Logistic_regression_Notebook.ipynb

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      "display_name": "Python 3",
      "language": "python",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/kopiladevkota/Python/blob/main/Logistic_regression_Notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XHyuIcRGixQy"
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MetALTTmQKkh"
      },
      "source": [
        "### Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWchGPfa9xW9"
      },
      "source": [
        "#importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHKgifnpjyvh"
      },
      "source": [
        "### Importing the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NB5xYyHg9xXA",
        "outputId": "7eaac4d1-42cd-4178-96e1-e9ccabc75020",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "data = pd.read_csv('data_cleaned.csv')\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>SibSp_0</th>\n",
              "      <th>SibSp_1</th>\n",
              "      <th>SibSp_2</th>\n",
              "      <th>SibSp_3</th>\n",
              "      <th>SibSp_4</th>\n",
              "      <th>SibSp_5</th>\n",
              "      <th>SibSp_8</th>\n",
              "      <th>Parch_0</th>\n",
              "      <th>Parch_1</th>\n",
              "      <th>Parch_2</th>\n",
              "      <th>Parch_3</th>\n",
              "      <th>Parch_4</th>\n",
              "      <th>Parch_5</th>\n",
              "      <th>Parch_6</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>22.0</td>\n",
              "      <td>7.2500</td>\n",
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              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>4</th>\n",
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            "text/plain": [
              "   Survived   Age     Fare  ...  Embarked_C  Embarked_Q  Embarked_S\n",
              "0         0  22.0   7.2500  ...           0           0           1\n",
              "1         1  38.0  71.2833  ...           1           0           0\n",
              "2         1  26.0   7.9250  ...           0           0           1\n",
              "3         1  35.0  53.1000  ...           0           0           1\n",
              "4         0  35.0   8.0500  ...           0           0           1\n",
              "\n",
              "[5 rows x 25 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hGxgnJmxj3nv"
      },
      "source": [
        "### Segregating variables: Independent and Dependent Variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rym4fnPq9xXG",
        "outputId": "103a10b9-f5d8-4ae3-e3a1-c42a9a1a42f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#seperating independent and dependent variables\n",
        "x = data.drop(['Survived'], axis=1)\n",
        "y = data['Survived']\n",
        "x.shape, y.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((891, 24), (891,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pAQY_EKnGpZ3"
      },
      "source": [
        "### Splitting the data into train set and the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-PcDK1re9xXM"
      },
      "source": [
        "# Importing the train test split function\n",
        "from sklearn.model_selection import train_test_split\n",
        "train_x,test_x,train_y,test_y = train_test_split(x,y, random_state = 56)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fl_ddY0xgfl5"
      },
      "source": [
        "### Normalising using *min_max_scaler*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tsoh_kCkgfmQ"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "outputId": "d06ed210-cafc-41ee-f6fd-ace7e7f90710",
        "id": "-OpAcJQ7gfmj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "cols = train_x.columns\n",
        "cols"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Fare', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female',\n",
              "       'Sex_male', 'SibSp_0', 'SibSp_1', 'SibSp_2', 'SibSp_3', 'SibSp_4',\n",
              "       'SibSp_5', 'SibSp_8', 'Parch_0', 'Parch_1', 'Parch_2', 'Parch_3',\n",
              "       'Parch_4', 'Parch_5', 'Parch_6', 'Embarked_C', 'Embarked_Q',\n",
              "       'Embarked_S'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "outputId": "eea41bdb-e250-4d8b-d72b-ac15b7322d04",
        "id": "TqAHX6eYgfmy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "train_x_scaled = scaler.fit_transform(train_x)\n",
        "train_x_scaled = pd.DataFrame(train_x_scaled, columns=cols)\n",
        "train_x_scaled.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>SibSp_0</th>\n",
              "      <th>SibSp_1</th>\n",
              "      <th>SibSp_2</th>\n",
              "      <th>SibSp_3</th>\n",
              "      <th>SibSp_4</th>\n",
              "      <th>SibSp_5</th>\n",
              "      <th>SibSp_8</th>\n",
              "      <th>Parch_0</th>\n",
              "      <th>Parch_1</th>\n",
              "      <th>Parch_2</th>\n",
              "      <th>Parch_3</th>\n",
              "      <th>Parch_4</th>\n",
              "      <th>Parch_5</th>\n",
              "      <th>Parch_6</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.371701</td>\n",
              "      <td>0.024350</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.396833</td>\n",
              "      <td>0.015127</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.786378</td>\n",
              "      <td>0.152164</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
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              "      <td>0.334004</td>\n",
              "      <td>0.412821</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Age      Fare  Pclass_1  ...  Embarked_C  Embarked_Q  Embarked_S\n",
              "0  0.371701  0.024350       0.0  ...         0.0         0.0         1.0\n",
              "1  0.334004  0.016908       0.0  ...         0.0         0.0         1.0\n",
              "2  0.396833  0.015127       0.0  ...         0.0         1.0         0.0\n",
              "3  0.786378  0.152164       1.0  ...         0.0         0.0         1.0\n",
              "4  0.334004  0.412821       1.0  ...         1.0         0.0         0.0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "outputId": "2ced4da2-ae4e-4f54-da25-269453cd9e25",
        "id": "p4BEqpnpgfnB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "test_x_scaled = scaler.transform(test_x)\n",
        "test_x_scaled = pd.DataFrame(test_x_scaled, columns=cols)\n",
        "test_x_scaled.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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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>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Pclass_1</th>\n",
              "      <th>Pclass_2</th>\n",
              "      <th>Pclass_3</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>SibSp_0</th>\n",
              "      <th>SibSp_1</th>\n",
              "      <th>SibSp_2</th>\n",
              "      <th>SibSp_3</th>\n",
              "      <th>SibSp_4</th>\n",
              "      <th>SibSp_5</th>\n",
              "      <th>SibSp_8</th>\n",
              "      <th>Parch_0</th>\n",
              "      <th>Parch_1</th>\n",
              "      <th>Parch_2</th>\n",
              "      <th>Parch_3</th>\n",
              "      <th>Parch_4</th>\n",
              "      <th>Parch_5</th>\n",
              "      <th>Parch_6</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.396833</td>\n",
              "      <td>0.143462</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.359135</td>\n",
              "      <td>0.129995</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.367921</td>\n",
              "      <td>0.014110</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.346569</td>\n",
              "      <td>0.025374</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.371701</td>\n",
              "      <td>0.054164</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Age      Fare  Pclass_1  ...  Embarked_C  Embarked_Q  Embarked_S\n",
              "0  0.396833  0.143462       0.0  ...         0.0         0.0         1.0\n",
              "1  0.359135  0.129995       1.0  ...         0.0         0.0         1.0\n",
              "2  0.367921  0.014110       0.0  ...         1.0         0.0         0.0\n",
              "3  0.346569  0.025374       0.0  ...         0.0         0.0         1.0\n",
              "4  0.371701  0.054164       1.0  ...         1.0         0.0         0.0\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WvsDKzjdyNWi"
      },
      "source": [
        "### Implementing Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yCG2gM5KyM-1"
      },
      "source": [
        "#importing Logistic Regression and metric F1-score\n",
        "from sklearn.linear_model import LogisticRegression as LogReg\n",
        "from sklearn.metrics import f1_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFrwDTRdybYF",
        "outputId": "be6a8867-192c-445b-a4ef-6ef855d43c89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        }
      },
      "source": [
        "# Creating instance of Logistic Regresssion\n",
        "logreg = LogReg()\n",
        "\n",
        "# Fitting the model\n",
        "logreg.fit(train_x, train_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ll87PoJuPI4n"
      },
      "source": [
        "### Making predictions using *predict* function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lCGfbmBI-kj",
        "outputId": "4947b5a9-9709-4629-f85a-d018c369aa1e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        }
      },
      "source": [
        "# Predicting over the Train\n",
        "train_predict = logreg.predict(train_x)\n",
        "train_predict"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
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              "       0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
              "       0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0,\n",
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              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
              "       1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0,\n",
              "       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
              "       0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "       0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1,\n",
              "       0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
              "       1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1,\n",
              "       1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0,\n",
              "       0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0,\n",
              "       1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1,\n",
              "       0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
              "       0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1,\n",
              "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0,\n",
              "       1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
              "       0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0,\n",
              "       1, 0, 0, 0, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zgQQS_abvKE",
        "outputId": "9c38b16d-544e-446e-f7f4-dd11003824be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Calculating f1-score\n",
        "k = f1_score(train_predict, train_y)\n",
        "print('Training f1_score', k )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training f1_score 0.7485148514851485\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wf4id-DOJCsn",
        "outputId": "cfc4c660-1865-426c-d37d-6a5ae26dd8a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Predicting over the Test Set and f1-score\n",
        "test_predict = logreg.predict(test_x)\n",
        "k = f1_score(test_predict, test_y)\n",
        "print('Test f1_score    ', k )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test f1_score     0.7500000000000001\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GYz-N4EVPapW"
      },
      "source": [
        "### Making predictions using *predict_proba* function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdyhO6NmdZ4_",
        "outputId": "1f9f1cb2-0f48-4d3f-951a-d42ee572bc44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "# Predicting over the Train\n",
        "train_predict = logreg.predict_proba(train_x)\n",
        "train_predict"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.50532331, 0.49467669],\n",
              "       [0.90358566, 0.09641434],\n",
              "       [0.87905533, 0.12094467],\n",
              "       ...,\n",
              "       [0.2318093 , 0.7681907 ],\n",
              "       [0.31396139, 0.68603861],\n",
              "       [0.04488945, 0.95511055]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uan07aK5l9oT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2344
        },
        "outputId": "fe421e79-fd6e-4723-9a78-97a58765e1f0"
      },
      "source": [
        "train_preds = train_predict[:,1]\n",
        "train_preds"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.49467669, 0.09641434, 0.12094467, 0.78269614, 0.63158872,\n",
              "       0.11959961, 0.82765257, 0.7683714 , 0.52316428, 0.08819509,\n",
              "       0.1090379 , 0.0883493 , 0.12710626, 0.09929288, 0.60742308,\n",
              "       0.08735934, 0.30854448, 0.12325226, 0.07440999, 0.28061931,\n",
              "       0.10585236, 0.21134153, 0.07037293, 0.59759559, 0.09323393,\n",
              "       0.52618686, 0.08819857, 0.55834168, 0.59353543, 0.13131481,\n",
              "       0.81433078, 0.08838533, 0.58424898, 0.12972101, 0.01941746,\n",
              "       0.59135528, 0.21086379, 0.13152659, 0.06123531, 0.27460277,\n",
              "       0.84284749, 0.39026594, 0.20569086, 0.71795561, 0.48472512,\n",
              "       0.94248268, 0.35733836, 0.23520213, 0.1737895 , 0.89340115,\n",
              "       0.13552776, 0.6395987 , 0.23012338, 0.60033766, 0.34034319,\n",
              "       0.64464168, 0.77324414, 0.28159562, 0.11224533, 0.30456352,\n",
              "       0.59461444, 0.28159562, 0.13033402, 0.4551247 , 0.10073283,\n",
              "       0.97659391, 0.12001885, 0.0883493 , 0.85823999, 0.67783378,\n",
              "       0.93271443, 0.7563801 , 0.89304445, 0.44658035, 0.83485807,\n",
              "       0.23687075, 0.86224164, 0.5521455 , 0.556213  , 0.13131481,\n",
              "       0.53475428, 0.13080701, 0.13479363, 0.02028353, 0.67893887,\n",
              "       0.9432239 , 0.67072637, 0.08032521, 0.26771886, 0.93212215,\n",
              "       0.29790265, 0.12972782, 0.09506527, 0.12972782, 0.20569086,\n",
              "       0.91146494, 0.34182001, 0.12711296, 0.5576294 , 0.5783447 ,\n",
              "       0.12972782, 0.12719478, 0.1023864 , 0.13393998, 0.13174758,\n",
              "       0.05561818, 0.24643285, 0.04412963, 0.74265326, 0.30335111,\n",
              "       0.23012338, 0.43051127, 0.1315252 , 0.12995991, 0.55187656,\n",
              "       0.32760278, 0.12145854, 0.92653897, 0.11603838, 0.13272705,\n",
              "       0.50877214, 0.50446104, 0.23100313, 0.39858577, 0.93613384,\n",
              "       0.31812669, 0.37540061, 0.60024775, 0.12971964, 0.66255097,\n",
              "       0.09337048, 0.09933289, 0.39033495, 0.90659736, 0.1315252 ,\n",
              "       0.02818343, 0.51625927, 0.06844663, 0.31246664, 0.90818749,\n",
              "       0.92985988, 0.12469721, 0.65739917, 0.10791447, 0.24055058,\n",
              "       0.24800573, 0.11246827, 0.93062869, 0.86925602, 0.90615758,\n",
              "       0.52613567, 0.48262492, 0.65730975, 0.07459497, 0.45144237,\n",
              "       0.08839993, 0.4437509 , 0.27149294, 0.09093194, 0.80336521,\n",
              "       0.2966227 , 0.26771886, 0.31632899, 0.66336138, 0.04872741,\n",
              "       0.27326413, 0.12329144, 0.82115988, 0.33371391, 0.93788361,\n",
              "       0.0538351 , 0.75574134, 0.6134784 , 0.28454157, 0.31718378,\n",
              "       0.47323337, 0.50943988, 0.38108974, 0.07837299, 0.09322373,\n",
              "       0.08473981, 0.24638667, 0.04720907, 0.11845463, 0.9244752 ,\n",
              "       0.35885659, 0.93105061, 0.25837712, 0.25428389, 0.16146308,\n",
              "       0.15120839, 0.0647929 , 0.58345553, 0.31977838, 0.12709822,\n",
              "       0.15554413, 0.28159562, 0.90164369, 0.64526579, 0.26149905,\n",
              "       0.45197123, 0.4048206 , 0.60024775, 0.1160012 , 0.21424431,\n",
              "       0.884498  , 0.62896431, 0.08616321, 0.76816081, 0.12972782,\n",
              "       0.62122978, 0.60024775, 0.13152659, 0.1493503 , 0.94556505,\n",
              "       0.0883493 , 0.07703544, 0.90441697, 0.60932217, 0.13721502,\n",
              "       0.12337767, 0.31132277, 0.08838533, 0.8922442 , 0.21407067,\n",
              "       0.1551267 , 0.0883493 , 0.71513823, 0.46679722, 0.13250207,\n",
              "       0.71702821, 0.46203718, 0.08990418, 0.27326413, 0.17513309,\n",
              "       0.73531781, 0.37661169, 0.87274215, 0.11580061, 0.04074664,\n",
              "       0.2399761 , 0.08925075, 0.21086379, 0.13513183, 0.65360013,\n",
              "       0.09929288, 0.65416472, 0.23012338, 0.5441826 , 0.12020913,\n",
              "       0.92176714, 0.09282684, 0.65981791, 0.79962107, 0.95907454,\n",
              "       0.15635212, 0.41266983, 0.13562405, 0.0883493 , 0.44622199,\n",
              "       0.04562253, 0.6255797 , 0.09070954, 0.13033264, 0.92779127,\n",
              "       0.32892647, 0.56469972, 0.08213707, 0.08838533, 0.08832109,\n",
              "       0.74045896, 0.12975376, 0.82615243, 0.67296769, 0.49191299,\n",
              "       0.27529282, 0.33587946, 0.07589363, 0.31448145, 0.32067379,\n",
              "       0.06751152, 0.11959961, 0.60023613, 0.09036726, 0.1013226 ,\n",
              "       0.44330354, 0.05052684, 0.85305458, 0.09619559, 0.82697055,\n",
              "       0.90374643, 0.56259311, 0.9425333 , 0.09598882, 0.03139889,\n",
              "       0.95011678, 0.059332  , 0.31887804, 0.63365499, 0.1159566 ,\n",
              "       0.54198067, 0.4416522 , 0.23498146, 0.11002084, 0.12727529,\n",
              "       0.04400797, 0.07704548, 0.31486046, 0.09323393, 0.82200514,\n",
              "       0.60024775, 0.44971545, 0.10222659, 0.23012338, 0.09004666,\n",
              "       0.1315252 , 0.11596278, 0.18333813, 0.11965941, 0.56061761,\n",
              "       0.3418579 , 0.12861395, 0.80779108, 0.9475971 , 0.45762581,\n",
              "       0.31887804, 0.60403943, 0.08826856, 0.13637164, 0.25904274,\n",
              "       0.12332931, 0.12972782, 0.01941746, 0.25197576, 0.0883493 ,\n",
              "       0.17305449, 0.10037982, 0.22750082, 0.80947391, 0.05563722,\n",
              "       0.2801312 , 0.93905837, 0.07985735, 0.07211812, 0.32696828,\n",
              "       0.43204777, 0.65034851, 0.04819653, 0.18905059, 0.49682703,\n",
              "       0.36412695, 0.13455834, 0.80419996, 0.28351017, 0.46485538,\n",
              "       0.8037342 , 0.05695514, 0.1455091 , 0.13123443, 0.24482714,\n",
              "       0.89857556, 0.62715994, 0.13131481, 0.26816842, 0.47892867,\n",
              "       0.80383369, 0.16629599, 0.08838533, 0.86657651, 0.96178513,\n",
              "       0.75655731, 0.93149598, 0.19834928, 0.65416472, 0.25471874,\n",
              "       0.44151655, 0.39049685, 0.03953925, 0.73429213, 0.86133913,\n",
              "       0.12322912, 0.72768088, 0.45840461, 0.12972782, 0.14218905,\n",
              "       0.93563596, 0.23012338, 0.31827688, 0.30297522, 0.57742835,\n",
              "       0.42842881, 0.35615347, 0.86104114, 0.08821314, 0.08838533,\n",
              "       0.80383369, 0.64360979, 0.06454558, 0.11603838, 0.56910787,\n",
              "       0.76515426, 0.76252929, 0.90241624, 0.49260953, 0.13201875,\n",
              "       0.1195389 , 0.38171778, 0.09045328, 0.88392128, 0.40151322,\n",
              "       0.11473301, 0.556213  , 0.24773539, 0.11935425, 0.05934819,\n",
              "       0.46059579, 0.9646317 , 0.92385155, 0.58615666, 0.84740279,\n",
              "       0.48479391, 0.60481188, 0.68764108, 0.25052583, 0.80917092,\n",
              "       0.16820637, 0.94406403, 0.10902618, 0.08788099, 0.07219579,\n",
              "       0.62617915, 0.86192859, 0.09377443, 0.06550657, 0.49238847,\n",
              "       0.60024775, 0.23277094, 0.18969902, 0.60010834, 0.22143724,\n",
              "       0.17192838, 0.08210974, 0.64998872, 0.31061891, 0.63238587,\n",
              "       0.70073603, 0.91629952, 0.82496275, 0.92966879, 0.79930408,\n",
              "       0.53829688, 0.12491084, 0.0883493 , 0.08990418, 0.1707594 ,\n",
              "       0.56026039, 0.12972236, 0.09688318, 0.22116717, 0.4302995 ,\n",
              "       0.07459497, 0.07671557, 0.884681  , 0.91407535, 0.96444694,\n",
              "       0.20394625, 0.14872585, 0.56643977, 0.25197576, 0.70846291,\n",
              "       0.75283305, 0.69991695, 0.91417762, 0.93249622, 0.08838533,\n",
              "       0.45485213, 0.7922384 , 0.22877754, 0.16629599, 0.60051738,\n",
              "       0.59441924, 0.62079948, 0.09337048, 0.25231206, 0.89797429,\n",
              "       0.13519276, 0.42483606, 0.40019654, 0.66270807, 0.097761  ,\n",
              "       0.11235976, 0.1652939 , 0.73184861, 0.72360161, 0.40800925,\n",
              "       0.89886593, 0.56533248, 0.23311252, 0.08210974, 0.08210974,\n",
              "       0.08209427, 0.4437509 , 0.10258932, 0.87542244, 0.08477355,\n",
              "       0.06766611, 0.85822194, 0.26194182, 0.04409294, 0.12341165,\n",
              "       0.61431573, 0.12972782, 0.92964737, 0.3300793 , 0.27326413,\n",
              "       0.13513183, 0.11228145, 0.09617166, 0.12087477, 0.26771886,\n",
              "       0.42273003, 0.76660663, 0.01941746, 0.50849347, 0.94892891,\n",
              "       0.33181711, 0.12640494, 0.89499361, 0.48498889, 0.23208141,\n",
              "       0.40048815, 0.72681172, 0.12404444, 0.04292316, 0.01941746,\n",
              "       0.0883493 , 0.53896207, 0.08714118, 0.09030173, 0.04176586,\n",
              "       0.93186964, 0.07067688, 0.57993344, 0.88536083, 0.35865633,\n",
              "       0.46937278, 0.76676753, 0.56210833, 0.09926156, 0.08819857,\n",
              "       0.08832109, 0.0883493 , 0.19950466, 0.60023905, 0.13152659,\n",
              "       0.60404234, 0.12974409, 0.10037982, 0.11288994, 0.73762978,\n",
              "       0.9336511 , 0.45016687, 0.30906849, 0.16629599, 0.88368183,\n",
              "       0.2655386 , 0.50636606, 0.70799257, 0.90149197, 0.72264043,\n",
              "       0.03720393, 0.60030286, 0.05510744, 0.60024775, 0.18490468,\n",
              "       0.38555732, 0.08872474, 0.16792612, 0.12259399, 0.72105091,\n",
              "       0.08220542, 0.11622071, 0.94141222, 0.23989388, 0.21086379,\n",
              "       0.05951233, 0.87410249, 0.03575625, 0.379492  , 0.39029299,\n",
              "       0.70758477, 0.84546252, 0.08819857, 0.44335722, 0.8206913 ,\n",
              "       0.89492353, 0.10918304, 0.03797031, 0.04612531, 0.03598554,\n",
              "       0.60024775, 0.13152659, 0.71513823, 0.05563722, 0.85750575,\n",
              "       0.6637941 , 0.10546804, 0.04800931, 0.11937609, 0.2173066 ,\n",
              "       0.38655285, 0.11334151, 0.62776426, 0.15160379, 0.97317166,\n",
              "       0.78752475, 0.04227144, 0.14095357, 0.13228653, 0.65416472,\n",
              "       0.08750878, 0.60016368, 0.22602497, 0.12972782, 0.48883613,\n",
              "       0.86912535, 0.19736364, 0.94390593, 0.77283984, 0.23208141,\n",
              "       0.14594586, 0.06928229, 0.13152659, 0.10574197, 0.057496  ,\n",
              "       0.08361603, 0.25428389, 0.67029426, 0.59605839, 0.70757475,\n",
              "       0.11246827, 0.12972373, 0.0883493 , 0.94324403, 0.31718378,\n",
              "       0.11278832, 0.74990387, 0.94908003, 0.33274537, 0.64758085,\n",
              "       0.53290546, 0.14336735, 0.94556505, 0.9485861 , 0.68592548,\n",
              "       0.1201031 , 0.94754242, 0.71490468, 0.08838533, 0.25138584,\n",
              "       0.69234726, 0.36327402, 0.49372619, 0.23498146, 0.63573355,\n",
              "       0.7681907 , 0.68603861, 0.95511055])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7_cPchudkR-"
      },
      "source": [
        "for i in range(0, len(train_preds)):\n",
        "  if(train_preds[i]>0.55):\n",
        "    train_preds[i] = 1\n",
        "  else:\n",
        "    train_preds[i] = 0\n",
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMS-8UHtm6ul",
        "outputId": "9bc47d7f-ce45-4d2d-846d-547e02184d53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Calculating f1-score\n",
        "k = f1_score(train_preds, train_y)\n",
        "print('Training f1_score', k )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training f1_score 0.7428571428571429\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wq2nBd4yaJLy"
      },
      "source": [
        "# Confusion matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_B0y2DfFZus5",
        "outputId": "05523d3b-19cf-413d-d839-3514d278a29a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cf= confusion_matrix(test_y, test_predict)\n",
        "print(cf)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[128  16]\n",
            " [ 22  57]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8iN2ugVDZkWS",
        "outputId": "26abffce-bb28-4786-81d6-ac09ad0e4402",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "from sklearn.metrics import classification_report as rep\n",
        "print(rep( test_y , test_predict ))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.89      0.87       144\n",
            "           1       0.78      0.72      0.75        79\n",
            "\n",
            "    accuracy                           0.83       223\n",
            "   macro avg       0.82      0.81      0.81       223\n",
            "weighted avg       0.83      0.83      0.83       223\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tx7pAs3cosaz"
      },
      "source": [
        "# Parameters of Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjMoVU-VoyVo",
        "outputId": "9e3cda88-342d-45a9-8f1f-82e77c1a00e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "# printing the coefficients\n",
        "logreg.coef_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.03483328,  0.00290015,  0.9995268 ,  0.09486387, -1.05437905,\n",
              "         1.17493827, -1.13492666,  0.87606757,  1.0844905 ,  0.48118362,\n",
              "        -0.58818134, -0.80578005, -0.43078847, -0.57698021,  0.23067525,\n",
              "         0.68993538, -0.08309876,  0.31876761, -0.5964179 , -0.19446201,\n",
              "        -0.32538794,  0.16857516,  0.15122525, -0.27978879]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MGWYxwjykZss"
      },
      "source": [
        "### Plotting the coefficients"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxkPFx7Yp3Du",
        "outputId": "e3a2c141-29a5-4fe1-c5cf-3aba7ecea7c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        }
      },
      "source": [
        "plt.figure(figsize=(8, 6), dpi=120, facecolor='w', edgecolor='b')\n",
        "x = range(len(train_x.columns))\n",
        "c = logreg.coef_.reshape(-1)\n",
        "plt.bar( x, c )\n",
        "plt.xlabel( \"Variables\")\n",
        "plt.ylabel('Coefficients')\n",
        "plt.title('Coefficient plot')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Coefficient plot')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qG9/4Ru1/P/3009e7TUVFhStUAADAZs8X6QIAABRUdkCNHDmyKeYAAABo9coO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\nAABWIkABAAAAgJX+D0SfaU/hI/TeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GWpGeLB-UsRE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7ac27612-3e35-4827-8bde-8b57e6a472b7"
      },
      "source": [
        "Coefficients = pd.DataFrame({\n",
        "    'Variable'    : train_x.columns,\n",
        "    'coefficient' : abs(c)\n",
        "})\n",
        "Coefficients.head()"
      ],
      "execution_count": null,
      "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>Variable</th>\n",
              "      <th>coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Age</td>\n",
              "      <td>0.034833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Fare</td>\n",
              "      <td>0.002900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Pclass_1</td>\n",
              "      <td>0.999527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Pclass_2</td>\n",
              "      <td>0.094864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pclass_3</td>\n",
              "      <td>1.054379</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Variable  coefficient\n",
              "0       Age     0.034833\n",
              "1      Fare     0.002900\n",
              "2  Pclass_1     0.999527\n",
              "3  Pclass_2     0.094864\n",
              "4  Pclass_3     1.054379"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWP-VAcstr5J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "outputId": "f7e7ec7c-6c82-4f7c-c2d0-8ebc166a6251"
      },
      "source": [
        "#selecting variables with high coefficient\n",
        "sig_var = Coefficients[Coefficients.coefficient > 0.3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-108-2419a9a0bb0c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    sig_var = Coefficients[Coefficients.coefficient > 0.3)\u001b[0m\n\u001b[0m                                                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdCZihb6uLNI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "e3bbf507-b9f1-4316-8aa0-b634c02701d5"
      },
      "source": [
        "subset = data[sig_var['Variable'].values]\n",
        "subset.head()"
      ],
      "execution_count": null,
      "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>Pclass_1</th>\n",
              "      <th>Pclass_3</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>SibSp_0</th>\n",
              "      <th>SibSp_1</th>\n",
              "      <th>SibSp_2</th>\n",
              "      <th>SibSp_3</th>\n",
              "      <th>SibSp_4</th>\n",
              "      <th>SibSp_5</th>\n",
              "      <th>SibSp_8</th>\n",
              "      <th>Parch_0</th>\n",
              "      <th>Parch_1</th>\n",
              "      <th>Parch_3</th>\n",
              "      <th>Parch_4</th>\n",
              "      <th>Parch_5</th>\n",
              "      <th>Parch_6</th>\n",
              "      <th>Embarked_C</th>\n",
              "      <th>Embarked_Q</th>\n",
              "      <th>Embarked_S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass_1  Pclass_3  Sex_female  ...  Embarked_C  Embarked_Q  Embarked_S\n",
              "0         0         1           0  ...           0           0           1\n",
              "1         1         0           1  ...           1           0           0\n",
              "2         0         1           1  ...           0           0           1\n",
              "3         1         0           1  ...           0           0           1\n",
              "4         0         1           0  ...           0           0           1\n",
              "\n",
              "[5 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aeEEVtBbvFI6"
      },
      "source": [
        "# Using train test split function\n",
        "train_x,test_x,train_y,test_y = train_test_split(subset, y, random_state = 56)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bP1qY6M2vFKX"
      },
      "source": [
        "### Implementing Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "outputId": "73ab52a8-76a7-42c6-bb7a-68a95907cda1",
        "id": "fT8yiasyvFKf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        }
      },
      "source": [
        "# Creating instance of Logistic Regresssion\n",
        "logreg = LogReg()\n",
        "\n",
        "# Fitting the model\n",
        "logreg.fit(train_x, train_y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nprgzSRivFKo"
      },
      "source": [
        "### Making predictions using *predict* function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "outputId": "29ee7fee-1345-4136-c585-feaeaca26bc1",
        "id": "sI8H3sxLvFKq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Predicting over the Train\n",
        "train_predict = logreg.predict(train_x)\n",
        "k = f1_score(train_predict, train_y)\n",
        "print('Training f1_score', k )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training f1_score 0.7076923076923077\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOjZbA_0uNMg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "outputId": "f9ff494b-8c19-4608-f610-9f3fbac010d2"
      },
      "source": [
        "plt.figure(figsize=(8, 6), dpi=120, facecolor='w', edgecolor='b')\n",
        "x = range(len(train_x.columns))\n",
        "c = logreg.coef_.reshape(-1)\n",
        "plt.bar( x, c )\n",
        "plt.xlabel( \"Variables\")\n",
        "plt.ylabel('Coefficients')\n",
        "plt.title('Coefficient plot')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Coefficient plot')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GjBlT+8/pDSGiNrG1H+EbMGBAdtlllyaeBgAASFLWV23cWAIAAKAAEQUAAFCAiAIAAChA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\nIQoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 960x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z9mFcQNrv3Ee"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}