KNN_Imputer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.impute import KNNImputer,SimpleImputer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')[['Age','Pclass','Fare','Survived']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    Age  Pclass     Fare  Survived\n",
       "0  22.0       3   7.2500         0\n",
       "1  38.0       1  71.2833         1\n",
       "2  26.0       3   7.9250         1\n",
       "3  35.0       1  53.1000         1\n",
       "4  35.0       3   8.0500         0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age         19.86532\n",
       "Pclass       0.00000\n",
       "Fare         0.00000\n",
       "Survived     0.00000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Survived'])\n",
    "y = df['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.7208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>16.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>47.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.0000</td>\n",
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       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>31.3875</td>\n",
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       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
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      "text/plain": [
       "      Age  Pclass     Fare\n",
       "30   40.0       1  27.7208\n",
       "10    4.0       3  16.7000\n",
       "873  47.0       3   9.0000\n",
       "182   9.0       3  31.3875\n",
       "876  20.0       3   9.8458"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNNImputer(n_neighbors=3,weights='distance')\n",
    "\n",
    "X_train_trf = knn.fit_transform(X_train)\n",
    "X_test_trf = knn.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7150837988826816"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "\n",
    "lr.fit(X_train_trf,y_train)\n",
    "\n",
    "y_pred = lr.predict(X_test_trf)\n",
    "\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comparision with Simple Imputer --> mean\n",
    "\n",
    "si = SimpleImputer()\n",
    "\n",
    "X_train_trf2 = si.fit_transform(X_train)\n",
    "X_test_trf2 = si.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6927374301675978"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "\n",
    "lr.fit(X_train_trf2,y_train)\n",
    "\n",
    "y_pred2 = lr.predict(X_test_trf2)\n",
    "\n",
    "accuracy_score(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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