D-23_Categorical_Encoding.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "00a0283d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7b12de8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Datasets/titanic.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "224eaffb",
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>4</th>\n",
       "      <td>4</td>\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",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>775</td>\n",
       "      <td>776</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Myhrman, Mr. Pehr Fabian Oliver Malkolm</td>\n",
       "      <td>male</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347078</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>156</td>\n",
       "      <td>157</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Gilnagh, Miss. Katherine \"Katie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35851</td>\n",
       "      <td>7.7333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>632</td>\n",
       "      <td>633</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Stahelin-Maeglin, Dr. Max</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13214</td>\n",
       "      <td>30.5000</td>\n",
       "      <td>B50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  PassengerId  Survived  Pclass  \\\n",
       "4             4            5         0       3   \n",
       "775         775          776         0       3   \n",
       "6             6            7         0       1   \n",
       "156         156          157         1       3   \n",
       "632         632          633         1       1   \n",
       "\n",
       "                                        Name     Sex   Age  SibSp  Parch  \\\n",
       "4                   Allen, Mr. William Henry    male  35.0      0      0   \n",
       "775  Myhrman, Mr. Pehr Fabian Oliver Malkolm    male  18.0      0      0   \n",
       "6                    McCarthy, Mr. Timothy J    male  54.0      0      0   \n",
       "156         Gilnagh, Miss. Katherine \"Katie\"  female  16.0      0      0   \n",
       "632                Stahelin-Maeglin, Dr. Max    male  32.0      0      0   \n",
       "\n",
       "     Ticket     Fare Cabin Embarked  \n",
       "4    373450   8.0500   NaN        S  \n",
       "775  347078   7.7500   NaN        S  \n",
       "6     17463  51.8625   E46        S  \n",
       "156   35851   7.7333   NaN        Q  \n",
       "632   13214  30.5000   B50        C  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06942e32",
   "metadata": {},
   "source": [
    "## Identify Categorical Features\n",
    "### Specifically identify *nominal* and *ordinal* features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "31502515",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Unnamed: 0   891 non-null    int64  \n",
      " 1   PassengerId  891 non-null    int64  \n",
      " 2   Survived     891 non-null    int64  \n",
      " 3   Pclass       891 non-null    int64  \n",
      " 4   Name         891 non-null    object \n",
      " 5   Sex          891 non-null    object \n",
      " 6   Age          714 non-null    float64\n",
      " 7   SibSp        891 non-null    int64  \n",
      " 8   Parch        891 non-null    int64  \n",
      " 9   Ticket       891 non-null    object \n",
      " 10  Fare         891 non-null    float64\n",
      " 11  Cabin        204 non-null    object \n",
      " 12  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(6), object(5)\n",
      "memory usage: 90.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f45f9b",
   "metadata": {},
   "source": [
    "# 1. Ordinal Categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43dc4455",
   "metadata": {},
   "source": [
    "### Here 'Embarked' is ordinal categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "183ffca0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['Embarked'].fillna('S', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "49022f8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(df.iloc[:,12:],df.iloc[:,2:3],test_size=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3ad4764c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(623, 1)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape\n",
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "150a89a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.fillna('S', inplace=True)\n",
    "X_test.fillna('S',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b139f6b",
   "metadata": {},
   "source": [
    "## Create an OrdinalEncoder object\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4c669be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OrdinalEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1275261",
   "metadata": {},
   "source": [
    "## Categories: Passed a list of lists\n",
    "\n",
    "- Each list represents each column from the data.\n",
    "- Each 'value' represents the total number of unique values in the column.\n",
    "- Values are passed in ascending order based on the amount of value.\n",
    "- For example, if we consider data of stars given to a hotel like 3-star Hotel, 5-star Hotel, 7-star Hotel, here the 7-star hotel has the maximum value.\n",
    "- The encoding will be applied in the following format:\n",
    "  - 3-star=0, 5-star=1, 7-star=2, and so on...\n",
    "- For instance, 'S' has the minimum value, and 'Q' has the maximum value.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cbdc131c",
   "metadata": {},
   "outputs": [],
   "source": [
    "oe=OrdinalEncoder(categories=[['S','C','Q']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf4076e6",
   "metadata": {},
   "source": [
    "# Always fit the object to 'Training Data' first and then transform training data and testing data later.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f0cfefe9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "OrdinalEncoder(categories=[['S', 'C', 'Q']])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oe.fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8ecdfcbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=oe.transform(X_train)\n",
    "X_test=oe.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "80782dd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [0.],\n",
       "       [1.],\n",
       "       [1.],\n",
       "       [0.],\n",
       "       [2.],\n",
       "       [1.]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fc16cd79",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "34745074",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:97: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n"
     ]
    }
   ],
   "source": [
    "le.fit(y_train)\n",
    "y_train=le.transform(y_train)\n",
    "y_test=le.transform(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9ab872f",
   "metadata": {},
   "source": [
    "#### I too didint understand what this warning said ..ignored it ;)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "aea4fa00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "dac87401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 0, 0, 0, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "669b1297",
   "metadata": {},
   "source": [
    "# Done That was it..! :)"
   ]
  }
 ],
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