Random_Forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "eqp7Q6rWnX20"
   },
   "outputs": [],
   "source": [
    "#Social_Network_Ads.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1jSlqZcZnmrD"
   },
   "source": [
    "**Importing the libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ACJTKMfInp-E"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Praveen\\Anaconda3\\lib\\site-packages\\pandas\\compat\\_optional.py:138: UserWarning: Pandas requires version '2.7.0' or newer of 'numexpr' (version '2.6.9' currently installed).\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fRZkaj-dnxIj"
   },
   "source": [
    "**Importing the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "SfTR4mVDnqBV"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('Social_Network_Ads.csv')\n",
    "df = df.iloc[:, 2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  EstimatedSalary  Purchased\n",
       "0   19            19000          0\n",
       "1   35            20000          0\n",
       "2   26            43000          0\n",
       "3   27            57000          0\n",
       "4   19            76000          0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Purchased'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                0\n",
       "EstimatedSalary    0\n",
       "Purchased          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.722071</td>\n",
       "      <td>70719.346049</td>\n",
       "      <td>0.376022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.681383</td>\n",
       "      <td>34772.173908</td>\n",
       "      <td>0.485047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.500000</td>\n",
       "      <td>43000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>71000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>89000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  EstimatedSalary   Purchased\n",
       "count  367.000000       367.000000  367.000000\n",
       "mean    37.722071     70719.346049    0.376022\n",
       "std     10.681383     34772.173908    0.485047\n",
       "min     18.000000     15000.000000    0.000000\n",
       "25%     29.500000     43000.000000    0.000000\n",
       "50%     37.000000     71000.000000    0.000000\n",
       "75%     46.000000     89000.000000    1.000000\n",
       "max     60.000000    150000.000000    1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.75\n",
      "70.75\n"
     ]
    }
   ],
   "source": [
    "# Age Column\n",
    "iqr = df['Age'].quantile(0.75) - df['Age'].quantile(0.25)\n",
    "upper_threshold = df['Age'].quantile(0.75) + (1.5 * iqr)\n",
    "lower_threshold = df['Age'].quantile(0.25) - (1.5 * iqr)\n",
    "print(lower_threshold)\n",
    "print(upper_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-26000.0\n",
      "158000.0\n"
     ]
    }
   ],
   "source": [
    "# EstimatedSalary Column\n",
    "iqr = df['EstimatedSalary'].quantile(0.75) - df['EstimatedSalary'].quantile(0.25)\n",
    "upper_threshold = df['EstimatedSalary'].quantile(0.75) + (1.5 * iqr)\n",
    "lower_threshold = df['EstimatedSalary'].quantile(0.25) - (1.5 * iqr)\n",
    "print(lower_threshold)\n",
    "print(upper_threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no outlier in two columns 'Age' and 'EstimatedSalary'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.167771</td>\n",
       "      <td>0.626164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <td>0.167771</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.375624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purchased</th>\n",
       "      <td>0.626164</td>\n",
       "      <td>0.375624</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                      Age  EstimatedSalary  Purchased\n",
       "Age              1.000000         0.167771   0.626164\n",
       "EstimatedSalary  0.167771         1.000000   0.375624\n",
       "Purchased        0.626164         0.375624   1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'EstimatedSalary')"
      ]
     },
     "execution_count": 14,
     "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": [
    "plt.scatter(df['Age'], df['EstimatedSalary'])\n",
    "plt.title('Age vs EstimateSalary')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('EstimatedSalary')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no Linear relationship between 'Age' and 'EstimatedSalary'. So, we have to use both the columns for the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperating as Features and Target\n",
    "x = df.iloc[:, :-1].values\n",
    "y = df.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9Thl3lvkn9Kr"
   },
   "source": [
    "**Splitting the dataset into the Training set and Test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "sp359H4XnqD3"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XNNxUncRoG6K"
   },
   "source": [
    "**Feature Scaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "loYjT47TnqIG",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.21938166e-02, -3.14033535e-01],\n",
       "       [ 8.54461939e-01,  1.00486561e+00],\n",
       "       [ 2.07142288e-01, -3.71376976e-01],\n",
       "       [-2.55228891e-01, -1.43223063e+00],\n",
       "       [ 1.03941041e+00,  2.03704755e+00],\n",
       "       [-6.25125834e-01,  1.16042272e-01],\n",
       "       [ 1.14668052e-01,  1.83634550e+00],\n",
       "       [-9.95022777e-01, -1.43223063e+00],\n",
       "       [ 3.92090760e-01,  1.35539042e-03],\n",
       "       [ 9.46936175e-01, -1.05949827e+00],\n",
       "       [-8.10074306e-01, -2.56690094e-01],\n",
       "       [-7.02804192e-02,  1.16042272e-01],\n",
       "       [-1.62754655e-01,  1.37759797e+00],\n",
       "       [ 6.69513467e-01, -7.15437622e-01],\n",
       "       [ 2.99616524e-01,  4.88774639e-01],\n",
       "       [ 2.99616524e-01, -5.43407299e-01],\n",
       "       [-1.62754655e-01, -4.57392138e-01],\n",
       "       [-1.08749701e+00,  2.88072595e-01],\n",
       "       [-1.62754655e-01, -1.99346653e-01],\n",
       "       [-2.55228891e-01, -4.86063858e-01],\n",
       "       [ 7.61987703e-01, -8.30124504e-01],\n",
       "       [-9.95022777e-01, -4.57392138e-01],\n",
       "       [ 1.50178159e+00,  9.76193887e-01],\n",
       "       [ 9.46936175e-01, -1.14551343e+00],\n",
       "       [ 9.46936175e-01, -8.30124504e-01],\n",
       "       [ 1.40930735e+00,  3.45416036e-01],\n",
       "       [ 1.96415277e+00,  3.74087757e-01],\n",
       "       [-6.25125834e-01,  1.37759797e+00],\n",
       "       [-2.55228891e-01, -1.42003212e-01],\n",
       "       [ 5.77039231e-01,  1.97970410e+00],\n",
       "       [-7.02804192e-02,  2.18040615e+00],\n",
       "       [ 3.92090760e-01,  2.26642131e+00],\n",
       "       [ 2.21938166e-02, -2.56690094e-01],\n",
       "       [-2.55228891e-01, -5.72079020e-01],\n",
       "       [-1.36491972e+00, -1.20285687e+00],\n",
       "       [-2.55228891e-01,  8.73705519e-02],\n",
       "       [ 1.96415277e+00, -8.01452784e-01],\n",
       "       [-9.02548542e-01,  4.31431198e-01],\n",
       "       [ 2.21938166e-02, -5.72079020e-01],\n",
       "       [-6.25125834e-01, -1.57558924e+00],\n",
       "       [-9.02548542e-01,  5.46118080e-01],\n",
       "       [ 1.40930735e+00,  9.76193887e-01],\n",
       "       [ 2.07142288e-01, -6.58094181e-01],\n",
       "       [-2.55228891e-01,  6.03461521e-01],\n",
       "       [-1.62754655e-01, -5.72079020e-01],\n",
       "       [ 9.46936175e-01,  7.46820123e-01],\n",
       "       [ 2.05662700e+00, -8.01452784e-01],\n",
       "       [-1.08749701e+00,  5.86988314e-02],\n",
       "       [-5.32651598e-01,  8.61507005e-01],\n",
       "       [ 4.84564996e-01,  1.80767378e+00],\n",
       "       [ 2.07142288e-01, -2.85361815e-01],\n",
       "       [-1.36491972e+00,  3.45416036e-01],\n",
       "       [-9.02548542e-01,  2.59400875e-01],\n",
       "       [-6.25125834e-01, -1.13331492e-01],\n",
       "       [-1.62754655e-01,  1.57830002e+00],\n",
       "       [-4.40177362e-01,  1.23423937e+00],\n",
       "       [-6.25125834e-01, -5.59880506e-02],\n",
       "       [ 1.96415277e+00,  5.17446359e-01],\n",
       "       [-1.82729090e+00, -7.44109343e-01],\n",
       "       [ 1.14668052e-01,  2.59400875e-01],\n",
       "       [ 1.14668052e-01,  2.02057434e-01],\n",
       "       [-1.36491972e+00, -1.43223063e+00],\n",
       "       [-7.17600070e-01, -6.00750740e-01],\n",
       "       [-9.02548542e-01, -1.08816999e+00],\n",
       "       [ 2.07142288e-01, -2.56690094e-01],\n",
       "       [-1.27244548e+00, -4.28720417e-01],\n",
       "       [-1.45739396e+00,  3.16744316e-01],\n",
       "       [ 1.22435888e+00,  2.18040615e+00],\n",
       "       [-1.62754655e-01,  2.12306271e+00],\n",
       "       [-7.02804192e-02, -4.86063858e-01],\n",
       "       [ 1.03941041e+00, -8.87467945e-01],\n",
       "       [ 9.46936175e-01,  1.75033034e+00],\n",
       "       [-5.32651598e-01,  1.34892625e+00],\n",
       "       [-6.25125834e-01, -3.42705256e-01],\n",
       "       [-1.73481666e+00, -1.26020031e+00],\n",
       "       [ 3.92090760e-01, -1.70674933e-01],\n",
       "       [-1.64234243e+00,  1.16042272e-01],\n",
       "       [ 1.31683312e+00,  5.74789800e-01],\n",
       "       [-3.47703127e-01,  1.29158281e+00],\n",
       "       [-1.64234243e+00,  4.60102918e-01],\n",
       "       [-1.27244548e+00, -1.23152859e+00],\n",
       "       [-1.08749701e+00, -1.54691752e+00],\n",
       "       [-2.55228891e-01,  2.59400875e-01],\n",
       "       [-8.10074306e-01, -1.20285687e+00],\n",
       "       [ 1.87167853e+00,  8.90178726e-01],\n",
       "       [-7.17600070e-01,  1.06220905e+00],\n",
       "       [ 2.99616524e-01,  5.86988314e-02],\n",
       "       [ 3.92090760e-01,  1.44713993e-01],\n",
       "       [-7.02804192e-02,  2.59400875e-01],\n",
       "       [ 1.50178159e+00, -1.26020031e+00],\n",
       "       [ 1.77920430e+00, -1.05949827e+00],\n",
       "       [ 3.92090760e-01,  8.73705519e-02]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "sc.fit_transform(x_train)\n",
    "sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_VB4b09roPhq"
   },
   "source": [
    "**Fitting Random Forest to the Training set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 216 candidates, totalling 1080 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=8,\n",
       "             param_grid={'max_depth': [170, 180, 190, 200, 210, 220],\n",
       "                         'min_samples_leaf': [2, 3, 4, 5],\n",
       "                         'min_samples_split': [2, 3, 4],\n",
       "                         'n_estimators': [10, 100, 500]},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier()\n",
    "\n",
    "param_grid = {\n",
    "    'n_estimators': [10, 100, 500],\n",
    "    'max_depth': [170, 180, 190, 200, 210, 220],\n",
    "    'min_samples_split': [2, 3, 4],\n",
    "    'min_samples_leaf': [2, 3, 4, 5]\n",
    "}\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "grid_rf_search = GridSearchCV(estimator = classifier, param_grid = param_grid, cv = 5, n_jobs = 8, verbose = 2)\n",
    "grid_rf_search.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=200, min_samples_leaf=5, min_samples_split=3,\n",
       "                       n_estimators=10)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_rf_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9309090909090909"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_rf_search.score(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8804347826086957"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_rf_search.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XOl6_ERWoYh2"
   },
   "source": [
    "**Predicting the Test set results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "7xFCuAXjnqPN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "# Predicting a new result\n",
    "print(grid_rf_search.predict(sc.transform([[22, 75520]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 1]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 1]]\n"
     ]
    }
   ],
   "source": [
    "# Predicting test set results\n",
    "y_pred = grid_rf_search.predict(x_test)\n",
    "print(np.concatenate((y_pred.reshape(len(y_pred),1), y_test.reshape(len(y_test),1)),1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pqaQ_HTZogaZ"
   },
   "source": [
    "**Making the Confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "DdqNOMK8nqSs"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[52  6]\n",
      " [ 5 29]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ko5EDqqiopBl"
   },
   "source": [
    "**Visualising the Training set results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "y7C-xZjFnqVC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x144fee8e828>"
      ]
     },
     "execution_count": 25,
     "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": [
    "from mlxtend.plotting import plot_decision_regions\n",
    "plot_decision_regions(x_train, y_train, grid_rf_search)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ExFSX84Hou9C"
   },
   "source": [
    "**Visualising the Test set results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "zHbPz86Noj6d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x144ff3cf908>"
      ]
     },
     "execution_count": 26,
     "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": [
    "plot_decision_regions(x_test, y_test, clf=grid_rf_search)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Random_Forest_Assignment16.ipynb",
   "provenance": []
  },
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}