              "3                                5                        0   \n",
              "4                                7                        0   \n",
              "\n",
              "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
              "0                             6                                     0   \n",
              "1                             0                                     0   \n",
              "2                             0                                     0   \n",
              "3                             0                                     0   \n",
              "4                             1                                     0   \n",
              "\n",
              "   NumberOfDependents  \n",
              "0                 2.0  \n",
              "1                 1.0  \n",
              "2                 0.0  \n",
              "3                 0.0  \n",
              "4                 0.0  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Read the DATA\n",
        "import pandas as pd\n",
        "df_train = pd.read_csv('cs-training.csv')\n",
        "\n",
        "df_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2022-07-20T18:41:48.092972Z",
          "iopub.status.busy": "2022-07-20T18:41:48.092544Z",
          "iopub.status.idle": "2022-07-20T18:41:48.098978Z",
          "shell.execute_reply": "2022-07-20T18:41:48.097679Z",
          "shell.execute_reply.started": "2022-07-20T18:41:48.092929Z"
        },
        "id": "2-o-5HCl9Jrm",
        "outputId": "e5e20950-c00e-4a2a-ad1f-9334e73f33d5",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(150000, 12)\n"
          ]
        }
      ],
      "source": [
        "#shape of data\n",
        "print(df_train.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2022-07-20T18:41:48.101435Z",
          "iopub.status.busy": "2022-07-20T18:41:48.100394Z",
          "iopub.status.idle": "2022-07-20T18:41:48.112769Z",
          "shell.execute_reply": "2022-07-20T18:41:48.111816Z",
          "shell.execute_reply.started": "2022-07-20T18:41:48.101386Z"
        },
        "id": "e9XaSqzt9Jrm",
        "outputId": "29d525ab-3acc-4dca-c66f-702daf9fcae1",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10026"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#check  dataset imbalance\n",
        "df_train['SeriousDlqin2yrs'].sum()\n",
        "#stratified k fold is necessary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-07-20T18:41:48.116613Z",
          "iopub.status.busy": "2022-07-20T18:41:48.116211Z",
          "iopub.status.idle": "2022-07-20T18:41:48.153149Z",
          "shell.execute_reply": "2022-07-20T18:41:48.152146Z",
          "shell.execute_reply.started": "2022-07-20T18:41:48.116580Z"
        },
        "id": "XplztuET9Jrn",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#minimal prepprocessing\n",
        "cols_missing = [col for col in df_train.columns if df_train[col].isnull().any()]\n",
        "\n",
        "df_train = df_train.drop(cols_missing,axis =1)\n",
        "\n",
        "for col in df_train.columns:\n",
        "    df_train[col] = df_train[col].astype(float)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-07-20T18:41:48.156335Z",
          "iopub.status.busy": "2022-07-20T18:41:48.155652Z",
          "iopub.status.idle": "2022-07-20T18:41:48.162479Z",
          "shell.execute_reply": "2022-07-20T18:41:48.161290Z",
          "shell.execute_reply.started": "2022-07-20T18:41:48.156296Z"
        },
        "id": "3xhfMobm9Jrn",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#import possibly good models\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "lr = LogisticRegression()\n",
        "gnb = GaussianNB()\n",
        "rf = RandomForestClassifier()\n",
        "xgb = XGBClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-07-20T18:41:48.166033Z",
          "iopub.status.busy": "2022-07-20T18:41:48.165712Z",
          "iopub.status.idle": "2022-07-20T18:41:48.173530Z",
          "shell.execute_reply": "2022-07-20T18:41:48.172641Z",
          "shell.execute_reply.started": "2022-07-20T18:41:48.166006Z"
        },
        "id": "OJFyZyBC9Jro",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#further necessary for baseline model\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.model_selection import cross_validate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-07-20T18:41:48.175630Z",
          "iopub.status.busy": "2022-07-20T18:41:48.175181Z",
          "iopub.status.idle": "2022-07-20T18:41:48.201644Z",
          "shell.execute_reply": "2022-07-20T18:41:48.200630Z",
          "shell.execute_reply.started": "2022-07-20T18:41:48.175580Z"
        },
        "id": "f-2oPQ489Jrp",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "\n",
        "X = df_train.drop(['Unnamed: 0','SeriousDlqin2yrs'],axis =1)\n",
        "y = df_train['SeriousDlqin2yrs']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-07-20T18:41:48.203738Z",
          "iopub.status.busy": "2022-07-20T18:41:48.203209Z",
          "iopub.status.idle": "2022-07-20T18:41:48.209294Z",
          "shell.execute_reply": "2022-07-20T18:41:48.208189Z",
          "shell.execute_reply.started": "2022-07-20T18:41:48.203700Z"
        },
        "id": "WgZqxjZp9Jrp",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#creating stratified k fold subsets\n",
        "folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-07-20T18:41:48.211557Z",
          "iopub.status.busy": "2022-07-20T18:41:48.210807Z",
          "iopub.status.idle": "2022-07-20T18:41:48.218489Z",
          "shell.execute_reply": "2022-07-20T18:41:48.217516Z",
          "shell.execute_reply.started": "2022-07-20T18:41:48.211351Z"
        },
        "id": "2s7R4YZ19Jrq",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#train , fit , predict , avg auc score\n",
        "def score(model):\n",
        "    scoring = ['precision', 'recall', 'f1','roc_auc']\n",
        "    return cross_validate(model,X,y,cv=folds,scoring = scoring,n_jobs = -1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-07-20T18:41:48.222869Z",
          "iopub.status.busy": "2022-07-20T18:41:48.222352Z",
          "iopub.status.idle": "2022-07-20T18:43:46.990219Z",
          "shell.execute_reply": "2022-07-20T18:43:46.989020Z",
          "shell.execute_reply.started": "2022-07-20T18:41:48.222832Z"
        },
        "id": "Pqo45j7J9Jrq",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "models = [lr,gnb,rf,xgb]\n",
        "scores_dictionary = {}\n",
        "for model in models:\n",
        "    scores_dictionary[model] = score(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2022-07-20T18:43:46.992519Z",
          "iopub.status.busy": "2022-07-20T18:43:46.992096Z",
          "iopub.status.idle": "2022-07-20T18:43:47.009080Z",
          "shell.execute_reply": "2022-07-20T18:43:47.007863Z",
          "shell.execute_reply.started": "2022-07-20T18:43:46.992454Z"
        },
        "id": "jmT2p9-i9Jrq",
        "outputId": "e18012b6-bdfe-422e-8afd-e13af5f7160b",
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{GaussianNB(): {'fit_time': array([0.06553149, 0.0556314 , 0.0536797 , 0.05550718, 0.03644848]),\n",
              "  'score_time': array([0.10261464, 0.10947967, 0.10660291, 0.10703135, 0.06457758]),\n",
              "  'test_f1': array([0.04547564, 0.03964134, 0.04356061, 0.05329593, 0.04590164]),\n",
              "  'test_precision': array([0.32885906, 0.36842105, 0.42990654, 0.42537313, 0.37692308]),\n",
              "  'test_recall': array([0.02442672, 0.02094763, 0.02294264, 0.02842893, 0.0244389 ]),\n",
              "  'test_roc_auc': array([0.70407366, 0.72240132, 0.71141584, 0.70730793, 0.71850749])},\n",
              " LogisticRegression(): {'fit_time': array([2.62828517, 2.18680334, 2.08196068, 2.2857542 , 1.80329108]),\n",
              "  'score_time': array([0.08495045, 0.0835557 , 0.0889523 , 0.08584189, 0.0555656 ]),\n",
              "  'test_f1': array([0.08615949, 0.04484733, 0.06403712, 0.06037736, 0.09845032]),\n",
              "  'test_precision': array([0.53409091, 0.51648352, 0.46      , 0.55652174, 0.57142857]),\n",
              "  'test_recall': array([0.04685942, 0.0234414 , 0.03441397, 0.0319202 , 0.05386534]),\n",
              "  'test_roc_auc': array([0.68880363, 0.69197631, 0.68224046, 0.68554511, 0.70338323])},\n",
              " RandomForestClassifier(): {'fit_time': array([26.34415579, 26.86141038, 26.86549163, 26.7599175 , 18.8852253 ]),\n",
              "  'score_time': array([1.99690771, 2.00087643, 1.94559717, 1.95045733, 1.53307343]),\n",
              "  'test_f1': array([0.28154632, 0.27702455, 0.27262734, 0.28478024, 0.3       ]),\n",
              "  'test_precision': array([0.52445652, 0.52209945, 0.51381215, 0.52406417, 0.53806452]),\n",
              "  'test_recall': array([0.19242273, 0.18852868, 0.18553616, 0.19551122, 0.20798005]),\n",
              "  'test_roc_auc': array([0.82851256, 0.83399059, 0.83686282, 0.82859287, 0.8310319 ])},\n",
              " XGBClassifier(): {'fit_time': array([10.36359692, 10.47247291, 10.19280624, 10.35437536,  6.26095128]),\n",
              "  'score_time': array([0.30203843, 0.30631804, 0.29197192, 0.29848552, 0.21006489]),\n",
              "  'test_f1': array([0.27456979, 0.28299008, 0.28538462, 0.28582288, 0.29017857]),\n",
              "  'test_precision': array([0.58949097, 0.6012966 , 0.62352941, 0.60063898, 0.57101025]),\n",
              "  'test_recall': array([0.17896311, 0.18503741, 0.18503741, 0.18753117, 0.19451372]),\n",
              "  'test_roc_auc': array([0.85871657, 0.86961779, 0.86270793, 0.86371216, 0.86341123])}}"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scores_dictionary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-07-20T18:43:47.011995Z",
          "iopub.status.busy": "2022-07-20T18:43:47.011335Z",
          "iopub.status.idle": "2022-07-20T18:43:47.022524Z",
          "shell.execute_reply": "2022-07-20T18:43:47.021455Z",
          "shell.execute_reply.started": "2022-07-20T18:43:47.011957Z"
        },
        "id": "bf2lyRHP9Jrr",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "Baseline_results = pd.DataFrame({'LR':[scores_dictionary[lr]['test_precision'].mean(),scores_dictionary[lr]['test_recall'].mean(),scores_dictionary[lr]['test_f1'].mean(),scores_dictionary[lr]['test_roc_auc'].mean()],\n",
        "                                'GNB':[scores_dictionary[gnb]['test_precision'].mean(),scores_dictionary[gnb]['test_recall'].mean(),scores_dictionary[gnb]['test_f1'].mean(),scores_dictionary[gnb]['test_roc_auc'].mean()],\n",
        "                                'RF':[scores_dictionary[rf]['test_precision'].mean(),scores_dictionary[rf]['test_recall'].mean(),scores_dictionary[rf]['test_f1'].mean(),scores_dictionary[rf]['test_roc_auc'].mean()],\n",
        "                                'XGB':[scores_dictionary[xgb]['test_precision'].mean(),scores_dictionary[xgb]['test_recall'].mean(),scores_dictionary[xgb]['test_f1'].mean(),scores_dictionary[xgb]['test_roc_auc'].mean()],},\n",
        "                               index = ['precision','recall','F1','ROC AUC'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "execution": {
          "iopub.execute_input": "2022-07-20T18:43:47.024670Z",
          "iopub.status.busy": "2022-07-20T18:43:47.023768Z",
          "iopub.status.idle": "2022-07-20T18:43:47.040033Z",
          "shell.execute_reply": "2022-07-20T18:43:47.038818Z",
          "shell.execute_reply.started": "2022-07-20T18:43:47.024631Z"
        },
        "id": "9KBAeJS49Jrr",
        "outputId": "d80fd71d-3f6f-4b8f-b863-d39a640c671c",
        "trusted": true
      },
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LR</th>\n",
              "      <th>GNB</th>\n",
              "      <th>RF</th>\n",
              "      <th>XGB</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>precision</th>\n",
              "      <td>0.527705</td>\n",
              "      <td>0.385897</td>\n",
              "      <td>0.524499</td>\n",
              "      <td>0.597193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>recall</th>\n",
              "      <td>0.038100</td>\n",
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              "      <td>0.193996</td>\n",
              "      <td>0.186217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1</th>\n",
              "      <td>0.070774</td>\n",
              "      <td>0.045575</td>\n",
              "      <td>0.283196</td>\n",
              "      <td>0.283789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ROC AUC</th>\n",
              "      <td>0.690390</td>\n",
              "      <td>0.712741</td>\n",
              "      <td>0.831798</td>\n",
              "      <td>0.863633</td>\n",
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            "text/plain": [
              "                 LR       GNB        RF       XGB\n",
              "precision  0.527705  0.385897  0.524499  0.597193\n",
              "recall     0.038100  0.024237  0.193996  0.186217\n",
              "F1         0.070774  0.045575  0.283196  0.283789\n",
              "ROC AUC    0.690390  0.712741  0.831798  0.863633"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Baseline_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-07-20T18:43:47.042618Z",
          "iopub.status.busy": "2022-07-20T18:43:47.041755Z",
          "iopub.status.idle": "2022-07-20T18:43:47.050802Z",
          "shell.execute_reply": "2022-07-20T18:43:47.049863Z",
          "shell.execute_reply.started": "2022-07-20T18:43:47.042579Z"
        },
        "id": "Ptd1mAZO9Jrr",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "pickle.dump(Baseline_results,open(\"BaselineResults\",\"wb\"))\n",
        "#good job"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "baseline-models.ipynb",
      "provenance": []
    },
    "kernelspec": {
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      "name": "python3"
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        "version": 3
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