advertising_ml_Random_Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Will the user click on ad?:) First attempt on ML. Made as a reference for myself..with random forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#enables matplotlib in jupyter\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read csv file to pandas dataframe\n",
    "train = pd.read_csv('advertising.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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",
       "  </tbody>\n",
       "</table>\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",
       "                           Ad Topic Line            City  Male     Country  \\\n",
       "0     Cloned 5thgeneration orchestration     Wrightburgh     0     Tunisia   \n",
       "1     Monitored national standardization       West Jodi     1       Nauru   \n",
       "2       Organic bottom-line service-desk        Davidton     0  San Marino   \n",
       "3  Triple-buffered reciprocal time-frame  West Terrifurt     1       Italy   \n",
       "4          Robust logistical utilization    South Manuel     0     Iceland   \n",
       "\n",
       "             Timestamp  Clicked on Ad  \n",
       "0  2016-03-27 00:53:11              0  \n",
       "1  2016-04-04 01:39:02              0  \n",
       "2  2016-03-13 20:35:42              0  \n",
       "3  2016-01-10 02:31:19              0  \n",
       "4  2016-06-03 03:36:18              0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#explore data\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ca6313e10>"
      ]
     },
     "execution_count": 4,
     "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": [
    "#check for NaN data (none found)\n",
    "sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ca5fcdcc0>"
      ]
     },
     "execution_count": 5,
     "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": [
    "#check the proportion of target data\n",
    "sns.countplot(x='Clicked on Ad', data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
      "Daily Time Spent on Site    1000 non-null float64\n",
      "Age                         1000 non-null int64\n",
      "Area Income                 1000 non-null float64\n",
      "Daily Internet Usage        1000 non-null float64\n",
      "Ad Topic Line               1000 non-null object\n",
      "City                        1000 non-null object\n",
      "Male                        1000 non-null int64\n",
      "Country                     1000 non-null object\n",
      "Timestamp                   1000 non-null object\n",
      "Clicked on Ad               1000 non-null int64\n",
      "dtypes: float64(3), int64(3), object(4)\n",
      "memory usage: 78.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#check dataframe info\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get rid of columns\n",
    "train.drop(['Ad Topic Line', 'City', 'Country', 'Timestamp'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Clicked on Ad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a Random Forests Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#small trick - enter train_test_split then shift+TAB, and find this line in examples. Saves typing time. \n",
    "#this line prepares data for training:\n",
    "# X_train = 70% of prepared dataset, without target column3\n",
    "# X_test = 30% of prepared dataset, left for model testing\n",
    "# y_train = 70% of target column\n",
    "# y_test = 30% of target column accordingly.\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(train.drop('Clicked on Ad',axis=1), \n",
    "                                                    train['Clicked on Ad'], test_size=0.30, \n",
    "                                                    random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=50,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(n_estimators=50)\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[152   5]\n",
      " [  8 135]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.97      0.96       157\n",
      "           1       0.96      0.94      0.95       143\n",
      "\n",
      "    accuracy                           0.96       300\n",
      "   macro avg       0.96      0.96      0.96       300\n",
      "weighted avg       0.96      0.96      0.96       300\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
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 },
 "nbformat": 4,
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