clicking_on_add_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3b32bfdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn as sk\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f8ba13fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "adv = pd.read_csv('advertising.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9dabd044",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Daily Time Spent on Site</th>\n",
       "      <th>Age</th>\n",
       "      <th>Area Income</th>\n",
       "      <th>Daily Internet Usage</th>\n",
       "      <th>Ad Topic Line</th>\n",
       "      <th>City</th>\n",
       "      <th>Male</th>\n",
       "      <th>Country</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Clicked on Ad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.95</td>\n",
       "      <td>35</td>\n",
       "      <td>61833.90</td>\n",
       "      <td>256.09</td>\n",
       "      <td>Cloned 5thgeneration orchestration</td>\n",
       "      <td>Wrightburgh</td>\n",
       "      <td>0</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>2016-03-27 00:53:11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.23</td>\n",
       "      <td>31</td>\n",
       "      <td>68441.85</td>\n",
       "      <td>193.77</td>\n",
       "      <td>Monitored national standardization</td>\n",
       "      <td>West Jodi</td>\n",
       "      <td>1</td>\n",
       "      <td>Nauru</td>\n",
       "      <td>2016-04-04 01:39:02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.47</td>\n",
       "      <td>26</td>\n",
       "      <td>59785.94</td>\n",
       "      <td>236.50</td>\n",
       "      <td>Organic bottom-line service-desk</td>\n",
       "      <td>Davidton</td>\n",
       "      <td>0</td>\n",
       "      <td>San Marino</td>\n",
       "      <td>2016-03-13 20:35:42</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74.15</td>\n",
       "      <td>29</td>\n",
       "      <td>54806.18</td>\n",
       "      <td>245.89</td>\n",
       "      <td>Triple-buffered reciprocal time-frame</td>\n",
       "      <td>West Terrifurt</td>\n",
       "      <td>1</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2016-01-10 02:31:19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.37</td>\n",
       "      <td>35</td>\n",
       "      <td>73889.99</td>\n",
       "      <td>225.58</td>\n",
       "      <td>Robust logistical utilization</td>\n",
       "      <td>South Manuel</td>\n",
       "      <td>0</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>2016-06-03 03:36:18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>72.97</td>\n",
       "      <td>30</td>\n",
       "      <td>71384.57</td>\n",
       "      <td>208.58</td>\n",
       "      <td>Fundamental modular algorithm</td>\n",
       "      <td>Duffystad</td>\n",
       "      <td>1</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>2016-02-11 21:49:00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>51.30</td>\n",
       "      <td>45</td>\n",
       "      <td>67782.17</td>\n",
       "      <td>134.42</td>\n",
       "      <td>Grass-roots cohesive monitoring</td>\n",
       "      <td>New Darlene</td>\n",
       "      <td>1</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>2016-04-22 02:07:01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>51.63</td>\n",
       "      <td>51</td>\n",
       "      <td>42415.72</td>\n",
       "      <td>120.37</td>\n",
       "      <td>Expanded intangible solution</td>\n",
       "      <td>South Jessica</td>\n",
       "      <td>1</td>\n",
       "      <td>Mongolia</td>\n",
       "      <td>2016-02-01 17:24:57</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>55.55</td>\n",
       "      <td>19</td>\n",
       "      <td>41920.79</td>\n",
       "      <td>187.95</td>\n",
       "      <td>Proactive bandwidth-monitored policy</td>\n",
       "      <td>West Steven</td>\n",
       "      <td>0</td>\n",
       "      <td>Guatemala</td>\n",
       "      <td>2016-03-24 02:35:54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>45.01</td>\n",
       "      <td>26</td>\n",
       "      <td>29875.80</td>\n",
       "      <td>178.35</td>\n",
       "      <td>Virtual 5thgeneration emulation</td>\n",
       "      <td>Ronniemouth</td>\n",
       "      <td>0</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>2016-06-03 21:43:21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  \\\n",
       "0                       68.95   35     61833.90                256.09   \n",
       "1                       80.23   31     68441.85                193.77   \n",
       "2                       69.47   26     59785.94                236.50   \n",
       "3                       74.15   29     54806.18                245.89   \n",
       "4                       68.37   35     73889.99                225.58   \n",
       "..                        ...  ...          ...                   ...   \n",
       "995                     72.97   30     71384.57                208.58   \n",
       "996                     51.30   45     67782.17                134.42   \n",
       "997                     51.63   51     42415.72                120.37   \n",
       "998                     55.55   19     41920.79                187.95   \n",
       "999                     45.01   26     29875.80                178.35   \n",
       "\n",
       "                             Ad Topic Line            City  Male  \\\n",
       "0       Cloned 5thgeneration orchestration     Wrightburgh     0   \n",
       "1       Monitored national standardization       West Jodi     1   \n",
       "2         Organic bottom-line service-desk        Davidton     0   \n",
       "3    Triple-buffered reciprocal time-frame  West Terrifurt     1   \n",
       "4            Robust logistical utilization    South Manuel     0   \n",
       "..                                     ...             ...   ...   \n",
       "995          Fundamental modular algorithm       Duffystad     1   \n",
       "996        Grass-roots cohesive monitoring     New Darlene     1   \n",
       "997           Expanded intangible solution   South Jessica     1   \n",
       "998   Proactive bandwidth-monitored policy     West Steven     0   \n",
       "999        Virtual 5thgeneration emulation     Ronniemouth     0   \n",
       "\n",
       "                    Country            Timestamp  Clicked on Ad  \n",
       "0                   Tunisia  2016-03-27 00:53:11              0  \n",
       "1                     Nauru  2016-04-04 01:39:02              0  \n",
       "2                San Marino  2016-03-13 20:35:42              0  \n",
       "3                     Italy  2016-01-10 02:31:19              0  \n",
       "4                   Iceland  2016-06-03 03:36:18              0  \n",
       "..                      ...                  ...            ...  \n",
       "995                 Lebanon  2016-02-11 21:49:00              1  \n",
       "996  Bosnia and Herzegovina  2016-04-22 02:07:01              1  \n",
       "997                Mongolia  2016-02-01 17:24:57              1  \n",
       "998               Guatemala  2016-03-24 02:35:54              0  \n",
       "999                  Brazil  2016-06-03 21:43:21              1  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0b6d9a2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 10 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Daily Time Spent on Site  1000 non-null   float64\n",
      " 1   Age                       1000 non-null   int64  \n",
      " 2   Area Income               1000 non-null   float64\n",
      " 3   Daily Internet Usage      1000 non-null   float64\n",
      " 4   Ad Topic Line             1000 non-null   object \n",
      " 5   City                      1000 non-null   object \n",
      " 6   Male                      1000 non-null   int64  \n",
      " 7   Country                   1000 non-null   object \n",
      " 8   Timestamp                 1000 non-null   object \n",
      " 9   Clicked on Ad             1000 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(4)\n",
      "memory usage: 78.3+ KB\n"
     ]
    }
   ],
   "source": [
    "adv.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60fa7693",
   "metadata": {},
   "outputs": [],
   "source": [
    "adv.drop('Ad Topic Line',axis =1,inplace=True)\n",
    "adv.drop('City',axis =1,inplace=True)\n",
    "adv.drop('Country',axis =1,inplace=True)\n",
    "adv.drop('Timestamp',axis =1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f4df461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Daily Time Spent on Site</th>\n",
       "      <th>Age</th>\n",
       "      <th>Area Income</th>\n",
       "      <th>Daily Internet Usage</th>\n",
       "      <th>Male</th>\n",
       "      <th>Clicked on Ad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>68.95</td>\n",
       "      <td>35</td>\n",
       "      <td>61833.90</td>\n",
       "      <td>256.09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.23</td>\n",
       "      <td>31</td>\n",
       "      <td>68441.85</td>\n",
       "      <td>193.77</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>69.47</td>\n",
       "      <td>26</td>\n",
       "      <td>59785.94</td>\n",
       "      <td>236.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74.15</td>\n",
       "      <td>29</td>\n",
       "      <td>54806.18</td>\n",
       "      <td>245.89</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>68.37</td>\n",
       "      <td>35</td>\n",
       "      <td>73889.99</td>\n",
       "      <td>225.58</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Daily Time Spent on Site  Age  Area Income  Daily Internet Usage  Male  \\\n",
       "0                     68.95   35     61833.90                256.09     0   \n",
       "1                     80.23   31     68441.85                193.77     1   \n",
       "2                     69.47   26     59785.94                236.50     0   \n",
       "3                     74.15   29     54806.18                245.89     1   \n",
       "4                     68.37   35     73889.99                225.58     0   \n",
       "\n",
       "   Clicked on Ad  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b20e4d6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = adv['Clicked on Ad']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da11b763",
   "metadata": {},
   "outputs": [],
   "source": [
    "adv.drop('Clicked on Ad',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f0000cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = adv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "49225c25",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c566c8ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87fef551",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9ace7a62",
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e688fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0f7c235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.885"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0a607a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c90d461a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1100 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adv.hist(figsize=(10,11))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "023ab984",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}