missing-values-imputation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing values is one of the most common problems that you will come across when performing data analysis. Furthermore, machine learning models require that a dataset does not contain any missing values before they can be fitted to the data. Therefore, it is crucial that we learn how to properly handle them.\n",
    "\n",
    "There are mainly two ways that we can deal with missing values in a dataset:\n",
    "\n",
    "1. If there are only a few rows with missing values or if a column has an overwhelming number of missing values, we can simply drop them from the dataset without running into the risk of losing too much information. \n",
    "2. An alternative approach to handling missing values is via imputation. Imputation is the process whereby the missing values in a dataset are replaced with some substituted values. \n",
    "\n",
    "For the purpose of this tutorial, we will examine the process of imputation and more specifically, we will learn the difference between a univariate approach to imputing versus a multivariate approach to imputing. \n",
    "\n",
    "Univariate imputation implies that we are only considering the values of a single column when performing imputation. Multivariate imputation, on the other hand, involves taking into account other features in the dataset when performing imputation.\n",
    "\n",
    "The multivariate approach to imputing is generally preferred over the univariate approach as it is more robust and provides our model with a more accurate representation of the missing values in order to make better predictions.\n",
    "\n",
    "In this tutorial, we will explore 3 different imputation techniques with reference to the [Titanic dataset](https://www.kaggle.com/c/titanic/data) on Kaggle:\n",
    "\n",
    "- [Simple imputer](https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html)\n",
    "- [Iterative imputer](https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer)\n",
    "- [KNN Imputer](https://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data wrangling\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "\n",
    "# Data visualisation\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Machine learning\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import SimpleImputer, IterativeImputer, KNNImputer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Remove warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Prepare dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.1 Import and read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"C:/Users/Jason Chong/Documents/Kaggle/Titanic/train.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.2 Drop unwanted columns "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purpose of this tutorial, I proceeded to drop the PassengerId, Name, Ticket and Cabin columns from the original dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</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>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>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>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>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>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S\n",
       "3         1       1  female  35.0      1      0  53.1000        S\n",
       "4         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop PassengerId, Name, Ticket and Cabin columns \n",
    "\n",
    "data = data.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis = 1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.3 Drop rows with missing Embarked values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age         177\n",
       "Embarked      2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing data\n",
    "\n",
    "missing = data.isnull().sum()\n",
    "missing = missing[missing > 0].sort_values(ascending = False)\n",
    "missing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because there are only two rows with missing Embarked values, I decided to simply drop those two rows from the dataset, leaving us with 177 rows with missing Age values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before:  (891, 8)\n",
      "After:  (889, 8)\n"
     ]
    }
   ],
   "source": [
    "# Drop rows with missing Embarked values \n",
    "\n",
    "print(\"Before: \", data.shape)\n",
    "data = data.loc[data['Embarked'].notnull(), :]\n",
    "print(\"After: \", data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age    177\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check missing values again\n",
    "\n",
    "missing = data.isnull().sum()\n",
    "missing[missing > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Explore Age feature\n",
    "\n",
    "Before we perform imputation on the Age column, let's briefly explore the Age feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'Correlation between features')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot heatmap \n",
    "\n",
    "correlation = data.corr()\n",
    "plt.figure(figsize = (7, 5))\n",
    "sns.heatmap(correlation, annot = True, fmt = '.2f', cmap = 'coolwarm')\n",
    "plt.title('Correlation between features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fare        0.093143\n",
       "Survived   -0.082446\n",
       "Parch      -0.187896\n",
       "SibSp      -0.307351\n",
       "Pclass     -0.365902\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age correlation with other features \n",
    "\n",
    "correlation['Age'].sort_values(ascending = False)[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the Age feature is slightly positively correlated with the Fare feature. In other words, passengers that are older generally pay a higher fare. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Passenger Age Distribution')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot passenger age distribution\n",
    "\n",
    "plt.figure(figsize = (8, 5))\n",
    "sns.distplot(data['Age'], label = 'Skewness: {:.2f}'.format(data['Age'].skew()))\n",
    "plt.legend(loc = 'best')\n",
    "plt.title('Passenger Age Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    712.000000\n",
       "mean      29.642093\n",
       "std       14.492933\n",
       "min        0.420000\n",
       "25%       20.000000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Age summary statistics \n",
    "\n",
    "data['Age'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Missing values imputation\n",
    "\n",
    "In this section, we will look at 3 different imputation techniques:\n",
    "\n",
    "1. Simple imputer\n",
    "2. Iterative imputer\n",
    "3. KNN Imputer\n",
    "\n",
    "Here, I have created a sample dataframe to demonstrate the differences between the 3 techniques. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>SibSp</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</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>71.2833</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>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>53.1000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp     Fare   Age\n",
       "0      1   7.2500  22.0\n",
       "1      1  71.2833  38.0\n",
       "2      0   7.9250  26.0\n",
       "3      1  53.1000  35.0\n",
       "4      0   8.0500  35.0\n",
       "5      0   8.4583   NaN"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create sample dataframe\n",
    "\n",
    "df = pd.DataFrame({'SibSp': [1, 1, 0, 1, 0, 0], \n",
    "                   'Fare': [7.25, 71.2833, 7.925, 53.1, 8.05, 8.4583], \n",
    "                   'Age': [22, 38, 26, 35, 35, np.nan]})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.1 Simple imputer\n",
    "\n",
    "Simple imputer follows a univariate approach to imputing missing values i.e. it only takes a single feature into consideration. Some of the most common uses of simple imputer are:\n",
    "\n",
    "- Mean\n",
    "- Median\n",
    "- Most frequent (mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average age:  31.2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 1.    ,  7.25  , 22.    ],\n",
       "       [ 1.    , 71.2833, 38.    ],\n",
       "       [ 0.    ,  7.925 , 26.    ],\n",
       "       [ 1.    , 53.1   , 35.    ],\n",
       "       [ 0.    ,  8.05  , 35.    ],\n",
       "       [ 0.    ,  8.4583, 31.2   ]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Average age: \", df['Age'].mean())\n",
    "simple_imp = SimpleImputer(missing_values = np.nan, strategy = 'mean')\n",
    "simple_imp.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, simple imputer has filled the missing value in the Age column with the average age which is 31.2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.2 Iterative imputer\n",
    "\n",
    "Iterative imputer is an example of a multivariate approach to imputation. It models the missing values in a column by using information from the other columns in a dataset. More specifically, it treats the column with missing values as a target variable while the remaining columns are used are predictor variables to predict the target variable. \n",
    "\n",
    "In our sample data frame, the Age column has one missing value on row 6 and is therefore assigned as the target variable in this scenario. This leaves the SibSp and Fare columns as our predictor variables. \n",
    "\n",
    "Iterative imputer will use the first 5 rows of the data frame to train a predictive model. Once the model is ready, it will then values in the SibSp and Fare columns of row 6 as inputs and predict the Age value for that row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  7.25      , 22.        ],\n",
       "       [ 1.        , 71.2833    , 38.        ],\n",
       "       [ 0.        ,  7.925     , 26.        ],\n",
       "       [ 1.        , 53.1       , 35.        ],\n",
       "       [ 0.        ,  8.05      , 35.        ],\n",
       "       [ 0.        ,  8.4583    , 28.50639495]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iterative_imp = IterativeImputer()\n",
    "iterative_imp.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the value predicted under iterative imputer is different to that under simple imputer.\n",
    "\n",
    "This is a more accurate approach to predict the missing Age value as it takes other features in our dataframe into account. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.3 KNN imputer\n",
    "\n",
    "Last but not least, we have KNN Imputer which is another multivariate imputation technique. KNN Imputer scans our dataframe for k nearest observations to the row with missing value. It will then proceed to fill the missing value with the average of those nearest observations. \n",
    "\n",
    "Here, I have set k to equal to 2 or in other words, I want KNN imputer to look for 2 observations that are nearest to row 6 and fill the missing age with the average age of those 2 rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.    ,  7.25  , 22.    ],\n",
       "       [ 1.    , 71.2833, 38.    ],\n",
       "       [ 0.    ,  7.925 , 26.    ],\n",
       "       [ 1.    , 53.1   , 35.    ],\n",
       "       [ 0.    ,  8.05  , 35.    ],\n",
       "       [ 0.    ,  8.4583, 30.5   ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_imp = KNNImputer(n_neighbors = 2)\n",
    "knn_imp.fit_transform(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a result, KNN imputer has taken row 3 and row 5 as the nearest observations for row 6.\n",
    "\n",
    "Therefore, the average age between row 3 and row 5 is (26 + 35) / 2 = 30.5. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Model accuracy under simple imputer and iterative imputer\n",
    "\n",
    "Now that we have a better understanding of how the different imputers work, we can move on to apply these techniques to our Titanic dataset and compare the model accuracy under each approach.\n",
    "\n",
    "We should expect to see our model perform better under multivariate imputation than univariate imputation as multivariate imputation provides a more accurate prediction of the missing values and thus allowing our model to make better predictions. \n",
    "\n",
    "In this section, we will build a column transformer which consists of a OneHotEncoder for encoding the Sex and Embarked columns as well as an imputer to impute the missing values in the Age column.\n",
    "\n",
    "Following that, we will chain the column transformer with a random forest classifier to predict the surival of the passengers on the Titanic. Finally, we will perform 10-fold cross-validation to compare the prediction results under univariate imputation versus under multivariate imputation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</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>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>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>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>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>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S\n",
       "3         1       1  female  35.0      1      0  53.1000        S\n",
       "4         0       3    male  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get predictor and target variables \n",
    "\n",
    "X = data.drop('Survived', axis = 1)\n",
    "Y = data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape:  (889, 7)\n",
      "Y shape:  (889,)\n"
     ]
    }
   ],
   "source": [
    "# Predictor and target variables shape\n",
    "\n",
    "print(\"X shape: \", X.shape)\n",
    "print(\"Y shape: \", Y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.1 Univariate imputation (simple imputer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8043156281920327"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate OneHotEncoder for Sex and Embarked columns\n",
    "ohe = OneHotEncoder()\n",
    "\n",
    "# Instantiate simple imputer \n",
    "simple_imp = SimpleImputer(missing_values = np.nan, strategy = 'mean')\n",
    "\n",
    "# Instantiate model\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "# Make column transformer with simple imputer\n",
    "column_transform = make_column_transformer(\n",
    "    (ohe, ['Sex', 'Embarked']),\n",
    "    (simple_imp, ['Age']),\n",
    "    remainder = 'passthrough')\n",
    "\n",
    "# Pipeline\n",
    "pipe = make_pipeline(column_transform, rf)\n",
    "\n",
    "# 10-fold cross-validation\n",
    "cross_val_score(pipe, X, Y, cv = 10, scoring = 'accuracy').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.2 Multivariate imputation (iterative imputer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8099336057201226"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate OneHotEncoder for Sex and Embarked columns\n",
    "ohe = OneHotEncoder()\n",
    "\n",
    "# Instantiate iterative imputer\n",
    "iterative_imp = IterativeImputer()\n",
    "\n",
    "# Instantiate model\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "# Make column transformer with iterative imputer\n",
    "column_transform = make_column_transformer(\n",
    "    (ohe, ['Sex', 'Embarked']),\n",
    "    (iterative_imp, ['Age']),\n",
    "    remainder = 'passthrough')\n",
    "\n",
    "# Pipeline\n",
    "pipe = make_pipeline(column_transform, rf)\n",
    "\n",
    "# 10-fold cross-validation \n",
    "cross_val_score(pipe, X, Y, cv = 10, scoring = 'accuracy').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although not a drastic difference, we can conclude that our model performed better under multivariate imputation as it achieved a higher mean cross-validation score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we have learned the difference between univariate imputation and multivariate imputation. In addition, We also looked at 3 different imputation techniques within Scikit-learn which include simple imputer, iterative imputer and KNN imputer. \n",
    "\n",
    "After comparing the prediction accuracy of our model using simple imputer versus iterative imputer, we can conclude that multivariate imputation results in better model predictions due to its lower mean cross-validation score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. References\n",
    "\n",
    "- [Scikit-learn imputation of missing values](https://scikit-learn.org/stable/modules/impute.html)\n",
    "- [Impute missing values using KNNImputer and IterativeImputer](https://www.youtube.com/watch?v=m_qKhnaYZlc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Follow me on other platforms \n",
    "\n",
    "- [Facebook](https://www.facebook.com/chongjason914)\n",
    "- [Instagram](https://www.instagram.com/chongjason914)\n",
    "- [Twitter](https://www.twitter.com/chongjason914)\n",
    "- [LinkedIn](https://www.linkedin.com/in/chongjason914)\n",
    "- [YouTube](https://www.youtube.com/channel/UCQXiCnjatxiAKgWjoUlM-Xg?view_as=subscriber)\n",
    "- [Medium](https://www.medium.com/@chongjason)"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}