CreditCardFraudDetection_Using_Logistic_Regression.ipynb

{
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  "metadata": {
    "colab": {
      "name": "CreditCardFraudDetection_Using_Logistic_Regression.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "-XrKaC23pEZv"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_D-XFP6QUIek",
        "outputId": "6edec910-d2a2-46af-e236-21461bda8b31"
      },
      "source": [
        "#if zip file stored in drive\n",
        "#!unzip -q \"/content/drive/archive.zip\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "unzip:  cannot find or open /content/drive/MyDrive/DATA/archive.zip, /content/drive/MyDrive/DATA/archive.zip.zip or /content/drive/MyDrive/DATA/archive.zip.ZIP.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jBNSmZ-FVeBL",
        "outputId": "c1e164b7-8cc2-48b1-e461-0a377f15bf1e"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import seaborn as sns\n",
        "from sklearn.linear_model import Lasso, LogisticRegression\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vtR8Pr4gVLA3"
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/DATA/creditcard.csv\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jeTg6nmh9kC5"
      },
      "source": [
        "___\n",
        "Exploring the data\n",
        "___"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-aP9bG_I_EW"
      },
      "source": [
        "def view(data):\n",
        "    shape=df.shape\n",
        "    print('The shape of the dataframe(Rows X Columns) is :',shape)\n",
        "    count = df['Class'].value_counts()  \n",
        "    print(\"Count of samples in each class:\\n\",count)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "du9COnEIJfRW",
        "outputId": "952a8232-6bf0-4d47-9ddc-64cdbc46f5d3"
      },
      "source": [
        "view(df)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The shape of the dataframe(Rows X Columns) is : (284807, 31)\n",
            "Count of samples in each class:\n",
            " 0    284315\n",
            "1       492\n",
            "Name: Class, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "id": "yGermKuhKWvi",
        "outputId": "3777a914-25a0-491e-9110-d60e1860231a"
      },
      "source": [
        "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
        "df.describe()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Time</th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "      <td>284807.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>94813.85958</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>-0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>-0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>-0.00000</td>\n",
              "      <td>-0.00000</td>\n",
              "      <td>-0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>-0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>-0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>-0.00000</td>\n",
              "      <td>-0.00000</td>\n",
              "      <td>88.34962</td>\n",
              "      <td>0.00173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>47488.14595</td>\n",
              "      <td>1.95870</td>\n",
              "      <td>1.65131</td>\n",
              "      <td>1.51626</td>\n",
              "      <td>1.41587</td>\n",
              "      <td>1.38025</td>\n",
              "      <td>1.33227</td>\n",
              "      <td>1.23709</td>\n",
              "      <td>1.19435</td>\n",
              "      <td>1.09863</td>\n",
              "      <td>1.08885</td>\n",
              "      <td>1.02071</td>\n",
              "      <td>0.99920</td>\n",
              "      <td>0.99527</td>\n",
              "      <td>0.95860</td>\n",
              "      <td>0.91532</td>\n",
              "      <td>0.87625</td>\n",
              "      <td>0.84934</td>\n",
              "      <td>0.83818</td>\n",
              "      <td>0.81404</td>\n",
              "      <td>0.77093</td>\n",
              "      <td>0.73452</td>\n",
              "      <td>0.72570</td>\n",
              "      <td>0.62446</td>\n",
              "      <td>0.60565</td>\n",
              "      <td>0.52128</td>\n",
              "      <td>0.48223</td>\n",
              "      <td>0.40363</td>\n",
              "      <td>0.33008</td>\n",
              "      <td>250.12011</td>\n",
              "      <td>0.04153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>-56.40751</td>\n",
              "      <td>-72.71573</td>\n",
              "      <td>-48.32559</td>\n",
              "      <td>-5.68317</td>\n",
              "      <td>-113.74331</td>\n",
              "      <td>-26.16051</td>\n",
              "      <td>-43.55724</td>\n",
              "      <td>-73.21672</td>\n",
              "      <td>-13.43407</td>\n",
              "      <td>-24.58826</td>\n",
              "      <td>-4.79747</td>\n",
              "      <td>-18.68371</td>\n",
              "      <td>-5.79188</td>\n",
              "      <td>-19.21433</td>\n",
              "      <td>-4.49894</td>\n",
              "      <td>-14.12985</td>\n",
              "      <td>-25.16280</td>\n",
              "      <td>-9.49875</td>\n",
              "      <td>-7.21353</td>\n",
              "      <td>-54.49772</td>\n",
              "      <td>-34.83038</td>\n",
              "      <td>-10.93314</td>\n",
              "      <td>-44.80774</td>\n",
              "      <td>-2.83663</td>\n",
              "      <td>-10.29540</td>\n",
              "      <td>-2.60455</td>\n",
              "      <td>-22.56568</td>\n",
              "      <td>-15.43008</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>54201.50000</td>\n",
              "      <td>-0.92037</td>\n",
              "      <td>-0.59855</td>\n",
              "      <td>-0.89036</td>\n",
              "      <td>-0.84864</td>\n",
              "      <td>-0.69160</td>\n",
              "      <td>-0.76830</td>\n",
              "      <td>-0.55408</td>\n",
              "      <td>-0.20863</td>\n",
              "      <td>-0.64310</td>\n",
              "      <td>-0.53543</td>\n",
              "      <td>-0.76249</td>\n",
              "      <td>-0.40557</td>\n",
              "      <td>-0.64854</td>\n",
              "      <td>-0.42557</td>\n",
              "      <td>-0.58288</td>\n",
              "      <td>-0.46804</td>\n",
              "      <td>-0.48375</td>\n",
              "      <td>-0.49885</td>\n",
              "      <td>-0.45630</td>\n",
              "      <td>-0.21172</td>\n",
              "      <td>-0.22839</td>\n",
              "      <td>-0.54235</td>\n",
              "      <td>-0.16185</td>\n",
              "      <td>-0.35459</td>\n",
              "      <td>-0.31715</td>\n",
              "      <td>-0.32698</td>\n",
              "      <td>-0.07084</td>\n",
              "      <td>-0.05296</td>\n",
              "      <td>5.60000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>84692.00000</td>\n",
              "      <td>0.01811</td>\n",
              "      <td>0.06549</td>\n",
              "      <td>0.17985</td>\n",
              "      <td>-0.01985</td>\n",
              "      <td>-0.05434</td>\n",
              "      <td>-0.27419</td>\n",
              "      <td>0.04010</td>\n",
              "      <td>0.02236</td>\n",
              "      <td>-0.05143</td>\n",
              "      <td>-0.09292</td>\n",
              "      <td>-0.03276</td>\n",
              "      <td>0.14003</td>\n",
              "      <td>-0.01357</td>\n",
              "      <td>0.05060</td>\n",
              "      <td>0.04807</td>\n",
              "      <td>0.06641</td>\n",
              "      <td>-0.06568</td>\n",
              "      <td>-0.00364</td>\n",
              "      <td>0.00373</td>\n",
              "      <td>-0.06248</td>\n",
              "      <td>-0.02945</td>\n",
              "      <td>0.00678</td>\n",
              "      <td>-0.01119</td>\n",
              "      <td>0.04098</td>\n",
              "      <td>0.01659</td>\n",
              "      <td>-0.05214</td>\n",
              "      <td>0.00134</td>\n",
              "      <td>0.01124</td>\n",
              "      <td>22.00000</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>139320.50000</td>\n",
              "      <td>1.31564</td>\n",
              "      <td>0.80372</td>\n",
              "      <td>1.02720</td>\n",
              "      <td>0.74334</td>\n",
              "      <td>0.61193</td>\n",
              "      <td>0.39856</td>\n",
              "      <td>0.57044</td>\n",
              "      <td>0.32735</td>\n",
              "      <td>0.59714</td>\n",
              "      <td>0.45392</td>\n",
              "      <td>0.73959</td>\n",
              "      <td>0.61824</td>\n",
              "      <td>0.66250</td>\n",
              "      <td>0.49315</td>\n",
              "      <td>0.64882</td>\n",
              "      <td>0.52330</td>\n",
              "      <td>0.39967</td>\n",
              "      <td>0.50081</td>\n",
              "      <td>0.45895</td>\n",
              "      <td>0.13304</td>\n",
              "      <td>0.18638</td>\n",
              "      <td>0.52855</td>\n",
              "      <td>0.14764</td>\n",
              "      <td>0.43953</td>\n",
              "      <td>0.35072</td>\n",
              "      <td>0.24095</td>\n",
              "      <td>0.09105</td>\n",
              "      <td>0.07828</td>\n",
              "      <td>77.16500</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>172792.00000</td>\n",
              "      <td>2.45493</td>\n",
              "      <td>22.05773</td>\n",
              "      <td>9.38256</td>\n",
              "      <td>16.87534</td>\n",
              "      <td>34.80167</td>\n",
              "      <td>73.30163</td>\n",
              "      <td>120.58949</td>\n",
              "      <td>20.00721</td>\n",
              "      <td>15.59499</td>\n",
              "      <td>23.74514</td>\n",
              "      <td>12.01891</td>\n",
              "      <td>7.84839</td>\n",
              "      <td>7.12688</td>\n",
              "      <td>10.52677</td>\n",
              "      <td>8.87774</td>\n",
              "      <td>17.31511</td>\n",
              "      <td>9.25353</td>\n",
              "      <td>5.04107</td>\n",
              "      <td>5.59197</td>\n",
              "      <td>39.42090</td>\n",
              "      <td>27.20284</td>\n",
              "      <td>10.50309</td>\n",
              "      <td>22.52841</td>\n",
              "      <td>4.58455</td>\n",
              "      <td>7.51959</td>\n",
              "      <td>3.51735</td>\n",
              "      <td>31.61220</td>\n",
              "      <td>33.84781</td>\n",
              "      <td>25691.16000</td>\n",
              "      <td>1.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Time           V1  ...       Amount        Class\n",
              "count 284807.00000 284807.00000  ... 284807.00000 284807.00000\n",
              "mean   94813.85958      0.00000  ...     88.34962      0.00173\n",
              "std    47488.14595      1.95870  ...    250.12011      0.04153\n",
              "min        0.00000    -56.40751  ...      0.00000      0.00000\n",
              "25%    54201.50000     -0.92037  ...      5.60000      0.00000\n",
              "50%    84692.00000      0.01811  ...     22.00000      0.00000\n",
              "75%   139320.50000      1.31564  ...     77.16500      0.00000\n",
              "max   172792.00000      2.45493  ...  25691.16000      1.00000\n",
              "\n",
              "[8 rows x 31 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yLiHk9z9MVI7"
      },
      "source": [
        "####Data distribution among both non-fraud and fraud classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTHpHmDS0b-5",
        "outputId": "b895e8ee-c819-452c-e590-c3435a1947c7"
      },
      "source": [
        "print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n",
        "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No Frauds 99.83 % of the dataset\n",
            "Frauds 0.17 % of the dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "8DWCX9uK6ES4",
        "outputId": "5b201a66-6db8-46cb-a064-a3814b97926e"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(15,10)) \n",
        "corr = df.corr()\n",
        "sns.heatmap(corr, cmap='coolwarm', annot_kws={'size':30}, ax=ax)\n",
        "ax.set_title(\"Imbalanced Correlation Matrix\", fontsize=14)\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "WvnMe1qL0ope",
        "outputId": "b54c4d89-aba8-446a-8488-bf4c1d5d6656"
      },
      "source": [
        "colors = [\"#0101DF\", \"#DF0101\"]\n",
        "\n",
        "sns.countplot('Class', data=df, palette=colors)\n",
        "plt.title('(0: No Fraud || 1: Fraud)', fontsize=14)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '(0: No Fraud || 1: Fraud)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ejNLl99NCbe"
      },
      "source": [
        "####Exploring the Time feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "nh0qcC8dy3wZ",
        "outputId": "69fd3655-e4f8-45e0-fb96-aae21bb53cce"
      },
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "plt.title('Distribution of Time Feature')\n",
        "sns.distplot(df.Time)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fae3c6c43d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0sXiELPwFwTa"
      },
      "source": [
        "###Since Amount and Time are having higher values from other features we have to scale them according to our given features and also Time feature is not important because it is of small duration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d986qIRwDXba"
      },
      "source": [
        "df['Scaled_Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))\n",
        "df = df.drop(['Time', 'Amount'], axis=1)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "IS1uAYuCXPpD",
        "outputId": "88682b37-ac21-4eef-f81a-1a69f74406ae"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>V1</th>\n",
              "      <th>V2</th>\n",
              "      <th>V3</th>\n",
              "      <th>V4</th>\n",
              "      <th>V5</th>\n",
              "      <th>V6</th>\n",
              "      <th>V7</th>\n",
              "      <th>V8</th>\n",
              "      <th>V9</th>\n",
              "      <th>V10</th>\n",
              "      <th>V11</th>\n",
              "      <th>V12</th>\n",
              "      <th>V13</th>\n",
              "      <th>V14</th>\n",
              "      <th>V15</th>\n",
              "      <th>V16</th>\n",
              "      <th>V17</th>\n",
              "      <th>V18</th>\n",
              "      <th>V19</th>\n",
              "      <th>V20</th>\n",
              "      <th>V21</th>\n",
              "      <th>V22</th>\n",
              "      <th>V23</th>\n",
              "      <th>V24</th>\n",
              "      <th>V25</th>\n",
              "      <th>V26</th>\n",
              "      <th>V27</th>\n",
              "      <th>V28</th>\n",
              "      <th>Class</th>\n",
              "      <th>Scaled_Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.35981</td>\n",
              "      <td>-0.07278</td>\n",
              "      <td>2.53635</td>\n",
              "      <td>1.37816</td>\n",
              "      <td>-0.33832</td>\n",
              "      <td>0.46239</td>\n",
              "      <td>0.23960</td>\n",
              "      <td>0.09870</td>\n",
              "      <td>0.36379</td>\n",
              "      <td>0.09079</td>\n",
              "      <td>-0.55160</td>\n",
              "      <td>-0.61780</td>\n",
              "      <td>-0.99139</td>\n",
              "      <td>-0.31117</td>\n",
              "      <td>1.46818</td>\n",
              "      <td>-0.47040</td>\n",
              "      <td>0.20797</td>\n",
              "      <td>0.02579</td>\n",
              "      <td>0.40399</td>\n",
              "      <td>0.25141</td>\n",
              "      <td>-0.01831</td>\n",
              "      <td>0.27784</td>\n",
              "      <td>-0.11047</td>\n",
              "      <td>0.06693</td>\n",
              "      <td>0.12854</td>\n",
              "      <td>-0.18911</td>\n",
              "      <td>0.13356</td>\n",
              "      <td>-0.02105</td>\n",
              "      <td>0</td>\n",
              "      <td>0.24496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.19186</td>\n",
              "      <td>0.26615</td>\n",
              "      <td>0.16648</td>\n",
              "      <td>0.44815</td>\n",
              "      <td>0.06002</td>\n",
              "      <td>-0.08236</td>\n",
              "      <td>-0.07880</td>\n",
              "      <td>0.08510</td>\n",
              "      <td>-0.25543</td>\n",
              "      <td>-0.16697</td>\n",
              "      <td>1.61273</td>\n",
              "      <td>1.06524</td>\n",
              "      <td>0.48910</td>\n",
              "      <td>-0.14377</td>\n",
              "      <td>0.63556</td>\n",
              "      <td>0.46392</td>\n",
              "      <td>-0.11480</td>\n",
              "      <td>-0.18336</td>\n",
              "      <td>-0.14578</td>\n",
              "      <td>-0.06908</td>\n",
              "      <td>-0.22578</td>\n",
              "      <td>-0.63867</td>\n",
              "      <td>0.10129</td>\n",
              "      <td>-0.33985</td>\n",
              "      <td>0.16717</td>\n",
              "      <td>0.12589</td>\n",
              "      <td>-0.00898</td>\n",
              "      <td>0.01472</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.34247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-1.35835</td>\n",
              "      <td>-1.34016</td>\n",
              "      <td>1.77321</td>\n",
              "      <td>0.37978</td>\n",
              "      <td>-0.50320</td>\n",
              "      <td>1.80050</td>\n",
              "      <td>0.79146</td>\n",
              "      <td>0.24768</td>\n",
              "      <td>-1.51465</td>\n",
              "      <td>0.20764</td>\n",
              "      <td>0.62450</td>\n",
              "      <td>0.06608</td>\n",
              "      <td>0.71729</td>\n",
              "      <td>-0.16595</td>\n",
              "      <td>2.34586</td>\n",
              "      <td>-2.89008</td>\n",
              "      <td>1.10997</td>\n",
              "      <td>-0.12136</td>\n",
              "      <td>-2.26186</td>\n",
              "      <td>0.52498</td>\n",
              "      <td>0.24800</td>\n",
              "      <td>0.77168</td>\n",
              "      <td>0.90941</td>\n",
              "      <td>-0.68928</td>\n",
              "      <td>-0.32764</td>\n",
              "      <td>-0.13910</td>\n",
              "      <td>-0.05535</td>\n",
              "      <td>-0.05975</td>\n",
              "      <td>0</td>\n",
              "      <td>1.16069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.96627</td>\n",
              "      <td>-0.18523</td>\n",
              "      <td>1.79299</td>\n",
              "      <td>-0.86329</td>\n",
              "      <td>-0.01031</td>\n",
              "      <td>1.24720</td>\n",
              "      <td>0.23761</td>\n",
              "      <td>0.37744</td>\n",
              "      <td>-1.38702</td>\n",
              "      <td>-0.05495</td>\n",
              "      <td>-0.22649</td>\n",
              "      <td>0.17823</td>\n",
              "      <td>0.50776</td>\n",
              "      <td>-0.28792</td>\n",
              "      <td>-0.63142</td>\n",
              "      <td>-1.05965</td>\n",
              "      <td>-0.68409</td>\n",
              "      <td>1.96578</td>\n",
              "      <td>-1.23262</td>\n",
              "      <td>-0.20804</td>\n",
              "      <td>-0.10830</td>\n",
              "      <td>0.00527</td>\n",
              "      <td>-0.19032</td>\n",
              "      <td>-1.17558</td>\n",
              "      <td>0.64738</td>\n",
              "      <td>-0.22193</td>\n",
              "      <td>0.06272</td>\n",
              "      <td>0.06146</td>\n",
              "      <td>0</td>\n",
              "      <td>0.14053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.15823</td>\n",
              "      <td>0.87774</td>\n",
              "      <td>1.54872</td>\n",
              "      <td>0.40303</td>\n",
              "      <td>-0.40719</td>\n",
              "      <td>0.09592</td>\n",
              "      <td>0.59294</td>\n",
              "      <td>-0.27053</td>\n",
              "      <td>0.81774</td>\n",
              "      <td>0.75307</td>\n",
              "      <td>-0.82284</td>\n",
              "      <td>0.53820</td>\n",
              "      <td>1.34585</td>\n",
              "      <td>-1.11967</td>\n",
              "      <td>0.17512</td>\n",
              "      <td>-0.45145</td>\n",
              "      <td>-0.23703</td>\n",
              "      <td>-0.03819</td>\n",
              "      <td>0.80349</td>\n",
              "      <td>0.40854</td>\n",
              "      <td>-0.00943</td>\n",
              "      <td>0.79828</td>\n",
              "      <td>-0.13746</td>\n",
              "      <td>0.14127</td>\n",
              "      <td>-0.20601</td>\n",
              "      <td>0.50229</td>\n",
              "      <td>0.21942</td>\n",
              "      <td>0.21515</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.07340</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        V1       V2      V3       V4  ...      V27      V28  Class  Scaled_Amount\n",
              "0 -1.35981 -0.07278 2.53635  1.37816  ...  0.13356 -0.02105      0        0.24496\n",
              "1  1.19186  0.26615 0.16648  0.44815  ... -0.00898  0.01472      0       -0.34247\n",
              "2 -1.35835 -1.34016 1.77321  0.37978  ... -0.05535 -0.05975      0        1.16069\n",
              "3 -0.96627 -0.18523 1.79299 -0.86329  ...  0.06272  0.06146      0        0.14053\n",
              "4 -1.15823  0.87774 1.54872  0.40303  ...  0.21942  0.21515      0       -0.07340\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyHAJI7_WHvW"
      },
      "source": [
        "#function for plotting confusion matrix\n",
        "def plot_cm(cm):\n",
        "  plt.figure(figsize = (15,3))\n",
        "  plt.subplots_adjust(hspace = 0.5, wspace = 0.5)\n",
        "  plt.subplot(1,2,1)\n",
        "  sns.heatmap(cm, annot = True, fmt = 'g', cmap = plt.cm.Blues)\n",
        "  plt.xlabel('Predicted Label')\n",
        "  plt.ylabel('True Label')\n",
        "  plt.title(\"Recall Score: {}%\".format(100*cm[1,1]/(cm[1,0]+cm[1,1])))\n",
        "  plt.show()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8UglIf4fe66"
      },
      "source": [
        "#K-Fold Cross validation\n",
        "###Finding the best parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxwd-v1UMtKA"
      },
      "source": [
        "#function for Kfold and Grid search\n",
        "def Grid_Kfold(X_data,y_data):\n",
        "  model_cv = GridSearchCV(estimator = LogisticRegression(),\n",
        "                        param_grid = {\"C\": [0.01, 0.1, 1, 10, 100]}, \n",
        "                        scoring= 'roc_auc', \n",
        "                        cv = KFold(n_splits=10, shuffle=True, random_state=42), \n",
        "                        n_jobs=3,\n",
        "                        verbose = 5,\n",
        "                        return_train_score=True)\n",
        "  model_cv.fit(X_data,y_data)\n",
        "  print('Best ROC AUC score: ', model_cv.best_score_)\n",
        "  #print the optimum value of hyperparameters\n",
        "  print('Best hyperparameters: ', model_cv.best_params_)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g2yHdVmGGox2"
      },
      "source": [
        "#Logistic Regression(L1) using Downsampling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMyL3ugRXgw8"
      },
      "source": [
        "X = df.iloc[:, df.columns != 'Class']\n",
        "y = df.iloc[:, df.columns == 'Class']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PvIvXuenmvem"
      },
      "source": [
        "fraud_sample = len(df[df.Class == 1])\n",
        "\n",
        "#getting index of fraud transactions\n",
        "fraud_indices = np.array(df[df.Class == 1].index)\n",
        "#getting index of normal transactions\n",
        "normal_indices = df[df.Class == 0].index\n",
        "#extract random samples to create sub-smaples \n",
        "random_normal_indices = np.random.choice(normal_indices, fraud_sample, replace=False)\n",
        "random_normal_indices = np.array(random_normal_indices)\n",
        "\n",
        "#combine both the samples,Total 984 samples\n",
        "us_indices = np.concatenate([fraud_indices, random_normal_indices])\n",
        "\n",
        "#creating sub-samples\n",
        "u_data = df.iloc[us_indices, :]\n",
        "X_undersample = u_data.iloc[:, u_data.columns != 'Class']\n",
        "y_undersample = u_data.iloc[:, u_data.columns == 'Class']\n",
        "\n",
        "#splitting the under sampled data\n",
        "X_train_undersample,X_test_undersample,y_train_undersample,y_test_undersample = train_test_split(X_undersample,y_undersample,test_size = 0.3,random_state = 0)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wI1rp3woarqi",
        "outputId": "749df968-6f73-49a4-b42c-731713ae99fe"
      },
      "source": [
        "Grid_Kfold(X_undersample,y_undersample)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 5 candidates, totalling 50 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=3)]: Using backend LokyBackend with 3 concurrent workers.\n",
            "[Parallel(n_jobs=3)]: Done  12 tasks      | elapsed:    2.1s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Best ROC AUC score:  0.9799775240608861\n",
            "Best hyperparameters:  {'C': 0.01}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=3)]: Done  50 out of  50 | elapsed:    3.2s finished\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "id": "MiK-ztxznrZe",
        "outputId": "e4b2a4b3-28cb-43d7-cfb2-ac3e31bdc54f"
      },
      "source": [
        "lr = LogisticRegression(C = 0.01, penalty = 'l1',solver='saga')\n",
        "lr.fit(X_train_undersample,y_train_undersample.values.ravel())\n",
        "y_pred_undersample = lr.predict(X_test.values)\n",
        "cnf_matrix_undersample = confusion_matrix(y_test, y_pred_undersample) \n",
        "#calling function\n",
        "plot_cm(cnf_matrix_undersample)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gTmJOtDQAP2"
      },
      "source": [
        "#Logistic Regression(L2) using Oversampling(SMOTE)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XqyU573cJLhW",
        "outputId": "3f6cca8b-6edc-4e48-e23f-2fbcd3a0804e"
      },
      "source": [
        "#creating two variables\n",
        "columns=df.columns\n",
        "#sepersting features without the traget variable\n",
        "features_columns=columns.delete(len(columns)-1)\n",
        "features=df[features_columns]\n",
        "#seperating the target class\n",
        "labels=df['Class']\n",
        "\n",
        "train_features, test_features, train_labels, test_labels = train_test_split(features,labels, test_size=0.3, random_state=0)\n",
        "#using SMOTE\n",
        "oversample=SMOTE(random_state=0)\n",
        "os_features,os_labels=oversample.fit_sample(train_features,train_labels)\n",
        "\n",
        "os_features = pd.DataFrame(os_features)\n",
        "os_labels = pd.DataFrame(os_labels)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D0NPtHbwcONq",
        "outputId": "39a682a2-860c-4e31-fa2b-04d044f79a91"
      },
      "source": [
        "Grid_Kfold(os_features,os_labels)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 5 candidates, totalling 50 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=3)]: Using backend LokyBackend with 3 concurrent workers.\n",
            "[Parallel(n_jobs=3)]: Done  12 tasks      | elapsed:   28.0s\n",
            "[Parallel(n_jobs=3)]: Done  50 out of  50 | elapsed:  2.8min finished\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Best ROC AUC score:  1.0\n",
            "Best hyperparameters:  {'C': 0.1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "AjVQxjcnLYVY",
        "outputId": "921313ff-7fa8-4b72-ada6-4f344cddb665"
      },
      "source": [
        "#training with best C score\n",
        "lr1 = LogisticRegression(C = 0.1, penalty = 'l2')\n",
        "lr1.fit(os_features,os_labels.values.ravel())\n",
        "y_pred = lr1.predict(test_features.values)\n",
        "\n",
        "cnf_matrix = confusion_matrix(test_labels, y_pred) \n",
        "plot_cm(cnf_matrix)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "htvhndqeFQtD"
      },
      "source": [
        "\n",
        "\n",
        "##Training with Feature Selection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mxi8BKnwLFFG",
        "outputId": "295e3963-c201-4af6-f201-9a68d21d2c95"
      },
      "source": [
        "sel_ = SelectFromModel(LogisticRegression(C=0.1, penalty='l1',solver='saga'))\n",
        "sel_.fit(os_features,os_labels.values.ravel())"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SelectFromModel(estimator=LogisticRegression(C=0.1, class_weight=None,\n",
              "                                             dual=False, fit_intercept=True,\n",
              "                                             intercept_scaling=1, l1_ratio=None,\n",
              "                                             max_iter=100, multi_class='auto',\n",
              "                                             n_jobs=None, penalty='l1',\n",
              "                                             random_state=None, solver='saga',\n",
              "                                             tol=0.0001, verbose=0,\n",
              "                                             warm_start=False),\n",
              "                max_features=None, norm_order=1, prefit=False, threshold=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F71KneEHPGFx",
        "outputId": "f97a9b75-a260-42ad-8651-4dc0d5f9dac6"
      },
      "source": [
        "sf = sel_.get_support()\n",
        "print(sf)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ True  True False  True  True  True  True  True False  True  True  True\n",
            "  True  True False  True  True False False False  True  True  True False\n",
            " False False False False  True]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dA_o3z4sPKLP",
        "outputId": "cf901c07-7f2e-4eec-d73e-4aeb3045fec1"
      },
      "source": [
        "print('total features: {}'.format((os_features.shape[1])))\n",
        "print('selected features: {}'.format(sf.sum()))\n",
        "print('features with coefficients shrank to zero: {}'.format(\n",
        "      np.sum(sel_.estimator_.coef_ == 0)))\n",
        "a = np.sum(sel_.estimator_.coef_ == 0)\n",
        "print('The number of features which can be removed :',a)\n",
        "X_train_selected = sel_.transform(os_features)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total features: 29\n",
            "selected features: 18\n",
            "features with coefficients shrank to zero: 9\n",
            "The number of features which can be removed : 9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mtzFcf31kWWp"
      },
      "source": [
        "###Training with selected features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-WDScYHCXoe",
        "outputId": "3c8e0ea4-304d-470f-b6b5-578bfc262fda"
      },
      "source": [
        "lr_l2 = LogisticRegression(C=0.1,penalty='l1',solver='saga')\n",
        "lr_l2.fit(X_train_selected,os_labels.values.ravel())"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:330: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
            "  \"the coef_ did not converge\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l1',\n",
              "                   random_state=None, solver='saga', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HjHvFXqWjS8l"
      },
      "source": [
        "from sklearn.metrics import (f1_score, roc_auc_score,confusion_matrix, accuracy_score,\n",
        "                             precision_score, recall_score)\n",
        "from IPython.display import HTML, display\n",
        "import tabulate\n",
        "def predict_and_evaluate(model, x_test, y_test):\n",
        "    predictions = model.predict(x_test)\n",
        "    accuracy = accuracy_score(y_test, predictions)\n",
        "    precision = precision_score(y_test, predictions)\n",
        "    recall = recall_score(y_test, predictions)\n",
        "    f1 = f1_score(y_test, predictions)\n",
        "    tn, fp, fn, tp = confusion_matrix(y_test, predictions).ravel()\n",
        "    \n",
        "    metrics = [round(accuracy,2),fp, fn, round(precision,2), round(recall,2), round(f1,2)]\n",
        "    table = [[model.__class__.__name__] + metrics]\n",
        "    display(HTML(tabulate.tabulate(table,headers=('Algorithm','accuracy','False Positives', \n",
        "                                                  'False Negatives', 'Precision', \n",
        "                                                  'Recall', 'F1 Score'), \n",
        "                                   tablefmt='html')))\n",
        "    #print(accuracy)\n",
        "    return [model.__class__.__name__] + metrics"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "id": "dH2BRfdrjhrD",
        "outputId": "e43facdf-c72e-4479-a4b3-f142be3fccb2"
      },
      "source": [
        "lr_res = predict_and_evaluate(lr,X_test,y_test)\n",
        "l1_res = predict_and_evaluate(lr1,X_test,y_test)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th>Algorithm         </th><th style=\"text-align: right;\">  accuracy</th><th style=\"text-align: right;\">  False Positives</th><th style=\"text-align: right;\">  False Negatives</th><th style=\"text-align: right;\">  Precision</th><th style=\"text-align: right;\">  Recall</th><th style=\"text-align: right;\">  F1 Score</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>LogisticRegression</td><td style=\"text-align: right;\">      0.93</td><td style=\"text-align: right;\">                0</td><td style=\"text-align: right;\">               22</td><td style=\"text-align: right;\">          1</td><td style=\"text-align: right;\">    0.85</td><td style=\"text-align: right;\">      0.92</td></tr>\n",
              "</tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table>\n",
              "<thead>\n",
              "<tr><th>Algorithm         </th><th style=\"text-align: right;\">  accuracy</th><th style=\"text-align: right;\">  False Positives</th><th style=\"text-align: right;\">  False Negatives</th><th style=\"text-align: right;\">  Precision</th><th style=\"text-align: right;\">  Recall</th><th style=\"text-align: right;\">  F1 Score</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "<tr><td>LogisticRegression</td><td style=\"text-align: right;\">         1</td><td style=\"text-align: right;\">                5</td><td style=\"text-align: right;\">                0</td><td style=\"text-align: right;\">       0.97</td><td style=\"text-align: right;\">       1</td><td style=\"text-align: right;\">      0.98</td></tr>\n",
              "</tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}