Telco-Cust-Class-Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification using K-Nearest Neighbour Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing all the required libraries and packages\n",
    "from sklearn import preprocessing\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About the dataset\n",
    "Imagine a telecommunications provider has segmented its customer base by service usage patterns, categorizing the customers into four groups. If demographic data can be used to predict group membership, the company can customize offers for individual prospective customers. It is a classification problem. That is, given the dataset, with predefined labels, we need to build a model to be used to predict class of a new or unknown case.\n",
    "\n",
    "The example focuses on using demographic data, such as region, age, and marital, to predict usage patterns.\n",
    "\n",
    "The target field, called custcat, has four possible values that correspond to the four customer groups, as follows: 1- Basic Service 2- E-Service 3- Plus Service 4- Total Service\n",
    "\n",
    "The objective is to build a classifier, to predict the class of unknown cases. We will use a specific type of classification called K nearest neighbour.\n",
    "\n",
    "Source of dataset: https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/teleCust1000t.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>region</th>\n",
       "      <th>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>ed</th>\n",
       "      <th>employ</th>\n",
       "      <th>retire</th>\n",
       "      <th>gender</th>\n",
       "      <th>reside</th>\n",
       "      <th>custcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>136.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   region  tenure  age  marital  address  income  ed  employ  retire  gender  \\\n",
       "0       2      13   44        1        9    64.0   4       5     0.0       0   \n",
       "1       3      11   33        1        7   136.0   5       5     0.0       0   \n",
       "2       3      68   52        1       24   116.0   1      29     0.0       1   \n",
       "3       2      33   33        0       12    33.0   2       0     0.0       1   \n",
       "4       2      23   30        1        9    30.0   1       2     0.0       0   \n",
       "\n",
       "   reside  custcat  \n",
       "0       2        1  \n",
       "1       6        4  \n",
       "2       2        3  \n",
       "3       1        1  \n",
       "4       4        3  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Loading the Dataset\n",
    "df = pd.read_csv(\"knn_dataset.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    281\n",
       "1    266\n",
       "4    236\n",
       "2    217\n",
       "Name: custcat, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"custcat\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 281 Plus Service, 266 Basic-service, 236 Total Service, and 217 E-Service customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['region', 'tenure', 'age', 'marital', 'address', 'income', 'ed',\n",
       "       'employ', 'retire', 'gender', 'reside', 'custcat'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['region', 'tenure', 'age', 'marital', 'address', 'income', 'ed',\n",
    "       'employ', 'retire', 'gender', 'reside']].values\n",
    "y = df[['custcat']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# displaying heatmap of correlation matrix\n",
    "plt.figure(figsize=(10, 10))\n",
    "dataplot = sns.heatmap(df.corr(), cmap=\"YlGnBu\", annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Correlation of region anf gender with custCat ie Target variable is very less\n",
    "- Dropping the region and gender column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>tenure</th>\n",
       "      <th>age</th>\n",
       "      <th>marital</th>\n",
       "      <th>address</th>\n",
       "      <th>income</th>\n",
       "      <th>ed</th>\n",
       "      <th>employ</th>\n",
       "      <th>retire</th>\n",
       "      <th>reside</th>\n",
       "      <th>custcat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>136.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>116.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</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>10</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>7</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>67</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>944.0</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>70</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>87.0</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>50</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>39.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     tenure  age  marital  address  income  ed  employ  retire  reside  \\\n",
       "0        13   44        1        9    64.0   4       5     0.0       2   \n",
       "1        11   33        1        7   136.0   5       5     0.0       6   \n",
       "2        68   52        1       24   116.0   1      29     0.0       2   \n",
       "3        33   33        0       12    33.0   2       0     0.0       1   \n",
       "4        23   30        1        9    30.0   1       2     0.0       4   \n",
       "..      ...  ...      ...      ...     ...  ..     ...     ...     ...   \n",
       "995      10   39        0        0    27.0   3       0     0.0       3   \n",
       "996       7   34        0        2    22.0   5       5     0.0       1   \n",
       "997      67   59        0       40   944.0   5      33     0.0       1   \n",
       "998      70   49        0       18    87.0   2      22     0.0       1   \n",
       "999      50   36        1        7    39.0   3       3     0.0       3   \n",
       "\n",
       "     custcat  \n",
       "0          1  \n",
       "1          4  \n",
       "2          3  \n",
       "3          1  \n",
       "4          3  \n",
       "..       ...  \n",
       "995        1  \n",
       "996        1  \n",
       "997        4  \n",
       "998        3  \n",
       "999        2  \n",
       "\n",
       "[1000 rows x 10 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([\"region\", \"gender\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Standardization give data zero mean and unit variance, it is good, especially for algorithms such as KNN which is based on distance of cases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.02696767, -1.055125  ,  0.18450456,  1.0100505 , -0.25303431,\n",
       "        -0.12650641,  1.0877526 , -0.5941226 , -0.22207644, -1.03459817,\n",
       "        -0.23065004],\n",
       "       [ 1.19883553, -1.14880563, -0.69181243,  1.0100505 , -0.4514148 ,\n",
       "         0.54644972,  1.9062271 , -0.5941226 , -0.22207644, -1.03459817,\n",
       "         2.55666158],\n",
       "       [ 1.19883553,  1.52109247,  0.82182601,  1.0100505 ,  1.23481934,\n",
       "         0.35951747, -1.36767088,  1.78752803, -0.22207644,  0.96655883,\n",
       "        -0.23065004],\n",
       "       [-0.02696767, -0.11831864, -0.69181243, -0.9900495 ,  0.04453642,\n",
       "        -0.41625141, -0.54919639, -1.09029981, -0.22207644,  0.96655883,\n",
       "        -0.92747794],\n",
       "       [-0.02696767, -0.58672182, -0.93080797,  1.0100505 , -0.25303431,\n",
       "        -0.44429125, -1.36767088, -0.89182893, -0.22207644, -1.03459817,\n",
       "         1.16300577]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preprocessing the dataset -> standardisation\n",
    "x_scaled = preprocessing.StandardScaler().fit_transform(x)\n",
    "x_scaled[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.026968</td>\n",
       "      <td>-1.055125</td>\n",
       "      <td>0.184505</td>\n",
       "      <td>1.010051</td>\n",
       "      <td>-0.253034</td>\n",
       "      <td>-0.126506</td>\n",
       "      <td>1.087753</td>\n",
       "      <td>-0.594123</td>\n",
       "      <td>-0.222076</td>\n",
       "      <td>-1.034598</td>\n",
       "      <td>-0.230650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.198836</td>\n",
       "      <td>-1.148806</td>\n",
       "      <td>-0.691812</td>\n",
       "      <td>1.010051</td>\n",
       "      <td>-0.451415</td>\n",
       "      <td>0.546450</td>\n",
       "      <td>1.906227</td>\n",
       "      <td>-0.594123</td>\n",
       "      <td>-0.222076</td>\n",
       "      <td>-1.034598</td>\n",
       "      <td>2.556662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.198836</td>\n",
       "      <td>1.521092</td>\n",
       "      <td>0.821826</td>\n",
       "      <td>1.010051</td>\n",
       "      <td>1.234819</td>\n",
       "      <td>0.359517</td>\n",
       "      <td>-1.367671</td>\n",
       "      <td>1.787528</td>\n",
       "      <td>-0.222076</td>\n",
       "      <td>0.966559</td>\n",
       "      <td>-0.230650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.026968</td>\n",
       "      <td>-0.118319</td>\n",
       "      <td>-0.691812</td>\n",
       "      <td>-0.990050</td>\n",
       "      <td>0.044536</td>\n",
       "      <td>-0.416251</td>\n",
       "      <td>-0.549196</td>\n",
       "      <td>-1.090300</td>\n",
       "      <td>-0.222076</td>\n",
       "      <td>0.966559</td>\n",
       "      <td>-0.927478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.026968</td>\n",
       "      <td>-0.586722</td>\n",
       "      <td>-0.930808</td>\n",
       "      <td>1.010051</td>\n",
       "      <td>-0.253034</td>\n",
       "      <td>-0.444291</td>\n",
       "      <td>-1.367671</td>\n",
       "      <td>-0.891829</td>\n",
       "      <td>-0.222076</td>\n",
       "      <td>-1.034598</td>\n",
       "      <td>1.163006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0 -0.026968 -1.055125  0.184505  1.010051 -0.253034 -0.126506  1.087753   \n",
       "1  1.198836 -1.148806 -0.691812  1.010051 -0.451415  0.546450  1.906227   \n",
       "2  1.198836  1.521092  0.821826  1.010051  1.234819  0.359517 -1.367671   \n",
       "3 -0.026968 -0.118319 -0.691812 -0.990050  0.044536 -0.416251 -0.549196   \n",
       "4 -0.026968 -0.586722 -0.930808  1.010051 -0.253034 -0.444291 -1.367671   \n",
       "\n",
       "         7         8         9         10  \n",
       "0 -0.594123 -0.222076 -1.034598 -0.230650  \n",
       "1 -0.594123 -0.222076 -1.034598  2.556662  \n",
       "2  1.787528 -0.222076  0.966559 -0.230650  \n",
       "3 -1.090300 -0.222076  0.966559 -0.927478  \n",
       "4 -0.891829 -0.222076 -1.034598  1.163006  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_x = pd.DataFrame(x_scaled)\n",
    "df_x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (800, 11) (800,)\n",
      "Test set: (200, 11) (200,)\n"
     ]
    }
   ],
   "source": [
    "#spliting the dataset into training and testing set\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split( x_scaled, y, test_size=0.2, random_state=4)\n",
    "y_train.resize(X_train.shape[0],)\n",
    "y_test.resize(X_test.shape[0],)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### K nearest neighbor (KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy at k= 1: 0.3\n",
      "Test accuracy at k= 2: 0.29\n",
      "Test accuracy at k= 3: 0.315\n",
      "Test accuracy at k= 4: 0.32\n",
      "Test accuracy at k= 5: 0.315\n",
      "Test accuracy at k= 6: 0.31\n",
      "Test accuracy at k= 7: 0.335\n",
      "Test accuracy at k= 8: 0.325\n",
      "Test accuracy at k= 9: 0.34\n",
      "Test accuracy at k= 10: 0.33\n",
      "Test accuracy at k= 11: 0.315\n",
      "Test accuracy at k= 12: 0.34\n",
      "Test accuracy at k= 13: 0.33\n",
      "Test accuracy at k= 14: 0.315\n",
      "Test accuracy at k= 15: 0.34\n",
      "Test accuracy at k= 16: 0.36\n",
      "Test accuracy at k= 17: 0.355\n",
      "Test accuracy at k= 18: 0.35\n",
      "Test accuracy at k= 19: 0.345\n",
      "Test accuracy at k= 20: 0.335\n",
      "Test accuracy at k= 21: 0.35\n",
      "Test accuracy at k= 22: 0.36\n",
      "Test accuracy at k= 23: 0.37\n",
      "Test accuracy at k= 24: 0.365\n",
      "Test accuracy at k= 25: 0.365\n",
      "Test accuracy at k= 26: 0.365\n",
      "Test accuracy at k= 27: 0.35\n",
      "Test accuracy at k= 28: 0.36\n",
      "Test accuracy at k= 29: 0.38\n",
      "Test accuracy at k= 30: 0.385\n",
      "Test accuracy at k= 31: 0.395\n",
      "Test accuracy at k= 32: 0.395\n",
      "Test accuracy at k= 33: 0.38\n",
      "Test accuracy at k= 34: 0.37\n",
      "Test accuracy at k= 35: 0.365\n",
      "Test accuracy at k= 36: 0.385\n",
      "Test accuracy at k= 37: 0.395\n",
      "Test accuracy at k= 38: 0.41\n",
      "Test accuracy at k= 39: 0.395\n",
      "Test accuracy at k= 40: 0.395\n",
      "Test accuracy at k= 41: 0.395\n",
      "Test accuracy at k= 42: 0.38\n",
      "Test accuracy at k= 43: 0.39\n",
      "Test accuracy at k= 44: 0.375\n"
     ]
    }
   ],
   "source": [
    "#Training and predicting the model using knn classifier\n",
    "ks = 45   # Total K \n",
    "accuracy=np.zeros(ks-1)\n",
    "for k in range(1,ks):     # Accuracy check for each k till total k\n",
    "#Train Model and Predict  \n",
    "    trained = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
    "    yhat = trained.predict(X_test)\n",
    "    print(\"Test accuracy at k= {}: {}\".format(k, metrics.accuracy_score(y_test, yhat)))\n",
    "    accuracy[k-1]=metrics.accuracy_score(y_test, yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot model accuracy for Different number of Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2197ba77eb0>]"
      ]
     },
     "execution_count": 12,
     "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": [
    "# k vs Accuracy graph\n",
    "plt.plot(range(1,ks),accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was 0.41 with k= 38\n"
     ]
    }
   ],
   "source": [
    "print( \"The best accuracy was\", accuracy.max(), \"with k=\", accuracy.argmax()+1) "
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}