05 - Data Preparation and Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Missing Value\n",
    "\n",
    "Sering kali, data rusak, atau hilang, kita perlu mengurusnya terlebih dahulu karena kedepannya data ini tidak berfungsi saat data hilang atau tidak lengkap."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imputing missing values dengan Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Country</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>44.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>27.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>30.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>38.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>France</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>France</td>\n",
       "      <td>48.0</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Germany</td>\n",
       "      <td>50.0</td>\n",
       "      <td>83000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>37.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country   Age   Salary Purchased\n",
       "0   France  44.0  72000.0        No\n",
       "1    Spain  27.0  48000.0       Yes\n",
       "2  Germany  30.0  54000.0        No\n",
       "3    Spain  38.0  61000.0        No\n",
       "4  Germany  40.0      NaN       Yes\n",
       "5   France  35.0  58000.0       Yes\n",
       "6    Spain   NaN  52000.0        No\n",
       "7   France  48.0  79000.0       Yes\n",
       "8  Germany  50.0  83000.0        No\n",
       "9   France  37.0  67000.0       Yes"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country      0\n",
       "Age          1\n",
       "Salary       1\n",
       "Purchased    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>France</td>\n",
       "      <td>44.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>27.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>30.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>38.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>France</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>France</td>\n",
       "      <td>48.0</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Germany</td>\n",
       "      <td>50.0</td>\n",
       "      <td>83000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>37.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country   Age   Salary Purchased\n",
       "0   France  44.0  72000.0        No\n",
       "1    Spain  27.0  48000.0       Yes\n",
       "2  Germany  30.0  54000.0        No\n",
       "3    Spain  38.0  61000.0        No\n",
       "5   France  35.0  58000.0       Yes\n",
       "7   France  48.0  79000.0       Yes\n",
       "8  Germany  50.0  83000.0        No\n",
       "9   France  37.0  67000.0       Yes"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Country</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchased</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>France</td>\n",
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       "      <td>No</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Spain</td>\n",
       "      <td>27.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>30.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>No</td>\n",
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       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>38.0</td>\n",
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       "      <td>No</td>\n",
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       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>France</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>France</td>\n",
       "      <td>48.0</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Germany</td>\n",
       "      <td>50.0</td>\n",
       "      <td>83000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>37.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country   Age   Salary Purchased\n",
       "0   France  44.0  72000.0        No\n",
       "1    Spain  27.0  48000.0       Yes\n",
       "2  Germany  30.0  54000.0        No\n",
       "3    Spain  38.0  61000.0        No\n",
       "4  Germany  40.0      NaN       Yes\n",
       "5   France  35.0  58000.0       Yes\n",
       "7   France  48.0  79000.0       Yes\n",
       "8  Germany  50.0  83000.0        No\n",
       "9   France  37.0  67000.0       Yes"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop kolom spesifik yang mengandung NaN \n",
    "df.dropna(subset=['Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.0</td>\n",
       "      <td>72000.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>48000.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>54000.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.0</td>\n",
       "      <td>61000.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>5</th>\n",
       "      <td>35.0</td>\n",
       "      <td>58000.0</td>\n",
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       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>52000.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>48.0</td>\n",
       "      <td>79000.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50.0</td>\n",
       "      <td>83000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>37.0</td>\n",
       "      <td>67000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age   Salary\n",
       "0  44.0  72000.0\n",
       "1  27.0  48000.0\n",
       "2  30.0  54000.0\n",
       "3  38.0  61000.0\n",
       "4  40.0      NaN\n",
       "5  35.0  58000.0\n",
       "6   NaN  52000.0\n",
       "7  48.0  79000.0\n",
       "8  50.0  83000.0\n",
       "9  37.0  67000.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, 1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Guntur\\AppData\\Local\\conda\\conda\\envs\\py3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
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       "      <th>Purchased</th>\n",
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       "      <td>Spain</td>\n",
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       "      <td>48000.0</td>\n",
       "      <td>Yes</td>\n",
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       "      <th>2</th>\n",
       "      <td>Germany</td>\n",
       "      <td>30.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>No</td>\n",
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       "      <th>3</th>\n",
       "      <td>Spain</td>\n",
       "      <td>38.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>No</td>\n",
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       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>40.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>France</td>\n",
       "      <td>35.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Spain</td>\n",
       "      <td>38.0</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>France</td>\n",
       "      <td>48.0</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Germany</td>\n",
       "      <td>50.0</td>\n",
       "      <td>83000.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>France</td>\n",
       "      <td>37.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country   Age   Salary Purchased\n",
       "0   France  44.0  72000.0        No\n",
       "1    Spain  27.0  48000.0       Yes\n",
       "2  Germany  30.0  54000.0        No\n",
       "3    Spain  38.0  61000.0        No\n",
       "4  Germany  40.0  61000.0       Yes\n",
       "5   France  35.0  58000.0       Yes\n",
       "6    Spain  38.0  52000.0        No\n",
       "7   France  48.0  79000.0       Yes\n",
       "8  Germany  50.0  83000.0        No\n",
       "9   France  37.0  67000.0       Yes"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace every occurrence of missing_values to one defined by strategy\n",
    "# which can be mean, median, mode. Axis = 0 means rows, 1 means column\n",
    "\n",
    "imputer = Imputer(missing_values='NaN', strategy='median', axis = 0)\n",
    "df.iloc[:, 1:3] = imputer.fit_transform(df.iloc[:, 1:3])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Encoding Data Kategori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Label Encoder will replace every categorical variable with number. Useful for replacing yes by 1, no by 0.\n",
    "# One Hot Encoder will create a separate column for every variable and give a value of 1 where the variable is present\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['No' 'Yes']\n",
      "['France' 'Germany' 'Spain']\n",
      "   Country   Age   Salary  Purchased\n",
      "0        0  44.0  72000.0          0\n",
      "1        2  27.0  48000.0          1\n",
      "2        1  30.0  54000.0          0\n",
      "3        2  38.0  61000.0          0\n",
      "4        1  40.0  61000.0          1\n",
      "5        0  35.0  58000.0          1\n",
      "6        2  38.0  52000.0          0\n",
      "7        0  48.0  79000.0          1\n",
      "8        1  50.0  83000.0          0\n",
      "9        0  37.0  67000.0          1\n"
     ]
    }
   ],
   "source": [
    "lable_encoder = LabelEncoder()\n",
    "temp = df.copy()\n",
    "temp.iloc[:, 3] = lable_encoder.fit_transform(df.iloc[:, 3])\n",
    "print(lable_encoder.classes_)\n",
    "temp.iloc[:, 0] = lable_encoder.fit_transform(df.iloc[:, 0])\n",
    "print(lable_encoder.classes_)\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Country_France</th>\n",
       "      <th>Country_Germany</th>\n",
       "      <th>Country_Spain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.0</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.0</td>\n",
       "      <td>54000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>38.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>61000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.0</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38.0</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>48.0</td>\n",
       "      <td>79000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50.0</td>\n",
       "      <td>83000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>37.0</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age   Salary  Country_France  Country_Germany  Country_Spain\n",
       "0  44.0  72000.0               1                0              0\n",
       "1  27.0  48000.0               0                0              1\n",
       "2  30.0  54000.0               0                1              0\n",
       "3  38.0  61000.0               0                0              1\n",
       "4  40.0  61000.0               0                1              0\n",
       "5  35.0  58000.0               1                0              0\n",
       "6  38.0  52000.0               0                0              1\n",
       "7  48.0  79000.0               1                0              0\n",
       "8  50.0  83000.0               0                1              0\n",
       "9  37.0  67000.0               1                0              0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# you can pass an array of indices of categorical features\n",
    "# one_hot_encoder = OneHotEncoder(categorical_features=[0])\n",
    "# temp = df.copy()\n",
    "# temp.iloc[:, 0] = one_hot_encoder.fit_transform(df.iloc[:, :0])\n",
    "# temp\n",
    "# you can achieve the same thing using get_dummies\n",
    "pd.get_dummies(df.iloc[:, :-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Binarizing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mengubah Data menjadi 0 dan 1.\n",
    "Kita akan mencoba dataset lain, yaitu dataset iris yang ada pada library scikit-learn. (https://archive.ics.uci.edu/ml/datasets/iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris_dataset = load_iris()\n",
    "X = iris_dataset.data\n",
    "y = iris_dataset.target\n",
    "feature_names = iris_dataset.feature_names\n",
    "print(feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.5, 3. , 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3. ,\n",
       "       3. , 4. , 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3. ,\n",
       "       3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3. ,\n",
       "       3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3. , 3.8, 3.2, 3.7, 3.3, 3.2, 3.2,\n",
       "       3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2. , 3. , 2.2, 2.9, 2.9,\n",
       "       3.1, 3. , 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3. , 2.8, 3. ,\n",
       "       2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3. , 3.4, 3.1, 2.3, 3. , 2.5, 2.6,\n",
       "       3. , 2.6, 2.3, 2.7, 3. , 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3. , 2.9,\n",
       "       3. , 3. , 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3. , 2.5, 2.8, 3.2, 3. ,\n",
       "       3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3. , 2.8, 3. ,\n",
       "       2.8, 3.8, 2.8, 2.8, 2.6, 3. , 3.4, 3.1, 3. , 3.1, 3.1, 3.1, 2.7,\n",
       "       3.2, 3.3, 3. , 2.5, 3. , 3.4, 3. ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kita akan mengubah 0 jika dibawah rata-rata, dan 1 jika diatas rata-rata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 0., 0., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 0., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
       "       0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 0., 1., 0., 1., 0.,\n",
       "       0., 1., 0., 0., 0., 1., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
       "       1., 1., 0., 1., 1., 1., 0., 1., 1., 0., 0., 0., 1., 0.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Binarizer\n",
    "binarizer_obj = Binarizer(threshold=X[:, 1].mean())\n",
    "X[:, 1:2] = binarizer_obj.fit_transform(X[:, 1].reshape(-1, 1))\n",
    "X[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Fitur Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Country   Age   Salary Purchased\n",
      "0   France  44.0  72000.0        No\n",
      "1    Spain  27.0  48000.0       Yes\n",
      "2  Germany  30.0  54000.0        No\n",
      "3    Spain  38.0  61000.0        No\n",
      "5   France  35.0  58000.0       Yes\n",
      "7   France  48.0  79000.0       Yes\n",
      "8  Germany  50.0  83000.0        No\n",
      "9   France  37.0  67000.0       Yes\n",
      "[[4.4e+01 7.2e+04]\n",
      " [2.7e+01 4.8e+04]\n",
      " [3.0e+01 5.4e+04]\n",
      " [3.8e+01 6.1e+04]\n",
      " [3.5e+01 5.8e+04]\n",
      " [4.8e+01 7.9e+04]\n",
      " [5.0e+01 8.3e+04]\n",
      " [3.7e+01 6.7e+04]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler\n",
    "\n",
    "df = pd.read_csv('Data.csv').dropna()\n",
    "print(df)\n",
    "X = df[[\"Age\", \"Salary\"]].values.astype(np.float64)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standardization\n",
      "[[ 0.69985807  0.58989097]\n",
      " [-1.51364653 -1.50749915]\n",
      " [-1.12302807 -0.98315162]\n",
      " [-0.08137885 -0.37141284]\n",
      " [-0.47199731 -0.6335866 ]\n",
      " [ 1.22068269  1.20162976]\n",
      " [ 1.48109499  1.55119478]\n",
      " [-0.211585    0.1529347 ]]\n",
      "Normalizing\n",
      "[[6.11110997e-04 9.99999813e-01]\n",
      " [5.62499911e-04 9.99999842e-01]\n",
      " [5.55555470e-04 9.99999846e-01]\n",
      " [6.22950699e-04 9.99999806e-01]\n",
      " [6.03448166e-04 9.99999818e-01]\n",
      " [6.07594825e-04 9.99999815e-01]\n",
      " [6.02409529e-04 9.99999819e-01]\n",
      " [5.52238722e-04 9.99999848e-01]]\n",
      "MinMax Scaling\n",
      "[[0.73913043 0.68571429]\n",
      " [0.         0.        ]\n",
      " [0.13043478 0.17142857]\n",
      " [0.47826087 0.37142857]\n",
      " [0.34782609 0.28571429]\n",
      " [0.91304348 0.88571429]\n",
      " [1.         1.        ]\n",
      " [0.43478261 0.54285714]]\n"
     ]
    }
   ],
   "source": [
    "standard_scaler = StandardScaler()\n",
    "normalizer = Normalizer()\n",
    "min_max_scaler = MinMaxScaler()\n",
    "\n",
    "print(\"Standardization\")\n",
    "print(standard_scaler.fit_transform(X))\n",
    "\n",
    "print(\"Normalizing\")\n",
    "print(normalizer.fit_transform(X))\n",
    "\n",
    "print(\"MinMax Scaling\")\n",
    "print(min_max_scaler.fit_transform(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Ekstraksi Fitur\n",
    "Pada pertemuan sebelumnya kalian telah mencoba membuat program WordCount. WordCount merupakan sebuah teknik dalam melakukan ekstraksi Fitur. Namun, kalian tidak perlu membuat sendiri. Scikit-Learn telah menyediakan librarynya. Ekstraksi Fitur ini nantinya akan berguna dalam pemrosesan klasifikasi, clustering, maupun teknik pembelajaran mesin lainnya.\n",
    "## 5.1 Count Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 8)\t1\n",
      "  (0, 52)\t1\n",
      "  (0, 9)\t1\n",
      "  (0, 70)\t1\n",
      "  (0, 73)\t1\n",
      "  (0, 38)\t1\n",
      "  (0, 20)\t1\n",
      "  (0, 63)\t1\n",
      "  (0, 71)\t1\n",
      "  (0, 75)\t1\n",
      "  (0, 57)\t1\n",
      "  (0, 33)\t1\n",
      "  (0, 28)\t1\n",
      "  (0, 31)\t1\n",
      "  (0, 66)\t1\n",
      "  (0, 37)\t1\n",
      "  (0, 16)\t1\n",
      "  (0, 36)\t1\n",
      "  (0, 76)\t1\n",
      "  (0, 18)\t1\n",
      "  (0, 29)\t1\n",
      "  (0, 21)\t2\n",
      "  (0, 15)\t2\n",
      "  (0, 67)\t1\n",
      "  (0, 24)\t1\n",
      "  :\t:\n",
      "  (2, 35)\t1\n",
      "  (2, 68)\t1\n",
      "  (2, 25)\t1\n",
      "  (2, 47)\t1\n",
      "  (2, 14)\t1\n",
      "  (2, 3)\t2\n",
      "  (2, 19)\t1\n",
      "  (2, 26)\t1\n",
      "  (2, 46)\t1\n",
      "  (2, 2)\t1\n",
      "  (2, 77)\t2\n",
      "  (2, 1)\t1\n",
      "  (2, 61)\t2\n",
      "  (2, 53)\t2\n",
      "  (2, 39)\t1\n",
      "  (2, 30)\t1\n",
      "  (2, 59)\t1\n",
      "  (2, 54)\t1\n",
      "  (2, 12)\t1\n",
      "  (2, 34)\t1\n",
      "  (2, 13)\t1\n",
      "  (2, 42)\t1\n",
      "  (2, 71)\t2\n",
      "  (2, 21)\t1\n",
      "  (2, 58)\t2\n",
      "{'pesawat': 58, 'membawa': 44, '178': 7, 'penumpang': 56, 'dewasa': 24, 'satu': 67, 'anak': 15, 'dan': 21, 'dua': 29, 'bayi': 18, 'ujar': 76, 'kepala': 36, 'bagian': 16, 'kerja': 37, 'sama': 66, 'humas': 31, 'direktorat': 28, 'jenderal': 33, 'perhubungan': 57, 'udara': 75, 'sindu': 71, 'rahayu': 63, 'dalam': 20, 'keterangan': 38, 'tertulisnya': 73, 'senin': 70, '29': 9, 'oktober': 52, '2018': 8, 'menyebutkan': 50, 'lion': 42, 'air': 13, 'jt': 34, '610': 12, 'penerbangan': 55, 'dari': 22, 'jakarta': 32, 'menuju': 49, 'pangkal': 54, 'pinang': 59, 'mengalami': 48, 'hilang': 30, 'kontak': 39, 'pada': 53, 'pukul': 61, '06': 1, '33': 10, 'wib': 77, 'dengan': 23, 'nomor': 51, 'registrasi': 64, 'pl': 60, 'lqp': 43, 'dilaporkan': 27, 'tertangkap': 72, 'radar': 62, 'koordinat': 40, '05': 0, '46': 11, '15': 5, '107': 4, '07': 2, '16': 6, 'menambahkan': 46, 'dijadwalkan': 26, 'berangkat': 19, '10': 3, 'akan': 14, 'mendarat': 47, 'di': 25, 'sebelum': 68, 'kata': 35, 'kru': 41, 'sempat': 69, 'meminta': 45, 'retrun': 65, 'to': 74, 'base': 17}\n",
      "[[0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 2 1 0 1 0 1 2 0 0 1 0 0 0 1 1 0 1 0 1 0 0\n",
      "  1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 1 0 0 1 1 0 0 1 1\n",
      "  0 1 0 1 1 0]\n",
      " [1 1 1 0 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 1 0 1 0\n",
      "  0 0 0 1 1 0 1 1 0 0 0 0 1 1 1 1 0 2 1 1 0 0 2 1 1 1 1 0 1 0 0 0 0 0 0 1\n",
      "  1 0 0 0 0 1]\n",
      " [0 1 1 2 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 1 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 1\n",
      "  0 0 0 1 0 1 1 0 0 1 1 1 0 0 0 0 0 2 1 0 0 0 2 1 0 2 0 0 0 1 0 0 1 1 0 2\n",
      "  0 0 1 0 0 2]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "docs = [\"Pesawat membawa 178 penumpang dewasa, satu anak-anak, dan dua bayi,ujar Kepala Bagian Kerja Sama dan Humas Direktorat Jenderal Perhubungan Udara, Sindu Rahayu dalam keterangan tertulisnya, Senin, 29 Oktober 2018.\",\"Sindu menyebutkan pesawat Lion Air JT-610 penerbangan dari Jakarta menuju Pangkal Pinang mengalami hilang kontak pada pukul 06.33 WIB. Pesawat dengan nomor registrasi PL LQP dilaporkan tertangkap radar pada koordinat 05 46.15 S - 107 07.16 E.\",\"Sindu menambahkan pesawat Lion Air JT-610 dijadwalkan berangkat pada pukul 06.10 WIB dan akan mendarat di Pangkal Pinang pada pukul 07.10 WIB. Sebelum hilang kontak, kata Sindu, kru pesawat sempat meminta retrun to base\"]\n",
    "X = cv.fit_transform(docs)\n",
    "print(X)\n",
    "print(cv.vocabulary_)\n",
    "print(X.todense())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dict Vectorizer\n",
    "\n",
    "DictVectorizer melakukan mapping dari dictionry wordcount ke Vektor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 0)\t1.0\n",
      "  (0, 1)\t1.0\n",
      "  (0, 2)\t2.0\n",
      "  (1, 0)\t1.0\n",
      "  (1, 1)\t2.0\n",
      "  (1, 2)\t3.0\n",
      "  (1, 3)\t1.0\n",
      "  (1, 4)\t1.0\n",
      "  (1, 5)\t2.0\n",
      "  (1, 6)\t3.0\n",
      "{'Aku': 0, 'suka': 1, 'makan': 2, 'tidak': 3, 'kambing': 4, 'bakar': 5, 'madu': 6}\n",
      "[[1. 1. 2. 0. 0. 0. 0.]\n",
      " [1. 2. 3. 1. 1. 2. 3.]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "\n",
    "docs = [{\"Aku\": 1, \"suka\": 1, \"makan\": 2}, {\"Aku\": 1, \"tidak\": 1, \"suka\": 2, \"makan\": 3, \"kambing\": 1, \"bakar\": 2, \"madu\": 3}]\n",
    "dv = DictVectorizer(sort=False)\n",
    "X = dv.fit_transform(docs)\n",
    "print(X)\n",
    "print(dv.vocabulary_)\n",
    "print(X.todense())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TfIdf Vectorizer:\n",
    "Word Count (Term Frekuensi dikali dengan Inverse Dokumen Frekuensi),\n",
    "\n",
    "Tutorial dapat dilihat pada link berikut:\n",
    "- https://datascience.mipa.ugm.ac.id/id/representasi-teks-dalam-vektor-part-1/\n",
    "- https://datascience.mipa.ugm.ac.id/id/representasi-teks-dalam-vektor-part-2/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.92276146 0.27249889 0.27249889 0.         0.        ]\n",
      " [0.         0.         0.45329466 0.45329466 0.76749457 0.        ]\n",
      " [0.6088451  0.         0.35959372 0.35959372 0.         0.6088451 ]]\n",
      "{'mayur': 3, 'is': 2, 'guitarist': 1, 'musician': 4, 'also': 0, 'programmer': 5}\n",
      "[[0 2 1 1 0 0]\n",
      " [0 0 1 1 1 0]\n",
      " [1 0 1 1 0 1]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "cv_vectorizer = CountVectorizer()\n",
    "docs = [\"Mayur is a Guitarist Guitarist\", \"Mayur is Musician\", \"Mayur is also a programmer\"]\n",
    "X_idf = tfidf_vectorizer.fit_transform(docs)\n",
    "X_cv = cv_vectorizer.fit_transform(docs)\n",
    "print(X_idf.todense())\n",
    "print(tfidf_vectorizer.vocabulary_)\n",
    "print(X_cv.todense())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GROUP PROJECT\n",
    "\n",
    "Tujuan dari Project ini adalah mengaplikasikan hal-hal yang telah dipelajari dari setiap pertemuan pada Digital Talent pada sebuah \"big dataset\" yang dipilih hingga akhirnya menemukan \"insight\" dari data tersebut.\n",
    "\n",
    "Grup Terdiri dari 4-5 orang, dan akan dipilihkan oleh pengajar secara acak.\n",
    "\n",
    "Dataset yang digunakan adalah GDELT Dataset (Tentatif) https://www.gdeltproject.org\n",
    "\n",
    "\n",
    "Terdapat milestone yang harus dilaporkan setiap minggunya dalam bentuk Pdf:\n",
    "\n",
    "Milestone 1 : \n",
    "- Deskripsi Project & Dataset. \n",
    "- Eksplorasi Data\n",
    "\n",
    "Milestone 2 : \n",
    "- Eksplorasi dengan Statistik Deskriptif\n",
    "- Research Question\n",
    "\n",
    "Milestone 3 :\n",
    "- Model Pembelajaran Mesin yang mungkin diterapkan\n",
    "- Dasar Pemilihan Metode\n",
    "\n",
    "Milestone 4 :\n",
    "- Pembahasan Mengenai Hasil dari riset yang telah dilakukan\n",
    "- Visualisasi Data dengan Tools yang diajarkan\n",
    "\n",
    "\n",
    "Milestone 5 :\n",
    "- Menjawab Research Question beserta kesimpulan dan pembahasan\n",
    "\n",
    "Milestone 6 : \n",
    "- Laporan Final \n",
    "- Pembuatan Presentasi dan Poster\n",
    "- Publikasi dalam paper (opsional) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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