KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "JolSMtbOfmc9"
   },
   "outputs": [],
   "source": [
    "#Social_Networks_Ads.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h_3tQC1Kf0ut"
   },
   "source": [
    "**Importing the libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "EObl2Z6IfyG4"
   },
   "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": "yxzr0NOWf73l"
   },
   "source": [
    "**Importing the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "nN9xC2r6fyMZ"
   },
   "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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       "    }\n",
       "\n",
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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": {
      "text/html": [
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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": {
    "id": "Z9gpSZOVfyPX"
   },
   "outputs": [],
   "source": [
    "# Seperating as Features and Target\n",
    "x = df.iloc[:, :-1].values\n",
    "y = df.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_lrDXgjIgGJ3"
   },
   "source": [
    "**Splitting the dataset into the Training set and Test set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "z2QWVTZUfyUB"
   },
   "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": "XmzkAVfggNrD"
   },
   "source": [
    "**Feature Scaling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "UZUJ4Xn4fyW6",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.81988405,  0.36832348],\n",
       "       [ 1.52319597, -0.87466316],\n",
       "       [-0.91634891,  0.51285681],\n",
       "       [-0.28387431, -0.12308984],\n",
       "       [-0.01281377, -0.29652984],\n",
       "       [ 0.16789326,  0.25269682],\n",
       "       [ 0.16789326, -0.35434317],\n",
       "       [-0.28387431,  0.05035016],\n",
       "       [-1.72953053, -1.25044982],\n",
       "       [-0.28387431, -0.73012983],\n",
       "       [-0.8259954 ,  0.31051015],\n",
       "       [ 1.70390299, -0.26762317],\n",
       "       [ 0.34860028,  0.59957681],\n",
       "       [ 0.61966083, -1.07700982],\n",
       "       [-0.10316728,  0.16597682],\n",
       "       [-0.73564188,  0.31051015],\n",
       "       [-0.10316728,  1.95819012],\n",
       "       [-0.01281377, -0.12308984],\n",
       "       [-0.46458134, -1.10591649],\n",
       "       [ 0.34860028, -0.44106317],\n",
       "       [-0.10316728,  0.22379015],\n",
       "       [ 1.16178191,  2.21835011],\n",
       "       [ 0.71001434,  0.77301681],\n",
       "       [-0.28387431, -0.4121565 ],\n",
       "       [-0.01281377, -0.2387165 ],\n",
       "       [ 0.07753974,  0.22379015],\n",
       "       [-0.46458134, -1.19263648],\n",
       "       [-1.63917702,  0.36832348],\n",
       "       [ 1.70390299, -1.04810315],\n",
       "       [ 0.98107488, -0.12308984],\n",
       "       [ 0.34860028,  1.00427014],\n",
       "       [ 0.25824677,  0.31051015],\n",
       "       [-0.1935208 ,  2.16053678],\n",
       "       [ 1.43284245,  1.1198968 ],\n",
       "       [-1.00670242,  0.54176348],\n",
       "       [-1.09705594,  0.33941682],\n",
       "       [-1.09705594,  0.48395015],\n",
       "       [ 0.98107488,  2.07381678],\n",
       "       [ 0.71001434,  0.13707015],\n",
       "       [-0.55493485, -1.48170314],\n",
       "       [-0.10316728,  0.05035016],\n",
       "       [-0.73564188,  1.35115013],\n",
       "       [-0.64528837,  1.40896346],\n",
       "       [ 0.07753974,  1.87147012],\n",
       "       [ 0.52930731,  2.01600345],\n",
       "       [-1.36811648, -1.19263648],\n",
       "       [-0.73564188,  0.28160349],\n",
       "       [-0.91634891,  1.55349679],\n",
       "       [-1.09705594, -1.51060981],\n",
       "       [-0.28387431,  0.54176348],\n",
       "       [ 0.25824677, -0.52778316],\n",
       "       [ 0.16789326,  2.10272345],\n",
       "       [ 0.07753974,  0.16597682],\n",
       "       [-0.91634891, -0.73012983],\n",
       "       [-0.1935208 , -0.55668983],\n",
       "       [-0.10316728,  2.16053678],\n",
       "       [-0.37422783,  1.32224346],\n",
       "       [-0.10316728, -0.4121565 ],\n",
       "       [-1.27776297,  0.57067015],\n",
       "       [-0.28387431,  0.62848348],\n",
       "       [-1.09705594, -0.75903649],\n",
       "       [ 0.4389538 ,  1.84256345],\n",
       "       [-0.10316728,  0.68629681],\n",
       "       [-0.73564188, -1.51060981],\n",
       "       [-1.18740945, -1.04810315],\n",
       "       [ 0.16789326,  0.05035016],\n",
       "       [-0.55493485,  1.90037678],\n",
       "       [-1.36811648,  0.36832348],\n",
       "       [ 1.43284245,  0.02144349],\n",
       "       [-0.28387431, -0.46996983],\n",
       "       [-1.72953053,  0.19488349],\n",
       "       [-0.91634891, -0.29652984],\n",
       "       [ 0.61966083, -1.25044982],\n",
       "       [ 0.25824677,  0.07925682],\n",
       "       [ 0.34860028,  0.10816349],\n",
       "       [ 0.25824677,  0.05035016],\n",
       "       [-0.46458134, -0.81684983],\n",
       "       [-1.54882351,  0.07925682],\n",
       "       [ 0.71001434, -0.81684983],\n",
       "       [ 0.89072137,  0.13707015],\n",
       "       [ 0.71001434,  1.3800568 ],\n",
       "       [ 1.25213542, -1.39498315],\n",
       "       [-1.27776297, -0.3254365 ],\n",
       "       [-0.1935208 , -0.4988765 ],\n",
       "       [ 0.89072137,  2.07381678],\n",
       "       [-0.01281377,  1.23552347],\n",
       "       [ 0.89072137, -1.04810315],\n",
       "       [-1.00670242, -1.42388981],\n",
       "       [-1.63917702,  0.48395015],\n",
       "       [-0.1935208 ,  0.85973681],\n",
       "       [-0.73564188,  0.57067015],\n",
       "       [ 1.0714284 , -1.42388981]])"
      ]
     },
     "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": "BWpz-G_KgaB5"
   },
   "source": [
    "**Fitting K-NN to the Training set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K = 0----Train Score = 0.9927272727272727----CV Score = 0.7410041407867496\n",
      "K = 1----Train Score = 0.8836363636363637----CV Score = 0.7768622038187256\n",
      "K = 2----Train Score = 0.8654545454545455----CV Score = 0.7963451805843109\n",
      "K = 3----Train Score = 0.8654545454545455----CV Score = 0.8089308718656543\n",
      "K = 4----Train Score = 0.8872727272727273----CV Score = 0.8202392454566368\n",
      "K = 5----Train Score = 0.850909090909091----CV Score = 0.826675868414999\n",
      "K = 6----Train Score = 0.84----CV Score = 0.8358390844260409\n",
      "K = 7----Train Score = 0.8290909090909091----CV Score = 0.823252242926156\n",
      "K = 8----Train Score = 0.8690909090909091----CV Score = 0.8196658615136876\n",
      "K = 9----Train Score = 0.850909090909091----CV Score = 0.8294243156199677\n",
      "K = 10----Train Score = 0.8472727272727273----CV Score = 0.8286507936507936\n",
      "K = 11----Train Score = 0.8363636363636363----CV Score = 0.826909362778928\n",
      "K = 12----Train Score = 0.8290909090909091----CV Score = 0.8215073613986658\n",
      "K = 13----Train Score = 0.8254545454545454----CV Score = 0.8136783988957902\n",
      "K = 14----Train Score = 0.8436363636363636----CV Score = 0.8108224062571889\n",
      "K = 15----Train Score = 0.8363636363636363----CV Score = 0.829228778467909\n",
      "K = 16----Train Score = 0.8436363636363636----CV Score = 0.8326386013342535\n",
      "K = 17----Train Score = 0.8----CV Score = 0.8375166781688522\n",
      "K = 18----Train Score = 0.8----CV Score = 0.8404485852311939\n",
      "K = 19----Train Score = 0.7963636363636364----CV Score = 0.8391350356567747\n"
     ]
    }
   ],
   "source": [
    "# Finding the best K value\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "k_range = range(1, 21)\n",
    "train_scores = []\n",
    "k_scores = []\n",
    "\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(x_train, y_train)\n",
    "    train_scores.append(knn.score(x_train, y_train))\n",
    "    scores = cross_val_score(knn, x, y, cv=5, scoring='roc_auc')\n",
    "    k_scores.append(scores.mean())\n",
    "for i in range(len(k_range)):\n",
    "    print(f'K = {i}----Train Score = {train_scores[i]}----CV Score = {k_scores[i]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cross-Validated AUROC')"
      ]
     },
     "execution_count": 19,
     "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": [
    "# ploting\n",
    "plt.plot(k_range, k_scores)\n",
    "plt.grid()\n",
    "plt.xlabel('Value of K for KNN')\n",
    "plt.ylabel('Cross-Validated AUROC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the best value of K is 7. After 7, the score decreases due to under fitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "5Wo68lCqfyc6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=7)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors=7)\n",
    "classifier.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GoGxoF-kgmlj"
   },
   "source": [
    "**Predicting the Test set results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "aEOlRLkefye3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n"
     ]
    }
   ],
   "source": [
    "# Predicting a new result\n",
    "print(classifier.predict(sc.transform([[22, 75520]])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[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",
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      " [0 1]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
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      " [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",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [1 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [1 0]\n",
      " [1 1]\n",
      " [0 0]\n",
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      " [0 1]\n",
      " [1 1]\n",
      " [0 0]\n",
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      " [0 0]\n",
      " [0 0]\n",
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      " [0 0]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [0 1]\n",
      " [0 1]\n",
      " [1 1]\n",
      " [0 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 0]\n",
      " [1 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 0]\n",
      " [1 1]\n",
      " [0 0]\n",
      " [0 1]]\n"
     ]
    }
   ],
   "source": [
    "# Predicting test set results\n",
    "y_pred = classifier.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": "cYfKp7MQgwX7"
   },
   "source": [
    "**Making the Confusion Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "zFhgq3Yufyh5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[53  7]\n",
      " [13 19]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)"
   ]
  }
 ],
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  "colab": {
   "name": "KNN Assignment13.ipynb",
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  },
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   "language": "python",
   "name": "python3"
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