4. End of Distribution imputation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End of Distribution imputation\n",
    "- In this method we replace missing values with far end values or extreme\n",
    "- Far end value means the values after 3rd stander deviation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('Titanic.csv',usecols=['Age','Fare','Survived'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### check null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived      0\n",
       "Age         177\n",
       "Fare          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - there is 177 null values are present in dataset "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform End of Distribution imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](https://i.ytimg.com/vi/iiRiOlkLa6A/maxresdefault.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- first find mean of Age feature "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = data['Age'].mean()\n",
    "mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- now find stander deviation of Age Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.526497332334044"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = data['Age'].std()\n",
    "std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now we have a mean and stander deviation of Age feature now we find the Far end values or extreme value\n",
    "- where extreme values means the values after 3rd stander deviation \n",
    "- so extreme = (Mean of Age Feature + ( 3 * stander deviation of Age Feature))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.27860964406095"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extreme = (mean + (3*std))\n",
    "extreme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- here we get a extreme value: 73.27860964406095"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### let's use some plot for better understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fbc7c63eb0>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "data.Age.plot(kind='kde',color='b')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1fbc5671160>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data['Age'],color='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\DS ENV\\Anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['Age'],color='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- from both plots we can see that extrame values are between ~ 70 to 80 in other word it is a outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a function to replace misssing in Age with extreme values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def End_distribution(df,variable,extreme):\n",
    "    df[variable+'_median'] = df[variable].fillna(df[variable].median())\n",
    "    df[variable+'_end_distribution'] = df[variable].fillna(extreme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "End_distribution(data,'Age',extreme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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_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",
       "      <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_end_distribution\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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1fbc7c68b50>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "data.Age_end_distribution.plot(kind='kde',color='b')\n",
    "data.Age_median.plot(kind='kde',color='y')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\DS ENV\\Anaconda\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Age_end_distribution'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q7V9Je2LEQX+ZmdfNxO1L1QywJBXxl3CSVMQAS1IRAyxJRQywJBX5P+HsVg3cJRyIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data['Age_end_distribution'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Now we can see that \n",
    "    - when we replace NaN with median then it show distortion\n",
    "    - but it overcome when we replace NaN with End of Distribution and outlier is also covered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I would be glad to hear your feedback on my Notebook!\n",
    "### Best regards,\n",
    "#### [Rushi](https://www.kaggle.com/rushikeshlavate)😊😊"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### You can also check other techniques of handling Missing values¶\n",
    "- [Mean/Mode/Median Imputation](https://www.kaggle.com/rushikeshlavate/mean-median-mode-imputation-on-titanic-dataset)\n",
    "- [Random Sample Imputation](https://www.kaggle.com/rushikeshlavate/random-sample-imputation-on-titanic-dataset)\n",
    "- [Capturing NaN values with a new feature](https://www.kaggle.com/rushikeshlavate/capturing-nan-values-with-a-new-feature)"
   ]
  }
 ],
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   "display_name": "Python 3",
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