Feature_Engineering 1_Missing_Value.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering - Handling Missing Value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Different types of Missing Data:\n",
    "1. ##### Missing Completely at Random, MCAR:\n",
    "A variable is missing completely at random (MCAR) if the probability of being missing is the same for all the observations. When data is MCAR, there is absolutely no relationship between the data missing and any other values, observed or missing, within the dataset. In other words, those missing data points are a random subset of the data. There is nothing systematic going on that makes some data more likely to be missing than other.\n",
    "2. ##### Missing Data Not At Random(MNAR): Systematic missing Values\n",
    "There is absolutely some relationship between the data missing and any other values, observed or missing, within the dataset.\n",
    "3. #### Missing At Random(MAR): \n",
    "Someone purposefully hides their information while filling info."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ways to handling Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Mean/ Median/Mode replacement\n",
    "2. Random Sample Imputation\n",
    "3. Capturing NAN values with a new feature\n",
    "4. End of Distribution imputation\n",
    "5. Arbitrary imputation\n",
    "6. Frequent categories imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Libraries and Dataset. Here using Titanic dataset\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</th>\n",
       "      <td>1</td>\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",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('titanic.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have 177 null values in Age, 2 in Embarked and 687 in Cabin\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>61</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>female</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113572</td>\n",
       "      <td>80.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                       Name  \\\n",
       "61            62         1       1                        Icard, Miss. Amelie   \n",
       "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "        Sex   Age  SibSp  Parch  Ticket  Fare Cabin Embarked  \n",
       "61   female  38.0      0      0  113572  80.0   B28      NaN  \n",
       "829  female  62.0      0      0  113572  80.0   B28      NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Just Exploring the Data\n",
    "df[df['Embarked'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7710437710437711"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df['cabin_null']=np.where(df['Cabin'].isnull(),1,0) #Here We are just creating new variable to know the importance of the missing data\n",
    "\n",
    "##find the percentage of null values\n",
    "df['cabin_null'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0    0.876138\n",
       "1    0.602339\n",
       "Name: cabin_null, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Survived'])['cabin_null'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Mean/ Median /Mode imputation\n",
    "When should we apply?\n",
    "Mean/median imputation has the assumption that the data are missing completely at random(MCAR). We solve this by replacing the NAN with Mean/Median/Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('titanic.csv',usecols=['Age','Fare','Survived']) # I am taking only some columes, you can use all cols.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0.000000\n",
       "Age         0.198653\n",
       "Fare        0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Lets go and see the percentage of missing values\n",
    "df.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Writing function to fill the missing data\n",
    "def impute_nan(df,variable,median):\n",
    "    df[variable+\"_median\"]=df[variable].fillna(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median=df.Age.median()\n",
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Age_median\n",
       "0         0  22.0   7.2500        22.0\n",
       "1         1  38.0  71.2833        38.0\n",
       "2         1  26.0   7.9250        26.0\n",
       "3         1  35.0  53.1000        35.0\n",
       "4         0  35.0   8.0500        35.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Passing the Variable as Age. You can pass any variable which you want to fill the missing value.\n",
    "impute_nan(df,'Age',median)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.526497332334044\n",
      "13.019696550973194\n"
     ]
    }
   ],
   "source": [
    "# As we can see, there are some difference in the standard deviation of orizional Age cols and Age_median. Difference should be minimum.\n",
    "print(df['Age'].std())\n",
    "print(df['Age_median'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived        0\n",
       "Age           177\n",
       "Fare            0\n",
       "Age_median      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here you can see, Age_median feature got created after the imputation, and it has 0 null values. You can do it in the same (Age) cols.\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23da423ba90>"
      ]
     },
     "execution_count": 15,
     "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": [
    "# Checking the difference between orizinal and imputed fearure with the help of graph\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "df['Age'].plot(kind='kde', ax=ax)\n",
    "df.Age_median.plot(kind='kde', ax=ax, color='red')\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(lines, labels, loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Disadvantages\n",
    "1. As we can see in the above graph, Change or Distortion in the original variance\n",
    "2. Impacts Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.Random Sample Imputation\n",
    "\n",
    "Aim: Random sample imputation consists of taking random observation from the dataset and we use this observation to replace the nan values\n",
    "\n",
    "When should it be used? \n",
    "It assumes that the data are missing completely at random(MCAR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Age_median\n",
       "0         0  22.0   7.2500        22.0\n",
       "1         1  38.0  71.2833        38.0\n",
       "2         1  26.0   7.9250        26.0\n",
       "3         1  35.0  53.1000        35.0\n",
       "4         0  35.0   8.0500        35.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We already lodad data above with 'Age','Fare' & 'Survived cols. We will use them for Random sample imputation\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived        0\n",
       "Age           177\n",
       "Fare            0\n",
       "Age_median      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here With the help of Random sample techniques, we will fill the missing value of 'Age' feature\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "423    28.00\n",
       "177    50.00\n",
       "305     0.92\n",
       "292    36.00\n",
       "889    26.00\n",
       "       ...  \n",
       "539    22.00\n",
       "267    25.00\n",
       "352    15.00\n",
       "99     34.00\n",
       "689    15.00\n",
       "Name: Age, Length: 177, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Listing Random sample for missing value\n",
    "df['Age'].dropna().sample(df['Age'].isnull().sum(),random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  5,  17,  19,  26,  28,  29,  31,  32,  36,  42,\n",
       "            ...\n",
       "            832, 837, 839, 846, 849, 859, 863, 868, 878, 888],\n",
       "           dtype='int64', length=177)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the index\n",
    "df[df['Age'].isnull()].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_nan(df,variable,median):\n",
    "    df[variable+\"_median\"]=df[variable].fillna(median)\n",
    "    df[variable+\"_random\"]=df[variable]\n",
    "    ##It will have the random sample to fill the na\n",
    "    random_sample=df[variable].dropna().sample(df[variable].isnull().sum(),random_state=0)\n",
    "    ##pandas need to have same index in order to merge the dataset\n",
    "    random_sample.index=df[df[variable].isnull()].index\n",
    "    df.loc[df[variable].isnull(),variable+'_random']=random_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "median=df.Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "impute_nan(df,\"Age\",median)  #Imputed the Age with Median for checking purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age_median</th>\n",
       "      <th>Age_random</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Age_median  Age_random\n",
       "0         0  22.0   7.2500        22.0        22.0\n",
       "1         1  38.0  71.2833        38.0        38.0\n",
       "2         1  26.0   7.9250        26.0        26.0\n",
       "3         1  35.0  53.1000        35.0        35.0\n",
       "4         0  35.0   8.0500        35.0        35.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived        0\n",
       "Age           177\n",
       "Fare            0\n",
       "Age_median      0\n",
       "Age_random      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here, Age_random feature got created with 0 null value.\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x23da4304340>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q1WPVqlUAfPrppwWlicqiU20oz5GQAN27l/34sDBwsrqXqloqe7rviRMnctttt9GkSRP69u3L3r17Szy+R48ejBw5km7dutG8eXMuv/zygn2zZs3iwQcf5PTp07Rq1YoZM2aU/wdwEXS6b+UZMjOtKbxfegn++c+ynfP009YSmRkZpc/dpIql0327D53uWylnDjk60JW3DSIrC1JToV4918SlbKXTfZdME4TyDOXp4pqv8GhqTRDVkk73XTJNEMoz5K8vXd4SBFjzMXUoOo2Y8jQ63bdS1VV+CaI8CaJRI+tZezJdtOrU1llVXcjvQBOE8gwJCdYguVq1yn6OTrdRIQICAkhJSdEkYSNjDCkpKQQEBJTrPK1iUp6hrAsFFVa3Lvj56ZTfFyk8PJyEhAR0Mk17BQQEEF6eEjSaIJSnSEgoX/USWF1bGzXSEsRF8vX1PWe6CVV1aBWT8gwXUoIAHU2tPJomCFX9ZWZCUlL5SxBgJQitYlIeShOEqv4OHrSetQShVLloglDV34WMgcjXqJFV+sjNrdiYlKoCNEGo6u9CxkDkCw211rE+erRiY1KqCtAEoaq/iylBFB5NrZSH0QShqr/8leTKM0gun46mVh5ME4Sq/sq7UFBhOppaeTBNEKr6K+9So4VpFZPyYJogVPV3oYPkAIKCoGZNLUEoj6QJQlVvp09DcjI0b37h19DpNpSH0gShqrf9+63nFi0u/Bo6WE55KE0QqnqrqAShbRDKA2mCUNXbvn3W88UkCK1iUh5KE4Sq3vbtA1/fs+tLX4jQUEhJgaysCgtLqapAE4Sq3vbtg2bNwOsi/qvnd3VNSqqQkJSqKjRBqOpt//6Lq14CHQuhPJYmCFW97dt38QlCp9tQHkoThKq+zpyx3tQrqgShCUJ5GE0Qqvo6cMB6rqgShFYxKQ+jCUJVXxUxBgIgIMCaDVZLEMrD+NgdgFIu4xgDYZo14/cDq4k5HEOdgDpc3epqGgU1Kt+1dDS18kCaIFT1FRfHwfq+3PnTX/n1wMqCzX7efjzR9wleGvQS/j7+ZbuWJgjlgVxaxSQiQ0Vkh4jEicgzTvaLiEx27N8kIj0c2wNE5E8R2SgiW0TkJVfGqaqnpH1buOIeISpxPUOaPEc3789pmj2Z+nIF/179b/pNv5yU0yllu1ijRtoGoTyOyxKEiHgDU4BrgQ7A7SLSochh1wIRjsc44APH9kzgSmNMV6AbMFRE+roqVlX9GGMYW38V8YE5NM55mT17+9O/dQTj+13NVY1eIjjzWdYnxtD74wGkZqSWfkEtQSgP5Moqpt5AnDFmD4CIzANGAFsLHTMCmG2MMcAaEakrImHGmETgpOMYX8fDuDBWVc18ETuPb8LSuWNbFw5FduPjByNp0zCoYP/mhEsY878GbDj+DIOm38C6B37C19u3+AuGhsKJE1bX2Ro1KuE7UMp+rqxiagLEF3qd4NhWpmNExFtEYoAk4AdjzFoXxqqqkezcbJ7/4Tm6HIb2pwfy6b29z0kOAJ3D67DqscfoU+8ZNiavYuS8CSVfVEdTKw/kygQhTrYVLQUUe4wxJtcY0w0IB3qLSCenNxEZJyJRIhKVnJx8UQGr6uGLLV+wO30fr/wMQ0ZcTni9QKfH1fT3YcX4ibQMuIWFcR/x0dqFxV9UR1MrD+TKBJEAFF7nMRw4VN5jjDGpwApgqLObGGM+MsZEGmMiQ0JCLjZmVQ289+f71M+ow3W7oNfVfUo8NsDXm1/u/5ga0oIJ391HQloxHzJ0NLXyQK5MEOuACBFpKSJ+wChgSZFjlgCjHb2Z+gJpxphEEQkRkboAIlIDGAxsd2GsqprYfGQzaw7+zhV7m4GfP4SHl3pO8/r1+Oi6mWSbVG6Y/bDzgzRBKA/ksgRhjMkBJgDfA9uAL4wxW0TkQRF50HHYMmAPEAd8DIx3bA8DfhGRTViJ5gdjzNeuilVVH9M3zEDw5aG9QXi1bl3mab7v6jmQ/qF/ZcOxL/noj2/PPyC/dKptEMqDiNWBqHqIjIw0UVFRdoehbGKMIfTNpqSnNybpf2kEdWoPixaV+fyjp9Jo8lYEflKb5Ge2EeBbpFdTSAjceit88IHzCyhVBYlItDEm0tk+nYtJVRtRh6JIOn2Qxj4DqJmwH9q0Kdf5wTXr8LfeL3MybzcPL5p0/gE6FkJ5GE0QqtqYs+kLMN48HNYHycyEDkXHZZbu5Wvuo75vez7d+gbJJ9PP3RkaqlVMyqNoglDVxldblxCQ14mbvRxrR3fsWO5reHt58/rgf5NNMvcvePncnaGhcKhoRzylqq8yJQgR+UpErhMRTSjKLR1KP0TCyZ2E+vemWeJea+MFlCAA7u99PU1r9GPp3vc5kFqo22uTJlaCyMurgIiVcn9lfcP/ALgD2CUir4vIJS6MSalyW7rjOwCGtx2CbN0KzZpBrVoXfL23hvw/8jjFQwtfPbuxaVPIzgYdkKk8RJkShDHmR2PMnUAPYB/wg4j8LiL3iEgJE9goVTm+3LwML1OHUd0HwNatF1S9VNhtXQfQtEZ/vj8wneSTjsn88sdUxMcXf6JS1UiZq4xEpAFwN3AfsAGYhJUwfnBJZEqVkTGGtYdWEmS6Edm0DmzfftEJAuBfA/9JLuk8uvTf1ob8BJGQcNHXVqoqKGsbxAJgFRAIXG+MucEY87kx5hEgqOSzlXKtval7OZmTTMfgPvjv3weZmRWSIMb2HkywTy++2jWV9IyTVhUTaIJQHqOsJYhpxpgOxpjXHFNxIyL+AMUNsFCqsizZ9jMAQyMGwpYt1sYKSBAiwhN9niXbpPLs8kkQHAx+fpoglMcoa4J4xcm2PyoyEKUu1Nfbf0VMICO79YPYWGtj+/YVcu2/XzGCIDoya/P75GKsnkzaBqE8RIkJQkRCRaQnUENEuotID8djEFZ1k1K2izmyhlpe7WnbqDasXw9t20JQxdR8+np7cVPEOE7mHGL6+i+saiYtQSgPUVoJYgjwJtY03G8DbzkeTwLPuTY0pUqXlpFGStZu2tWLREQgKgp69qzQe7w85B588hrx/1a+aTVUa4JQHqLEJUeNMbOAWSJyizHmq0qKSakyW7ZzJWAY0LwfJCVZb96RFdss1rxBLbrXv511qe+wJrwDfRMSrMFyZZwpVqmqqrQqprscX7YQkSeLPiohPqVK9PW2lWCEmzsNhOhoa2MFlyAAnhv0MGIC+T//jZCVBUePVvg9lHI3pX0Equl4DgJqOXkoZavoxPX4EU6v5uFW9RJA9+4Vfp/hnVrRyPtavvfaQmIQWs2kPEJpVUwfOp5fqpxwlCqf/emxhNXohq+3l1WCaNcOateu8Pv4eHtxd5f7eD3mK6Z3h+fj46FHjwq/j1LupKwD5d4Qkdoi4isiP4nI0ULVT0rZYn9qIhl5SXRu1M3a4IIG6sIe6Hc5tbI68lFPyN2/z2X3UcpdlLWV7RpjzAlgOJAAtAX+7rKolCqDr7f9DsDlzXtb6zQcPOjSBNEiuCYdgkdxoC4si//ZZfdRyl2UNUHkT8g3DJhrjDnmoniUKrOVe9cCMOySS882UFdwD6aixve9nZBTXkzJ0nGiqvora4JYKiLbgUjgJxEJATJcF5ZSpduUFIOvCaNjaJhVvSTikgbqwkZ0a8Y1e0JZXi+Z/an7XXovpexW1um+nwEuBSKNMdnAKWCEKwNTqjTx6VtoFHCJNUAuOtoaQX0Ra0CURZ0avgzP6okYmLruQ5feSym7lWekT3tgpIiMBm4FrnFNSEqVLuXUMU7lHaJt/S7Whuhol1cv5evZqTdDdsMn0dPJzcutlHsqZYey9mL6FGvKjf5AL8dDZ3FVtlm+aw0AvcN7wOHDLm+gLqxV707cHQPJmUf4ea82Vqvqq8RxEIVEAh2MMcaVwShVViv3rQdgcJveldZAnc+7TRtu2AH+Of58FD2dq1tfXSn3VaqylbWKKRYIdWUgSpVHTOJmvEwtLmvRptIaqAu0aEFADnQ/3JzFOxaSlpFWOfdVqpKVNUEEA1tF5HsRWZL/cGVgSpVkT9p2avu0ooafz9kR1BU0xXepgoIwDRsy8kAI2XmZfLHli8q5r1KVrKxVTBNdGYRS5WGMISVzNx3qDLM2REfDlVdWagzSti1/OZzBP/Ka8mHUdO7veX+l3l+pylDWbq6/AvsAX8fX64D1LoxLqWJtPbKXXE7RsWEnSEyEQ4cqrYG6wCWX0OjQfoJyryL68Bp2peyq3PsrVQnK2ovpfmA+kN/xuwmwyFVBKVWS73euA+CyZt1dOsV3idq1w/toMsMaXAN4MSNmRuXeX6lKUNY2iIeBfsAJAGPMLqChq4JSqiRr4mMAuKZtTytBVGYDdb5LLgHg3rqBBOR2Z8aGT8kzeZUbg1IuVtYEkWmMycp/ISI+gHZ5VbbYkhyLLw1o17Cx1YPpkksqr4E6nyNBXJadTH0Gc/hUAiv3r6zcGJRysbImiF9F5DmghohcDXwJLHVdWEoVL+HkToL9W1svoqMrv3oJoEUL8PXFf08cN15yA14EMmPDrMqPQykXKmuCeAZIBjYDDwDLgBdKO0lEhorIDhGJE5FnnOwXEZns2L9JRHo4tjcVkV9EZJuIbBGRx8r+LanqLCM7m/ScfbSq295qoE5MtCdB+PhARARs385fIiOokdOPL7d+yens05Ufi1IuUtZeTHlYjdLjjTG3GmM+Lm1UtYh4A1OAa4EOwO0i0qHIYdcCEY7HOOADx/Yc4G/GmPZAX+BhJ+cqD/TrnliMZNEtrDOsd3SksyNBgFXNtHUr/Vo3oInfEM7knGLx9sX2xKKUC5SYIByf8CeKyFFgO7BDRJJF5J9luHZvIM4Ys8fRfjGP82eAHQHMNpY1QF0RCTPGJBpj1gMYY9KBbVg9p5SHW7HbSgoDW/Y420DdrZs9wXTtCnFx+GSc4c5uQ/AxIXyyfqY9sSjlAqWVIB7H6r3UyxjTwBhTH+gD9BORJ0o5twkQX+h1Aue/yZd6jIi0ALoDa0u5n/IAUYc2ATA4oqdVgqiEKb6L1bUrGAObN3NLz6YE5lzBz/t+JDE90Z54lKpgpSWI0cDtxpi9+RuMMXuAuxz7SiJOthWtlirxGBEJAr4CHncseXr+TUTGiUiUiEQlJyeXEpKq6nYd20oNrzDq1ahtJYgePewLJr/kEhNDh7DadK47HEMec2Pn2heTUhWotATha4w5WnSjMSaZs8uQFicBaFrodThwqKzHiIgvVnKYY4xZUNxNjDEfGWMijTGRISEhpYSkqrojZ3bROLAtJCdDfLx97Q8AzZpB3boQE4OIcFevy/DLa8u06Jn2xaRUBSotQWRd4D6wpuOIEJGWIuIHjAKKTvC3BBjtaOvoC6QZYxJFRIBPgG3GmLdLuY/yEAmpJ8gwCbRr0OFsA7WdJYj89o+NGwEY0a0xQblXsC1lM5uObLIvLqUqSGkJoquInHDySAc6l3SiMSYHmAB8j9XI/IUxZouIPCgiDzoOWwbsAeKAj4Hxju39gL8CV4pIjOMx7MK+RVVd/LAzBiSX3uFdzyaIyh5BXVTXrrBpE+TmElanBoOa3ojgzeyNs+2NS6kKUGKCMMZ4G2NqO3nUMsaUVsWEMWaZMaatMaa1MeZVx7apxpipjq+NMeZhx/7Oxpgox/bfjDFijOlijOnmeCyriG9YVV2/7dsAwOA2kVaCaN3aquKxU8+ecPo0bN0KwB2RnQjI7cXMmM/IycuxNzalLlJ51qRWylabjmwGvIgM72R1cbWzeinfpZdaz3/8AcDQTqHU4ypSzhzhpz0/2RiYUhdPE4SqMvaf2E4d32b4p5+GvXvdI0G0bg3BwfD77wDU9Pfhpkuux8sEMTNGq5lU1aYJQlUJGdm5HM/aS/Na7WCDVdVkaw+mfCJWKcJRggD4S2QrAnP7s2D7AtIz020MTqmLowlCVQkbDx4hRxLp3KjT2TUg7G6gznfppbBzJ6SkANC/TTAtA4eRlZvBgm3F9tBWyu1pglBVws9xG0AMlzXrZjVQN2tmVe24g8sus55XrwbAy0t4oO9QfPLC+DBKFxJSVZcmCFUl/BlvjTUY1Kqn/SOoi+rbF2rUgJ/ONkr/JbIZtfOuZM3BlcSnxZdwslLuSxOEqm9oKVQAABxUSURBVBK2pWzFC1/a+oda1Tnu0P6Qz98fBgyAH38s2BRSy59hbf6CwTAz5lMbg1PqwmmCUG7PGMPBkzsJDmiJz+Yt1ka7ZnAtzuDB1liIQ2dnkxnfvx/+uR34MGompcyOr5Rb0gSh3F7C8TOcMftoU689xFjrUbtNA3W+wYOt50KliL6t6tMicCgHT+4i6lCUTYEpdeE0QSi3F3XgILleyXQP62wliOBgaNzY7rDO1aULhIbC0rMr8YoIT/YfA8aX/6yaZmNwSl0YTRDK7a3ca5UaBrbqYSWI7t2t8QfuxMsLbrwRvv0Wzpwp2Dy6T0fqSl+W7vqC7NxsGwNUqvw0QSi3t/6Q1YOpZ0gH2LzZ/dof8t10E5w6BT/8ULApwNebWy65g4y8VKZH6ZgIVbVoglBuL+74dnwkgBaHMyAry30TxKBBUKcOLFx4zuaXh96Ft6nHG7+9Z09cSl0gTRDKraVnZHMsazeNa7bBa6NjjQV3TRB+fnD99bBkiZXIHMLqBHFZ6Cj2nPyNlXs32higUuWjCUK5tW2J6WR77ad9cCer/SEgwFqH2l2NGgXHjsGyc2enf/f6fyDGhye+ft2mwJQqP00Qyq2t3bePXDlO36ZdrATRuTP4+NgdVvGGDLF6M82cec7mrk1a0KX+MDakLCI2sejKu0q5J00Qyq39tt/qwdS3aTcrQbhr9VI+Hx/461/hm28gKemcXW8OexYjGUxY9KZNwSlVPpoglFvbfGQzAJ1y6ltVN+6eIADuvhtycuDTc6fYGNymLy1r9ea3I7OIiU9yfq5SbkQThHJbGdm5JJzaRqBPXZrsPGxtdLcR1M506AD9+8OUKZCbe86uScNeJleOce/8N3T6DeX2NEEot7XzSDqZspuIep2QjRutwXGdO9sdVtk88YS16t3ixedsHt7uatrW7cXG1Fks2bjfpuCUKhtNEMptbUxIIUv20ye8p9X+EBEBQUF2h1U2I0ZAy5bw3/+es1lEmDzsFXK9jvLEN29zOivHpgCVKp0mCOW2Vu3dCJJN/+aRVaOBujBvb3j0UfjtN1i37pxd17S5mh6N+rM/exb/941O4qfclyYI5baiE621p3sEtbaqa6pSggAYOxbq1YOXXjpns4jw8YhJ5Ek6H0T/h7V7UmwKUKmSaYJQbik7N4+9qVvwEX/axTsmv6tqCaJ2bfj7360ur2vWnLOrR1gP7ur8V9J9lzLhi285lalVTcr9aIJQbml7Yjqn2U3LOu3x2Wh1da1yCQLgkUcgJARefPG8Xf+++jVq+PoTe/o/vLBws/ZqUm5HE4RyS+sPHCPbaw+9mnS32h8aNrRGKFc1QUHw9NPWQkKFZnkFaFyrMW9d8x8yvDfx6eaZfL5O165W7kUThHJLq/ZsJ09OclmznrBhg3uuAVFWDz8MrVrBY49B9rlrQozrOY7Lmw0g3X8Gzy1ZwdZDJ2wKUqnzaYJQbmlNwp8ARNbvBLGxEBlpc0QXISDA6u66bRu8d+6U317ixfQRn+DrY0j2e4Nxn67l2KmsYi6kVOXSBKHczvFTWcSf3ISP+NH9iFijkXv1sjusi3P99TB0KPzrX3DgwDm72tRvw0fXf8hJs4Wtpz5h/JxosnPzbApUqbM0QSi3E5OQSqbXdi5p0BW/aGuyvipdggCremzKFMjLg3vvhSIN0nd0voN7u99LqvcX/LzvOyYu2WJToEqdpQlCuZ21e46Q5RXHoJaXQVQUhIVBkyZ2h3XxWrWCN9+0Gqw/+OC83ZOvnUz3sO6k1XiTGX+u5NM/9lV6iEoVpglCuZ3lu9ZhJIvLm19mjUKu6qWHwh54wFoz4m9/sxrfCwn0DWTxqMU0CKxDes1XeXHpH/wed9SmQJXSBKHczOmsHDYnWdNP9K3TEXbsqPrtD4WJwOzZEBwMN98MKeeOog6vHc6iUYvIkeOkBb7GA3PWsD/llE3BKk/n0gQhIkNFZIeIxInIM072i4hMduzfJCI9Cu2bLiJJIhLryhiVe4nef5wzsp0GNRrRdFeSVVdfnUoQYI3p+OorOHQIbr0VMjLO2d27SW9mjJhBau5mDvIu42ZHcSYrt5iLKeU6LksQIuINTAGuBToAt4tIhyKHXQtEOB7jgMIVszOBoa6KT7mn3+OOkuEdy+XN+iNRjonsqlMJIl/v3jB9OqxYAXfeed66EaM6jeLFAS9ynOWsS5nNczrSWtnAlSWI3kCcMWaPMSYLmAeMKHLMCGC2sawB6opIGIAxZiVwzIXxKTf03Y4YciWZIW0GW/MXtWxpVcdUR3feaY2PWLDg7Cp0hUwcNJFbO9xKqu8M5mxcyGdrdP0IVblcmSCaAIXnDkhwbCvvMcpDHDmRwabk1QBc2eIKa6rs/v1tjsrFHn8cXnkFPvsMRo6EzMyCXV7ixawbZ9E9tDvHA97k+W+Wsf7AcRuDVZ7GlQnC2bwIRcvIZTmm5JuIjBORKBGJSk5OLs+pys38sj2JDK9NNKrZmIgUICkJLr/c7rBc7/nnYdIkqyQxfDikpxfsCvQNZPHtiwkOrEOS38uM++xnUk/rSGtVOVyZIBKApoVehwOHLuCYEhljPjLGRBpjIkNCQi4oUOUeftiWSJbPJq5pfRWy2ipJVPsSRL5HH4WZM+GXX+CKK6zk6BBeO5zFty8Cr1S2ZE7kmQUbtD1CVQpXJoh1QISItBQRP2AUsKTIMUuA0Y7eTH2BNGNMogtjUm7qREY2y+NWkUMa10VcB6tWQYMGcMkldodWecaMsdaw3roV+vWDPXsKdvVu0ptpN3xMhlcsc7e/yRdROvOrcj2XJQhjTA4wAfge2AZ8YYzZIiIPisiDjsOWAXuAOOBjYHz++SIyF/gDaCciCSJyr6tiVfb7dnMiJ8zv+Hr5cW3EtVaC6N+/6s7geqGuuw5+/hmOHYPLLrOmOne4q8tdjOsxjhO+83lq6Qx2J5+0MVDlCVw6DsIYs8wY09YY09oY86pj21RjzFTH18YY87Bjf2djTFShc283xoQZY3yNMeHGmE9cGauy11fRCWT7rWVwq6uonXISdu/2jPYHZ/r2tRro/fxgwACr2slh0rWT6BTSlUTvN7n/s2/JzNHxEcp1dCS1st3eo6f4bX8UZ8whRrQbAcuXWzsGD7Y3MDu1bw+//w7NmlmzwH75JQABPgEsHDUffx9hdeoL/Ps7HUeqXEcThLLdtFV7OO37I/7e/tzW8Tb47jtr9bguXewOzV7h4VZVW69eVhfY998HrOnBZ980gyyvnby59kV+3am995RraIJQtjqYeoYvoneT6fcrN7W/ifp+daylOYcM8bz2B2fq1bN+HsOHWyvTffopALd0uIWHez1Kus9S7pn3DsnpmaVcSKny0wShKsSRk0f4YN0HPPfTc0yNmkpietk6o73+7XbSvJaTkXuChyIfsqb3PnbMqlZRlho1rCqmK6+EsWML1rZ+e8h/6N6oD/vy3ubeOQvJy9Our6piaYJQF236hum0ebcN45eN59+r3+Chbx6i2TstuG/xeNIz04s9b9GGgyzeuIfsGovo17Qflze73JrEzscHrrmmEr+DKsDf3xpI16GDNQvshg34efux9I6vqOUfxLeJf+e9FZvsjlJVM5og1EV5Z8073LvkXur5dKBx5nuEn15E44wPCMi6kk82TCX0jbY8+82cc0b/GmNYHHOQv8/fSGCDRaRmJfLqla9aw+o//9xKDvXr2/Y9ua06deDbb61qp+uvh4MHaVK7CQtHfk6u1yGeW/EIMToVh6pAUp1GZEZGRpqoqKjSD1QV4ofdPzDksyEEe/en1umnGDegLbf0CCckyJ99KaeYvu47pm35B6fz4qmdey1XN/4bzeo1YMeRdLYcOkFo6Cb+THueMd3GMGPEDPjjD6vv/+zZ8Ne/2v3tua9Nm6wxIm3awMqVEBTES7/8PyaufJ7m3vcT/fi7NAjytztKVUWISLQxxumc+pog1AU5kXmCju934thJoWn2JGbfM4DeLc//1H866zTjv36a2Zun4Ct1aOg1nCY121KrThw/J3xGZONIfhnzC4G+gTB+vDUFdlIS1K5tw3dVhXz7rdVwPXw4LFiA8fLi2k9v4fu9C+lT5yVWTngBPx+tIFClKylB6P8gdUFe+PkFDp44SJ0zj/LJ6P5OkwNAoF8gM29+lz/u/YOrWl9KQu5nrD3xT349OJd7ut3Dj3/90UoOaWlWyWHUKE0OZXHttTB5MixZAn//OyLCojv+R8cGfVib+jJj/jdd52tSF83H7gBU1bPn+B4+WDeVmjnX8MxVN9AvKwlueQB+/BGysqx++w88YL3Ze3sD0Ce8D8vuXMbxM8dJOJFAeO1w6tWod/aiH38Mp07BI4/Y9F1VQQ8/DDt3WmtKREQQ8NBDrLr3W9q/25vP90wgeIEv794yxu4oVRWmVUyq3EZ+eTvzty5kUJ25LG9q8L7zDggMhNtus56XLbPeuHr3tt74SxvwlpYGrVpBz55nR1GrssnNhREjrMGF33wDQ4ZwOP0InadcTkrGfh7r/gH/HTHW7iiVG9MqJlVhth/dzhdb5xGUfT1Twv3xHjUSuneH7dvhww+tT7PbtlkL4Ozda73pv/wyZGcXf9Gnn4bjx+H11yvvG6kuvL1h7lzo1MlK0LGxhNZqROzDq2ng34p3NtzPXfP+pdVN6oJoglDl8urK/yDGj3ub3M4lj94HLVpYn14bNjx7kJeXtZzmtm3wl7/AP/9pTUAX62TeoBkzrMTyt79Bjx6V9n1UK7VqwddfQ1CQNRvs4cM0qhXC9sfW0Czwcubs+D/6TL2Vk5k6+6sqH00QqsySTiUxL/YzgnKv5KVfF8DRozB/vtU/35kGDWDOHGvwW3y8lQAeeMAaCbxqlVWHfu+9cPXV1rKb6sKFh8PSpdbvZMQIOH2aBoF12PXkj1wafD/rjiyk2dudWH1grd2RqipEE4QqszdXTybHZPGIzwBqfT4HnnwSunYt/cSbb4YtW+Duu62eStdcY01jPXWq1bV18WJrpLC6OD17Wgl53Tq46SY4eRI/Hx9Wj/+QhzvN4kTmKS6f0Y9/fD+RnLwcu6NVVYA2UqsyOZN9huA3mkBmBClLcwg4mmRVIQUFle9CaWkQHQ2ZmRAZCbpMbMWbPh3uv98qsS1das2MCyzetIMxCx4gTX6lfYMeLLp9Lm0btLU5WGU3baRWF+39Pz/hdM5xHszqQ0DMepg4sfzJAazqqCuvtPrxa3JwjbFjrVLZli1WB4KffwZgRJd2xEz4hi41/sWOo7vo9H5X3lkzmTyTZ3PAyl1pglClyjN5vP7bW/jlteGV5authunRo+0OS5Vk+HBYuxbq1rUWXnr0UUhLo0VwTdY+8SIPXbIQn+xOPPH9YwyccRUH0g7YHbFyQ5ogVKk+37yIoxn7uCO7HzU2rIfnngNfX7vDUqXp3Nlqjxg/Ht57z1qlbvp0AsTw3qir+OzGBYTmPsbv8WvpMKUTs2Jm2R2xcjOaIFSpXvjxNbzzgpn0Q6y1BOYYHZ1bZQQFWclh7Vqrp9O990LHjjBvHjd3a8KqCS9zWdB0cjKbc/fiu7l/yf1k5ujiQ8qiCUKV6Me41exJj+L63IHUjoqGZ58FPz+7w1Ll1auXlSQWLrR+f7ffDt270yb6N76fcAvjO86idvZtTNswjf7TB3Io/ZDdESs3oAlClehv376CmJpM/Tne+gR6zz12h6QulAjceCPExFjdYU+ehGHDCBxyNW83z2bajW/ROPc51idupMv73VmboGMmPJ0mCFWsDQe3syllOVfm9aPRH3/CM8/oeIXqwNsb7rjD6qb83nvWc9++3Pbei6wYdT+RNd4n7bQX/acPYHbMp3ZHq2ykCUIV6+GlLwFefPjrMWjSxKq/VtWHn581mn33bqvjwZw5RAzqza+NfLi37f/wzmnHmMWjeXTZU+Tm5dodrbKBJgjlVHTCDv44PJ9L8y6l9cooq/QQEGB3WMoVgoLg1Vdh/Xpo2ZKAu0fzwRfv8FnvD6mTex3vrnuLAdOv5UTmCbsjVZVME4Ry6p6vngVg1m9noHFjuO8+myNSLte5M/z+O7z5JrJ8ObeOvo4tjW4kwvcxfk/4iXaTe7IjeZfdUapKpAlCnWdx7Fo2H1/MtaYfEb9EWdUPWnrwDN7e1sy6MTHQrh1NJtzP1jV7uaPe6ySdOkLnDyKZvm6J3VGqSqJzMalzZOXkEvbvSNKy40j4IphQasDGjTowzhPl5lrre7zwAtSsydKnHuEvWTPJMPFc2nAM825/m2b16todpbpIOheTKrP75/+XYzkxvHiqH6Hb9llvEJocPJO3Nzz1lFWaaNuW6597iYT17ekbcBV/JM8gYlJn7p77IQnHT9sdqXIRLUGoAotjY7h5fn+aeDVm76t78b7hRvjyS7vDUu4gNxfeftuapPHMGZaM6sN9EXEkex0lILcrV4c/xBMDbuTyiBB8vPVzZ1VSUglCE4QCYMeRZHpMvZws4tm6NJSI+JPWbKDBwXaHptzJ0aMwaRK89x5Z6am8P6gm/9c3m+O+WdTIbk5Dv5sZ1ek2/hJ5Cd3C6+LlJXZHrEqhCUKVaFfSMfp8OIzjueuYt6UTIxdtheXL4Yor7A5NuavMTFi2DObN48wvPzCn6XHe6QtbGoJPrhdNTkZQs9Ywru40ghu7tOfS1g3w9/G2O2rlhG0JQkSGApMAb2CaMeb1IvvFsX8YcBq42xizviznOqMJovwWb47lzgW3c4pYJm2L4NHPd8G0aTooTpVdXh7ExGCWLyf6z4XMyYrm80tySawFYqDpiXrUz+1Cs4ihXNP5KoZ2aE+r4JpYf/7KbrYkCBHxBnYCVwMJwDrgdmPM1kLHDAMewUoQfYBJxpg+ZTnXGU0QZbcx4TCPLn2L345MxctkMOvnWtyx9qSVHHStB3UxMjPJ/f031v8yl+/2LOd7/3jWhEOuo2miTkYAtfMa0qBmKxqFdaBVSFMiQsJo06Axzeo2JKRmCMGBwfh566SQlaGkBOHjwvv2BuKMMXscQcwDRgCF3+RHALONlaXWiEhdEQkDWpThXFUGuXmGpPSTxB09xLr4Xfy5ew3R8Ss4kBtNlk8Glx/yY9qCHNqGNYE/ZlrrGit1Mfz98b7iKnpdcRW9gBfT0zm98ic2rJ7PH3Gr2JwTz566B9iTc4AYswKKmTg2MNeP2nmB1JFa1PWtSz3/+jQIDCGkTijBdRvTqEFTwuo3oXGdRjStE0qdgFr4ePloyaQCuTJBNAHiC71OwCollHZMkzKeW2E6PRJAplceRs6WpvK/MlL4tTlnuxQ+rmC7Ofd1/gWl6HH5r43T7SXH4eT8QvsLx5UncLLI/HoNzsDtO+G+GG/6X3IlvDcGbrvN6taoVEWrVYvA626k33U30g/gzBnYvh22buXUlk3sT9jDkaMJpJ5KIjXnBKlep0nzyeR4QBbJNbM4GphKcmA8m2vC0UDIPFb8rbzywD8X/HIF/xzBL0/AWH8T1kMQU/DnSP6fvABihKqaWmpn+xH1/skKv64rE4Szn3XR+qzijinLudYFRMYB4wCaNWtWnvgKNM1uQA45joAK/usU/CuFXhc+RgwgUvCGLOddQTAUHmxy7hFS3PYin4CcHV+wRaz/2AX7Ha+tBCb4eAn1TACNTCDNfOvSp1EHItq3x+vmDtClC9SoUbYfklIVpUYNa63s7t2pyZ10ADo4Oy4rC06cwKSlcTo5hVPJxzmVfIxjKYmkpB3h+KkjHMs8zvG8dFLMSc7kZZJlssgy2WRJDlleeWRLLgawVt02Bc9gfagyxsoWptCHv6ooiECXXNeVCSIBaFrodTjnFyaLO8avDOcCYIz5CPgIrDaICwn026kHL+Q0pZQr+flBcDASHEzN1q2p6djc0tagPIsrR7SsAyJEpKWI+AGjgKKTuCwBRoulL5BmjEks47lKKaVcyGUlCGNMjohMAL7H6qo63RizRUQedOyfCizD6sEUh9XN9Z6SznVVrEoppc6nA+WUUsqD6WR9Simlyk0ThFJKKac0QSillHJKE4RSSimnNEEopZRyqlr1YhKRZGD/BZwaDByt4HAqgsZVPu4aF7hvbBpX+bhrXHDhsTU3xoQ421GtEsSFEpGo4rp52UnjKh93jQvcNzaNq3zcNS5wTWxaxaSUUsopTRBKKaWc0gRh+cjuAIqhcZWPu8YF7hubxlU+7hoXuCA2bYNQSinllJYglFJKOeXxCUJEnhIRIyLBhbY9KyJxIrJDRIZUcjz/EZHtIrJJRBaKSF13iKtQDEMd948TkWfsiMERR1MR+UVEtonIFhF5zLG9voj8ICK7HM/1bIrPW0Q2iMjX7hKXY0nf+Y7/X9tE5FI3iesJx+8wVkTmikiAXXGJyHQRSRKR2ELbio2lsv4mi4nL9e8VxhiPfWAtSvQ91tiJYMe2DsBGwB9rbZLdgHclxnQN4OP4+t/Av90hLkcM3o77tsJa1Gkj0MGm310Y0MPxdS1gp+Nn9AbwjGP7M/k/PxviexL4H/C147XtcQGzgPscX/sBde2OC2t54b1ADcfrL4C77YoLGAD0AGILbXMaS2X+TRYTl8vfKzy9BPFf4B+cu5zpCGCeMSbTGLMXa62K3pUVkDFmuTEmx/FyDdZqerbH5dAbiDPG7DHGZAHzHHFVOmNMojFmvePrdGAb1pvNCKw3QhzPN1Z2bCISDlwHTCu02da4RKQ21pvMJwDGmCxjTKrdcTn4ADVExAcIxFo90pa4jDErgaKrXhcXS6X9TTqLqzLeKzw2QYjIDcBBY8zGIruaAPGFXic4ttlhLPCt42t3iMsdYjiPiLQAugNrgUbGWpUQx3NDG0J6B+uDR16hbXbH1QpIBmY4qr6miUhNu+MyxhwE3gQOAIlYq0outzuuIoqLxZ3+HlzyXuHKNaltJyI/AqFOdj0PPIdVRDvvNCfbKrSrV0lxGWMWO455HsgB5lRWXGXgDjGcQ0SCgK+Ax40xJ0SchVip8QwHkowx0SIyyNZgzuWDVUXxiDFmrYhMwqousZWjPn8EVlVIKvCliNxlb1Rl5hZ/D658r6jWCcIYM9jZdhHpjPUfcqPjDSUcWC8ivbGybdNCh4djFXldHleh+MYAw4GrjKNSsTLiKgN3iKGAiPhiJYc5xpgFjs1HRCTMGJMoImFAUiWH1Q+4QUSGAQFAbRH5zA3iSgASjDFrHa/nYyUIu+MaDOw1xiQDiMgC4DI3iKuw4mKx/e/B1e8VHlnFZIzZbIxpaIxpYYxpgfUD7WGMOQwsAUaJiL+ItAQigD8rKzYRGQo8DdxgjDldaJetcTmsAyJEpKWI+AGjHHFVOrEy+yfANmPM24V2LQHGOL4eAyyuzLiMMc8aY8Id/69GAT8bY+5yg7gOA/Ei0s6x6Spgq91xYVUt9RWRQMfv9Cqs9iS74yqsuFiq/3uFK1rcq9oD2IejF5Pj9fNYLf87gGsrOZY4rPrDGMdjqjvEVSiGYVg9hnZjVYnZ9Tvrj1Vs3lToZzUMaAD8BOxyPNe3McZBnO3FZHtcQDcgyvEzWwTUc5O4XgK2A7HAp1i9b2yJC5iL1RaSjfXB8d6SYqmsv8li4nL5e4WOpFZKKeWUR1YxKaWUKp0mCKWUUk5pglBKKeWUJgillFJOaYJQSinllCYIpZRSTmmCUEop5ZQmCKWUUk79fxdH1qllCa9yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "df['Age'].plot(kind='kde', ax=ax)\n",
    "df.Age_median.plot(kind='kde', ax=ax, color='red')\n",
    "df.Age_random.plot(kind='kde', ax=ax, color='green')\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(lines, labels, loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, there is less distortion in variance compared to the Age_median. So we will use Age_random feature for our use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Capturing NAN values with a new feature\n",
    "\n",
    "It works well if the data are not missing completely at random.\n",
    "We will create a new feature \"Age_NAN\" and fill 1 where is null value and 0 for rest.\n",
    "Again, we will fill 'Age' null value with any suitabel techniques.\n",
    "This techniques will help us to know the importance of the missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
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       "      <td>26.0</td>\n",
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      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('titanic.csv', usecols=['Age','Fare','Survived'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df['Age_NAN']=np.where(df['Age'].isnull(),1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Age_NAN\n",
       "0         0  22.0   7.2500        0\n",
       "1         1  38.0  71.2833        0\n",
       "2         1  26.0   7.9250        0\n",
       "3         1  35.0  53.1000        0\n",
       "4         0  35.0   8.0500        0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Age.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Age'].fillna(df.Age.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <th>8</th>\n",
       "      <td>1</td>\n",
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       "      <td>11.1333</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   Survived   Age     Fare  Age_NAN\n",
       "0         0  22.0   7.2500        0\n",
       "1         1  38.0  71.2833        0\n",
       "2         1  26.0   7.9250        0\n",
       "3         1  35.0  53.1000        0\n",
       "4         0  35.0   8.0500        0\n",
       "5         0  28.0   8.4583        1\n",
       "6         0  54.0  51.8625        0\n",
       "7         0   2.0  21.0750        0\n",
       "8         1  27.0  11.1333        0\n",
       "9         1  14.0  30.0708        0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Advantages\n",
    "1. Easy to implement\n",
    "2. Captures the importance of missing values\n",
    "\n",
    "##### Disadvantages\n",
    "1. Creating Additional Features(Curse of Dimensionality)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### End of Distribution imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>71.2833</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
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      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('titanic.csv', usecols=['Age','Fare','Survived'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23da42ae970>"
      ]
     },
     "execution_count": 36,
     "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": [
    "df.Age.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.27860964406095"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are taking extreme value (Genearly after 3 std.) and will replace the null value with this extreme value.\n",
    "extreme=df.Age.mean()+3*df.Age.std()\n",
    "extreme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23da485ccd0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANTElEQVR4nO3db2xdd3nA8e8TG0Zij0GcLipuxQVd1I41ozQWa+k0raFFoUGwjb5oJNS82MQblIRq0kTVaHE098WkaVprsWnVxppsUyfxZxtKo0BKs0nbC5gN7ZKuLdyNADG0SV2tXZIOcPLbi3McTEixfeN7n5P2+5Es+17H5/fUuffb45/j4yilIEnqv1XZA0jSa5UBlqQkBliSkhhgSUpigCUpyeBy/vC6detKq9Xq0SiS9Oo0PT39fCnligvvX1aAW60WU1NTKzeVJL0GRMS3L3a/WxCSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJDLAkJTHAkpTEAEtSEgMsSUkMsCQlMcCSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJlvU74dR7k5OTdDqdvq87MzMDwOjoaN/XXop2u8327duzx5BWlAFumE6nw+NHn+LsmrV9XXfgzIsAPPuD5j0kBs68kD2C1BPNe7aJs2vW8vK1t/d1zdVPHwDo+7pLMT+b9GrjHrAkJTHAkpTEAEtSEgMsSUkMsCQlMcCSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJDLAkJTHAkpTEAEtSEgMsSUkMsCQlMcCSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJDLAkJTHAkpTEAEtSEgMsSUkMsCQlMcCSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJDLAkJTHAkpTEAEtSEgMsSUkMsCQl6UuAJycnmZyc7MdSknrI5/LKGuzHIp1Opx/LSOoxn8sryy0ISUpigCUpiQGWpCQGWJKSGGBJSmKAJSmJAZakJAZYkpIYYElKYoAlKYkBlqQkBliSkhhgSUpigCUpiQGWpCQGWJKSGGBJSmKAJSmJAZakJAZYkpIYYElKYoAlKYkBlqQkBliSkhhgSUpigCUpiQGWpCQGWJKSGGBJSmKAJSmJAZakJAZYkpIYYElKYoAlKYkBlqQkBliSkhhgSUpigCUpiQGW1LWpqSk2bdrE9PQ0ALOzs+zYsYPZ2dllH6vT6bBlyxamp6fZsWMHU1NTbNmyhU6ns+jHXsq6mcc2wJK6Nj4+zrlz59i9ezcAe/fu5ciRI+zbt2/Zx5qYmOD06dPs3r2bI0eOMD4+zunTp5mYmFj0Yy9l3cxjG2BJXZmamuLUqVMAnDp1isOHD3Pw4EFKKRw8eHBZZ4ydTodjx46dP1Yp5fyxjx079jPPgmdnZ7tedzG9PDbA4Ioe7RXMzMzw8ssvs3Pnzn4sd1nrdDqs+mHJHqNRVv3fS3Q6/+vjpwE6nQ6rV68GqrPfhe67777zb589e5Z9+/Zx9913L+m4i53lTkxM8NBDD130fXv37uXcuXNdrbuYXh4blnAGHBEfi4ipiJg6efLkii0s6fI2f4Y6b25ujrm5ufNvHzp0aMnHmj/77eb9jz76aNfrLqaXx4YlnAGXUh4EHgQYGxvr6tRsdHQUgPvvv7+bD39N2blzJ9P//Vz2GI1y7g1vpP329T5+GmDhVyHDw8M/EeHBwSonc3NzDA4Octttty35uK1W62dGttVqveL7br31Vg4cONDVuovp5bHBPWBJXbpwC+Lee+9l1aoqKQMDA9x1111LPtauXbu6fv+2bdu6XncxvTw2GGBJXRobG2N4eBiozoZvueUWNm/eTESwefNmRkZGlnysdrt9/ix3eHiYiDh/7FarRbvdfsWPHRkZ6XrdxfTy2GCAJV2C8fFxVq1axZ49e4DqjHHDhg1dnSnu2rWLoaEh9uzZw4YNGxgfH2doaGjRs+NLXTfz2H35VxCSXp3GxsZ47LHHzt8eGRnhgQce6OpY7XabRx55BICNGzcCnL+9mEtZN/PYngFLUhIDLElJDLAkJTHAkpTEAEtSEgMsSUkMsCQlMcCSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJDLAkJTHAkpTEAEtSEgMsSUkMsCQlMcCSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJDLAkJTHAkpTEAEtSEgMsSUkMsCQlMcCSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJBvuxSLvd7scyknrM5/LK6kuAt2/f3o9lJPWYz+WV5RaEJCUxwJKUxABLUhIDLElJDLAkJTHAkpTEAEtSEgMsSUkMsCQlMcCSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJDLAkJTHAkpTEAEtSEgMsSUkMsCQlMcCSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJDLAkJTHAkpTEAEtSEgMsSUkMsCQlMcCSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJBrMH0E8bOPMCq58+0Oc1ZwH6vu5SDJx5AVifPYa04gxww7Tb7ZR1Z2bmABgdbWLo1qd9XqReMsANs3379uwRJPWJe8CSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJDLAkJTHAkpTEAEtSEgMsSUkMsCQlMcCSlMQAS1ISAyxJSQywJCUxwJKUxABLUhIDLElJDLAkJYlSytL/cMRJ4NvLXGMd8PwyP6Zfmjqbcy1PU+eC5s7mXMtzqXO9tZRyxYV3LivA3YiIqVLKWE8X6VJTZ3Ou5WnqXNDc2ZxreXo1l1sQkpTEAEtSkn4E+ME+rNGtps7mXMvT1LmgubM51/L0ZK6e7wFLki7OLQhJSmKAJSlJTwMcEZsj4pmI6ETEJ3u51iJzfDoiTkTE0QX3rY2IQxHxzfr1mxPmujoiDkfEUxHxZETsbMJsEfGGiPhqRDxRz7WnCXMtmG8gIr4eEfsbNtexiDgSEY9HxFRTZouIN0XEZyPi6fqxdlP2XBFxTf15mn95KSI+kT3Xgvnurh/7RyPi4fo5seKz9SzAETEAfAr4APBOYGtEvLNX6y3iIWDzBfd9EvhyKeUdwJfr2/02B/xeKeWXgBuBj9efo+zZfgBsKqW8C7ge2BwRNzZgrnk7gacW3G7KXAC3lFKuX/BvRpsw2/3AwVLKtcC7qD53qXOVUp6pP0/XAxuBM8A/ZM8FEBGjwA5grJRyHTAA3NmT2UopPXkBbgK+uOD2PcA9vVpvCfO0gKMLbj8DXFm/fSXwTNZsC2b6J+C2Js0GrAG+BvxqE+YCrqof/JuA/U36uwSOAesuuC91NuCNwLeov+HelLkumOX9wL81ZS5gFPgusBYYBPbXM674bL3cgpj/j5h3vL6vKdaXUr4PUL/+xcxhIqIFvBv4Cg2Yrf4y/3HgBHColNKIuYA/BX4fOLfgvibMBVCAL0XEdER8rCGzvR04Cfx1vW3zlxEx1IC5FroTeLh+O32uUsoM8MfAd4DvAy+WUr7Ui9l6GeC4yH3+m7eLiIhh4HPAJ0opL2XPA1BKOVuqLw+vAt4TEddlzxQRHwROlFKms2d5BTeXUm6g2nb7eET8evZAVGdwNwB/Xkp5N3Ca3C2anxARrwc+BHwme5Z59d7uh4G3AW8BhiLio71Yq5cBPg5cveD2VcD3erjecj0XEVcC1K9PZAwREa+jiu/flVI+36TZAEop/wP8M9UeevZcNwMfiohjwN8DmyLibxswFwCllO/Vr09Q7We+pwGzHQeO11/BAHyWKsjZc837APC1Uspz9e0mzHUr8K1SyslSyo+AzwPv7cVsvQzwvwPviIi31f+XuxP4Qg/XW64vANvqt7dR7b/2VUQE8FfAU6WUP2nKbBFxRUS8qX57NdUD8unsuUop95RSriqltKgeT4+VUj6aPRdARAxFxM/Pv021Z3g0e7ZSyrPAdyPimvqu9wH/mT3XAlv58fYDNGOu7wA3RsSa+jn6PqpvXK78bD3ezL4d+AbwX8C9/d5MXzDHw1R7OT+iOiP4HWCE6ps536xfr02Y69eotmX+A3i8frk9ezbgV4Cv13MdBf6gvj/9c7Zgxt/gx9+ES5+Laq/1ifrlyfnHe0Nmux6Yqv8+/xF4c0PmWgPMAr+w4L70ueo59lCddBwF/gb4uV7M5o8iS1ISfxJOkpIYYElKYoAlKYkBlqQkBliSkhhgXRYi4rciokTEtdmzSCvFAOtysRX4V6ofwJBeFQywGq++VsbNVD9Ac2d936qI+LP6mq37I+JARNxRv29jRPxLfVGcL87/+KjUNAZYl4PfpLqe7TeAFyLiBuC3qS4xugH4XarLn85fW2MSuKOUshH4NHBfxtDSYgazB5CWYCvVZSihugjPVuB1wGdKKeeAZyPicP3+a4DrgEPVj/EzQPVj6FLjGGA1WkSMUF18/bqIKFRBLVRXG7vohwBPllJu6tOIUtfcglDT3QHsK6W8tZTSKqVcTfUbHp4HPlLvBa+nujgPVL+14IqIOL8lERG/nDG4tBgDrKbbyk+f7X6O6kLZx6muVvUXVL9J5MVSyg+pov1HEfEE1RXm3tu/caWl82poumxFxHAp5VS9TfFVqt9I8Wz2XNJSuQesy9n++sLxrwf+0PjqcuMZsCQlcQ9YkpIYYElKYoAlKYkBlqQkBliSkvw/6OWB6Kr+rvcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Trying to see the exteme value by using box plot\n",
    "import seaborn as sns\n",
    "sns.boxplot('Age',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_nan(df,variable,median,extreme):\n",
    "    df[variable+\"_end_distribution\"]=df[variable].fillna(extreme)\n",
    "    df[variable].fillna(median,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "impute_nan(df,'Age',df.Age.median(),extreme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age_end_distribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare  Age_end_distribution\n",
       "0         0  22.0   7.2500                  22.0\n",
       "1         1  38.0  71.2833                  38.0\n",
       "2         1  26.0   7.9250                  26.0\n",
       "3         1  35.0  53.1000                  35.0\n",
       "4         0  35.0   8.0500                  35.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23da48ca430>"
      ]
     },
     "execution_count": 42,
     "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": [
    "df['Age'].hist(bins=50)  #You can see most of the value is in between 27-29. Becaue we have filled the missing value by median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23da49aa760>"
      ]
     },
     "execution_count": 43,
     "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": [
    "df['Age_end_distribution'].hist(bins=50) #You can see most of the value is in between 70-75. Becaue we have filled the missing value by extreme value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23da4a974f0>"
      ]
     },
     "execution_count": 44,
     "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": [
    "# Now we can see, we dont have any outliers if we take the feature Age_end_distribution. \n",
    "sns.boxplot('Age_end_distribution',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Arbitrary Value Imputation\n",
    "It consists of replacing NAN by an arbitrary value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived   Age     Fare\n",
       "0         0  22.0   7.2500\n",
       "1         1  38.0  71.2833\n",
       "2         1  26.0   7.9250\n",
       "3         1  35.0  53.1000\n",
       "4         0  35.0   8.0500"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"titanic.csv\", usecols=[\"Age\",\"Fare\",\"Survived\"])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_nan(df,variable):\n",
    "    df[variable+'_zero']=df[variable].fillna(0)\n",
    "    df[variable+'_hundred']=df[variable].fillna(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23da4ae5fa0>"
      ]
     },
     "execution_count": 47,
     "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": [
    "df['Age'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disadvantages\n",
    "\n",
    "- Distorts the original distribution of the variable\n",
    "- If missingess is not important, it may mask the predictive power of the original variable by distorting its distribution\n",
    "- Hard to decide which value to use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### How To Handle Categroical Missing Values\n",
    "\n",
    "##### Frequent Category Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('house_price.csv', usecols=['BsmtQual','FireplaceQu','GarageType','SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 4)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BsmtQual        37\n",
       "FireplaceQu    690\n",
       "GarageType      81\n",
       "SalePrice        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice      0.000000\n",
       "BsmtQual       0.025342\n",
       "GarageType     0.055479\n",
       "FireplaceQu    0.472603\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean().sort_values(ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute the frequency with every feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23da4ba9190>"
      ]
     },
     "execution_count": 52,
     "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": [
    "df['BsmtQual'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23da48a9400>"
      ]
     },
     "execution_count": 53,
     "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": [
    "df.groupby(['BsmtQual'])['BsmtQual'].count().sort_values(ascending=False).plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23da4c65eb0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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31hdOnxcOXcAE/TnwS1V1I/z4V+aHgZ8btKrxeRfdta4Dk5wN/CrwtmFLarzNfeQn/VJgBV13utbOimZGqL4JuIfuL9lob5ImRqjOMcrxay38Gc5I8lxgRVVd0J/97VNVtwxd186a7c+pwT+7I4Dj+8XLq+qGIeuB9s/cF8NP+tERqgC/M7KtgCcteEVjlOQ/AW+gG+U4OvBlX+ALw1Q1fkneDqyku5/wBXR9+j8EHLe9102JTUnez8NHiG8asJ5J2IvuxkQF7DlwLUDjZ+4zkhwLXFdV9/XL+wJHVtUVw1amHenHJuwH/CmwZmTTfa38KgFI8hW6OUmumplzpZWz27632hl01xVC10333FZGGyf5feBU4GN0x3cK8JGq+uNB61ok4X418MzqDzbJbsDGFiZlmpHkDODCqvpWv7wf8PKqOnfYynbOYpkYLcmVVXXMTPNTkr2B/zPN4Z7kALopFJ4MXAP8aVV9Z9iqxi/JDcDRVfWv/fKedF/STxuyrsUy5W9q5Fusqn5Ee01Sr50Jdui6YwGvHbCecdkEbOz/27TNfxsHrGvc1iX5a+DxSV4LfBp478A17awP0g1a+itgH+CcYcuZmFt5+KClPYB/GqaUh7QWcHO5OcmbgJneJG+gu7jakt2SZOTXyRJg8L62O2tmYrTWVdU7k7wA+A5du/vvV9VlA5e1s55YVf+9f/4PSa4atJrJuR+4rh+BW8ALgM8neRcMNxJ3sYT76+m6K72N7n/+Bto4qx11Kd3Z33vojvH1wKeGLWnnJTmiqr6eZNYmtKpqJjCq6rIkV9D/u0yy/5Q3O6VvHpy52L9kdHnKj23UxTw07QfAZweq42EWS5v7cVX1hR2tm2b9dYTVdNP8hi7s39s3QU2tJOdX1eokn5llc037CNwZSV5HNy/J9+kGwMx0153a3k5JbuWhY9nWVB/bqCQvAj65q/1bWyzh3uxdbmYkObOqztnROu2aktwEPLuq7h26lnHq5zU/tKpuH7qWSUnyIeDZdL1lLtgV+rhD480ySZ4NPAdYluS3RzY9lq5PaktW8cgLVq+ZZd1USvLq2dZX1QcXupYJ+SfauTvYj1VVJbmYdkajPkJVvaqfcfblwAVJim6swodnul8Poelwp7uguA/dce47sv47dEOEp97IrcwOb/xWZqMz7D2GbjTgVXQ9MlpwFt00GVfQ3nz1X0ryrKr68tCFTEpVfSfJx+gGML0Z+BXgd5K8q6r+aoiamg73/u4vn0vy/ar6H6PbkpwK3DRMZWO1KG5lVlVvHF3uBzf9zRy7T6O/Bi6n6w++S7XdjsHzgNcluY2ua2QT038k+Y9V9fEkL6a7+c+/o/s7eUxV3ZNkL+AGuq6gC1+fbe7tSHIY3dwkn+4HUiwd8mfhJCXZHfja0ANFxiXJF6vqOUPXMQn938tHmJkUblqNDDj7IPC+2Wa5THJ8VW0YoLy2z9zT3ST6l4GDZ/qc9vYFWppPmn7gy2pgf7oziEPopgA+fnuvmxZJ/paH5nXfje7ON+uGq2jsPpNkNfC3NDbx28jMngewC9yhaNyqatbrQf22QYIdGg934E66kYwv4eETFR1GexevzgCOAa4AqKqb+n9MrXjnyPMHgNuqastQxUzAK/rHs0bWTf3EbwBJXkLXZPjTdDOXHkbXXHHUkHWNwRHbTGY3Y5dodmo63Kvqq8BXk1xI9xfpFcCvAbfQdVtqyf1V9YOu5xkkWcp27mA0bfrrJwAkeQJtXSxufSTuHwHHAp+uqqOTPI827u97C/DioYuYS9NzyyR5Sj9j29eAdwN30F1neF5VvXvY6sbuc0neCuzZD2P/CN1P/KmW5Ngkn03y8SRHJ7kWuBa4O0kzd9dKcmo/WylJ3jZzvEPXNSY/rKpv0k2RsVtVfQb42aGLGoMfVNVtc/03dHFNhzvwdbo25xdX1XP7Lkkt3YJu1BpgK11vi9cBn2QXuBvMGLwb+BO6O/dcDvxWVT0R+Hm6aYBb8XtVdV9/w44T6G5L956BaxqXbyXZh26q3wuTnEPXtDbtvpDkiCTH98f3Y7vCiUfr4f5S4Bt0F6vem+R4Zh8KPfX6oc//G3hDVf1qVb13dCbMKba0qi6tqo8A36iqLwFU1dcHrmvcZk46TgLOq6pLmPKJ35I8OclxwMl017jeQjff0TeBN27vtVPi/wKX0B3LtUlOHtn2J8OU9JCmw72qLq6qlwFH0E3m8xa6+xyel+SXBi1uTNL5gyT30v1SuTHJ1r45qgWjfb6/v822Fr68ZvxzP+XvrwGfTHeDi2n/9/mXdDdV+W5V/aiqHqiqtXS/Kv9g2NLG4rXAz1XVKcAvAL+X5Mx+2+AnkYuin/uodDd/OBV4WQuTTiV5C113z9XV328zyZPopjf+VFX9zyHr21lJHuShgS978lAvpwCPqardh6ptnPoBLycC1/Q9nQ4CnlFVlw5c2qOW5Nqqevoc266pqmcsdE3jlOT6qjpyZHkf4KPA9cDzq2rQ6wqLLtxbk+4uUy/YdsKpdDdYvrT6W7ZpOmzbF3yaJ9xKsrmqnvyTbpsWSS4HfruqvjKybinwAeCVVTXo/FXT/rNPsPtsMwlW1Va6myxrCiR5ST8z5C3A5/rHvx+2qp325X5w3cMkOZ02bpD9arprej/WNz29mu6C/6Ca7ue+SPzgUW7TrqXFvuBvBi5O8koeCvOVdBeKf2WwqsZke4PodoV7RdgsM+VG2qQfsYmG2qRbl2RjVa1M8lW6my3/KP1Ns4eubWf1X1Qzbe/XVdXlQ9azWHjmPuWGbtfT2GzbF/we2ugLTj9oabY7aWmCPHOXdgFJ9qbr6rkb8ErgccCF/chO6SdmuEu7mJm5cxoZhKaB2FtGGtBimTtHC88zd2lASTYCb6VrhjkfeGFVfSnJEXT34HScgh4Vz9ylYS2WuXO0wAx3aViLZe4cLTCbZaQBLZa5c7TwDHdJapDNMpLUIMNdkhpkuEtSgwx3SWqQ4S5JDfr/7q0rQQGdn8sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['GarageType'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23da4cbd220>"
      ]
     },
     "execution_count": 55,
     "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": [
    "df['FireplaceQu'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Attchd'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['GarageType'].value_counts().index[0] # We can use df['GarageType'].mode()[0] as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_nan(df,variable):\n",
    "    most_frequent_category=df[variable].mode()[0]\n",
    "    df[variable].fillna(most_frequent_category,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in ['BsmtQual','FireplaceQu','GarageType']:\n",
    "    impute_nan(df,feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BsmtQual       0.0\n",
       "FireplaceQu    0.0\n",
       "GarageType     0.0\n",
       "SalePrice      0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Advantages\n",
    "1. Easy To implement\n",
    "2. Fater way to implement\n",
    "#### Disadvantages\n",
    "1. Since we are using the more frequent labels, it may use them in an over respresented way, if there are many nan's\n",
    "2. It distorts the relation of the most frequent label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Adding a variable to capture NAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>BsmtQual</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BsmtQual FireplaceQu GarageType  SalePrice\n",
       "0       Gd         NaN     Attchd     208500\n",
       "1       Gd          TA     Attchd     181500\n",
       "2       Gd          TA     Attchd     223500\n",
       "3       TA          Gd     Detchd     140000\n",
       "4       Gd          TA     Attchd     250000"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('house_price.csv', usecols=['BsmtQual','FireplaceQu','GarageType','SalePrice'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "df['BsmtQual_Var']=np.where(df['BsmtQual'].isnull(),1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>BsmtQual</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>BsmtQual_Var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>208500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>181500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>223500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>140000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>250000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BsmtQual FireplaceQu GarageType  SalePrice  BsmtQual_Var\n",
       "0       Gd         NaN     Attchd     208500             0\n",
       "1       Gd          TA     Attchd     181500             0\n",
       "2       Gd          TA     Attchd     223500             0\n",
       "3       TA          Gd     Detchd     140000             0\n",
       "4       Gd          TA     Attchd     250000             0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TA'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['BsmtQual'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "frequent=df['BsmtQual'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BsmtQual'].fillna(frequent,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>FireplaceQu</th>\n",
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       "      <td>181500</td>\n",
       "      <td>0</td>\n",
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       "      <td>TA</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BsmtQual FireplaceQu GarageType  SalePrice  BsmtQual_Var\n",
       "0       Gd         NaN     Attchd     208500             0\n",
       "1       Gd          TA     Attchd     181500             0\n",
       "2       Gd          TA     Attchd     223500             0\n",
       "3       TA          Gd     Detchd     140000             0\n",
       "4       Gd          TA     Attchd     250000             0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['FireplaceQu_Var']=np.where(df['FireplaceQu'].isnull(),1,0)\n",
    "frequent=df['FireplaceQu'].mode()[0]\n",
    "df['FireplaceQu'].fillna(frequent,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
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       "      <td>140000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>250000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "  BsmtQual FireplaceQu GarageType  SalePrice  BsmtQual_Var  FireplaceQu_Var\n",
       "0       Gd          Gd     Attchd     208500             0                1\n",
       "1       Gd          TA     Attchd     181500             0                0\n",
       "2       Gd          TA     Attchd     223500             0                0\n",
       "3       TA          Gd     Detchd     140000             0                0\n",
       "4       Gd          TA     Attchd     250000             0                0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Suppose if you have more frequent categories, we just replace NAN with a new category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('house_price.csv', usecols=['BsmtQual','FireplaceQu','GarageType','SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BsmtQual FireplaceQu GarageType  SalePrice\n",
       "0       Gd         NaN     Attchd     208500\n",
       "1       Gd          TA     Attchd     181500\n",
       "2       Gd          TA     Attchd     223500\n",
       "3       TA          Gd     Detchd     140000\n",
       "4       Gd          TA     Attchd     250000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_nan(df,variable):\n",
    "    df[variable+\"newvar\"]=np.where(df[variable].isnull(),\"Missing\",df[variable])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in ['BsmtQual','FireplaceQu','GarageType']:\n",
    "    impute_nan(df,feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Gd</td>\n",
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       "      <td>Attchd</td>\n",
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       "      <th>3</th>\n",
       "      <td>TA</td>\n",
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       "      <td>Detchd</td>\n",
       "      <td>140000</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
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       "      <th>4</th>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>250000</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  BsmtQual FireplaceQu GarageType  SalePrice BsmtQualnewvar FireplaceQunewvar  \\\n",
       "0       Gd         NaN     Attchd     208500             Gd           Missing   \n",
       "1       Gd          TA     Attchd     181500             Gd                TA   \n",
       "2       Gd          TA     Attchd     223500             Gd                TA   \n",
       "3       TA          Gd     Detchd     140000             TA                Gd   \n",
       "4       Gd          TA     Attchd     250000             Gd                TA   \n",
       "\n",
       "  GarageTypenewvar  \n",
       "0           Attchd  \n",
       "1           Attchd  \n",
       "2           Attchd  \n",
       "3           Detchd  \n",
       "4           Attchd  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['BsmtQual','FireplaceQu','GarageType'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Attchd</td>\n",
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       "      <td>TA</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   SalePrice BsmtQualnewvar FireplaceQunewvar GarageTypenewvar\n",
       "0     208500             Gd           Missing           Attchd\n",
       "1     181500             Gd                TA           Attchd\n",
       "2     223500             Gd                TA           Attchd\n",
       "3     140000             TA                Gd           Detchd\n",
       "4     250000             Gd                TA           Attchd"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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