Logistic Regression - Prediction Customer Clicks On Facebook Ad.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_face = pd.read_csv(\"Facebook_Ads_2.csv\",encoding=\"latin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>emails</th>\n",
       "      <th>Country</th>\n",
       "      <th>Time Spent on Site</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Martina Avila</td>\n",
       "      <td>cubilia.Curae.Phasellus@quisaccumsanconvallis.edu</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>25.649648</td>\n",
       "      <td>55330.06006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Harlan Barnes</td>\n",
       "      <td>eu.dolor@diam.co.uk</td>\n",
       "      <td>Belize</td>\n",
       "      <td>32.456107</td>\n",
       "      <td>79049.07674</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Naomi Rodriquez</td>\n",
       "      <td>vulputate.mauris.sagittis@ametconsectetueradip...</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>20.945978</td>\n",
       "      <td>41098.60826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Jade Cunningham</td>\n",
       "      <td>malesuada@dignissim.com</td>\n",
       "      <td>Cook Islands</td>\n",
       "      <td>54.039325</td>\n",
       "      <td>37143.35536</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Cedric Leach</td>\n",
       "      <td>felis.ullamcorper.viverra@egetmollislectus.net</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>34.249729</td>\n",
       "      <td>37355.11276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Names                                             emails  \\\n",
       "0    Martina Avila  cubilia.Curae.Phasellus@quisaccumsanconvallis.edu   \n",
       "1    Harlan Barnes                                eu.dolor@diam.co.uk   \n",
       "2  Naomi Rodriquez  vulputate.mauris.sagittis@ametconsectetueradip...   \n",
       "3  Jade Cunningham                            malesuada@dignissim.com   \n",
       "4     Cedric Leach     felis.ullamcorper.viverra@egetmollislectus.net   \n",
       "\n",
       "        Country  Time Spent on Site       Salary  Clicked  \n",
       "0      Bulgaria           25.649648  55330.06006        0  \n",
       "1        Belize           32.456107  79049.07674        1  \n",
       "2       Algeria           20.945978  41098.60826        0  \n",
       "3  Cook Islands           54.039325  37143.35536        1  \n",
       "4        Brazil           34.249729  37355.11276        0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_face.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>emails</th>\n",
       "      <th>Country</th>\n",
       "      <th>Time Spent on Site</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>494</td>\n",
       "      <td>Rigel</td>\n",
       "      <td>egestas.blandit.Nam@semvitaealiquam.com</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>19.222746</td>\n",
       "      <td>44969.13495</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>495</td>\n",
       "      <td>Walter</td>\n",
       "      <td>ligula@Cumsociis.ca</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>22.665662</td>\n",
       "      <td>41686.20425</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>496</td>\n",
       "      <td>Vanna</td>\n",
       "      <td>Cum.sociis.natoque@Sedmolestie.edu</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>35.320239</td>\n",
       "      <td>23989.80864</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>497</td>\n",
       "      <td>Pearl</td>\n",
       "      <td>penatibus.et@massanonante.com</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>26.539170</td>\n",
       "      <td>31708.57054</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>498</td>\n",
       "      <td>Nell</td>\n",
       "      <td>Quisque.varius@arcuVivamussit.net</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>32.386148</td>\n",
       "      <td>74331.35442</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Names                                   emails                Country  \\\n",
       "494   Rigel  egestas.blandit.Nam@semvitaealiquam.com  Sao Tome and Principe   \n",
       "495  Walter                      ligula@Cumsociis.ca                  Nepal   \n",
       "496   Vanna       Cum.sociis.natoque@Sedmolestie.edu               Zimbabwe   \n",
       "497   Pearl            penatibus.et@massanonante.com            Philippines   \n",
       "498    Nell        Quisque.varius@arcuVivamussit.net               Botswana   \n",
       "\n",
       "     Time Spent on Site       Salary  Clicked  \n",
       "494           19.222746  44969.13495        0  \n",
       "495           22.665662  41686.20425        0  \n",
       "496           35.320239  23989.80864        0  \n",
       "497           26.539170  31708.57054        0  \n",
       "498           32.386148  74331.35442        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_face.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names</th>\n",
       "      <th>emails</th>\n",
       "      <th>Country</th>\n",
       "      <th>Time Spent on Site</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>494</td>\n",
       "      <td>Rigel</td>\n",
       "      <td>egestas.blandit.Nam@semvitaealiquam.com</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>19.222746</td>\n",
       "      <td>44969.13495</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>495</td>\n",
       "      <td>Walter</td>\n",
       "      <td>ligula@Cumsociis.ca</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>22.665662</td>\n",
       "      <td>41686.20425</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>496</td>\n",
       "      <td>Vanna</td>\n",
       "      <td>Cum.sociis.natoque@Sedmolestie.edu</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>35.320239</td>\n",
       "      <td>23989.80864</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>497</td>\n",
       "      <td>Pearl</td>\n",
       "      <td>penatibus.et@massanonante.com</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>26.539170</td>\n",
       "      <td>31708.57054</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>498</td>\n",
       "      <td>Nell</td>\n",
       "      <td>Quisque.varius@arcuVivamussit.net</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>32.386148</td>\n",
       "      <td>74331.35442</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "      Names                                   emails                Country  \\\n",
       "494   Rigel  egestas.blandit.Nam@semvitaealiquam.com  Sao Tome and Principe   \n",
       "495  Walter                      ligula@Cumsociis.ca                  Nepal   \n",
       "496   Vanna       Cum.sociis.natoque@Sedmolestie.edu               Zimbabwe   \n",
       "497   Pearl            penatibus.et@massanonante.com            Philippines   \n",
       "498    Nell        Quisque.varius@arcuVivamussit.net               Botswana   \n",
       "\n",
       "     Time Spent on Site       Salary  Clicked  \n",
       "494           19.222746  44969.13495        0  \n",
       "495           22.665662  41686.20425        0  \n",
       "496           35.320239  23989.80864        0  \n",
       "497           26.539170  31708.57054        0  \n",
       "498           32.386148  74331.35442        1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_face.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore/Vizualize Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "click = df_face[df_face[\"Clicked\"]==1]\n",
    "no_click = df_face[df_face[\"Clicked\"]==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total : 499\n",
      "People Who Clicked : 250\n",
      "People Who Not Clicked : 249\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total : {len(df_face)}\")\n",
    "print(f\"People Who Clicked : {len(click)}\")\n",
    "print(f\"People Who Not Clicked : {len(no_click)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21996235e48>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,14))\n",
    "plt.subplot(211)\n",
    "sns.scatterplot(x=\"Time Spent on Site\",y=\"Salary\",data=df_face)\n",
    "plt.subplot(212)\n",
    "sns.scatterplot(x=\"Time Spent on Site\",y=\"Salary\",data=df_face,hue=\"Clicked\",palette=\"summer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21996318808>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.boxplot(x=\"Clicked\",y=\"Salary\",data=df_face)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2199814dec8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.boxplot(x=\"Clicked\",y= \"Time Spent on Site\",data=df_face)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x219986bef48>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FZeaaftC44Pp94BzgVOA7wHmrXdcS6t8EXFievxT4Lxq3SvhL4NrSfi3wsfL8UuCrNMb5bwPuL+2nA4+WnxvL841l3jeBi8s6XwXestr9LnW9H/gccFeZ/gJwZXn+WeAPyvM/BD5bnl8JfL48P6/s7xcDZ5f3wUlr9T0B7AHeXZ6fCmyoeT8Dm4EfAKc17d+ra9vPwG8DFwL7mtr6vl9bbWPRWlf7H0EHv8yLga81TV8HXLfadfXQnzuAN9L4xOym0rYJeLg8vxG4qmn5h8v8q4Abm9pvLG2bgO81tT9vuVXs55nAPcAlwF3lzfoEcPKJ+5XGSKmLy/OTy3Jx4r6eX24tvieAXy3hFie0V7ufaQT6j0pInVz285tr3M/AGM8P9L7v11bbWOwxCKdc5t808w6VtoFT/sR8LXA/MJqZjwOUny8vi7Xq72LthxZoX21/DfwZ8PMy/WvA05l5vEw31/mLvpX5x8ryS/1drKZzgB8D/1BOM/19RKyj4v2cmYeBjwM/BB6nsd/2Uvd+nrcS+7XVNloahEDv6BYDa11EjABfAv4kM3+y2KILtGUX7asmIn4HOJqZe5ubF1g028wbmD7TOOK8EPjbzHwt8CyNP5NbGfg+l3O6l9M4TfIKYB2Nu6+eqKb93M6q9nEQAn3gbzEQEafQCPNbM/P20nwkIjaV+ZuAo6W9VX8Xaz9zgfbV9DrgdyPiII27cF5C44h9Q0TMf5ituc5f9K3MXw88ydJ/F6vpEHAoM+8v01+kEfA17+c3AD/IzB9n5k+B24HfpO79PG8l9murbbQ0CIE+0LcYKFesbwL2Z+YnmmbdCcxf6d5B49z6fPs7y9XybcCx8ufW14A3RcTGcmT0JhrnFx8HnomIbWVb72x6rVWRmddl5pmZOUZjf/1bZr4duBe4oix2Yp/nfxdXlOWztF9ZRkecDZxL4wLSmntPZOb/AD+KiFeVptcD36Xi/UzjVMu2iPiVUtN8n6vdz01WYr+22kZrq3lRZQkXJC6lMTrk+8AHV7ueJdb+WzT+hHoQeKA8LqVx7vAe4ED5eXpZPmh8ccj3gVlgvOm13gU8Uh6/39Q+Duwr6/wNJ1yYW+X+T/DLUS7n0PiH+gjwT8CLS/tLyvQjZf45Tet/sPTrYZpGdazF9wRwATBT9vU/0xjNUPV+Bj4MfK/U9Y80RqpUtZ+B22hcI/gpjSPqa1Ziv7baxmIPP/ovSZUYhFMukqQOGOiSVAkDXZIqYaBLUiUMdEmqhIEuSZUw0CWpEv8PNJmt6ZAghLsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_face[\"Salary\"].hist(bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21998bf0588>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df_face[\"Time Spent on Site\"].hist(bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "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>Names</th>\n",
       "      <th>emails</th>\n",
       "      <th>Country</th>\n",
       "      <th>Time Spent on Site</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Martina Avila</td>\n",
       "      <td>cubilia.Curae.Phasellus@quisaccumsanconvallis.edu</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>25.649648</td>\n",
       "      <td>55330.06006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Harlan Barnes</td>\n",
       "      <td>eu.dolor@diam.co.uk</td>\n",
       "      <td>Belize</td>\n",
       "      <td>32.456107</td>\n",
       "      <td>79049.07674</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Naomi Rodriquez</td>\n",
       "      <td>vulputate.mauris.sagittis@ametconsectetueradip...</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>20.945978</td>\n",
       "      <td>41098.60826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Jade Cunningham</td>\n",
       "      <td>malesuada@dignissim.com</td>\n",
       "      <td>Cook Islands</td>\n",
       "      <td>54.039325</td>\n",
       "      <td>37143.35536</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Cedric Leach</td>\n",
       "      <td>felis.ullamcorper.viverra@egetmollislectus.net</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>34.249729</td>\n",
       "      <td>37355.11276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Names                                             emails  \\\n",
       "0    Martina Avila  cubilia.Curae.Phasellus@quisaccumsanconvallis.edu   \n",
       "1    Harlan Barnes                                eu.dolor@diam.co.uk   \n",
       "2  Naomi Rodriquez  vulputate.mauris.sagittis@ametconsectetueradip...   \n",
       "3  Jade Cunningham                            malesuada@dignissim.com   \n",
       "4     Cedric Leach     felis.ullamcorper.viverra@egetmollislectus.net   \n",
       "\n",
       "        Country  Time Spent on Site       Salary  Clicked  \n",
       "0      Bulgaria           25.649648  55330.06006        0  \n",
       "1        Belize           32.456107  79049.07674        1  \n",
       "2       Algeria           20.945978  41098.60826        0  \n",
       "3  Cook Islands           54.039325  37143.35536        1  \n",
       "4        Brazil           34.249729  37355.11276        0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_face.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the \"emails\",\"Names\",\"Country\"\n",
    "df_face.drop([\"emails\",\"Names\",\"Country\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "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>Time Spent on Site</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Clicked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>25.649648</td>\n",
       "      <td>55330.06006</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>32.456107</td>\n",
       "      <td>79049.07674</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>20.945978</td>\n",
       "      <td>41098.60826</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>54.039325</td>\n",
       "      <td>37143.35536</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>34.249729</td>\n",
       "      <td>37355.11276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time Spent on Site       Salary  Clicked\n",
       "0           25.649648  55330.06006        0\n",
       "1           32.456107  79049.07674        1\n",
       "2           20.945978  41098.60826        0\n",
       "3           54.039325  37143.35536        1\n",
       "4           34.249729  37355.11276        0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_face.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets Normalize The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_face.drop(\"Clicked\",axis=1).values\n",
    "y = df_face[\"Clicked\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(499, 2)\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(499,)\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(y.shape)\n",
    "print(type(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "X = sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-7.99457658e-01,  1.28257713e-01],\n",
       "       [-5.10286870e-02,  1.37859159e+00],\n",
       "       [-1.31666684e+00, -6.21944817e-01],\n",
       "       [ 2.32223270e+00, -8.30443624e-01],\n",
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       "       [-1.08480907e+00, -7.86291560e-01],\n",
       "       [ 1.38267785e+00,  7.78269562e-01],\n",
       "       [ 6.05017234e-01,  4.21569136e-01],\n",
       "       [ 4.28762764e-01,  1.17038786e+00],\n",
       "       [-1.71553149e-01,  9.27029232e-02],\n",
       "       [-2.30791221e+00,  6.30805286e-02],\n",
       "       [ 3.93751298e-01, -2.41424855e-01],\n",
       "       [-1.49843916e+00, -1.00450601e-01],\n",
       "       [-7.51719512e-02, -1.16628841e+00],\n",
       "       [ 2.28098620e+00,  1.65189364e-02],\n",
       "       [-1.90921852e+00,  1.13647954e-01],\n",
       "       [ 1.06851142e-01,  2.78900567e-01],\n",
       "       [ 1.21969727e+00,  5.24899798e-01],\n",
       "       [-2.16629216e+00, -5.90937478e-01],\n",
       "       [-1.14994093e+00, -2.41675560e+00],\n",
       "       [ 1.20517638e+00,  1.29016696e+00],\n",
       "       [-3.22365808e-01,  1.27372563e+00],\n",
       "       [-6.48493466e-01, -1.63506165e-01],\n",
       "       [-2.27442560e-01, -1.17735631e+00],\n",
       "       [-2.14563526e-01, -1.22150728e+00],\n",
       "       [ 6.26794649e-01, -2.40037655e-01],\n",
       "       [ 9.99539856e-01,  4.82253821e-01],\n",
       "       [-9.11163106e-01, -1.32337060e+00],\n",
       "       [-2.08254258e+00, -3.83457878e-01],\n",
       "       [ 1.48257113e+00,  1.68256408e-01],\n",
       "       [-4.57989979e-01,  8.55755068e-01],\n",
       "       [-1.53834725e+00,  6.36975234e-01],\n",
       "       [ 2.68027594e-01, -1.46264059e+00],\n",
       "       [ 2.31823509e+00, -4.49316213e-01],\n",
       "       [ 1.12735434e+00, -6.25400558e-01],\n",
       "       [ 1.18473700e-01, -7.70880173e-01],\n",
       "       [ 6.87656629e-01,  1.31802826e+00],\n",
       "       [ 4.05529430e-01,  6.40325553e-01],\n",
       "       [ 1.09773526e+00,  1.34446404e+00],\n",
       "       [ 5.10943282e-01, -6.59638389e-01],\n",
       "       [-2.79280647e+00, -1.58447584e+00],\n",
       "       [-5.11310233e-02, -1.12628909e+00],\n",
       "       [ 1.19093972e+00, -1.11701595e-01],\n",
       "       [ 1.27887913e+00,  3.00539351e-01],\n",
       "       [-4.10243162e-01, -1.15753545e-02],\n",
       "       [ 2.18200890e-01,  1.56454875e-01],\n",
       "       [ 4.72072688e-01,  1.54992105e-01],\n",
       "       [ 1.87441076e-01,  4.63852044e-01],\n",
       "       [-9.84468940e-02, -1.24880708e-01],\n",
       "       [ 4.79229983e-01, -1.44345059e-01],\n",
       "       [ 1.69663462e+00,  1.11208820e-01],\n",
       "       [ 8.09930826e-01, -1.72046521e+00],\n",
       "       [-2.10129200e-01, -9.14068144e-02],\n",
       "       [ 7.62151072e-01,  2.21682152e+00],\n",
       "       [ 3.03362900e-02,  1.90588939e+00],\n",
       "       [ 1.40123857e+00,  1.31693631e+00],\n",
       "       [ 8.47383498e-01,  3.46881340e-01],\n",
       "       [ 1.51842223e+00,  8.97182126e-01],\n",
       "       [-4.25875625e-01, -7.26998696e-01],\n",
       "       [ 5.19235713e-01, -3.24748638e-01],\n",
       "       [ 6.62743265e-02, -1.15680141e-01],\n",
       "       [-9.08416002e-01, -8.62416406e-01],\n",
       "       [-5.00122693e-01, -1.42137651e+00],\n",
       "       [-2.78093845e-01,  3.49097910e-01],\n",
       "       [ 5.07133827e-01,  7.57085151e-03],\n",
       "       [-9.67986770e-01, -2.74018308e-01],\n",
       "       [-3.47623362e-01, -1.18353500e+00],\n",
       "       [ 9.00127361e-02,  9.03336004e-01],\n",
       "       [ 7.35118466e-01,  4.70422049e-01],\n",
       "       [ 5.27050651e-01,  1.44743530e-01],\n",
       "       [-1.63019618e-01,  1.19257227e+00],\n",
       "       [ 8.62103501e-01,  4.25322958e-01],\n",
       "       [ 1.42843937e+00,  1.07942714e+00],\n",
       "       [-2.36068144e+00, -9.41129513e-01],\n",
       "       [ 4.34747198e-01,  1.84558386e+00],\n",
       "       [ 7.92715785e-01,  1.19646227e+00],\n",
       "       [-1.38412304e+00, -7.82259943e-01],\n",
       "       [-1.27725848e+00,  7.52297316e-01],\n",
       "       [-1.73989886e+00,  3.33780501e-01],\n",
       "       [ 1.29105293e+00,  1.18761807e+00],\n",
       "       [-8.30822155e-01,  1.80117605e-01],\n",
       "       [-1.07088607e+00, -5.56898156e-01],\n",
       "       [-6.60486714e-01,  4.49203455e-01],\n",
       "       [ 1.46184378e-01, -6.28570805e-01],\n",
       "       [-5.87596986e-01,  6.82896020e-01],\n",
       "       [ 1.01120803e-01, -1.78517437e+00],\n",
       "       [ 6.38603734e-03,  1.46624390e+00],\n",
       "       [-1.46834772e-01, -1.23734947e+00],\n",
       "       [ 2.46884600e-02,  1.57072173e+00],\n",
       "       [-1.55636639e+00, -7.10749996e-01],\n",
       "       [ 6.46145133e-01,  1.05983228e+00],\n",
       "       [ 3.20443973e-01,  1.28450995e-01],\n",
       "       [ 1.06931056e+00,  8.62704790e-01],\n",
       "       [ 1.29534992e-01,  1.14768065e+00],\n",
       "       [-7.23528829e-01, -8.26128261e-01],\n",
       "       [ 1.12386746e+00,  5.76429012e-01],\n",
       "       [-3.77350625e-01, -1.41877223e+00],\n",
       "       [-1.31874898e+00, -2.34326440e-01],\n",
       "       [-6.92868827e-01,  1.24451155e+00],\n",
       "       [ 2.04452218e+00,  1.04577832e+00],\n",
       "       [ 7.08248950e-01,  1.47664601e+00],\n",
       "       [-5.77249761e-01, -2.57793682e+00],\n",
       "       [-2.99815039e-01, -4.78395457e-01],\n",
       "       [ 9.55037645e-01,  1.47119015e+00],\n",
       "       [-1.82782453e+00, -1.05304777e+00],\n",
       "       [-7.28129436e-01,  6.59010772e-01],\n",
       "       [ 3.93423635e-01,  2.18334976e+00],\n",
       "       [ 1.14375260e+00,  3.71621874e-01],\n",
       "       [ 2.24544291e-01,  3.78344206e-01],\n",
       "       [-9.70629827e-01, -1.79172680e+00],\n",
       "       [ 2.39410715e-01,  8.82447136e-01],\n",
       "       [ 7.19277726e-01, -1.00438525e+00],\n",
       "       [ 7.94587947e-01,  1.13587727e+00],\n",
       "       [-5.24625542e-01,  5.26287084e-01],\n",
       "       [-3.05048160e-01, -1.72320179e+00],\n",
       "       [-5.70715635e-01, -3.05925470e-01],\n",
       "       [-9.83818173e-01, -1.43310102e+00],\n",
       "       [-2.20285872e-01, -1.78035137e+00],\n",
       "       [-8.10221766e-01, -1.67739289e+00],\n",
       "       [ 5.85621734e-01,  2.22471293e+00],\n",
       "       [ 4.81997019e-01,  4.82652448e-01],\n",
       "       [-7.92902568e-01,  1.40635901e-01],\n",
       "       [ 6.26603767e-01,  2.88253836e-01],\n",
       "       [-1.31149405e+00, -8.81682059e-01],\n",
       "       [-8.48086524e-01,  1.31739164e+00],\n",
       "       [-3.51687074e-01,  1.28325152e+00],\n",
       "       [ 3.31256892e-01, -1.12033884e+00],\n",
       "       [ 1.82927627e+00,  6.98873547e-01],\n",
       "       [ 3.12995886e-01,  1.72720935e+00],\n",
       "       [-5.51094361e-01, -7.00131706e-01],\n",
       "       [ 1.31599336e+00,  9.41421783e-02],\n",
       "       [ 3.44927602e-01, -1.47132096e-01],\n",
       "       [ 1.72135322e-01, -1.59685526e+00],\n",
       "       [ 5.06769182e-01,  5.24684010e-01],\n",
       "       [-1.08090975e+00, -6.45661787e-01],\n",
       "       [-3.02098814e-01, -1.79861741e+00],\n",
       "       [ 1.12889157e+00,  1.98190953e+00],\n",
       "       [ 1.48805910e+00,  9.27229065e-01],\n",
       "       [ 2.63141389e-01,  1.02742605e-01],\n",
       "       [-2.96569336e-01, -1.02427718e+00],\n",
       "       [ 1.27842887e+00,  6.42418929e-01],\n",
       "       [-8.42943155e-01, -4.29119606e-01],\n",
       "       [-1.91132465e-01,  4.15148736e-01],\n",
       "       [ 1.02626226e+00,  7.20450068e-01],\n",
       "       [-2.89151763e-01,  6.44961019e-01],\n",
       "       [-3.48394024e-01,  3.48103203e-02],\n",
       "       [ 3.00478046e-01,  1.24373063e+00],\n",
       "       [-2.16905779e+00, -1.51110615e+00],\n",
       "       [-8.27867118e-01, -2.76806472e-01],\n",
       "       [ 6.33635710e-01, -5.09246868e-01],\n",
       "       [ 6.26106703e-01,  7.50557005e-01],\n",
       "       [ 2.01173160e-01, -2.02638847e-01],\n",
       "       [-3.86929102e-01, -1.11683559e-01],\n",
       "       [-3.81072741e-01, -2.05002079e+00],\n",
       "       [ 3.42595298e-02, -1.00778802e+00],\n",
       "       [-2.76609826e+00, -9.84487785e-01],\n",
       "       [-1.26962083e+00, -7.39867378e-01],\n",
       "       [-9.02821987e-01,  8.84221540e-01],\n",
       "       [ 3.30774731e-03, -5.14590296e-01],\n",
       "       [-4.39656187e-01, -1.33815008e+00],\n",
       "       [-1.21727002e-01, -9.06545636e-01],\n",
       "       [-6.13638244e-01, -5.97782954e-01],\n",
       "       [ 4.48526308e-01, -4.17928816e-01],\n",
       "       [ 9.68142138e-02, -3.89771134e-01],\n",
       "       [ 2.62399033e-01, -2.13904662e+00],\n",
       "       [-4.13832022e-01, -7.11787278e-01],\n",
       "       [ 5.13026747e-01, -1.53519500e+00],\n",
       "       [ 4.57969682e-01,  2.03857045e+00],\n",
       "       [-1.96516240e+00,  2.35892897e+00],\n",
       "       [-8.30558309e-01, -8.27878447e-01],\n",
       "       [ 3.74335737e-01,  1.24675686e+00],\n",
       "       [ 4.39769398e-01,  4.62387961e-01],\n",
       "       [-6.00890099e-01, -6.74237655e-01],\n",
       "       [ 5.31031094e-01,  1.09595804e+00],\n",
       "       [-1.10130678e+00, -9.69857225e-01],\n",
       "       [ 4.67665396e-01,  5.37880931e-01],\n",
       "       [ 1.87463590e+00,  9.98778697e-01],\n",
       "       [ 1.27332419e+00, -3.98894338e-02],\n",
       "       [ 5.59128087e-01,  5.32813464e-01],\n",
       "       [-7.01364125e-01, -1.01686333e-01],\n",
       "       [ 9.65145405e-01,  8.56079168e-02],\n",
       "       [-2.73372941e-01, -6.56301972e-01],\n",
       "       [-5.10850236e-01,  1.55500352e+00],\n",
       "       [ 8.59447839e-01, -9.26391980e-01],\n",
       "       [-1.02959805e+00, -5.17888726e-01],\n",
       "       [ 5.41314780e-01,  2.60639388e-01],\n",
       "       [ 1.31656657e+00,  9.98590718e-01],\n",
       "       [-1.06031983e+00, -6.39528470e-01],\n",
       "       [ 4.50249526e-01,  1.06273571e+00],\n",
       "       [-8.61257819e-01,  4.61587932e-01],\n",
       "       [-1.34304341e+00, -2.41164554e+00],\n",
       "       [ 1.01932774e+00, -2.57057820e-01],\n",
       "       [ 1.09575096e+00,  2.77556768e-01],\n",
       "       [-1.15244247e+00, -8.95045507e-01],\n",
       "       [ 5.22730195e-01,  4.81677546e-01],\n",
       "       [-1.37440455e+00, -6.73839622e-01],\n",
       "       [ 4.37877245e-01, -1.07083877e+00],\n",
       "       [ 4.71567012e-02, -4.61818265e-02],\n",
       "       [ 9.48297203e-01,  1.52645424e-01],\n",
       "       [ 1.13932638e+00,  6.46623388e-02],\n",
       "       [-9.44448413e-01,  3.67763347e-01],\n",
       "       [-9.95654359e-01, -1.55281483e+00],\n",
       "       [ 2.09814834e-01, -1.44736166e-01],\n",
       "       [ 4.67658202e-01,  5.72131754e-01],\n",
       "       [-3.07006494e+00, -2.93483588e-01],\n",
       "       [-9.13201126e-01, -9.58097186e-01],\n",
       "       [-1.50615103e+00, -4.17912301e-01],\n",
       "       [-1.12757275e+00, -5.90970043e-01],\n",
       "       [ 2.63907387e-01, -1.52382503e+00],\n",
       "       [-7.01646996e-01, -1.11693508e+00],\n",
       "       [-5.87212695e-02,  1.12989966e+00]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train.shape (399, 2)\n",
      "X_test shape (100, 2)\n",
      "y_train shape (399,)\n",
      "y_test shape (100,)\n"
     ]
    }
   ],
   "source": [
    "print(f\"X_train.shape {X_train.shape}\")\n",
    "print(f\"X_test shape {X_test.shape}\")\n",
    "print(f\"y_train shape {y_train.shape}\")\n",
    "print(f\"y_test shape {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\raada\\Anaconda3\\lib\\site-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"
     ]
    },
    {
     "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)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_model = LogisticRegression()\n",
    "lin_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lin_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.94\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.94      0.94        47\n",
      "           1       0.94      0.94      0.94        53\n",
      "\n",
      "    accuracy                           0.94       100\n",
      "   macro avg       0.94      0.94      0.94       100\n",
      "weighted avg       0.94      0.94      0.94       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[44  3]\n",
      " [ 3 50]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2199c20bf08>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "cn = confusion_matrix(y_test,y_pred)\n",
    "print(cn)\n",
    "sns.heatmap(cn,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Training And Testing Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_set,y_set = X_train,y_train\n",
    "X1,X2 = np.meshgrid(np.arange(start=X_set[:0].min() - 1,stop=X_set[:]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}