logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "17e38d20",
   "metadata": {
    "id": "17e38d20"
   },
   "source": [
    "# Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c911f148",
   "metadata": {
    "executionInfo": {
     "elapsed": 1586,
     "status": "ok",
     "timestamp": 1648478914517,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "c911f148"
   },
   "outputs": [],
   "source": [
    "# Import modules\n",
    "import pandas as pd\n",
    "\n",
    "# Create data frame\n",
    "df = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f395b2d",
   "metadata": {
    "id": "7f395b2d"
   },
   "source": [
    "# Check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2000572d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1648478914520,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "2000572d",
    "outputId": "e12a0d15-9769-4b99-b5ca-b5c57d3f6e54"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basket_icon_click          0\n",
      "basket_add_list            0\n",
      "basket_add_detail          0\n",
      "sort_by                    0\n",
      "image_picker               0\n",
      "account_page_click         0\n",
      "promo_banner_click         0\n",
      "detail_wishlist_add        0\n",
      "list_size_dropdown         0\n",
      "closed_minibasket_click    0\n",
      "checked_delivery_detail    0\n",
      "checked_returns_detail     0\n",
      "sign_in                    0\n",
      "saw_checkout               0\n",
      "saw_sizecharts             0\n",
      "saw_delivery               0\n",
      "saw_account_upgrade        0\n",
      "saw_homepage               0\n",
      "device_mobile              0\n",
      "device_computer            0\n",
      "device_tablet              0\n",
      "returning_user             0\n",
      "loc_uk                     0\n",
      "ordered                    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for the sum of null values for each feature\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a6766d1",
   "metadata": {
    "id": "9a6766d1"
   },
   "source": [
    "# Check for outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66918f3c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1102,
     "status": "ok",
     "timestamp": 1648478915608,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "66918f3c",
    "outputId": "94daf207-3f6c-4ebc-fcb6-86c31796a204"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers: 0\n"
     ]
    }
   ],
   "source": [
    "# Create function to implement IQR method\n",
    "def remove_outlier_IQR(df):\n",
    "    Q1 = df.quantile(0.25)\n",
    "    Q3 = df.quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    df_final = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR)))]\n",
    "    return df_final\n",
    " \n",
    "# Remove outliers\n",
    "df_outlier_removed = remove_outlier_IQR(df)\n",
    "df_outlier_removed = pd.DataFrame(df_outlier_removed)\n",
    "ind_diff = df.index.difference(df_outlier_removed.index)\n",
    "    \n",
    "for i in range(0, len(ind_diff),1):\n",
    "    df_final = df.drop([ind_diff[i]])\n",
    "    df = df_final\n",
    "    \n",
    "# Check number of outliers removed\n",
    "print('Number of outliers:', len(ind_diff))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c69429e",
   "metadata": {
    "id": "5c69429e"
   },
   "source": [
    "# Feature Selection - Pearson's Correlation Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ccc7c7ee",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 977
    },
    "executionInfo": {
     "elapsed": 4416,
     "status": "ok",
     "timestamp": 1648478920014,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "ccc7c7ee",
    "outputId": "3a0db9ca-42e5-4a4d-d2e1-c1c6dd98aa28"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import modules\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Plot heatmap\n",
    "plt.figure(figsize = (20,15))\n",
    "sns.heatmap(df.corr(), linewidth = 2, annot=True, cmap=\"Blues\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04db8c08",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 503,
     "status": "ok",
     "timestamp": 1648478920502,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "04db8c08",
    "outputId": "1fa032dc-3c60-43e6-b786-9950c3b29517"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "basket_icon_click          0.428334\n",
      "basket_add_list            0.287666\n",
      "basket_add_detail          0.414420\n",
      "list_size_dropdown         0.154867\n",
      "closed_minibasket_click    0.140011\n",
      "checked_delivery_detail    0.798720\n",
      "sign_in                    0.665556\n",
      "saw_checkout               0.708986\n",
      "saw_homepage               0.157778\n",
      "ordered                    1.000000\n",
      "Name: ordered, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Correlation with output variable\n",
    "cor = df.corr()\n",
    "cor_target = abs(cor[\"ordered\"])\n",
    "\n",
    "# Selecting features that are highly correlated  with the output\n",
    "relevant_features = cor_target[cor_target>0.1]\n",
    "print(relevant_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0810bc6c",
   "metadata": {
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1648478920505,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "0810bc6c"
   },
   "outputs": [],
   "source": [
    "# Keep the higly correleated features and output\n",
    "df_features = df[['basket_add_detail', 'saw_homepage', 'saw_checkout', 'list_size_dropdown', 'basket_add_list', 'basket_icon_click', 'sign_in', 'closed_minibasket_click', 'checked_delivery_detail', 'ordered']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "33b2558c",
   "metadata": {
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1648478920507,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "33b2558c"
   },
   "outputs": [],
   "source": [
    "# Select features and output\n",
    "X = df_features.iloc[:, 0:9]\n",
    "y = df_features.iloc[:, 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "40dd6e9d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 977
    },
    "executionInfo": {
     "elapsed": 982,
     "status": "ok",
     "timestamp": 1648478921477,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "40dd6e9d",
    "outputId": "92f8d9a2-7c49-4c5c-d26f-bbe9374269fd"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot heatmap\n",
    "plt.figure(figsize = (20,15))\n",
    "sns.heatmap(X.corr(), linewidth=2, annot=True, cmap=\"Blues\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5ab45704",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1648478921479,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "5ab45704",
    "outputId": "3c7fb360-db8f-4722-bd65-adeb2922c4a6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of correlated features: 1\n",
      "Names of correlated features: {'sign_in'}\n"
     ]
    }
   ],
   "source": [
    "# Select highly correlated features\n",
    "# Remove the first feature that is correlated above the threshold with any other feature\n",
    "def correlation(dataset, threshold):\n",
    "    col_corr = set()  # Set of all the names of correlated columns\n",
    "    corr_matrix = dataset.corr()\n",
    "    for i in range(len(corr_matrix.columns)):\n",
    "        for j in range(i):\n",
    "            if abs(corr_matrix.iloc[i, j]) > threshold: # We are interested in absolute coeff value\n",
    "                colname = corr_matrix.columns[i]  # Getting the name of column\n",
    "                col_corr.add(colname)\n",
    "    return col_corr\n",
    "\n",
    "corr_features = correlation(X, 0.9)\n",
    "\n",
    "print('Number of correlated features:', len(set(corr_features)))\n",
    "print('Names of correlated features:', corr_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb4a713d",
   "metadata": {
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1648478921481,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "bb4a713d"
   },
   "outputs": [],
   "source": [
    "# Drop correlated features\n",
    "X = X.drop(corr_features,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac05806",
   "metadata": {
    "id": "cac05806"
   },
   "source": [
    "# Train Logistic Regression Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "66364738",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 536382,
     "status": "ok",
     "timestamp": 1648479457851,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "66364738",
    "outputId": "f2652a5c-4bc1-4088-e884-fb2bf49b92ad"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
      "135 fits failed out of a total of 300.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "15 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 464, in _check_solver\n",
      "    raise ValueError(\"penalty='none' is not supported for the liblinear solver\")\n",
      "ValueError: penalty='none' is not supported for the liblinear solver\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "15 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
      "    % (solver, penalty)\n",
      "ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "15 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
      "    % (solver, penalty)\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "15 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
      "    % (solver, penalty)\n",
      "ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "15 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
      "    % (solver, penalty)\n",
      "ValueError: Solver newton-cg supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "15 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
      "    % (solver, penalty)\n",
      "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "15 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 459, in _check_solver\n",
      "    solver\n",
      "ValueError: Only 'saga' solver supports elasticnet penalty, got solver=liblinear.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "15 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
      "    % (solver, penalty)\n",
      "ValueError: Solver sag supports only 'l2' or 'none' penalties, got elasticnet penalty.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "15 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1473, in fit\n",
      "    % self.l1_ratio\n",
      "ValueError: l1_ratio must be between 0 and 1; got (l1_ratio=None)\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [0.99291361 0.99291361        nan 0.99291361 0.99291361        nan\n",
      "        nan 0.99291361        nan 0.99291361 0.98700358 0.98700358\n",
      " 0.99034444 0.98700358 0.98700358        nan        nan        nan\n",
      "        nan        nan 0.99291361 0.99291361        nan 0.99291361\n",
      " 0.99291361        nan        nan 0.99291361        nan 0.99291361\n",
      " 0.99291361 0.99291361 0.99291361 0.99291361 0.99291361        nan\n",
      "        nan        nan        nan        nan 0.99291361 0.99291361\n",
      "        nan 0.99291361 0.99291361        nan        nan 0.99291361\n",
      "        nan 0.99291361 0.99291361 0.99291361 0.99291361 0.99291361\n",
      " 0.99291361        nan        nan        nan        nan        nan\n",
      " 0.99291361 0.99291361        nan 0.99291361 0.99291361        nan\n",
      "        nan 0.99291361        nan 0.99291361 0.99291361 0.99291361\n",
      " 0.99291361 0.99291361 0.99291361        nan        nan        nan\n",
      "        nan        nan 0.99291361 0.99291361        nan 0.99291361\n",
      " 0.99291361        nan        nan 0.99291361        nan 0.99291361\n",
      " 0.99291361 0.99291361 0.99291361 0.99291361 0.99291361        nan\n",
      "        nan        nan        nan        nan]\n",
      "  category=UserWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the train scores are non-finite: [0.99291361 0.99291361        nan 0.99291361 0.99291361        nan\n",
      "        nan 0.99291361        nan 0.99291361 0.98699417 0.98699417\n",
      " 0.99034444 0.98699417 0.98699417        nan        nan        nan\n",
      "        nan        nan 0.99291361 0.99291361        nan 0.99291361\n",
      " 0.99291361        nan        nan 0.99291361        nan 0.99291361\n",
      " 0.99291361 0.99291361 0.99291361 0.99291361 0.99291361        nan\n",
      "        nan        nan        nan        nan 0.99291361 0.99291361\n",
      "        nan 0.99291361 0.99291361        nan        nan 0.99291361\n",
      "        nan 0.99291361 0.99291361 0.99291361 0.99291361 0.99291361\n",
      " 0.99291361        nan        nan        nan        nan        nan\n",
      " 0.99291361 0.99291361        nan 0.99291361 0.99291361        nan\n",
      "        nan 0.99291361        nan 0.99291361 0.99291361 0.99291361\n",
      " 0.99291361 0.99291361 0.99291361        nan        nan        nan\n",
      "        nan        nan 0.99291361 0.99291361        nan 0.99291361\n",
      " 0.99291361        nan        nan 0.99291361        nan 0.99291361\n",
      " 0.99291361 0.99291361 0.99291361 0.99291361 0.99291361        nan\n",
      "        nan        nan        nan        nan]\n",
      "  category=UserWarning,\n",
      "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:1484: UserWarning: Setting penalty='none' will ignore the C and l1_ratio parameters\n",
      "  \"Setting penalty='none' will ignore the C and l1_ratio parameters\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 0.001, 'penalty': 'none', 'solver': 'newton-cg'}\n"
     ]
    }
   ],
   "source": [
    "# Import modules\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import numpy as np\n",
    "\n",
    "# Split training and testing data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)\n",
    "\n",
    "# Instantiate the model (using the default parameters)\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "# Defining parameter range for hyperparameter tuning \n",
    "grid_vals = {'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
    "\t        'penalty': ['none', 'l1', 'l2', 'elasticnet'],\n",
    "            'C': [0.001, 0.01, 0.1, 1, 10]}\n",
    "\n",
    "grid = GridSearchCV(estimator = logreg, param_grid=grid_vals, scoring='accuracy', \n",
    "                    cv=3, return_train_score=True) \n",
    "\n",
    "# Fitting the model for grid search \n",
    "grid.fit(X_train, y_train) \n",
    "\n",
    "# Print best parameters after tuning \n",
    "print('Best parameters:', grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "atnWS6SA8z46",
   "metadata": {
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1648479457854,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "atnWS6SA8z46"
   },
   "outputs": [],
   "source": [
    "# Save the model for the best parameters\n",
    "logreg_best = grid.best_estimator_\n",
    "\n",
    "# Predict labels\n",
    "y_pred = logreg_best.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a855720",
   "metadata": {
    "id": "0a855720"
   },
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d49cda2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 322
    },
    "executionInfo": {
     "elapsed": 677,
     "status": "ok",
     "timestamp": 1648479458523,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "0d49cda2",
    "outputId": "5ea945d1-3e9c-4205-fe12-e96e447ccc36"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Imort modules\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Generate the confusion matrix\n",
    "cf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "ax = sns.heatmap(cf_matrix, annot=True, cmap='Blues', fmt=\".0f\")\n",
    "\n",
    "ax.set_title('Logistic Regression Confusion Matrix \\n');\n",
    "ax.set_xlabel('\\nPredicted Values')\n",
    "ax.set_ylabel('Actual Values ');\n",
    "\n",
    "# Ticket labels\n",
    "ax.xaxis.set_ticklabels(['False','True'])\n",
    "ax.yaxis.set_ticklabels(['False','True'])\n",
    "\n",
    "# Display the visualization of the Confusion Matrix.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f6b587",
   "metadata": {
    "id": "31f6b587"
   },
   "source": [
    "# Classification Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "de5ada0e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 69,
     "status": "ok",
     "timestamp": 1648479458524,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "de5ada0e",
    "outputId": "ce303c9d-a10a-4c6d-f9cf-4336c6432c2f",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "Did not order       1.00      0.99      1.00    130841\n",
      "      Ordered       0.87      0.99      0.92      5780\n",
      "\n",
      "     accuracy                           0.99    136621\n",
      "    macro avg       0.93      0.99      0.96    136621\n",
      " weighted avg       0.99      0.99      0.99    136621\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import modules\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "# Create target labels\n",
    "target_names = ['Did not order', 'Ordered']\n",
    "\n",
    "# Print classification report\n",
    "print(classification_report(y_test, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "42d5a557",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 831,
     "status": "ok",
     "timestamp": 1648479459308,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "42d5a557",
    "outputId": "5dddd661-372a-4b97-af96-e43dfdb3b13a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.866959\n",
      "Recall: 0.991003\n",
      "Accuracy: 0.993186\n",
      "F1 Score: 0.924841\n",
      "AUC: 0.992143\n"
     ]
    }
   ],
   "source": [
    "# Import modules\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
    "from sklearn import metrics\n",
    "\n",
    "# Calculate Precision, Recall, Accuracy and F1 Score\n",
    "print('Precision: %.6f' % precision_score(y_test, y_pred))\n",
    "print('Recall: %.6f' % recall_score(y_test, y_pred))\n",
    "print('Accuracy: %.6f' % accuracy_score(y_test, y_pred))\n",
    "print('F1 Score: %.6f' % f1_score(y_test, y_pred))\n",
    "print('AUC: %.6f' % metrics.roc_auc_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caf1e0eb",
   "metadata": {
    "id": "caf1e0eb"
   },
   "source": [
    "# ROC - AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a0c25fc4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1648479459313,
     "user": {
      "displayName": "Konstantinos Katsantouris",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiUf4n9AKStKHKbBLHcJPt_Nr0RbCOusvz5jZe7VA=s64",
      "userId": "14226418817707827179"
     },
     "user_tz": -180
    },
    "id": "a0c25fc4",
    "outputId": "680bebc1-2da3-434b-8e18-75dde72d0e22"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import modules\n",
    "from sklearn import metrics\n",
    "\n",
    "# Define metrics\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred)\n",
    "auc = metrics.roc_auc_score(y_test, y_pred)\n",
    "\n",
    "# Create ROC curve\n",
    "plt.plot(fpr, tpr, label = \"AUC=\" + str(auc))\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.legend(loc = 4)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "logistic_regression.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}