Feature Engineering- MeanMedianDay 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Values- Feature Engineering- Day 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lifecycle of a Data Science Projects\n",
    "1. Data Collection Statergy---from company side,3rd party APi's,Surveys,Surveys\n",
    "2. Feature Engineering---Handling Missing Values\n",
    "\n",
    "Why are their Missing values?? Survey--Depression Survey\n",
    "1. They hesitate to put down the information\n",
    "2. Survey informations are not that valid\n",
    "3. Men--salary\n",
    "4. Women---age\n",
    "5. People may have died----NAN\n",
    "\n",
    "Data Science Projects---Dataset should be collected from multiple sources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What are the different types of Missing Data?\n",
    "\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  </thead>\n",
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       "    <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",
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       "    <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",
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      "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Embarked'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7710437710437711"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df['cabin_null']=np.where(df['Cabin'].isnull(),1,0)\n",
    "\n",
    "##find the percentage of null values\n",
    "df['cabin_null'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked', 'cabin_null'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived\n",
       "0    0.876138\n",
       "1    0.602339\n",
       "Name: cabin_null, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Survived'])['cabin_null'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing At Random(MAR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Men---hide their salary\n",
    "Women---hide their age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### All the techniques of handling ,issing values\n",
    "\n",
    "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\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 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 the most frequent occurance of the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
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       "    <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",
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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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('titanic.csv',usecols=['Age','Fare','Survived'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0.000000\n",
       "Age         0.198653\n",
       "Fare        0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Lets go and see the percentage of missing values\n",
    "df.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_nan(df,variable,median):\n",
    "    df[variable+\"_median\"]=df[variable].fillna(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median=df.Age.median()\n",
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impute_nan(df,'Age',median)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.526497332334042\n",
      "13.019696550973201\n"
     ]
    }
   ],
   "source": [
    "print(df['Age'].std())\n",
    "print(df['Age_median'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x273541c2828>"
      ]
     },
     "execution_count": 25,
     "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",
    "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": [
    "### Advantages And Disadvantages of Mean/Median Imputation\n",
    "\n",
    "#### Advantages\n",
    "1. Easy to implement(Robust to outliers)\n",
    "2. Faster way to obtain the complete dataset\n",
    "#### Disadvantages\n",
    "1. Change or Distortion in the original variance\n",
    "2. Impacts Correlation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}