Case Study #06 - Data Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "471934c4",
   "metadata": {},
   "source": [
    "Case Study #06 - Data Preprocessing by Jayaram K"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e29f8e62",
   "metadata": {},
   "source": [
    "Do the following on titanic dataset.\n",
    "\n",
    "1. Load the dataset into python environment\n",
    "2. Make ‘PassengerId’ as the index column\n",
    "3. Check the basic details of the dataset\n",
    "4. Fill in all the missing values present in all the columns in the dataset\n",
    "5. Check and handle outliers in at least 3 columns in the dataset\n",
    "6. Do min max scaling on the feature set (Take ‘Survived’ as target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "32290538",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baef9887",
   "metadata": {},
   "source": [
    "# 1. Load the dataset into python environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "d0b62be6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data to Python environment\n",
    "data = pd.read_csv('titanic_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "8d4ad5bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>126</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Nicola-Yarred, Master. Elias</td>\n",
       "      <td>male</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2651</td>\n",
       "      <td>11.2417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>304</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Keane, Miss. Nora A</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>226593</td>\n",
       "      <td>12.3500</td>\n",
       "      <td>E101</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>445</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johannesen-Bratthammer, Mr. Bernt</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65306</td>\n",
       "      <td>8.1125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>224</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Nenkoff, Mr. Christo</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349234</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>741</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hawksford, Mr. Walter James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16988</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>D45</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                               Name     Sex  \\\n",
       "125          126         1       3       Nicola-Yarred, Master. Elias    male   \n",
       "303          304         1       2                Keane, Miss. Nora A  female   \n",
       "444          445         1       3  Johannesen-Bratthammer, Mr. Bernt    male   \n",
       "223          224         0       3               Nenkoff, Mr. Christo    male   \n",
       "740          741         1       1        Hawksford, Mr. Walter James    male   \n",
       "\n",
       "      Age  SibSp  Parch  Ticket     Fare Cabin Embarked  \n",
       "125  12.0      1      0    2651  11.2417   NaN        C  \n",
       "303   NaN      0      0  226593  12.3500  E101        Q  \n",
       "444   NaN      0      0   65306   8.1125   NaN        S  \n",
       "223   NaN      0      0  349234   7.8958   NaN        S  \n",
       "740   NaN      0      0   16988  30.0000   D45        S  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying five random rows\n",
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b259e1c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b2bb7d2a",
   "metadata": {},
   "source": [
    "# 2. Make ‘PassengerId’ as the index column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "1050c44d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c5a8042",
   "metadata": {},
   "source": [
    "- Data set containing 11 columns and 891 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "262ff40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['PassengerId'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "fe6ef6b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PassengerId column contains 891 unique values which is equal to data set raw size. \n",
    "# So, changing the column, PassengerId into index, since it contains only the unique PassengerIds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "2739cecd",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>Fare</th>\n",
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       "      <th>PassengerId</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.set_index('PassengerId')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c56fc6b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3acea8d0",
   "metadata": {},
   "source": [
    "# 3. Check the basic details of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "63107103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Name      891 non-null    object \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   Ticket    891 non-null    object \n",
      " 8   Fare      891 non-null    float64\n",
      " 9   Cabin     204 non-null    object \n",
      " 10  Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# Getting concise summary of the dataset\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "a5ec75cb",
   "metadata": {},
   "outputs": [
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</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>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</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>38.000000</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  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the statistical summary of the dataset.\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82f15674",
   "metadata": {},
   "source": [
    "- From the above table we can find the Total count, Mean, Standard Deviation, Min & Max value and percentile of each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "5b6ccc8c",
   "metadata": {},
   "outputs": [
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       "        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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>347082</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Name   Sex  Ticket    Cabin Embarked\n",
       "count                       891   891     891      204      889\n",
       "unique                      891     2     681      147        3\n",
       "top     Braund, Mr. Owen Harris  male  347082  B96 B98        S\n",
       "freq                          1   577       7        4      644"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the statistical summary of the categorical columns in dataset.\n",
    "data.describe(include = object)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76aa32b",
   "metadata": {},
   "source": [
    "- The 'top' is the most common value and 'freq' is the most common value’s frequency."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3286a7ba",
   "metadata": {},
   "source": [
    "Data description:\n",
    "\n",
    "1. Survival - Survival (0 = No; 1 = Yes). Not included in test.csv file.\n",
    "2. Pclass - Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)\n",
    "3. Name - Name\n",
    "4. Sex - Sex\n",
    "5. Age - Age\n",
    "6. Sibsp - Number of Siblings/Spouses Aboard\n",
    "7. Parch - Number of Parents/Children Aboard\n",
    "8. Ticket - Ticket Number\n",
    "9. Fare - Passenger Fare\n",
    "10. Cabin - Cabin\n",
    "11. Embarked - Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "826c6df0",
   "metadata": {},
   "source": [
    "# 4. Fill in all the missing values present in all the columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "6a4b4c76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Name          0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Ticket        0\n",
       "Fare          0\n",
       "Cabin       687\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for Null values in the data\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "d3d437f6",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Handling the missing values in Age column\n",
    "plt.hist('Age',data = data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "df2aa8d7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# We see that the data is not normally distributed, it skewed to right.\n",
    "# So, we fill the missing values with median using fillna() method\n",
    "data['Age'] = data['Age'].fillna(data['Age'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "4781823b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling the missing values in Cabin column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa97ac33",
   "metadata": {},
   "source": [
    "  The 'Cabin' column has got 687 missing values and the total count of raw is 891. Hence, we can see 'Cabin' column got around 77% missing value.  \n",
    "  Since, Cabin column is having a high no.of missing values. It's better not to consider it for further calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "3bfbdbef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the Cabin column from the data set.\n",
    "data = data.drop('Cabin',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "e6cd8864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['S', 'C', 'Q', nan], dtype=object)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handling the missing values in Embarked column\n",
    "data['Embarked'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "3f285aa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    S\n",
       "dtype: object"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the mode of Cabin column\n",
    "data['Embarked'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "21d2a508",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only one mode for the Embarked column, so we replace the missing values with this mode.\n",
    "data['Embarked'] = data['Embarked'].fillna(data['Embarked'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "fa207214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Name        0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Ticket      0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re- Checking for Null values in the data\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c811cdae",
   "metadata": {},
   "source": [
    " - We successfully handled all the missing values present in the data set. No null values present in the data set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "755d6710",
   "metadata": {},
   "source": [
    "# 5. Check and handle outliers in at least 3 columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "7c372d1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      int64\n",
       "Pclass        int64\n",
       "Name         object\n",
       "Sex          object\n",
       "Age         float64\n",
       "SibSp         int64\n",
       "Parch         int64\n",
       "Ticket       object\n",
       "Fare        float64\n",
       "Embarked     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "e920ad8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We now plot box plots for all the numerical columns to find outliers, if any."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "bb61c842",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (18,8))\n",
    "\n",
    "plt.subplot(2,3,1)\n",
    "plt.boxplot(data['Survived'])\n",
    "plt.title('Box plot of Survived')\n",
    "\n",
    "\n",
    "plt.subplot(2,3,2)\n",
    "plt.boxplot(data['Pclass'])\n",
    "plt.title('Box plot of Pclass')\n",
    "\n",
    "plt.subplot(2,3,3)\n",
    "plt.boxplot(data['Age'])\n",
    "plt.title('Box plot of Age')\n",
    "\n",
    "\n",
    "plt.subplot(2,3,4)\n",
    "plt.boxplot(data['SibSp'])\n",
    "plt.title('Box plot of SibSp')\n",
    "\n",
    "plt.subplot(2,3,5)\n",
    "plt.boxplot(data['Parch'])\n",
    "plt.title('Box plot of Parch')\n",
    "\n",
    "plt.subplot(2,3,6)\n",
    "plt.boxplot(data['Fare'])\n",
    "plt.title('Box plot of Fare')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f568a70",
   "metadata": {},
   "source": [
    "- We can see that there are some outliers in Age, SibSp, Parch and Fare columns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92036ed9",
   "metadata": {},
   "source": [
    "There are different ways to handle the outlier:\n",
    "\n",
    "- We may delete outlier values if it is due to data entry error.\n",
    "- We may impute outlier using mean, median, or zero and thus there is no loss of data. \n",
    "- Quantile based flooring and capping.\n",
    "- Another option is treating the outlier separately in the statistical model. So, we treat them in separate groups (with outlier, deleting outlier, impute outlier) and build individual model for each group and then check the output and find out what is the best way to handle the outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "a8135638",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Checking the outliers in Age column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "0a93fb32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      29.361582\n",
       "std       13.019697\n",
       "min        0.420000\n",
       "25%       22.000000\n",
       "50%       28.000000\n",
       "75%       35.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the statistical summary of the Age column\n",
    "data['Age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b0b8176",
   "metadata": {},
   "source": [
    "- We can conclude that there is no data entry error, because the Age minimum and maximum range between 0 to 80. So, we don't need to delete any outlier value.\n",
    "- However, in order to reduce the spread of data, we may impute the outliers with the help of Inter Quartile Range (IQR)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "9d741c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling the outlier with the help of Inter Quartile Range(IQR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "4dda1300",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding the respective value for the percentiles at 25, 50 and 75.\n",
    "Q1 = np.percentile(data['Age'], 25, interpolation = 'midpoint')\n",
    "Q2 = np.percentile(data['Age'], 50, interpolation = 'midpoint')\n",
    "Q3 = np.percentile(data['Age'], 75, interpolation = 'midpoint')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "def283d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the IQR\n",
    "IQR = Q3 - Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "339b7483",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding the lower and upper limit\n",
    "low_limit = Q1 - 1.5 * IQR\n",
    "upp_limit = Q3 + 1.5 * IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "74a596f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The no.of outliers present in the Age column: 66\n"
     ]
    }
   ],
   "source": [
    "# Now we will find out the outliers by considering that all values below lower and all values above upper limit as outliers.\n",
    "\n",
    "outlier = []\n",
    "for i in data['Age']:\n",
    "    if ((i < low_limit) or ( i > upp_limit )):\n",
    "        outlier.append(i)\n",
    "\n",
    "print('The no.of outliers present in the Age column:',len(outlier))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "94e3b1fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Since we don't consider it as data entry errors, we will not delete these outliers. \n",
    "# We will impute the outliers with median value of Age column because median is not affected by the outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "52cb47aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_median = data['Age'].median() \n",
    "data['Age'] = np.where(data['Age'] > upp_limit , age_median , np.where(data['Age'] < low_limit, age_median, data['Age']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "1249bedc",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Re-Checking the Age column after handling the outliers\n",
    "plt.boxplot(data['Age'])\n",
    "plt.title('Box plot of Age after handling Outliers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "d6ddbfad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      28.476992\n",
       "std        9.793559\n",
       "min        3.000000\n",
       "25%       23.750000\n",
       "50%       28.000000\n",
       "75%       33.000000\n",
       "max       54.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a52610bb",
   "metadata": {},
   "source": [
    "- We see there is a significant drop in the outliers, because the mean and median are now really close and the standard deviation reduced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "c8f811bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Checking the outliers in Fare column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "bc111e58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      32.204208\n",
       "std       49.693429\n",
       "min        0.000000\n",
       "25%        7.910400\n",
       "50%       14.454200\n",
       "75%       31.000000\n",
       "max      512.329200\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the statistical summary of the Fare column\n",
    "data['Fare'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "84dacd6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The no.of outliers present in the Fare column: 116\n"
     ]
    }
   ],
   "source": [
    "# Handling the outlier with the help of Inter Quartile Range (IQR)\n",
    "\n",
    "# Finding the respective value for the percentiles at 25, 50 and 75.\n",
    "Q1 = np.percentile(data['Fare'], 25, interpolation = 'midpoint')\n",
    "Q2 = np.percentile(data['Fare'], 50, interpolation = 'midpoint')\n",
    "Q3 = np.percentile(data['Fare'], 75, interpolation = 'midpoint')\n",
    "\n",
    "# Calculte the IQR\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Finding the lower and upper limit\n",
    "low_limit = Q1 - 1.5 * IQR\n",
    "upp_limit = Q3 + 1.5 * IQR\n",
    "\n",
    "# Finding the outliers by considering that all values below lower and all values above upper limit as outliers.\n",
    "outlier = []\n",
    "\n",
    "for i in data['Fare']:\n",
    "    if ((i < low_limit) or ( i > upp_limit )):\n",
    "        outlier.append(i)\n",
    "\n",
    "print('The no.of outliers present in the Fare column:',len(outlier))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20505a87",
   "metadata": {},
   "source": [
    "- We see that there are 116 outliers. Hence deleting this huge number of values will affect the data set.\n",
    "- We also don't know by 'Fare' it means price of single ticket for one individual or it also contain total fare paid by one family or group.\n",
    "- Hence, we can't say it as an incorrect entry, so imputation is the best way to handle the outliers here because we don’t lose any data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35838ba4",
   "metadata": {},
   "source": [
    " - We see that, the mean is much higher than median and standard deviation is also too high.\n",
    " - Most of the 'Fare' column value lies above the 75th percentile. That means higher Fare values.\n",
    " - So, we will impute all the values above upp_limit using capping and set the cap to upp_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "ac07a80a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-placing all the values above the upp_limit with the upp_limit value. \n",
    "data[\"Fare\"] = np.where(data[\"Fare\"] > upp_limit, upp_limit, data['Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "22bb9e05",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Re-Checking the Age column after removing the outliers\n",
    "plt.boxplot(data['Fare'])\n",
    "plt.title('Box plot of Fare after handling outliers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "43f837bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean      24.046813\n",
       "std       20.481625\n",
       "min        0.000000\n",
       "25%        7.910400\n",
       "50%       14.454200\n",
       "75%       31.000000\n",
       "max       65.634400\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Fare'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc81c101",
   "metadata": {},
   "source": [
    "- We see there is no outliers and the standard deviation is reduced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "db26a57d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Checking the outliers in SibSp column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "3d62102d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 3 4 2 5 8]\n"
     ]
    }
   ],
   "source": [
    "print(data['SibSp'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "e3d7f571",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5,6))\n",
    "sibSp_count = sns.countplot(x='SibSp',data=data)\n",
    "plt.title('Count plot of SibSp')\n",
    "plt.ylabel('Count')\n",
    "plt.bar_label(sibSp_count.containers[0],fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "4525f159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean       0.523008\n",
       "std        1.102743\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        1.000000\n",
       "max        8.000000\n",
       "Name: SibSp, dtype: float64"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['SibSp'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "f2f6a3d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We understand that SibSp column represent the no.of Siblings/Spouses abroad in the ship.\n",
    "# Since they are unique by its own, we don't have to consider them as outliers and keep the SibSp column as same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "471f0a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Checking the outliers in Parch column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "3f4995df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 5 3 4 6]\n"
     ]
    }
   ],
   "source": [
    "print(data['Parch'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "8f7f6072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5,6))\n",
    "parch_count = sns.countplot(x='Parch', data = data)\n",
    "plt.title('Count plot of Parch')\n",
    "plt.ylabel('Count')\n",
    "plt.bar_label(parch_count.containers[0],fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "c48bdcd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    891.000000\n",
       "mean       0.381594\n",
       "std        0.806057\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        0.000000\n",
       "max        6.000000\n",
       "Name: Parch, dtype: float64"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Parch'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "10e28a4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Same like SibSp column, the Parch column represent the no.of Parents/Children abroad in the ship.\n",
    "# Hence, they are unique by its own, and thus we don't have to consider them as outliers and keep the Parch column as same."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff450242",
   "metadata": {},
   "source": [
    "# 6. Do min max scaling on the feature set (Take ‘Survived’ as target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b6dbab",
   "metadata": {},
   "source": [
    "Feature scaling is the process of normalising the range of features in a dataset.\n",
    "- Datasets often contain features that are varying in degrees of magnitude, range and units. Therefore, in order for machine learning models to interpret these features on the same scale, we need to perform feature scaling."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18185a46",
   "metadata": {},
   "source": [
    "The most common techniques of feature scaling are Normalization and Standardization.\n",
    "- MinMaxScaler is the Scikit-learn function for normalisation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91aeda08",
   "metadata": {},
   "source": [
    "MinMaxScaler transform features by scaling each feature to a given range.\n",
    "\n",
    "- This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g., between zero and one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "591820fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d1e453d4",
   "metadata": {},
   "source": [
    "We will compute Linear regression, a model that assumes a linear relationship between the input features (X) and the target feature (y)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "69b54a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We first calculate it from the normal data(without scaling) and then perform a MinMax scaling to check whether it helping to make better model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "08d4347b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a new data set which contains only numerical features\n",
    "sub_data = data.select_dtypes(exclude= 'object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "22ec2f9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making 'Survived' as target and rest into input\n",
    "X = sub_data.drop('Survived',axis = 1)\n",
    "y = sub_data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "9c9cc249",
   "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>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>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.308642</td>\n",
       "      <td>28.476992</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>24.046813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.836071</td>\n",
       "      <td>9.793559</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>20.481625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</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>2.000000</td>\n",
       "      <td>23.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\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>3.000000</td>\n",
       "      <td>33.000000</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>3.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>65.634400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Pclass         Age       SibSp       Parch        Fare\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000\n",
       "mean     2.308642   28.476992    0.523008    0.381594   24.046813\n",
       "std      0.836071    9.793559    1.102743    0.806057   20.481625\n",
       "min      1.000000    3.000000    0.000000    0.000000    0.000000\n",
       "25%      2.000000   23.750000    0.000000    0.000000    7.910400\n",
       "50%      3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      3.000000   33.000000    1.000000    0.000000   31.000000\n",
       "max      3.000000   54.000000    8.000000    6.000000   65.634400"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the statistical summary of the X dataset(input/independent features)\n",
    "X.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "7d1dee6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we will make train and test data and then create Linear Regression model and finally make prediction about target.\n",
    "# Importing necessary libraries\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 42, test_size = .20)\n",
    "\n",
    "lr = linear_model.LinearRegression()\n",
    "model = lr.fit(X_train,y_train)\n",
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "81f8d9be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE value is:  0.19581214722484636\n",
      "R squared value is:  0.1925332034451348\n"
     ]
    }
   ],
   "source": [
    "# We will find the Mean Squared Error(MSE) and R-Square to evaluate the model\n",
    "# Importing necessary libraries\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print('MSE value is: ', mean_squared_error(y_test, predictions))\n",
    "\n",
    "print('R squared value is: ', r2_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "a129efb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we will perform MinMax scaling on the feature set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "d7a7de65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "f0d362f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create MinMaxScaler with scaling each feature to a range 0 to 1\n",
    "min_max = preprocessing.MinMaxScaler(feature_range=(0,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "e56f2572",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting into data, and then transforming it.\n",
    "X = min_max.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "5812fff2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the scaled and trasformed data back into a Dataframe\n",
    "X = pd.DataFrame(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "ea663260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.654321</td>\n",
       "      <td>0.499549</td>\n",
       "      <td>0.065376</td>\n",
       "      <td>0.063599</td>\n",
       "      <td>0.366375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.418036</td>\n",
       "      <td>0.192031</td>\n",
       "      <td>0.137843</td>\n",
       "      <td>0.134343</td>\n",
       "      <td>0.312056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.500000</td>\n",
       "      <td>0.406863</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.120522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.220223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.472313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0           1           2           3           4\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000\n",
       "mean     0.654321    0.499549    0.065376    0.063599    0.366375\n",
       "std      0.418036    0.192031    0.137843    0.134343    0.312056\n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000\n",
       "25%      0.500000    0.406863    0.000000    0.000000    0.120522\n",
       "50%      1.000000    0.490196    0.000000    0.000000    0.220223\n",
       "75%      1.000000    0.588235    0.125000    0.000000    0.472313\n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the statistical summary of the scaled and trasformed X dataset\n",
    "X.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76eb08ed",
   "metadata": {},
   "source": [
    "- We successfully scaled and transformed the data using MinMaxScaler setting Min to 0 and Max to 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "ed4989a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we will again create a regression model and check whether MinMaxScaler helped improving the model \n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 42, test_size = .20)\n",
    "\n",
    "lr = linear_model.LinearRegression()\n",
    "model = lr.fit(X_train,y_train)\n",
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "7bcab5d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE value is:  0.19581214722484633\n",
      "R squared value is:  0.19253320344513503\n"
     ]
    }
   ],
   "source": [
    "print('MSE value is: ', mean_squared_error(y_test, predictions))\n",
    "\n",
    "print('R squared value is: ', r2_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86cbd8dd",
   "metadata": {},
   "source": [
    "- We see there is no significant diffrence in the MSE and R squared value. Only a slight diffrence, MSE reducing and R-Squared incrasing.\n",
    "- So we can not say MinMax scalling significantly helped, but it made some minute changes to make it better. Because we know, we need to make MSE closer to zero and R squared to one.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f3bf999",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "034232da",
   "metadata": {},
   "source": [
    "Thank You."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47bf128d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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