main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iphone Purchase Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Importing the Libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import StratifiedKFold \n",
    "from sklearn.linear_model import LogisticRegression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Importing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('https://raw.githubusercontent.com/omairaasim/machine_learning/master/project_11_k_nearest_neighbor/iphone_purchase_records.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Salary  Purchase Iphone\n",
       "0    Male   19   19000                0\n",
       "1    Male   35   20000                0\n",
       "2  Female   26   43000                0\n",
       "3  Female   27   57000                0\n",
       "4    Male   19   76000                0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>Male</td>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Age  Salary  Purchase Iphone\n",
       "395  Female   46   41000                1\n",
       "396    Male   51   23000                1\n",
       "397  Female   50   20000                1\n",
       "398    Male   36   33000                0\n",
       "399  Female   49   36000                1"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Getting the shape of the Df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 4)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Getting basic Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Gender           400 non-null    object\n",
      " 1   Age              400 non-null    int64 \n",
      " 2   Salary           400 non-null    int64 \n",
      " 3   Purchase Iphone  400 non-null    int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 12.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NO NaN values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Getting the Statistical Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.655000</td>\n",
       "      <td>69742.500000</td>\n",
       "      <td>0.357500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.482877</td>\n",
       "      <td>34096.960282</td>\n",
       "      <td>0.479864</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>29.750000</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>70000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>46.000000</td>\n",
       "      <td>88000.000000</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         Salary  Purchase Iphone\n",
       "count  400.000000     400.000000       400.000000\n",
       "mean    37.655000   69742.500000         0.357500\n",
       "std     10.482877   34096.960282         0.479864\n",
       "min     18.000000   15000.000000         0.000000\n",
       "25%     29.750000   43000.000000         0.000000\n",
       "50%     37.000000   70000.000000         0.000000\n",
       "75%     46.000000   88000.000000         1.000000\n",
       "max     60.000000  150000.000000         1.000000"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* No.of Males & Females\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    204\n",
       "Male      196\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* No.of Iphones purchased by each Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Female    77\n",
       "Male      66\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Purchase Iphone']==1,'Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Encoding the Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "newdf.Gender=le.fit_transform(newdf.Gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Gender  Age  Salary  Purchase Iphone\n",
       "0       1   19   19000                0\n",
       "1       1   35   20000                0\n",
       "2       0   26   43000                0\n",
       "3       0   27   57000                0\n",
       "4       1   19   76000                0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    204\n",
       "1    196\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 4 columns):\n",
      " #   Column           Non-Null Count  Dtype\n",
      "---  ------           --------------  -----\n",
      " 0   Gender           400 non-null    int32\n",
      " 1   Age              400 non-null    int64\n",
      " 2   Salary           400 non-null    int64\n",
      " 3   Purchase Iphone  400 non-null    int64\n",
      "dtypes: int32(1), int64(3)\n",
      "memory usage: 11.1 KB\n"
     ]
    }
   ],
   "source": [
    "newdf.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Getting the Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchase Iphone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.073741</td>\n",
       "      <td>-0.060435</td>\n",
       "      <td>-0.042469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>-0.073741</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.155238</td>\n",
       "      <td>0.622454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salary</th>\n",
       "      <td>-0.060435</td>\n",
       "      <td>0.155238</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.362083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purchase Iphone</th>\n",
       "      <td>-0.042469</td>\n",
       "      <td>0.622454</td>\n",
       "      <td>0.362083</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Gender       Age    Salary  Purchase Iphone\n",
       "Gender           1.000000 -0.073741 -0.060435        -0.042469\n",
       "Age             -0.073741  1.000000  0.155238         0.622454\n",
       "Salary          -0.060435  0.155238  1.000000         0.362083\n",
       "Purchase Iphone -0.042469  0.622454  0.362083         1.000000"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* HeatMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(newdf.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Pairplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2d4e95e1f60>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Plotting the Heatmap for the NaN values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(newdf.isna(),annot=True , cmap='Greens')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Variablizing the Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=newdf.iloc[:,:-1]\n",
    "y=newdf.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "skf=StratifiedKFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index,test_index in skf.split(x,y):\n",
    "    x_train,x_test=x.iloc[train_index],x.iloc[test_index]\n",
    "    y_train,y_test=y.iloc[train_index],y.iloc[test_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale=StandardScaler()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Fitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=scale.fit_transform(x_train)\n",
    "x_test=scale.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Testing the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_knn_pred=knn.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* Df for the actual and Predicted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "look=pd.DataFrame({'Actual':y_test,'Predicted':y_knn_pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual  Predicted\n",
       "266       0          0\n",
       "267       0          0\n",
       "269       0          0\n",
       "270       0          1\n",
       "276       0          0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "look.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Performance Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[46,  5],\n",
       "       [ 7, 22]], dtype=int64)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_knn_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_knn_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "lis=[i for i in range(3,100) if i%2!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8875\n"
     ]
    }
   ],
   "source": [
    "acc=[]\n",
    "dic={}\n",
    "for i in lis:\n",
    "    knn=KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(x_train,y_train)\n",
    "    y_knn_pred=knn.predict(x_test)\n",
    "    acc.append(accuracy_score(y_test,y_knn_pred))\n",
    "    dic[i]=acc\n",
    "\n",
    "print(max(acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Model Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "log=LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Fitting the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Predicting the Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_log_pred=log.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Accuracy Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.725"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_log_pred)"
   ]
  }
 ],
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