Assignment overfitting and underfitting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71ff0524",
   "metadata": {},
   "source": [
    "# Problem statement:-\n",
    "- <strong> We are given dataset of titanic in which we have to predict survival chance of passanger with the help of features given like pclass, sex, age, sibsp, parch, fare, embarked, class, who, etc... with the help of these features we have to predict survival of passenger. </strong>\n",
    "- We have to detect Overfitting and Underfitting issue and comapre the differnce between logistic Regression and KNeighbor algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "99aa65ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libaries \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b42cca9d",
   "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>PassengerID</th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerID  survived  pclass     sex   age  sibsp  parch     fare  \\\n",
       "0            0         0       3    male  22.0      1      0   7.2500   \n",
       "1            1         1       1  female  38.0      1      0  71.2833   \n",
       "2            2         1       3  female  26.0      0      0   7.9250   \n",
       "3            3         1       1  female  35.0      1      0  53.1000   \n",
       "4            4         0       3    male  35.0      0      0   8.0500   \n",
       "\n",
       "  embarked  class    who  adult_male deck  embark_town alive  alone  \n",
       "0        S  Third    man        True  NaN  Southampton    no  False  \n",
       "1        C  First  woman       False    C    Cherbourg   yes  False  \n",
       "2        S  Third  woman       False  NaN  Southampton   yes   True  \n",
       "3        S  First  woman       False    C  Southampton   yes  False  \n",
       "4        S  Third    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('titanic.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f10da701",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 16)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d9555d",
   "metadata": {},
   "source": [
    "# Observation\n",
    "- There are 891 rows and 16 columns in dataset.\n",
    "- Each row contain information about individual person."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b45c160b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerID      0\n",
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age            177\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking null values in dataset\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9336999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerID     0.000000\n",
       "survived        0.000000\n",
       "pclass          0.000000\n",
       "sex             0.000000\n",
       "age            19.865320\n",
       "sibsp           0.000000\n",
       "parch           0.000000\n",
       "fare            0.000000\n",
       "embarked        0.224467\n",
       "class           0.000000\n",
       "who             0.000000\n",
       "adult_male      0.000000\n",
       "deck           77.216611\n",
       "embark_town     0.224467\n",
       "alive           0.000000\n",
       "alone           0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking null values %\n",
    "data.isnull().sum()/len(data) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9952e3c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 16 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerID  891 non-null    int64  \n",
      " 1   survived     891 non-null    int64  \n",
      " 2   pclass       891 non-null    int64  \n",
      " 3   sex          891 non-null    object \n",
      " 4   age          714 non-null    float64\n",
      " 5   sibsp        891 non-null    int64  \n",
      " 6   parch        891 non-null    int64  \n",
      " 7   fare         891 non-null    float64\n",
      " 8   embarked     889 non-null    object \n",
      " 9   class        891 non-null    object \n",
      " 10  who          891 non-null    object \n",
      " 11  adult_male   891 non-null    bool   \n",
      " 12  deck         203 non-null    object \n",
      " 13  embark_town  889 non-null    object \n",
      " 14  alive        891 non-null    object \n",
      " 15  alone        891 non-null    bool   \n",
      "dtypes: bool(2), float64(2), int64(5), object(7)\n",
      "memory usage: 99.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# fixing missing values\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f7af6bf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "si = SimpleImputer(strategy='median')\n",
    "si"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3871eb83",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['age'] = si.fit_transform(data[['age']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9cf24799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.age.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dbf4466a",
   "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>PassengerID</th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerID  survived  pclass     sex   age  sibsp  parch     fare  \\\n",
       "0            0         0       3    male  22.0      1      0   7.2500   \n",
       "1            1         1       1  female  38.0      1      0  71.2833   \n",
       "2            2         1       3  female  26.0      0      0   7.9250   \n",
       "3            3         1       1  female  35.0      1      0  53.1000   \n",
       "4            4         0       3    male  35.0      0      0   8.0500   \n",
       "\n",
       "  embarked  class    who  adult_male deck  embark_town alive  alone  \n",
       "0        S  Third    man        True  NaN  Southampton    no  False  \n",
       "1        C  First  woman       False    C    Cherbourg   yes  False  \n",
       "2        S  Third  woman       False  NaN  Southampton   yes   True  \n",
       "3        S  First  woman       False    C  Southampton   yes  False  \n",
       "4        S  Third    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "14f0d6fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping unnecessary columns \n",
    "col_ = ['PassengerID','alive','embarked','deck']\n",
    "data.drop(col_,1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30d2f58a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare  class    who  \\\n",
       "0         0       3    male  22.0      1      0   7.2500  Third    man   \n",
       "1         1       1  female  38.0      1      0  71.2833  First  woman   \n",
       "2         1       3  female  26.0      0      0   7.9250  Third  woman   \n",
       "3         1       1  female  35.0      1      0  53.1000  First  woman   \n",
       "4         0       3    male  35.0      0      0   8.0500  Third    man   \n",
       "\n",
       "   adult_male  embark_town  alone  \n",
       "0        True  Southampton  False  \n",
       "1       False    Cherbourg  False  \n",
       "2       False  Southampton   True  \n",
       "3       False  Southampton  False  \n",
       "4        True  Southampton   True  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c7ffa716",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "469a578d",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_col = data.select_dtypes(['int','float'])\n",
    "cat_col = data.select_dtypes(['object','bool'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4854a920",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>889.000000</td>\n",
       "      <td>889.000000</td>\n",
       "      <td>889.000000</td>\n",
       "      <td>889.000000</td>\n",
       "      <td>889.000000</td>\n",
       "      <td>889.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.382452</td>\n",
       "      <td>2.311586</td>\n",
       "      <td>29.315152</td>\n",
       "      <td>0.524184</td>\n",
       "      <td>0.382452</td>\n",
       "      <td>32.096681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486260</td>\n",
       "      <td>0.834700</td>\n",
       "      <td>12.984932</td>\n",
       "      <td>1.103705</td>\n",
       "      <td>0.806761</td>\n",
       "      <td>49.697504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         survived      pclass         age       sibsp       parch        fare\n",
       "count  889.000000  889.000000  889.000000  889.000000  889.000000  889.000000\n",
       "mean     0.382452    2.311586   29.315152    0.524184    0.382452   32.096681\n",
       "std      0.486260    0.834700   12.984932    1.103705    0.806761   49.697504\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   22.000000    0.000000    0.000000    7.895800\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   35.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_col.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e86ca868",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "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": [
    "sns.boxplot(num_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "412bf239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex            2\n",
       "class          3\n",
       "who            3\n",
       "adult_male     2\n",
       "embark_town    3\n",
       "alone          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "022f0f95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking for outliers in num_col\n",
    "fig = plt.figure(figsize=(9,5))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.boxplot(num_col['age'])\n",
    "plt.ylabel(\"Age\")\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.boxplot(num_col['fare'])\n",
    "plt.xlabel(\"Fare\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b14de6e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def outlier_treatment(data,col_name):\n",
    "    q3 = data[col_name].quantile(0.75)\n",
    "    q1 = data[col_name].quantile(0.25) \n",
    "    iqr = q3-q1\n",
    "    uw = q3+1.5*iqr\n",
    "    lw = q1-1.5*iqr\n",
    "    data[col_name] = np.where(data[col_name]>uw,data[col_name].quantile(0.85),data[col_name])\n",
    "    data[col_name] = np.where(data[col_name]<lw,data[col_name].quantile(0.85),data[col_name])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c2dd3b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outlier treatment of age and fare column\n",
    "outlier_treatment(data,'age')\n",
    "outlier_treatment(data,'fare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "79795662",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age     0.022789\n",
       "fare    0.931830\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['age','fare']].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0065c5b5",
   "metadata": {},
   "source": [
    "# Encoding in cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5596a813",
   "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>sex</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sex  class    who  adult_male  embark_town  alone\n",
       "0    male  Third    man        True  Southampton  False\n",
       "1  female  First  woman       False    Cherbourg  False\n",
       "2  female  Third  woman       False  Southampton   True\n",
       "3  female  First  woman       False  Southampton  False\n",
       "4    male  Third    man        True  Southampton   True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_col.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "67e4e75f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dic1 = {'True':1,'False':0}\n",
    "dic2 = {'male':1,'female':0}\n",
    "dic3 = {'First':1,'Second':2,'Third':3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8aa75b35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# labeling sex,class,adult_male,alone column\n",
    "data['sex'] = data['sex'].replace(dic2)\n",
    "data['class'] = data['class'].replace(dic3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "27da948a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ohe on who and embark_town column\n",
    "embark_ohe = pd.get_dummies(data['embark_town'])\n",
    "who_ohe = pd.get_dummies(data['who'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "34c0b063",
   "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>Cherbourg</th>\n",
       "      <th>Queenstown</th>\n",
       "      <th>Southampton</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cherbourg  Queenstown  Southampton\n",
       "0            0           0            1\n",
       "1            1           0            0\n",
       "2            0           0            1\n",
       "3            0           0            1\n",
       "4            0           0            1\n",
       "..         ...         ...          ...\n",
       "886          0           0            1\n",
       "887          0           0            1\n",
       "888          0           0            1\n",
       "889          1           0            0\n",
       "890          0           1            0\n",
       "\n",
       "[889 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embark_ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "5441bc7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>child</th>\n",
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       "      <th>woman</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     child  man  woman\n",
       "0        0    1      0\n",
       "1        0    0      1\n",
       "2        0    0      1\n",
       "3        0    0      1\n",
       "4        0    1      0\n",
       "..     ...  ...    ...\n",
       "886      0    1      0\n",
       "887      0    0      1\n",
       "888      0    0      1\n",
       "889      0    1      0\n",
       "890      0    1      0\n",
       "\n",
       "[889 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "who_ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "192fb7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# concatinating embark_ohe, who_ohe\n",
    "data = pd.concat([data,embark_ohe,who_ohe],1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "68d69124",
   "metadata": {},
   "outputs": [],
   "source": [
    "# droping embark_town and who\n",
    "data.drop(['who','embark_town'],1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3c75b50b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[['adult_male','alone']] = data[['adult_male','alone']].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "68a8b684",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>class</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>alone</th>\n",
       "      <th>Cherbourg</th>\n",
       "      <th>Queenstown</th>\n",
       "      <th>Southampton</th>\n",
       "      <th>child</th>\n",
       "      <th>man</th>\n",
       "      <th>woman</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.25000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56.37664</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.92500</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.10000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.05000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass  sex   age  sibsp  parch      fare  class  adult_male  \\\n",
       "0         0       3    1  22.0      1      0   7.25000      3           1   \n",
       "1         1       1    0  38.0      1      0  56.37664      1           0   \n",
       "2         1       3    0  26.0      0      0   7.92500      3           0   \n",
       "3         1       1    0  35.0      1      0  53.10000      1           0   \n",
       "4         0       3    1  35.0      0      0   8.05000      3           1   \n",
       "\n",
       "   alone  Cherbourg  Queenstown  Southampton  child  man  woman  \n",
       "0      0          0           0            1      0    1      0  \n",
       "1      0          1           0            0      0    0      1  \n",
       "2      1          0           0            1      0    0      1  \n",
       "3      0          0           0            1      0    0      1  \n",
       "4      1          0           0            1      0    1      0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7c4c0a00",
   "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>age</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>889.000000</td>\n",
       "      <td>889.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.490439</td>\n",
       "      <td>22.766094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.410016</td>\n",
       "      <td>18.084628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.500000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age        fare\n",
       "count  889.000000  889.000000\n",
       "mean    29.490439   22.766094\n",
       "std     10.410016   18.084628\n",
       "min      3.000000    0.000000\n",
       "25%     23.500000    7.895800\n",
       "50%     28.000000   14.454200\n",
       "75%     36.000000   31.000000\n",
       "max     54.000000   65.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['age','fare']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b44e2c0c",
   "metadata": {},
   "source": [
    "# Feature scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "501cc2f6",
   "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>StandardScaler()</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\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss = StandardScaler()\n",
    "ss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a4a15f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[['age','fare']] = ss.fit_transform(data[['age','fare']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6347d7e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>age</th>\n",
       "      <th>fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.719947</td>\n",
       "      <td>-0.858454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.817900</td>\n",
       "      <td>1.859561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.335485</td>\n",
       "      <td>-0.821109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.529554</td>\n",
       "      <td>1.678275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.529554</td>\n",
       "      <td>-0.814193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>-0.239370</td>\n",
       "      <td>-0.540326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>-1.008293</td>\n",
       "      <td>0.400228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>-0.143254</td>\n",
       "      <td>0.037838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>-0.335485</td>\n",
       "      <td>0.400228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0.241208</td>\n",
       "      <td>-0.830791</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>889 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age      fare\n",
       "0   -0.719947 -0.858454\n",
       "1    0.817900  1.859561\n",
       "2   -0.335485 -0.821109\n",
       "3    0.529554  1.678275\n",
       "4    0.529554 -0.814193\n",
       "..        ...       ...\n",
       "886 -0.239370 -0.540326\n",
       "887 -1.008293  0.400228\n",
       "888 -0.143254  0.037838\n",
       "889 -0.335485  0.400228\n",
       "890  0.241208 -0.830791\n",
       "\n",
       "[889 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['age','fare']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b094efe1",
   "metadata": {},
   "source": [
    "### Preprocessing and cleaning of dataset is done "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be97fedb",
   "metadata": {},
   "source": [
    "# Seperating data into x and y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "25d5cc55",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop('survived',1)\n",
    "y = data['survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ff1fb11",
   "metadata": {},
   "source": [
    "# Spliting x and y into train and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e98148a4",
   "metadata": {},
   "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=0.30,random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3195e82d",
   "metadata": {},
   "source": [
    "# Applying Machine learning algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5860fcee",
   "metadata": {},
   "source": [
    "### KNeighborClassifer "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4b505d9c",
   "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>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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knc = KNeighborsClassifier()\n",
    "knc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6aff38e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" 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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" 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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knc.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d172a55c",
   "metadata": {},
   "source": [
    "# Predicting value on x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "af18140a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n",
       "       0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_knc = knc.predict(x_test)\n",
    "y_pred_knc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b4e8c06",
   "metadata": {},
   "source": [
    "# Checking accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "13ed2046",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7827715355805244"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
    "accuracy_score(y_pred_knc,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02436e64",
   "metadata": {},
   "source": [
    "# Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "884e93e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[142,  33],\n",
       "       [ 25,  67]], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_pred_knc,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a67dd72",
   "metadata": {},
   "source": [
    "# Checking for overfitting and underfitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "25122dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" 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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" 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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6c121536",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_train = knc.predict(x_train)\n",
    "y_pred_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b9ca2b7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3541213482133634"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mse_train = np.sqrt(mean_squared_error(y_pred_train,y_train))\n",
    "mse_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "705f7044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" 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-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" 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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knc.fit(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "866892c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
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       "       0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
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       "       0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_test = knc.predict(x_test)\n",
    "y_pred_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "48784964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3722590214168318"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_test = np.sqrt(mean_squared_error(y_pred_test,y_test))\n",
    "mse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3a73ac2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Erron on train data set :  0.3541213482133634\n",
      "Mean Squared Erron on test data set :  0.3722590214168318\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Squared Erron on train data set : \",mse_train)\n",
    "print(\"Mean Squared Erron on test data set : \",mse_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a65f6562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01813767320346843"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_test-mse_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2622892c",
   "metadata": {},
   "source": [
    "<strong> Since the difference of error between train and test data is not to much so there is no case of overfitting and underfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6164cb37",
   "metadata": {},
   "source": [
    "# Applying logistic regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3760f870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" 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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" 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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5312110b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" 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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" 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": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa3c4415",
   "metadata": {},
   "source": [
    "# Predicting value of x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "78aeae74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n",
       "       0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_lr = lr.predict(x_test)\n",
    "y_pred_lr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "700b3d4d",
   "metadata": {},
   "source": [
    "# Checking accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ef76d015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8052434456928839"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred_lr,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a892af8",
   "metadata": {},
   "source": [
    "# Checking for Overfitting and underfitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d28aed78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" 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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" 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": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "bd69396b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,\n",
       "       0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,\n",
       "       1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predicting value of x_train \n",
    "y_pred_train_lr = lr.predict(x_train)\n",
    "y_pred_train_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ff211095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4186181524447669"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_train_lr = np.sqrt(mean_squared_error(y_pred_train_lr,y_train))\n",
    "mse_train_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6f2317c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "28b8ca2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1,\n",
       "       0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0,\n",
       "       0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predicting value on x_test\n",
    "y_pred_test_lr = lr.predict(x_test)\n",
    "y_pred_test_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "241633fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40594828179293563"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_test_lr = np.sqrt(mean_squared_error(y_pred_test_lr,y_test))\n",
    "mse_test_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e5d13633",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Erron on train data set :  0.4186181524447669\n",
      "Mean Squared Erron on test data set :  0.40594828179293563\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean Squared Erron on train data set : \",mse_train_lr)\n",
    "print(\"Mean Squared Erron on test data set : \",mse_test_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a5e5ed09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.012669870651831283"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_train_lr-mse_test_lr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bfd19ee",
   "metadata": {},
   "source": [
    "<strong> Since the difference of error between train and test data is not to much so there is no case of overfitting and underfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22b7ce67",
   "metadata": {},
   "source": [
    "# confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2f9d5a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[141,  26],\n",
       "       [ 26,  74]], dtype=int64)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_pred_lr,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23dcb49a",
   "metadata": {},
   "source": [
    "# Cross validation for logistic Regression algo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "152c9409",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.79775281, 0.82022472, 0.7752809 , 0.85393258, 0.82022472,\n",
       "       0.78651685, 0.82022472, 0.78651685, 0.83146067, 0.85227273])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(lr,x,y,cv=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b1178a8",
   "metadata": {},
   "source": [
    "# Observation \n",
    "- From above we can see the score of logistic Regression on different set of train and test data\n",
    "- Here we can observe the minimum score is 0.7752809 and maximum score is 0.85393258"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d47bf5a7",
   "metadata": {},
   "source": [
    "# Cross validation for KNeighborClassifier algo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "ce5a14fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.79775281, 0.84269663, 0.74157303, 0.79775281, 0.85393258,\n",
       "       0.84269663, 0.85393258, 0.7752809 , 0.84269663, 0.82954545])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(knc,x,y,cv=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b99e60e",
   "metadata": {},
   "source": [
    "# Observation \n",
    "- From above we can see score of KNeighborClassifier on different set of train and test data.\n",
    "- Here we can observe that minimum score is 0.74157303 and maximum score is 0.85393258"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68316953",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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