Handling Missing Values.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1c7b2634",
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked\n",
       "0           0       3    male  22.0      1      0   7.2500   NaN        S\n",
       "1           1       1  female  38.0      1      0  71.2833   C85        C\n",
       "2           1       3  female  26.0      0      0   7.9250   NaN        S\n",
       "3           1       1  female  35.0      1      0  53.1000  C123        S\n",
       "4           0       3    male  35.0      0      0   8.0500   NaN        S\n",
       "..        ...     ...     ...   ...    ...    ...      ...   ...      ...\n",
       "886         0       2    male  27.0      0      0  13.0000   NaN        S\n",
       "887         1       1  female  19.0      0      0  30.0000   B42        S\n",
       "888         0       3  female   NaN      1      2  23.4500   NaN        S\n",
       "889         1       1    male  26.0      0      0  30.0000  C148        C\n",
       "890         0       3    male  32.0      0      0   7.7500   NaN        Q\n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "df=pd.read_csv('https://raw.githubusercontent.com/krishnaik06/Feature-Engineering-Live-sessions/master/titanic.csv',usecols=['Survived','Pclass','Sex','Age','SibSp','Parch','Fare','Cabin','Embarked'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d266adf5",
   "metadata": {},
   "source": [
    "### 1) Replace with most frequent values / Frequent categories imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c5c48c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sex'].value_counts() # To get the most frequent value of a column we can use the method mode .\n",
    "#It will return the value that appears most often. It can be multiple values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12d00ad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        male\n",
       "1      female\n",
       "2      female\n",
       "3      female\n",
       "4        male\n",
       "        ...  \n",
       "886      male\n",
       "887    female\n",
       "888    female\n",
       "889      male\n",
       "890      male\n",
       "Name: Sex, Length: 891, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sex'].fillna('male') # here we are doing it manually"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc839bce",
   "metadata": {},
   "source": [
    "### 2) Apply clasifier algorithm to predict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88ceb931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "Age         177\n",
       "SibSp         0\n",
       "Parch         0\n",
       "Fare          0\n",
       "Cabin       687\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "721210b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "886    1\n",
       "887    0\n",
       "888    0\n",
       "889    1\n",
       "890    1\n",
       "Name: Sex, Length: 891, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sex']=np.where(df['Sex']=='male',1,0)\n",
    "df['Sex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aba09b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperate null values from age feature \n",
    "test_data=df[df['Age'].isnull()]\n",
    "train_data=df[df['Age'].isnull()==False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "51ad60d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=train_data.drop(['Age','Cabin','Embarked'],axis=1)\n",
    "y_train=train_data['Age']\n",
    "X_test=test_data.drop(['Age','Cabin','Embarked'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a877d070",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regressor=LinearRegression()\n",
    "regressor.fit(X_train,y_train)\n",
    "y_predicted=regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5f20c5d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_7256/2267555641.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  test_data.loc[test_data.Age.isnull(),'Age']=y_predicted\n"
     ]
    }
   ],
   "source": [
    "# replace missing values with predicted values\n",
    "test_data.loc[test_data.Age.isnull(),'Age']=y_predicted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5fb8a94",
   "metadata": {},
   "source": [
    "### 3) Apply Unsupervised ML (Clustering)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0a097da",
   "metadata": {},
   "source": [
    "### 4) Capturing NAN values with a new feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ad352a0",
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
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       "      <th>Cabin_val</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Cabin Embarked  \\\n",
       "0         0       3    male  22.0      1      0   7.2500    G6        S   \n",
       "1         1       1  female  38.0      1      0  71.2833   C85        C   \n",
       "2         1       3  female  26.0      0      0   7.9250    G6        S   \n",
       "3         1       1  female  35.0      1      0  53.1000  C123        S   \n",
       "4         0       3    male  35.0      0      0   8.0500    G6        S   \n",
       "\n",
       "   Cabin_val  \n",
       "0          1  \n",
       "1          0  \n",
       "2          1  \n",
       "3          0  \n",
       "4          1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.read_csv('https://raw.githubusercontent.com/krishnaik06/Feature-Engineering-Live-sessions/master/titanic.csv',usecols=['Survived','Pclass','Sex','Age','SibSp','Parch','Fare','Cabin','Embarked'])\n",
    "df2['Cabin_val']=np.where(df['Cabin'].isnull(),1,0)\n",
    "df2['Cabin'].fillna(df2['Cabin'].mode()[2],inplace=True)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c5cb94d",
   "metadata": {},
   "source": [
    "### 5) Create a new category for nan values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b87e0bf2",
   "metadata": {},
   "source": [
    "Suppose if you have more frequent categories, we just replace NAN with a new category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4efd1f1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Missing</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Missing</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Missing</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare    Cabin Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500  Missing        S\n",
       "1         1       1  female  38.0      1      0  71.2833      C85        C\n",
       "2         1       3  female  26.0      0      0   7.9250  Missing        S\n",
       "3         1       1  female  35.0      1      0  53.1000     C123        S\n",
       "4         0       3    male  35.0      0      0   8.0500  Missing        S"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3=pd.read_csv('https://raw.githubusercontent.com/krishnaik06/Feature-Engineering-Live-sessions/master/titanic.csv',usecols=['Survived','Pclass','Sex','Age','SibSp','Parch','Fare','Cabin','Embarked'])\n",
    "df3['Cabin']=np.where(df3['Cabin'].isnull(),\"Missing\",df3['Cabin'])\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2e1481",
   "metadata": {},
   "source": [
    "### 6) Sort values and impute with median  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2aa0dbe4",
   "metadata": {},
   "source": [
    "When should we apply? Mean/median imputation has the assumption that the data are missing completely at random(MCAR). We solve this by replacing the NAN with the most frequent occurance of the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd246b5a",
   "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",
       "  </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"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4=pd.read_csv('https://raw.githubusercontent.com/krishnaik06/Feature-Engineering-Live-sessions/master/titanic.csv',usecols=['Age','Fare','Survived'])\n",
    "df4.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cfe5c8fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median=df['Age'].sort_values().median()\n",
    "df['Age'].fillna(median,inplace=True)\n",
    "df['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b2a2a0c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f27fdfdcf70>"
      ]
     },
     "execution_count": 14,
     "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": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "df['Age'].plot(kind='kde', ax=ax)\n",
    "lines, labels = ax.get_legend_handles_labels()\n",
    "ax.legend(lines, labels, loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1edbb794",
   "metadata": {},
   "source": [
    "Advantages of mean/median/mode imputation\n",
    "\n",
    "    >Easy to implement(Robust to outliers)\n",
    "    >Faster way to obtain the complete dataset #### Disadvantages\n",
    "    >Change or Distortion in the original variance\n",
    "    >Impacts Correlation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c4f7d8",
   "metadata": {},
   "source": [
    "### 7) Replacing with Arbitrary Value / Arbitrary Value Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29c9d9f0",
   "metadata": {},
   "source": [
    "this technique was derived from kaggle competition It consists of replacing NAN by an arbitrary value we want to look for a value that is at the end of the distribution. Replacing the NA by arbitrary values should be used when there are reasons to believe that the NA is not missing at random. In situations like this, we would not like to replace it with the median or the mean and therefore make the NA look like the majority of our observations. Instead, we want to flag the fact that the observation is missing.\n",
    "https://medium.com/analytics-vidhya/feature-engineering-part-1-arbitrary-value-imputation-e81444bd79b2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8d0acea2",
   "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",
       "  </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"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5=pd.read_csv('https://raw.githubusercontent.com/krishnaik06/Feature-Engineering-Live-sessions/master/titanic.csv',usecols=['Age','Fare','Survived'])\n",
    "df5.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9ae66766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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31hqnfr+/NprG8R8o0TCmMtyls8lGPwxuvHyLO6KmGO5D8DcASWcrw12aMKOa9lHbDHdJz+Myz8k3tnBPsgv4DWAb8JGqumlc15I0GKcW2zWWpZBJtgH/EXgd8DLguiQvG8e1JEnPN64791cBj1bVVwGSLAHXAF8c0/UkbYFRTddsdJ79u7b33Sf1lqoa/UmTXwR2VdU/6bbfBPy9qnrHmtfsAfZ0my8FvjTApS4Anhyyu5NmGmuG6azbmqfDMDX/rap6ca8dZ+wPqlW1D9g3zDmSHKiq+RF1aSJMY80wnXVb83QYV83j+viBY8Ala7Yv7tokSVtgXOH+J8ClSXYm+UHgWuDOMV1LkrTOWKZlqupkkncAf8DqUshbquqhMVxqqGmdCTWNNcN01m3N02EsNY/lD6qSpDPLj/yVpAYZ7pLUoIkN9yS7knwpyaNJ9p7p/oxDkkuSfDbJF5M8lOSdXfuLktyT5Cvd1/PPdF9HLcm2JH+a5K5ue2eSz3fjfXv3h/pmJNmR5ONJHknycJK/3/o4J/kX3ff14SS3JfmhFsc5yS1Jjic5vKat59hm1W929T+Y5JWDXnciw32KPt7gJHBjVb0MeDXw9q7OvcC9VXUpcG+33Zp3Ag+v2f63wIeq6m8DTwM3nJFejc9vAL9fVX8H+AlWa292nJNcBPwzYL6qLmN14cW1tDnO+4Fd69o2GtvXAZd2jz3Ahwe96ESGO2s+3qCq/hI49fEGTamqx6vqC93zb7P6P/xFrNZ6a/eyW4HXn5EOjkmSi4GfBT7SbQd4LfDx7iVN1ZzkPOA1wM0AVfWXVfUtGh9nVlfr/XCSc4AfAR6nwXGuqvuAp9Y1bzS21wAfrVX3AzuSXDjIdSc13C8C/mzN9tGurVlJ5oBXAJ8HZqvq8W7XnwOzZ6pfY/IfgF8Bvt9t/xjwrao62W23Nt47gW8Av9dNRX0kyXYaHueqOga8H/g/rIb6CeAgbY/zWhuN7ciybVLDfaokmQHuAP55VT2zdl+trmVtZj1rkp8DjlfVwTPdly10DvBK4MNV9QrgWdZNwTQ4zuezepe6E/gbwHaeP3UxFcY1tpMa7lPz8QZJfoDVYP8vVfWJrvmJU7+qdV+Pn6n+jcFPAj+f5Air022vZXU+ekf36zu0N95HgaNV9flu++Oshn3L4/zTwNeq6htV9T3gE6yOfcvjvNZGYzuybJvUcJ+Kjzfo5ppvBh6uqg+u2XUncH33/Hrg01vdt3GpqndV1cVVNcfquP7Pqvol4LPAL3Yva63mPwf+LMlLu6arWP147GbHmdXpmFcn+ZHu+/xUzc2O8zobje2dwJu7VTOvBk6smb7pT1VN5AO4Gvgy8Bjw62e6P2Oq8adY/XXtQeCB7nE1q3PQ9wJfAf4H8KIz3dcx1b8A3NU9fwnwx8CjwH8Dzj3T/RtxrVcAB7qx/hRwfuvjDPxr4BHgMPCfgXNbHGfgNlb/rvA9Vn9Lu2GjsQXC6krAx4BDrK4mGui6fvyAJDVoUqdlJEkvwHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDfp/+yPKuMykMy8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df5['Age'].fillna(100,inplace=True)\n",
    "df5['Age'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adb916c3",
   "metadata": {},
   "source": [
    "### 8) Forward Fill - Backward fill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7e9827ac",
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.4583</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\n",
       "5         0   NaN   8.4583"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6=pd.read_csv('https://raw.githubusercontent.com/krishnaik06/Feature-Engineering-Live-sessions/master/titanic.csv',usecols=['Age','Fare','Survived'])\n",
    "df6.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3e7f29fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df6.fillna(method='ffill', inplace=True)\n",
    "#df6.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fd14d852",
   "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>8.4583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.8625</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\n",
       "5         0  54.0   8.4583\n",
       "6         0  54.0  51.8625"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.fillna(method='bfill', inplace=True)\n",
    "df6.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75161237",
   "metadata": {},
   "source": [
    "### 9) End of Distribution imputation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "950c89bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "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": [
    "df7=pd.read_csv('https://raw.githubusercontent.com/krishnaik06/Feature-Engineering-Live-sessions/master/titanic.csv',usecols=['Age','Fare','Survived'])\n",
    "df7.Age.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "de88446e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "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": [
    "extreme=df7.Age.mean()+3*df7.Age.std()\n",
    "def impute_nan(df,variable,median,extreme):\n",
    "    df7[variable+\"_end_distribution\"]=df7[variable].fillna(extreme)\n",
    "    df7[variable].fillna(median,inplace=True)\n",
    "impute_nan(df7,'Age',df7.Age.median(),extreme)\n",
    "df7['Age'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6ae9568f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "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": [
    "df7['Age_end_distribution'].hist(bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75fda5a1",
   "metadata": {},
   "source": [
    "### 10) Random Sample Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e820862",
   "metadata": {},
   "source": [
    "Aim: Random sample imputation consists of taking random observation from the dataset and we use this observation to replace the nan values\n",
    "When should it be used? It assumes that the data are missing completely at random(MCAR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "94f12036",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8=pd.read_csv('https://raw.githubusercontent.com/krishnaik06/Feature-Engineering-Live-sessions/master/titanic.csv',usecols=['Age','Fare','Survived'])\n",
    "##It will have the random sample to fill the na\n",
    "random_sample=df8['Age'].dropna().sample(df8['Age'].isnull().sum(),random_state=0)\n",
    "##pandas need to have same index in order to merge the dataset\n",
    "random_sample.index=df8[df8['Age'].isnull()].index\n",
    "df8.loc[df8['Age'].isnull(),'Age']=random_sample\n",
    "df8['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6f058f7",
   "metadata": {},
   "source": [
    "### 11) Interpolation "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e0abdb1",
   "metadata": {},
   "source": [
    "Linear Interpolation simply means to estimate a missing value by connecting dots in a straight line in increasing order. In short, It estimates the unknown value in the same increasing order from previous values. The default method used by Interpolation is Linear so while applying it we did not need to specify it.\n",
    "https://www.analyticsvidhya.com/blog/2021/06/power-of-interpolation-in-python-to-fill-missing-values/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a85ca27c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9=pd.read_csv('https://raw.githubusercontent.com/krishnaik06/Feature-Engineering-Live-sessions/master/titanic.csv',usecols=['Age','Fare','Survived'])\n",
    "df9.interpolate(method=\"polynomial\", order=1,inplace=True) # polynomial of order 1 is linear\n",
    "df9['Age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f31cfaaf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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