Data_Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>no</th>\n",
       "      <th>nama</th>\n",
       "      <th>tanggal_lahir</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>gaji</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ani</td>\n",
       "      <td>3/2/1993</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>budi</td>\n",
       "      <td>3/1/1992</td>\n",
       "      <td>laki-laki</td>\n",
       "      <td>2000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>susi</td>\n",
       "      <td>4/5/2000</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>1350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>wati</td>\n",
       "      <td>5/20/1995</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>10000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>ati</td>\n",
       "      <td>4/23/1994</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>3400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>ina</td>\n",
       "      <td>5/6/1991</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>5000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>nia</td>\n",
       "      <td>4/5/1990</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>2500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>dodi</td>\n",
       "      <td>4/3/1998</td>\n",
       "      <td>laki-laki</td>\n",
       "      <td>3400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>dedi</td>\n",
       "      <td>6/20/1993</td>\n",
       "      <td>laki-laki</td>\n",
       "      <td>8900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>roni</td>\n",
       "      <td>9/20/2001</td>\n",
       "      <td>laki-laki</td>\n",
       "      <td>2100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   no  nama tanggal_lahir jenis_kelamin      gaji\n",
       "0   1   ani      3/2/1993     perempuan   1000000\n",
       "1   2  budi      3/1/1992     laki-laki   2000000\n",
       "2   3  susi      4/5/2000     perempuan   1350000\n",
       "3   4  wati     5/20/1995     perempuan  10000000\n",
       "4   5   ati     4/23/1994     perempuan   3400000\n",
       "5   6   ina      5/6/1991     perempuan   5000000\n",
       "6   7   nia      4/5/1990     perempuan   2500000\n",
       "7   8  dodi      4/3/1998     laki-laki   3400000\n",
       "8   9  dedi     6/20/1993     laki-laki   8900000\n",
       "9  10  roni     9/20/2001     laki-laki   2100000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('employee.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   no             10 non-null     int64 \n",
      " 1   nama           10 non-null     object\n",
      " 2   tanggal_lahir  10 non-null     object\n",
      " 3   jenis_kelamin  10 non-null     object\n",
      " 4   gaji           10 non-null     int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 528.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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.boxplot(data = data, x = 'jenis_kelamin', y = 'gaji')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>jk_laki-laki</th>\n",
       "      <th>jk_perempuan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   jk_laki-laki  jk_perempuan\n",
       "0             0             1\n",
       "1             1             0\n",
       "2             0             1\n",
       "3             0             1\n",
       "4             0             1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jk_dummies = pd.get_dummies(data['jenis_kelamin'], prefix='jk')\n",
    "jk_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>no</th>\n",
       "      <th>nama</th>\n",
       "      <th>tanggal_lahir</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>gaji</th>\n",
       "      <th>jk_laki-laki</th>\n",
       "      <th>jk_perempuan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ani</td>\n",
       "      <td>3/2/1993</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>budi</td>\n",
       "      <td>3/1/1992</td>\n",
       "      <td>laki-laki</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>susi</td>\n",
       "      <td>4/5/2000</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>1350000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>wati</td>\n",
       "      <td>5/20/1995</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>10000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>ati</td>\n",
       "      <td>4/23/1994</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>3400000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   no  nama tanggal_lahir jenis_kelamin      gaji  jk_laki-laki  jk_perempuan\n",
       "0   1   ani      3/2/1993     perempuan   1000000             0             1\n",
       "1   2  budi      3/1/1992     laki-laki   2000000             1             0\n",
       "2   3  susi      4/5/2000     perempuan   1350000             0             1\n",
       "3   4  wati     5/20/1995     perempuan  10000000             0             1\n",
       "4   5   ati     4/23/1994     perempuan   3400000             0             1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jk = pd.concat([data, jk_dummies], axis = 1)\n",
    "data_jk.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "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>no</th>\n",
       "      <th>nama</th>\n",
       "      <th>tanggal_lahir</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>gaji</th>\n",
       "      <th>jk_laki-laki</th>\n",
       "      <th>jk_perempuan</th>\n",
       "      <th>jk_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ani</td>\n",
       "      <td>3/2/1993</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>budi</td>\n",
       "      <td>3/1/1992</td>\n",
       "      <td>laki-laki</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>susi</td>\n",
       "      <td>4/5/2000</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>1350000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>wati</td>\n",
       "      <td>5/20/1995</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>10000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>ati</td>\n",
       "      <td>4/23/1994</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>3400000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   no  nama tanggal_lahir jenis_kelamin      gaji  jk_laki-laki  jk_perempuan  \\\n",
       "0   1   ani      3/2/1993     perempuan   1000000             0             1   \n",
       "1   2  budi      3/1/1992     laki-laki   2000000             1             0   \n",
       "2   3  susi      4/5/2000     perempuan   1350000             0             1   \n",
       "3   4  wati     5/20/1995     perempuan  10000000             0             1   \n",
       "4   5   ati     4/23/1994     perempuan   3400000             0             1   \n",
       "\n",
       "   jk_label  \n",
       "0         1  \n",
       "1         0  \n",
       "2         1  \n",
       "3         1  \n",
       "4         1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jk['jk_label'] = le.fit_transform(data['jenis_kelamin'])\n",
    "data_jk.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standar Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1000000,  2000000,  1350000, 10000000,  3400000,  5000000,\n",
       "        2500000,  3400000,  8900000,  2100000], dtype=int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['gaji'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>no</th>\n",
       "      <th>nama</th>\n",
       "      <th>tanggal_lahir</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>gaji</th>\n",
       "      <th>jk_laki-laki</th>\n",
       "      <th>jk_perempuan</th>\n",
       "      <th>jk_label</th>\n",
       "      <th>sc_gaji</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ani</td>\n",
       "      <td>3/2/1993</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.001779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>budi</td>\n",
       "      <td>3/1/1992</td>\n",
       "      <td>laki-laki</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.663911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>susi</td>\n",
       "      <td>4/5/2000</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>1350000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.883525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>wati</td>\n",
       "      <td>5/20/1995</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>10000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.039035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>ati</td>\n",
       "      <td>4/23/1994</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>3400000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.190896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   no  nama tanggal_lahir jenis_kelamin      gaji  jk_laki-laki  jk_perempuan  \\\n",
       "0   1   ani      3/2/1993     perempuan   1000000             0             1   \n",
       "1   2  budi      3/1/1992     laki-laki   2000000             1             0   \n",
       "2   3  susi      4/5/2000     perempuan   1350000             0             1   \n",
       "3   4  wati     5/20/1995     perempuan  10000000             0             1   \n",
       "4   5   ati     4/23/1994     perempuan   3400000             0             1   \n",
       "\n",
       "   jk_label   sc_gaji  \n",
       "0         1 -1.001779  \n",
       "1         0 -0.663911  \n",
       "2         1 -0.883525  \n",
       "3         1  2.039035  \n",
       "4         1 -0.190896  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jk['sc_gaji'] = sc.fit_transform(data['gaji'].to_numpy().reshape(-1, 1))\n",
    "data_jk.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Min-Max Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "mms = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>no</th>\n",
       "      <th>nama</th>\n",
       "      <th>tanggal_lahir</th>\n",
       "      <th>jenis_kelamin</th>\n",
       "      <th>gaji</th>\n",
       "      <th>jk_laki-laki</th>\n",
       "      <th>jk_perempuan</th>\n",
       "      <th>jk_label</th>\n",
       "      <th>sc_gaji</th>\n",
       "      <th>mms_gaji</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ani</td>\n",
       "      <td>3/2/1993</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>1000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.001779</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>budi</td>\n",
       "      <td>3/1/1992</td>\n",
       "      <td>laki-laki</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.663911</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>susi</td>\n",
       "      <td>4/5/2000</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>1350000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.883525</td>\n",
       "      <td>0.038889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>wati</td>\n",
       "      <td>5/20/1995</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>10000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.039035</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>ati</td>\n",
       "      <td>4/23/1994</td>\n",
       "      <td>perempuan</td>\n",
       "      <td>3400000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.190896</td>\n",
       "      <td>0.266667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   no  nama tanggal_lahir jenis_kelamin      gaji  jk_laki-laki  jk_perempuan  \\\n",
       "0   1   ani      3/2/1993     perempuan   1000000             0             1   \n",
       "1   2  budi      3/1/1992     laki-laki   2000000             1             0   \n",
       "2   3  susi      4/5/2000     perempuan   1350000             0             1   \n",
       "3   4  wati     5/20/1995     perempuan  10000000             0             1   \n",
       "4   5   ati     4/23/1994     perempuan   3400000             0             1   \n",
       "\n",
       "   jk_label   sc_gaji  mms_gaji  \n",
       "0         1 -1.001779  0.000000  \n",
       "1         0 -0.663911  0.111111  \n",
       "2         1 -0.883525  0.038889  \n",
       "3         1  2.039035  1.000000  \n",
       "4         1 -0.190896  0.266667  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jk['mms_gaji'] = mms.fit_transform(data['gaji'].to_numpy().reshape(-1, 1))\n",
    "data_jk.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data_jk['sc_gaji'])\n",
    "plt.title('Standard Scaler')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data_jk['mms_gaji'])\n",
    "plt.title('Min-Max Scaler')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data_jk['gaji'])\n",
    "plt.title('Default')\n",
    "plt.show()"
   ]
  }
 ],
 "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
}