Missing-Value1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are the types of missing values?\n",
    "1. MCAR: Missing Completely At Random, A variable is mssing completely at ramdom if the probability of being missing the same for all the observations. When data is MCAR, there is absolutely no ralationship 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 mssing than other.\n",
    "\n",
    "2. Mssing Data Not At Random(MNAR): Systematic missing values: There is absolutely some relationship between the data mssing and any other values, observed or missing, within the dataset.\n",
    "\n",
    "3. Missing At Random (MAR):  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MCAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passenger_id</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",
       "      <th>boat</th>\n",
       "      <th>body</th>\n",
       "      <th>home.dest</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1216</td>\n",
       "      <td>3</td>\n",
       "      <td>Smyth, Miss. Julia</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>335432</td>\n",
       "      <td>7.7333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>699</td>\n",
       "      <td>3</td>\n",
       "      <td>Cacic, Mr. Luka</td>\n",
       "      <td>male</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315089</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1267</td>\n",
       "      <td>3</td>\n",
       "      <td>Van Impe, Mrs. Jean Baptiste (Rosalie Paula Go...</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>345773</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>449</td>\n",
       "      <td>2</td>\n",
       "      <td>Hocking, Mrs. Elizabeth (Eliza Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>29105</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cornwall / Akron, OH</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>576</td>\n",
       "      <td>2</td>\n",
       "      <td>Veal, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28221</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Barre, Co Washington, VT</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   passenger_id  pclass                                               name  \\\n",
       "0          1216       3                                 Smyth, Miss. Julia   \n",
       "1           699       3                                    Cacic, Mr. Luka   \n",
       "2          1267       3  Van Impe, Mrs. Jean Baptiste (Rosalie Paula Go...   \n",
       "3           449       2              Hocking, Mrs. Elizabeth (Eliza Needs)   \n",
       "4           576       2                                    Veal, Mr. James   \n",
       "\n",
       "      sex   age  sibsp  parch  ticket     fare cabin embarked boat  body  \\\n",
       "0  female   NaN      0      0  335432   7.7333   NaN        Q   13   NaN   \n",
       "1    male  38.0      0      0  315089   8.6625   NaN        S  NaN   NaN   \n",
       "2  female  30.0      1      1  345773  24.1500   NaN        S  NaN   NaN   \n",
       "3  female  54.0      1      3   29105  23.0000   NaN        S    4   NaN   \n",
       "4    male  40.0      0      0   28221  13.0000   NaN        S  NaN   NaN   \n",
       "\n",
       "                  home.dest  survived  \n",
       "0                       NaN         1  \n",
       "1                   Croatia         0  \n",
       "2                       NaN         0  \n",
       "3      Cornwall / Akron, OH         1  \n",
       "4  Barre, Co Washington, VT         0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "passenger_id      0\n",
       "pclass            0\n",
       "name              0\n",
       "sex               0\n",
       "age             174\n",
       "sibsp             0\n",
       "parch             0\n",
       "ticket            0\n",
       "fare              1\n",
       "cabin           659\n",
       "embarked          1\n",
       "boat            542\n",
       "body            777\n",
       "home.dest       386\n",
       "survived          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cabin_null'] = np.where(df.cabin.isnull(), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7752941176470588"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## find the percentage of nan values\n",
    "(df.cabin_null == 1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['passenger_id', 'pclass', 'name', 'sex', 'age', 'sibsp', 'parch',\n",
       "       'ticket', 'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest',\n",
       "       'survived', 'cabin_null'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5942492012779552"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cabin_null[df.survived == 1].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8808193668528864"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cabin_null[df.survived == 0].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Men ---> hide their salary\n",
    "# Women ---> hide their age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### All the techniques of handling, missing 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 Category Imputation"
   ]
  },
  {
   "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)\n",
    "We solve this by replacing the NaN with the most fequent occurace of the variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('titanic.csv', usecols=['age', 'fare', 'survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54.0</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    age     fare  survived\n",
       "0   NaN   7.7333         1\n",
       "1  38.0   8.6625         0\n",
       "2  30.0  24.1500         0\n",
       "3  54.0  23.0000         1\n",
       "4  40.0  13.0000         0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0.204706\n",
       "fare        0.001176\n",
       "survived    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## percentage of missing values\n",
    "df.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_nan(df, variable, median):\n",
    "    df[variable + \"_median\"] = df[variable].fillna(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "impute_nan(df, 'age', df.age.median())\n",
    "impute_nan(df, 'fare', df.fare.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>survived</th>\n",
       "      <th>age_median</th>\n",
       "      <th>fare_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.7333</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.7333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>8.6625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>24.1500</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>24.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54.0</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>23.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>13.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    age     fare  survived  age_median  fare_median\n",
       "0   NaN   7.7333         1        28.0       7.7333\n",
       "1  38.0   8.6625         0        38.0       8.6625\n",
       "2  30.0  24.1500         0        30.0      24.1500\n",
       "3  54.0  23.0000         1        54.0      23.0000\n",
       "4  40.0  13.0000         0        40.0      13.0000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>survived</th>\n",
       "      <th>age_median</th>\n",
       "      <th>fare_median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>60.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>14.1083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  fare  survived  age_median  fare_median\n",
       "416  60.5   NaN         0        60.5      14.1083"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.fare.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.562243434125529\n",
      "12.999022882894222\n"
     ]
    }
   ],
   "source": [
    "print(df.age.std())\n",
    "print(df.age_median.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jgaur\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age_median', ylabel='Density'>"
      ]
     },
     "execution_count": 35,
     "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.distplot(df.age_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jgaur\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Density'>"
      ]
     },
     "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": [
    "sns.distplot(df.age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17ea742a5b0>"
      ]
     },
     "execution_count": 41,
     "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",
    "ax.legend()"
   ]
  },
  {
   "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",
    "\n",
    "### Disadvantages \n",
    "1. change and Distortion in the original data\n",
    "2. It 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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}