IBDA3111_VictorChendra_202000338_TransformasiData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: center;\">\n",
    "    <hr>\n",
    "    <h2>Prapemrosesan & Rekayasa Data - IBDA3111</h2>\n",
    "    <h3>Data Transforms - Assignment</h3>\n",
    "    <h4>Victor Chendra - <b>202000338</b></h4>\n",
    "    <hr>\n",
    "</div>\n",
    "Dataset source: <a href=\"https://data.jakarta.go.id/dataset/kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk-tahun-2019-kpi\">data.jakarta.go.id</a>\n",
    "\n",
    "<h4> Important!!</h4>\n",
    "Ditemukan bahwa pada dataset april, ada nama kolom dengan huruf besar (pencapaian_Waktu_tempuh (berbeda sendiri)). Jadi ketika dicoba untuk menggabungkannya (concat), kita akan mendapatkan kolom tambahan. <br> <br>\n",
    "\n",
    "Jadi solusinya saya rename colomnnya secara langsung di file csv nya (hard code) menjadi \"pencapaian_waktu_tempuh\". Sehingga kita akan tetap mendapatkan jumlah kolom dataset yang sama dengan semuanya.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Library needed\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from prettytable import PrettyTable\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>waktu</th>\n",
       "      <th>arah</th>\n",
       "      <th>koridor</th>\n",
       "      <th>target_kecepatan</th>\n",
       "      <th>jarak</th>\n",
       "      <th>target_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan_per_koridor</th>\n",
       "      <th>pencapaian_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pagi</td>\n",
       "      <td>Selatan - Pusat</td>\n",
       "      <td>Lenteng Agung - Ps. Minggu - Supomo - dr. Saha...</td>\n",
       "      <td>20.5</td>\n",
       "      <td>19.62</td>\n",
       "      <td>57.43</td>\n",
       "      <td>22.70</td>\n",
       "      <td>63.83</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pagi</td>\n",
       "      <td>Selatan - Pusat</td>\n",
       "      <td>Cilandak KKO - Warung Buncit - Mampang Prapata...</td>\n",
       "      <td>20.5</td>\n",
       "      <td>13.36</td>\n",
       "      <td>39.10</td>\n",
       "      <td>25.43</td>\n",
       "      <td>37.71</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pagi</td>\n",
       "      <td>Selatan - Pusat</td>\n",
       "      <td>Pangeran Antasari - Prapanca - Iskandarsyah - ...</td>\n",
       "      <td>20.5</td>\n",
       "      <td>11.13</td>\n",
       "      <td>32.58</td>\n",
       "      <td>27.54</td>\n",
       "      <td>26.99</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  waktu             arah                                            koridor  \\\n",
       "0  pagi  Selatan - Pusat  Lenteng Agung - Ps. Minggu - Supomo - dr. Saha...   \n",
       "1  pagi  Selatan - Pusat  Cilandak KKO - Warung Buncit - Mampang Prapata...   \n",
       "2  pagi  Selatan - Pusat  Pangeran Antasari - Prapanca - Iskandarsyah - ...   \n",
       "\n",
       "   target_kecepatan  jarak  target_waktu_tempuh  \\\n",
       "0              20.5  19.62                57.43   \n",
       "1              20.5  13.36                39.10   \n",
       "2              20.5  11.13                32.58   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                             22.70                    63.83   \n",
       "1                             25.43                    37.71   \n",
       "2                             27.54                    26.99   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0                 22.98  \n",
       "1                 22.98  \n",
       "2                 22.98  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(986, 9)\n"
     ]
    }
   ],
   "source": [
    "# Concat all 12 dataset into only 1 dataframe (combine)\n",
    "def load_dataset():\n",
    "    januari = pd.read_csv(\"kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk-januari-2019.csv\")\n",
    "    februari = pd.read_csv(\"februari-2019-data-kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk.csv\")\n",
    "    maret = pd.read_csv(\"maret-2019-data-kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk.csv\")\n",
    "    april = pd.read_csv(\"april-2019-data-kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk.csv\")\n",
    "    mei = pd.read_csv(\"mei-2019-data-kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk.csv\")\n",
    "    juni = pd.read_csv(\"juni-2019-data-kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk.csv\")\n",
    "    juli = pd.read_csv(\"juli-2019-data-kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk.csv\")\n",
    "    agustus = pd.read_csv(\"agustus-2019-data-kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk.csv\")\n",
    "    september = pd.read_csv(\"september-2019-data-kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk.csv\")\n",
    "    oktober = pd.read_csv(\"oktober-2019-data-kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk.csv\")\n",
    "    november = pd.read_csv(\"november-2019-data-kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk.csv\")\n",
    "    desember = pd.read_csv(\"desember-2019-data-kecepatan-rata-rata-di-41-koridor-jalan-utama-pada-jam-sibuk.csv\")\n",
    "\n",
    "    df = pd.concat([januari, februari, maret, april, mei, juni, juli, agustus, september, oktober, november, desember], ignore_index=True)\n",
    "    return df, df.columns\n",
    "\n",
    "# define function to swap columns\n",
    "def swap_columns(df, col1, col2):\n",
    "    col_list = list(df.columns)\n",
    "    x, y = col_list.index(col1), col_list.index(col2)\n",
    "    col_list[y], col_list[x] = col_list[x], col_list[y]\n",
    "    df = df[col_list]\n",
    "    return df\n",
    "\n",
    "# Load dataset\n",
    "df, column_name = load_dataset()\n",
    "column_name = list(column_name)\n",
    "\n",
    "# swap \"pencapaian_kecepatan\" and \"pencapaian_waktu_tempuh\"\n",
    "df = swap_columns(df, 'pencapaian_kecepatan', 'pencapaian_waktu_tempuh')\n",
    "column_name[-2], column_name[-1] = column_name[-1], column_name[-2]\n",
    "\n",
    "# Save\n",
    "unique_waktu = len(df[\"waktu\"].unique())\n",
    "unique_arah = len(df[\"arah\"].unique())\n",
    "unique_koridor = len(df[\"koridor\"].unique())\n",
    "\n",
    "# Display dataset\n",
    "display(df.head(3))\n",
    "print(df.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Clean dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. Drop duplicated rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicated rows:\n",
      "True\n",
      "    waktu arah koridor  target_kecepatan  jarak  target_waktu_tempuh  \\\n",
      "411   NaN  NaN     NaN               NaN    NaN                  NaN   \n",
      "\n",
      "     pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
      "411                               NaN                      NaN   \n",
      "\n",
      "     pencapaian_kecepatan  \n",
      "411                   NaN  \n",
      "\n",
      "Shape before delete:  (986, 9)\n",
      "Shape after delete :  (985, 9)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dups = df.duplicated()\n",
    "print(f\"Duplicated rows:\")\n",
    "print(dups.any())\n",
    "print(df[dups])\n",
    "\n",
    "print(f\"\\nShape before delete:  {df.shape}\")\n",
    "df.drop_duplicates(inplace=True)\n",
    "print(f\"Shape after delete :  {df.shape}\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why drop the duplicated rows? Because we don't want our model become bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. Drop rows that contain NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Nan Value:\n",
      "waktu                                1\n",
      "arah                                 1\n",
      "koridor                              1\n",
      "target_kecepatan                    42\n",
      "jarak                                1\n",
      "target_waktu_tempuh                  1\n",
      "pencapaian_kecepatan_per_koridor     1\n",
      "pencapaian_waktu_tempuh              1\n",
      "pencapaian_kecepatan                 1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Let's check how many NaN value in each column\n",
    "print(f\"Total Nan Value:\")\n",
    "print(df.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape before: (985, 9)\n",
      "Shape  after: (943, 9)\n",
      "\n",
      "Total Nan Value:\n",
      "waktu                               0\n",
      "arah                                0\n",
      "koridor                             0\n",
      "target_kecepatan                    0\n",
      "jarak                               0\n",
      "target_waktu_tempuh                 0\n",
      "pencapaian_kecepatan_per_koridor    0\n",
      "pencapaian_waktu_tempuh             0\n",
      "pencapaian_kecepatan                0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Drop rows that contain NaN values\n",
    "print(f\"Shape before: {df.shape}\")\n",
    "df.dropna(inplace=True)\n",
    "print(f\"Shape  after: {df.shape}\\n\")\n",
    "\n",
    "# Let's check how many NaN value in each column\n",
    "print(f\"Total Nan Value:\")\n",
    "print(df.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why drop the NaN value rows? Because our model cannot calculate when there is a NaN value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is something wrong..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Terdapat typo pada datasetnya (categorical columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['pagi' 'sore' 'Pagi' 'Sore']\n",
      "['Selatan - Pusat' 'Timur - Pusat' 'Utara - Pusat' 'Barat - Pusat'\n",
      " 'Pusat - Selatan' 'Pusat - Timur' 'Pusat - Utara' 'Pusat - Barat'\n",
      " 'Utara - Pusa' 'selatan - Pusat']\n"
     ]
    }
   ],
   "source": [
    "# Let's check unique values on categorical columns (except column index 2)\n",
    "for i in range(2):\n",
    "    print(df.iloc[:, i].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "↑ ↑ Typo with same meaning: <br>\n",
    "<ul>\n",
    "    <li>\"pagi\" & \"Pagi\"</li>\n",
    "    <li>\"sore\" & \"Sore\"</li>\n",
    "    <li>\"Utara - Pusat\" with \"Utara - Pusa\" </li>\n",
    "    <li>\"Selatan - Pusat\" with \"selatan - Pusat\"</li>\n",
    "</ul>\n",
    "<br>\n",
    "Let's fix this!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace/update typo values\n",
    "df[column_name[0]] = df[column_name[0]].replace([\"pagi\"], \"Pagi\")\n",
    "df[column_name[0]] = df[column_name[0]].replace([\"sore\"], \"Sore\")\n",
    "\n",
    "df[column_name[1]] = df[column_name[1]].replace([\"Utara - Pusa\"], \"Utara - Pusat\")\n",
    "df[column_name[1]] = df[column_name[1]].replace([\"selatan - Pusat\"], \"Selatan - Pusat\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Pagi' 'Sore']\n",
      "['Selatan - Pusat' 'Timur - Pusat' 'Utara - Pusat' 'Barat - Pusat'\n",
      " 'Pusat - Selatan' 'Pusat - Timur' 'Pusat - Utara' 'Pusat - Barat']\n"
     ]
    }
   ],
   "source": [
    "# Let's check if it's already updated\n",
    "for i in range(2):\n",
    "    print(df.iloc[:, i].unique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DONE ✅"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Hitung akurasi model linear regression (cross validation) tanpa menggunakan scaling maupun encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Linear Regression function\n",
    "def do_linear_regression(df):\n",
    "    # Split X, y\n",
    "    df = df.values\n",
    "    X, y = df[:, :-1], df[:, -1]\n",
    "\n",
    "    # Define LR model, Pipeline, KFold\n",
    "    model = LinearRegression()\n",
    "    pipeline = Pipeline(steps=[('m', model)])\n",
    "    cv = KFold(n_splits=15, shuffle=True, random_state=42)\n",
    "    \n",
    "    # Evaluate the pipeline (It return an array with lenght of n_splits ↑)\n",
    "    accuracy = cross_val_score(model, X, y, scoring='r2', cv=cv, n_jobs=-1)\n",
    "    mean_cross_val = cross_val_score(pipeline, X, y, cv=cv, scoring='neg_mean_absolute_error', n_jobs=-1)\n",
    "    std_cross_val = cross_val_score(pipeline, X, y, cv=cv, scoring='neg_mean_absolute_error', n_jobs=-1)\n",
    "    \n",
    "    # Calculate\n",
    "    accuracy = np.mean(accuracy)\n",
    "    mean_cross_val = (np.mean(np.absolute(mean_cross_val)))\n",
    "    std_cross_val = np.std(std_cross_val)\n",
    "    \n",
    "    # print results\n",
    "    print(f\"Accuracy LR    = {accuracy*100: .4f}%\")\n",
    "    print(f\"Cross val Mean = {mean_cross_val: .4f}\")\n",
    "    print(f\"Cross val Std  = {std_cross_val: .4f}\")\n",
    "    \n",
    "    return (accuracy, mean_cross_val, std_cross_val)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target_kecepatan</th>\n",
       "      <th>jarak</th>\n",
       "      <th>target_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan_per_koridor</th>\n",
       "      <th>pencapaian_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.5</td>\n",
       "      <td>19.62</td>\n",
       "      <td>57.43</td>\n",
       "      <td>22.70</td>\n",
       "      <td>63.83</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.5</td>\n",
       "      <td>13.36</td>\n",
       "      <td>39.10</td>\n",
       "      <td>25.43</td>\n",
       "      <td>37.71</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.5</td>\n",
       "      <td>11.13</td>\n",
       "      <td>32.58</td>\n",
       "      <td>27.54</td>\n",
       "      <td>26.99</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target_kecepatan  jarak  target_waktu_tempuh  \\\n",
       "0              20.5  19.62                57.43   \n",
       "1              20.5  13.36                39.10   \n",
       "2              20.5  11.13                32.58   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                             22.70                    63.83   \n",
       "1                             25.43                    37.71   \n",
       "2                             27.54                    26.99   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0                 22.98  \n",
       "1                 22.98  \n",
       "2                 22.98  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 6) \n",
      "\n",
      "Accuracy LR    =  64.0561%\n",
      "Cross val Mean =  2.0215\n",
      "Cross val Std  =  0.1775\n"
     ]
    }
   ],
   "source": [
    "# Display dataset\n",
    "display(df.iloc[:, 3:].head(3))\n",
    "print(df.iloc[:, 3:].shape, \"\\n\")\n",
    "\n",
    "# Do linear regression\n",
    "pure_score = do_linear_regression(df=df.iloc[:, 3:])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Buatlah histogram untuk tiga cara: (normalisasi, standarisasi, dan robust scaler) untuk fitur numerik, lalu hitunglah akurasi model regresi linear (gunakan cross validation) untuk tiga cara ini (dengan memakai fitur numerik saja)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define scaler function\n",
    "def normalization(df, min_scale: int = 0, max_scale: int = 1):\n",
    "    scaled = MinMaxScaler(feature_range=(min_scale, max_scale)).fit_transform(df)\n",
    "    return scaled\n",
    "\n",
    "def standardization(df):\n",
    "    scaled = StandardScaler().fit_transform(df)\n",
    "    return scaled\n",
    "\n",
    "def robust_scaler(df):\n",
    "    scaled = RobustScaler().fit_transform(df)\n",
    "    return scaled\n",
    "\n",
    "# Define function for plotting histogram\n",
    "def histogram(df, set_title: str):\n",
    "    figure, ax = plt.subplots(2,3, constrained_layout=True, figsize=(18, 7))\n",
    "\n",
    "    # Set title for overall plot\n",
    "    figure.suptitle(set_title) # or plt.suptitle('Main title')\n",
    "\n",
    "    for i in range(df.shape[1]):\n",
    "        plt.subplot(2, 3, i+1)\n",
    "        axes = sns.histplot(data=df, x=df.columns[i], kde=True)\n",
    "        axes.lines[0].set_color('crimson')\n",
    "\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target_kecepatan</th>\n",
       "      <th>jarak</th>\n",
       "      <th>target_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan_per_koridor</th>\n",
       "      <th>pencapaian_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.5</td>\n",
       "      <td>19.62</td>\n",
       "      <td>57.43</td>\n",
       "      <td>22.70</td>\n",
       "      <td>63.83</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.5</td>\n",
       "      <td>13.36</td>\n",
       "      <td>39.10</td>\n",
       "      <td>25.43</td>\n",
       "      <td>37.71</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.5</td>\n",
       "      <td>11.13</td>\n",
       "      <td>32.58</td>\n",
       "      <td>27.54</td>\n",
       "      <td>26.99</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target_kecepatan  jarak  target_waktu_tempuh  \\\n",
       "0              20.5  19.62                57.43   \n",
       "1              20.5  13.36                39.10   \n",
       "2              20.5  11.13                32.58   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                             22.70                    63.83   \n",
       "1                             25.43                    37.71   \n",
       "2                             27.54                    26.99   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0                 22.98  \n",
       "1                 22.98  \n",
       "2                 22.98  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 6)\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>target_kecepatan</th>\n",
       "      <th>jarak</th>\n",
       "      <th>target_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan_per_koridor</th>\n",
       "      <th>pencapaian_waktu_tempuh</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.875392</td>\n",
       "      <td>0.791712</td>\n",
       "      <td>0.224421</td>\n",
       "      <td>0.723518</td>\n",
       "      <td>0.284382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.384796</td>\n",
       "      <td>0.391930</td>\n",
       "      <td>0.301583</td>\n",
       "      <td>0.347582</td>\n",
       "      <td>0.284382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.210031</td>\n",
       "      <td>0.249727</td>\n",
       "      <td>0.361221</td>\n",
       "      <td>0.193293</td>\n",
       "      <td>0.284382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target_kecepatan     jarak  target_waktu_tempuh  \\\n",
       "0               0.3  0.875392             0.791712   \n",
       "1               0.3  0.384796             0.391930   \n",
       "2               0.3  0.210031             0.249727   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                          0.224421                 0.723518   \n",
       "1                          0.301583                 0.347582   \n",
       "2                          0.361221                 0.193293   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0              0.284382  \n",
       "1              0.284382  \n",
       "2              0.284382  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 6) \n",
      "\n",
      "Accuracy LR    =  64.0561%\n",
      "Cross val Mean =  0.0942\n",
      "Cross val Std  =  0.0083\n"
     ]
    }
   ],
   "source": [
    "# Normalization\n",
    "scaled = normalization(df.iloc[:, 3:], 0, 1)\n",
    "df1_norm = pd.DataFrame(scaled, columns=column_name[3:])\n",
    "\n",
    "# Before normalization\n",
    "display(df.iloc[:, 3:].head(3))\n",
    "print(df.iloc[:, 3:].shape)\n",
    "\n",
    "# After normalization\n",
    "display(df1_norm.head(3))\n",
    "print(df1_norm.shape, \"\\n\")\n",
    "\n",
    "# Do linear regression\n",
    "nrm_score = do_linear_regression(df=df1_norm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target_kecepatan</th>\n",
       "      <th>jarak</th>\n",
       "      <th>target_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan_per_koridor</th>\n",
       "      <th>pencapaian_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20.5</td>\n",
       "      <td>19.62</td>\n",
       "      <td>57.43</td>\n",
       "      <td>22.70</td>\n",
       "      <td>63.83</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.5</td>\n",
       "      <td>13.36</td>\n",
       "      <td>39.10</td>\n",
       "      <td>25.43</td>\n",
       "      <td>37.71</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.5</td>\n",
       "      <td>11.13</td>\n",
       "      <td>32.58</td>\n",
       "      <td>27.54</td>\n",
       "      <td>26.99</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target_kecepatan  jarak  target_waktu_tempuh  \\\n",
       "0              20.5  19.62                57.43   \n",
       "1              20.5  13.36                39.10   \n",
       "2              20.5  11.13                32.58   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                             22.70                    63.83   \n",
       "1                             25.43                    37.71   \n",
       "2                             27.54                    26.99   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0                 22.98  \n",
       "1                 22.98  \n",
       "2                 22.98  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 6)\n"
     ]
    },
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       "      <th>pencapaian_kecepatan_per_koridor</th>\n",
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       "      <td>-0.614791</td>\n",
       "      <td>1.511478</td>\n",
       "      <td>1.665935</td>\n",
       "      <td>-0.587928</td>\n",
       "      <td>1.718837</td>\n",
       "      <td>-0.683991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.614791</td>\n",
       "      <td>-0.549380</td>\n",
       "      <td>-0.377027</td>\n",
       "      <td>-0.090900</td>\n",
       "      <td>-0.282108</td>\n",
       "      <td>-0.683991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.614791</td>\n",
       "      <td>-1.283520</td>\n",
       "      <td>-1.103711</td>\n",
       "      <td>0.293251</td>\n",
       "      <td>-1.103322</td>\n",
       "      <td>-0.683991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target_kecepatan     jarak  target_waktu_tempuh  \\\n",
       "0         -0.614791  1.511478             1.665935   \n",
       "1         -0.614791 -0.549380            -0.377027   \n",
       "2         -0.614791 -1.283520            -1.103711   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                         -0.587928                 1.718837   \n",
       "1                         -0.090900                -0.282108   \n",
       "2                          0.293251                -1.103322   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0             -0.683991  \n",
       "1             -0.683991  \n",
       "2             -0.683991  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 6) \n",
      "\n",
      "Accuracy LR    =  64.0561%\n",
      "Cross val Mean =  0.4687\n",
      "Cross val Std  =  0.0412\n"
     ]
    }
   ],
   "source": [
    "# Standardization\n",
    "scaled = standardization(df.iloc[:, 3:])\n",
    "df1_stdz = pd.DataFrame(scaled, columns=column_name[3:])\n",
    "\n",
    "# Before standardization\n",
    "display(df.iloc[:, 3:].head(3))\n",
    "print(df.iloc[:, 3:].shape)\n",
    "\n",
    "# After standardization\n",
    "display(df1_stdz.head(3))\n",
    "print(df1_stdz.shape, \"\\n\")\n",
    "\n",
    "# Do linear regression\n",
    "stdz_score = do_linear_regression(df=df1_stdz)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. Robust Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <td>20.5</td>\n",
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       "      <td>57.43</td>\n",
       "      <td>22.70</td>\n",
       "      <td>63.83</td>\n",
       "      <td>22.98</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.5</td>\n",
       "      <td>13.36</td>\n",
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       "      <td>37.71</td>\n",
       "      <td>22.98</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.5</td>\n",
       "      <td>11.13</td>\n",
       "      <td>32.58</td>\n",
       "      <td>27.54</td>\n",
       "      <td>26.99</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target_kecepatan  jarak  target_waktu_tempuh  \\\n",
       "0              20.5  19.62                57.43   \n",
       "1              20.5  13.36                39.10   \n",
       "2              20.5  11.13                32.58   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                             22.70                    63.83   \n",
       "1                             25.43                    37.71   \n",
       "2                             27.54                    26.99   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0                 22.98  \n",
       "1                 22.98  \n",
       "2                 22.98  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 6)\n"
     ]
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       "      <td>-0.353615</td>\n",
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       "      <td>-0.333333</td>\n",
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       "      <td>-0.838275</td>\n",
       "      <td>0.339298</td>\n",
       "      <td>-0.704752</td>\n",
       "      <td>-0.362663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target_kecepatan     jarak  target_waktu_tempuh  \\\n",
       "0         -0.333333  1.262755             1.394429   \n",
       "1         -0.333333 -0.334184            -0.252471   \n",
       "2         -0.333333 -0.903061            -0.838275   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                         -0.353615                 1.262146   \n",
       "1                          0.037223                -0.132408   \n",
       "2                          0.339298                -0.704752   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0             -0.362663  \n",
       "1             -0.362663  \n",
       "2             -0.362663  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 6) \n",
      "\n",
      "Accuracy LR    =  64.0561%\n",
      "Cross val Mean =  0.3093\n",
      "Cross val Std  =  0.0272\n"
     ]
    }
   ],
   "source": [
    "# Robust Scaler\n",
    "scaled = robust_scaler(df.iloc[:, 3:])\n",
    "df1_rs = pd.DataFrame(scaled, columns=column_name[3:])\n",
    "\n",
    "# Before Robust Scaler\n",
    "display(df.iloc[:, 3:].head(3))\n",
    "print(df.iloc[:, 3:].shape)\n",
    "\n",
    "# After Robust Scaler\n",
    "display(df1_rs.head(3))\n",
    "print(df1_rs.shape, \"\\n\")\n",
    "\n",
    "# Do linear regression\n",
    "RS_score = do_linear_regression(df=df1_rs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram(df.iloc[:, 3:], \"Original Dataset\")\n",
    "histogram(df1_norm, \"Normalization data\")\n",
    "histogram(df1_stdz, \"Standardization data\")\n",
    "histogram(df1_rs, \"Robust Scaler data\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Notes:\n",
    "Saya telah mencoba 3 jenis scaling diatas terhadap dataset, namun tidak untuk variabel outputnya (y) atau scaling hanya pada variabel input. Namun hasil accuracy yang dihasilkan sama persis, kecuali nilai MAE nya, dimana nilainya sama dengan nilai MAE jika tidak melakukan scaling sama sekali (nilai MAE sama nilai MAR pada no 1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Gunakan onehot encoder dan ordinal encoder terhadap dataset untuk fitur kategorial. Lalu hitunglah akurasi model regresi linear (gunakan cross validation) untuk dua cara ini (dengan memakai fitur kategorial saja)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encoding(df):\n",
    "    df = df.copy()\n",
    "\n",
    "    encoded = OneHotEncoder(sparse=False).fit_transform(df.iloc[:, :3])\n",
    "    encoded = pd.DataFrame(encoded)\n",
    "\n",
    "    encoded.reset_index(inplace=True)\n",
    "    df.reset_index(inplace=True)\n",
    "    \n",
    "    encoded.drop([\"index\"], axis=1, inplace=True)\n",
    "    df.drop([\"index\"], axis=1, inplace=True)\n",
    "\n",
    "    return encoded.join(df.iloc[:, 3:])\n",
    "\n",
    "def ordinal_encoder(df):\n",
    "    df = df.copy()\n",
    "\n",
    "    encoded = OrdinalEncoder().fit_transform(df.iloc[:, :3])\n",
    "    encoded = pd.DataFrame(encoded, columns=column_name[:3])\n",
    "    \n",
    "    encoded.reset_index(inplace=True)\n",
    "    df.reset_index(inplace=True)\n",
    "    \n",
    "    encoded.drop([\"index\"], axis=1, inplace=True)\n",
    "    df.drop([\"index\"], axis=1, inplace=True)\n",
    "\n",
    "    return encoded.join(df.iloc[:, 3:])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>waktu</th>\n",
       "      <th>arah</th>\n",
       "      <th>koridor</th>\n",
       "      <th>target_kecepatan</th>\n",
       "      <th>jarak</th>\n",
       "      <th>target_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan_per_koridor</th>\n",
       "      <th>pencapaian_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pagi</td>\n",
       "      <td>Selatan - Pusat</td>\n",
       "      <td>Lenteng Agung - Ps. Minggu - Supomo - dr. Saha...</td>\n",
       "      <td>20.5</td>\n",
       "      <td>19.62</td>\n",
       "      <td>57.43</td>\n",
       "      <td>22.70</td>\n",
       "      <td>63.83</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pagi</td>\n",
       "      <td>Selatan - Pusat</td>\n",
       "      <td>Cilandak KKO - Warung Buncit - Mampang Prapata...</td>\n",
       "      <td>20.5</td>\n",
       "      <td>13.36</td>\n",
       "      <td>39.10</td>\n",
       "      <td>25.43</td>\n",
       "      <td>37.71</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pagi</td>\n",
       "      <td>Selatan - Pusat</td>\n",
       "      <td>Pangeran Antasari - Prapanca - Iskandarsyah - ...</td>\n",
       "      <td>20.5</td>\n",
       "      <td>11.13</td>\n",
       "      <td>32.58</td>\n",
       "      <td>27.54</td>\n",
       "      <td>26.99</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "  waktu             arah                                            koridor  \\\n",
       "0  Pagi  Selatan - Pusat  Lenteng Agung - Ps. Minggu - Supomo - dr. Saha...   \n",
       "1  Pagi  Selatan - Pusat  Cilandak KKO - Warung Buncit - Mampang Prapata...   \n",
       "2  Pagi  Selatan - Pusat  Pangeran Antasari - Prapanca - Iskandarsyah - ...   \n",
       "\n",
       "   target_kecepatan  jarak  target_waktu_tempuh  \\\n",
       "0              20.5  19.62                57.43   \n",
       "1              20.5  13.36                39.10   \n",
       "2              20.5  11.13                32.58   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                             22.70                    63.83   \n",
       "1                             25.43                    37.71   \n",
       "2                             27.54                    26.99   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0                 22.98  \n",
       "1                 22.98  \n",
       "2                 22.98  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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      "(943, 9)\n"
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       "     0    1    2    3    4    5    6    7    8    9  ...   47   48   49   50  \\\n",
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       "\n",
       "   target_kecepatan  jarak  target_waktu_tempuh  \\\n",
       "0              20.5  19.62                57.43   \n",
       "1              20.5  13.36                39.10   \n",
       "2              20.5  11.13                32.58   \n",
       "3              20.5  16.25                47.56   \n",
       "4              20.5  15.87                46.45   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                             22.70                    63.83   \n",
       "1                             25.43                    37.71   \n",
       "2                             27.54                    26.99   \n",
       "3                             22.20                    50.82   \n",
       "4                             25.99                    42.06   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0                 22.98  \n",
       "1                 22.98  \n",
       "2                 22.98  \n",
       "3                 22.98  \n",
       "4                 22.98  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 57)\n",
      "Accuracy LR    =  82.1780%\n",
      "Cross val Mean =  1.3533\n",
      "Cross val Std  =  0.1518\n"
     ]
    }
   ],
   "source": [
    "# Before one hot encoding\n",
    "display(df.head(3))\n",
    "print(df.shape)\n",
    "\n",
    "# One hot encoding\n",
    "df_onehot = one_hot_encoding(df)\n",
    "\n",
    "# After one hot encoding\n",
    "display(df_onehot.head())\n",
    "print(df_onehot.shape)\n",
    "\n",
    "# Do linear regression\n",
    "one_hot_score = do_linear_regression(df=df_onehot)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. Ordinal encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>waktu</th>\n",
       "      <th>arah</th>\n",
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       "      <td>Selatan - Pusat</td>\n",
       "      <td>Lenteng Agung - Ps. Minggu - Supomo - dr. Saha...</td>\n",
       "      <td>20.5</td>\n",
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       "      <th>1</th>\n",
       "      <td>Pagi</td>\n",
       "      <td>Selatan - Pusat</td>\n",
       "      <td>Cilandak KKO - Warung Buncit - Mampang Prapata...</td>\n",
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       "      <th>2</th>\n",
       "      <td>Pagi</td>\n",
       "      <td>Selatan - Pusat</td>\n",
       "      <td>Pangeran Antasari - Prapanca - Iskandarsyah - ...</td>\n",
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       "      <td>32.58</td>\n",
       "      <td>27.54</td>\n",
       "      <td>26.99</td>\n",
       "      <td>22.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
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       "  waktu             arah                                            koridor  \\\n",
       "0  Pagi  Selatan - Pusat  Lenteng Agung - Ps. Minggu - Supomo - dr. Saha...   \n",
       "1  Pagi  Selatan - Pusat  Cilandak KKO - Warung Buncit - Mampang Prapata...   \n",
       "2  Pagi  Selatan - Pusat  Pangeran Antasari - Prapanca - Iskandarsyah - ...   \n",
       "\n",
       "   target_kecepatan  jarak  target_waktu_tempuh  \\\n",
       "0              20.5  19.62                57.43   \n",
       "1              20.5  13.36                39.10   \n",
       "2              20.5  11.13                32.58   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                             22.70                    63.83   \n",
       "1                             25.43                    37.71   \n",
       "2                             27.54                    26.99   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0                 22.98  \n",
       "1                 22.98  \n",
       "2                 22.98  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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      "(943, 9)\n"
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      ],
      "text/plain": [
       "   waktu  arah  koridor  target_kecepatan  jarak  target_waktu_tempuh  \\\n",
       "0    0.0   5.0     16.0              20.5  19.62                57.43   \n",
       "1    0.0   5.0      0.0              20.5  13.36                39.10   \n",
       "2    0.0   5.0     25.0              20.5  11.13                32.58   \n",
       "3    0.0   5.0     29.0              20.5  16.25                47.56   \n",
       "4    0.0   5.0     26.0              20.5  15.87                46.45   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                             22.70                    63.83   \n",
       "1                             25.43                    37.71   \n",
       "2                             27.54                    26.99   \n",
       "3                             22.20                    50.82   \n",
       "4                             25.99                    42.06   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0                 22.98  \n",
       "1                 22.98  \n",
       "2                 22.98  \n",
       "3                 22.98  \n",
       "4                 22.98  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 9)\n",
      "Accuracy LR    =  69.1674%\n",
      "Cross val Mean =  1.9023\n",
      "Cross val Std  =  0.1592\n"
     ]
    }
   ],
   "source": [
    "# Before ordinal encoding\n",
    "display(df.head(3))\n",
    "print(df.shape)\n",
    "\n",
    "# Ordinal encoding\n",
    "df_ordinal = ordinal_encoder(df)\n",
    "\n",
    "# After Ordinal encoding\n",
    "display(df_ordinal.head())\n",
    "print(df_ordinal.shape)\n",
    "\n",
    "# Do linear regression\n",
    "ordinal_score = do_linear_regression(df=df_ordinal)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Gunakan kombinasi transformasi data apapun (lebih dari 2) untuk semua fitur. Lalu hitunglah akurasi model regresi linear (gunakan cross validation) untuk cara ini (dengan memakai semua fitur yang sudah tertransformasi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A. Encoding - Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. One hot encoding - Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python 3.10.2\\lib\\site-packages\\sklearn\\utils\\validation.py:1858: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "c:\\Python 3.10.2\\lib\\site-packages\\sklearn\\utils\\validation.py:1858: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n"
     ]
    },
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       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                          0.224421                 0.723518   \n",
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       "\n",
       "   pencapaian_kecepatan  \n",
       "0              0.284382  \n",
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       "\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 57)\n",
      "Accuracy LR    =  82.8621%\n",
      "Cross val Mean =  0.0620\n",
      "Cross val Std  =  0.0067\n"
     ]
    }
   ],
   "source": [
    "# One hot encoding x normalization\n",
    "ohe_x_norm = normalization(df_onehot, 0, 1)\n",
    "ohe_x_norm = pd.DataFrame(ohe_x_norm, columns=df_onehot.columns)\n",
    "\n",
    "# Display dataset\n",
    "display(ohe_x_norm.head())\n",
    "print(ohe_x_norm.shape)\n",
    "\n",
    "# Do linear regression\n",
    "ohe_x_norm_score = do_linear_regression(df=ohe_x_norm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. One hot encoding - Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "c:\\Python 3.10.2\\lib\\site-packages\\sklearn\\utils\\validation.py:1858: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "c:\\Python 3.10.2\\lib\\site-packages\\sklearn\\utils\\validation.py:1858: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n"
     ]
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       "2  2.203893 -0.372678 -0.372678  ... -0.158114 -0.158114 -0.158114 -0.158114   \n",
       "3  2.203893 -0.372678 -0.372678  ... -0.158114 -0.158114 -0.158114 -0.158114   \n",
       "4  2.203893 -0.372678 -0.372678  ... -0.158114 -0.158114 -0.158114 -0.158114   \n",
       "\n",
       "   target_kecepatan     jarak  target_waktu_tempuh  \\\n",
       "0         -0.614791  1.511478             1.665935   \n",
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       "4         -0.614791  0.276938             0.442164   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                         -0.587928                 1.718837   \n",
       "1                         -0.090900                -0.282108   \n",
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       "4                          0.011055                 0.051128   \n",
       "\n",
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       "2             -0.683991  \n",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 57)\n",
      "Accuracy LR    =  81.9174%\n",
      "Cross val Mean =  0.3178\n",
      "Cross val Std  =  0.0330\n"
     ]
    }
   ],
   "source": [
    "# One hot encoding x standardization\n",
    "ohe_x_stdz = standardization(df_onehot)\n",
    "ohe_x_stdz = pd.DataFrame(ohe_x_stdz, columns=df_onehot.columns)\n",
    "\n",
    "# Display dataset\n",
    "display(ohe_x_stdz.head())\n",
    "print(ohe_x_stdz.shape)\n",
    "\n",
    "# Do linear regression\n",
    "ohe_x_stdz_score = do_linear_regression(df=ohe_x_stdz)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. One hot encoding - Robust scaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Python 3.10.2\\lib\\site-packages\\sklearn\\utils\\validation.py:1858: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n",
      "c:\\Python 3.10.2\\lib\\site-packages\\sklearn\\utils\\validation.py:1858: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  warnings.warn(\n"
     ]
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       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                         -0.353615                 1.262146   \n",
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       "3                         -0.425197                 0.567539   \n",
       "4                          0.117394                 0.099840   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0             -0.362663  \n",
       "1             -0.362663  \n",
       "2             -0.362663  \n",
       "3             -0.362663  \n",
       "4             -0.362663  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 57)\n",
      "Accuracy LR    =  82.9257%\n",
      "Cross val Mean =  0.2031\n",
      "Cross val Std  =  0.0227\n"
     ]
    }
   ],
   "source": [
    "# One hot encoding x robust scaler\n",
    "ohe_x_RS = robust_scaler(df_onehot)\n",
    "ohe_x_RS = pd.DataFrame(ohe_x_RS, columns=df_onehot.columns)\n",
    "\n",
    "# Display dataset\n",
    "display(ohe_x_RS.head())\n",
    "print(ohe_x_RS.shape)\n",
    "\n",
    "# Do linear regression\n",
    "ohe_x_RS_score = do_linear_regression(df=ohe_x_RS)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. Ordinal encoder - Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "   waktu      arah  koridor  target_kecepatan     jarak  target_waktu_tempuh  \\\n",
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       "4    0.0  0.714286    0.650               0.3  0.581505             0.552236   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                          0.224421                 0.723518   \n",
       "1                          0.301583                 0.347582   \n",
       "2                          0.361221                 0.193293   \n",
       "3                          0.210288                 0.536269   \n",
       "4                          0.317411                 0.410190   \n",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "text": [
      "(943, 9)\n",
      "Accuracy LR    =  69.1674%\n",
      "Cross val Mean =  0.0887\n",
      "Cross val Std  =  0.0074\n"
     ]
    }
   ],
   "source": [
    "# Ordinal encoder x Normalization\n",
    "ordinal_x_norm = normalization(df_ordinal)\n",
    "ordinal_x_norm = pd.DataFrame(ordinal_x_norm, columns=df_ordinal.columns)\n",
    "\n",
    "# Display dataset\n",
    "display(ordinal_x_norm.head())\n",
    "print(ordinal_x_norm.shape)\n",
    "\n",
    "# Do linear regression\n",
    "ordinal_x_norm_score = do_linear_regression(df=ordinal_x_norm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5. Ordinal encoder - Standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       "      waktu     arah   koridor  target_kecepatan     jarak  \\\n",
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       "4 -0.957427  0.60104  0.507093         -0.614791  0.276938   \n",
       "\n",
       "   target_waktu_tempuh  pencapaian_kecepatan_per_koridor  \\\n",
       "0             1.665935                         -0.587928   \n",
       "1            -0.377027                         -0.090900   \n",
       "2            -1.103711                          0.293251   \n",
       "3             0.565878                         -0.678959   \n",
       "4             0.442164                          0.011055   \n",
       "\n",
       "   pencapaian_waktu_tempuh  pencapaian_kecepatan  \n",
       "0                 1.718837             -0.683991  \n",
       "1                -0.282108             -0.683991  \n",
       "2                -1.103322             -0.683991  \n",
       "3                 0.722195             -0.683991  \n",
       "4                 0.051128             -0.683991  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 9)\n",
      "Accuracy LR    =  69.1674%\n",
      "Cross val Mean =  0.4411\n",
      "Cross val Std  =  0.0369\n"
     ]
    }
   ],
   "source": [
    "# Ordinal encoder x Standardization\n",
    "ordinal_x_stdz = standardization(df_ordinal)\n",
    "ordinal_x_stdz = pd.DataFrame(ordinal_x_stdz, columns=df_ordinal.columns)\n",
    "\n",
    "# Display dataset\n",
    "display(ordinal_x_stdz.head())\n",
    "print(ordinal_x_stdz.shape)\n",
    "\n",
    "# Do linear regression\n",
    "ordinal_x_stdz_score = do_linear_regression(df=ordinal_x_stdz)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 6. Ordinal encoder - Robust scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>waktu</th>\n",
       "      <th>arah</th>\n",
       "      <th>koridor</th>\n",
       "      <th>target_kecepatan</th>\n",
       "      <th>jarak</th>\n",
       "      <th>target_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan_per_koridor</th>\n",
       "      <th>pencapaian_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan</th>\n",
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       "      <td>0.45</td>\n",
       "      <td>-0.333333</td>\n",
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       "      <td>0.507637</td>\n",
       "      <td>-0.425197</td>\n",
       "      <td>0.567539</td>\n",
       "      <td>-0.362663</td>\n",
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       "      <th>4</th>\n",
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       "      <td>0.30</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.306122</td>\n",
       "      <td>0.407907</td>\n",
       "      <td>0.117394</td>\n",
       "      <td>0.099840</td>\n",
       "      <td>-0.362663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   waktu      arah  koridor  target_kecepatan     jarak  target_waktu_tempuh  \\\n",
       "0    0.0  0.333333    -0.20         -0.333333  1.262755             1.394429   \n",
       "1    0.0  0.333333    -1.00         -0.333333 -0.334184            -0.252471   \n",
       "2    0.0  0.333333     0.25         -0.333333 -0.903061            -0.838275   \n",
       "3    0.0  0.333333     0.45         -0.333333  0.403061             0.507637   \n",
       "4    0.0  0.333333     0.30         -0.333333  0.306122             0.407907   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                         -0.353615                 1.262146   \n",
       "1                          0.037223                -0.132408   \n",
       "2                          0.339298                -0.704752   \n",
       "3                         -0.425197                 0.567539   \n",
       "4                          0.117394                 0.099840   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0             -0.362663  \n",
       "1             -0.362663  \n",
       "2             -0.362663  \n",
       "3             -0.362663  \n",
       "4             -0.362663  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 9)\n",
      "Accuracy LR    =  69.1674%\n",
      "Cross val Mean =  0.2911\n",
      "Cross val Std  =  0.0244\n"
     ]
    }
   ],
   "source": [
    "# Ordinal encoder x robust scaler\n",
    "ordinal_x_RS = robust_scaler(df_ordinal)\n",
    "ordinal_x_RS = pd.DataFrame(ordinal_x_RS, columns=df_ordinal.columns)\n",
    "\n",
    "# Display dataset\n",
    "display(ordinal_x_RS.head())\n",
    "print(ordinal_x_RS.shape)\n",
    "\n",
    "# Do linear regression\n",
    "ordinal_x_RS_score = do_linear_regression(df=ordinal_x_RS)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### B. Scaling - Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. Normalization - One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "     0    1    2    3    4    5    6    7    8    9  ...   47   48   49   50  \\\n",
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       "\n",
       "   target_kecepatan     jarak  target_waktu_tempuh  \\\n",
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       "4               0.3  0.581505             0.552236   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                          0.224421                 0.723518   \n",
       "1                          0.301583                 0.347582   \n",
       "2                          0.361221                 0.193293   \n",
       "3                          0.210288                 0.536269   \n",
       "4                          0.317411                 0.410190   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0              0.284382  \n",
       "1              0.284382  \n",
       "2              0.284382  \n",
       "3              0.284382  \n",
       "4              0.284382  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 57)\n",
      "Accuracy LR    =  82.8621%\n",
      "Cross val Mean =  0.0620\n",
      "Cross val Std  =  0.0067\n"
     ]
    }
   ],
   "source": [
    "# Normalization - OHE\n",
    "norm_x_ohe = df_onehot.iloc[:, :-df1_norm.shape[1]]\n",
    "norm_x_ohe = norm_x_ohe.join(df1_norm)\n",
    "\n",
    "# Display dataset\n",
    "display(norm_x_ohe.head())\n",
    "print(norm_x_ohe.shape)\n",
    "\n",
    "# Do linear regression\n",
    "norm_x_ohe_score = do_linear_regression(df=norm_x_ohe)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2. Standardization - One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "      <td>-0.683991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-0.614791</td>\n",
       "      <td>-1.283520</td>\n",
       "      <td>-1.103711</td>\n",
       "      <td>0.293251</td>\n",
       "      <td>-1.103322</td>\n",
       "      <td>-0.683991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.614791</td>\n",
       "      <td>0.402038</td>\n",
       "      <td>0.565878</td>\n",
       "      <td>-0.678959</td>\n",
       "      <td>0.722195</td>\n",
       "      <td>-0.683991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.614791</td>\n",
       "      <td>0.276938</td>\n",
       "      <td>0.442164</td>\n",
       "      <td>0.011055</td>\n",
       "      <td>0.051128</td>\n",
       "      <td>-0.683991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
       "</div>"
      ],
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       "     0    1    2    3    4    5    6    7    8    9  ...   47   48   49   50  \\\n",
       "0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
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       "2  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "3  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "4  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "\n",
       "   target_kecepatan     jarak  target_waktu_tempuh  \\\n",
       "0         -0.614791  1.511478             1.665935   \n",
       "1         -0.614791 -0.549380            -0.377027   \n",
       "2         -0.614791 -1.283520            -1.103711   \n",
       "3         -0.614791  0.402038             0.565878   \n",
       "4         -0.614791  0.276938             0.442164   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                         -0.587928                 1.718837   \n",
       "1                         -0.090900                -0.282108   \n",
       "2                          0.293251                -1.103322   \n",
       "3                         -0.678959                 0.722195   \n",
       "4                          0.011055                 0.051128   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0             -0.683991  \n",
       "1             -0.683991  \n",
       "2             -0.683991  \n",
       "3             -0.683991  \n",
       "4             -0.683991  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 57)\n",
      "Accuracy LR    =  82.8384%\n",
      "Cross val Mean =  0.3079\n",
      "Cross val Std  =  0.0322\n"
     ]
    }
   ],
   "source": [
    "# Standardization - OHE\n",
    "stdz_x_ohe = df_onehot.iloc[:, :-df1_stdz.shape[1]]\n",
    "stdz_x_ohe = stdz_x_ohe.join(df1_stdz)\n",
    "\n",
    "# Display dataset\n",
    "display(stdz_x_ohe.head())\n",
    "print(stdz_x_ohe.shape)\n",
    "\n",
    "# Do linear regression\n",
    "stdz_x_ohe_score = do_linear_regression(df=stdz_x_ohe)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. Robust scaler - One hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "      <th>target_kecepatan</th>\n",
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       "     0    1    2    3    4    5    6    7    8    9  ...   47   48   49   50  \\\n",
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       "\n",
       "   target_kecepatan     jarak  target_waktu_tempuh  \\\n",
       "0         -0.333333  1.262755             1.394429   \n",
       "1         -0.333333 -0.334184            -0.252471   \n",
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       "4         -0.333333  0.306122             0.407907   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                         -0.353615                 1.262146   \n",
       "1                          0.037223                -0.132408   \n",
       "2                          0.339298                -0.704752   \n",
       "3                         -0.425197                 0.567539   \n",
       "4                          0.117394                 0.099840   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0             -0.362663  \n",
       "1             -0.362663  \n",
       "2             -0.362663  \n",
       "3             -0.362663  \n",
       "4             -0.362663  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 57)\n",
      "Accuracy LR    =  82.9088%\n",
      "Cross val Mean =  0.2029\n",
      "Cross val Std  =  0.0228\n"
     ]
    }
   ],
   "source": [
    "# Robust scaler - OHE\n",
    "rs_x_ohe = df_onehot.iloc[:, :-df1_rs.shape[1]]\n",
    "rs_x_ohe = rs_x_ohe.join(df1_rs)\n",
    "\n",
    "# Display dataset\n",
    "display(rs_x_ohe.head())\n",
    "print(rs_x_ohe.shape)\n",
    "\n",
    "# Do linear regression\n",
    "rs_x_ohe_score = do_linear_regression(df=rs_x_ohe)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. Normalization - Ordinal encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "   waktu  arah  koridor  target_kecepatan     jarak  target_waktu_tempuh  \\\n",
       "0    0.0   5.0     16.0               0.3  0.875392             0.791712   \n",
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       "3    0.0   5.0     29.0               0.3  0.611285             0.576445   \n",
       "4    0.0   5.0     26.0               0.3  0.581505             0.552236   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                          0.224421                 0.723518   \n",
       "1                          0.301583                 0.347582   \n",
       "2                          0.361221                 0.193293   \n",
       "3                          0.210288                 0.536269   \n",
       "4                          0.317411                 0.410190   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0              0.284382  \n",
       "1              0.284382  \n",
       "2              0.284382  \n",
       "3              0.284382  \n",
       "4              0.284382  "
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     },
     "metadata": {},
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    },
    {
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     "text": [
      "(943, 9)\n",
      "Accuracy LR    =  69.1674%\n",
      "Cross val Mean =  0.0887\n",
      "Cross val Std  =  0.0074\n"
     ]
    }
   ],
   "source": [
    "# Normalization - Ordinal encoding\n",
    "norm_x_ordinal = df_ordinal.iloc[:, :-df1_norm.shape[1]]\n",
    "norm_x_ordinal = norm_x_ordinal.join(df1_norm)\n",
    "\n",
    "# Display dataset\n",
    "display(norm_x_ordinal.head())\n",
    "print(norm_x_ordinal.shape)\n",
    "\n",
    "# Do linear regression\n",
    "norm_x_ordinal_score = do_linear_regression(df=norm_x_ordinal)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5. Standardization - Ordinal encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>waktu</th>\n",
       "      <th>arah</th>\n",
       "      <th>koridor</th>\n",
       "      <th>target_kecepatan</th>\n",
       "      <th>jarak</th>\n",
       "      <th>target_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan_per_koridor</th>\n",
       "      <th>pencapaian_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-0.614791</td>\n",
       "      <td>1.511478</td>\n",
       "      <td>1.665935</td>\n",
       "      <td>-0.587928</td>\n",
       "      <td>1.718837</td>\n",
       "      <td>-0.683991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.614791</td>\n",
       "      <td>-0.549380</td>\n",
       "      <td>-0.377027</td>\n",
       "      <td>-0.090900</td>\n",
       "      <td>-0.282108</td>\n",
       "      <td>-0.683991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-0.614791</td>\n",
       "      <td>-1.283520</td>\n",
       "      <td>-1.103711</td>\n",
       "      <td>0.293251</td>\n",
       "      <td>-1.103322</td>\n",
       "      <td>-0.683991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-0.614791</td>\n",
       "      <td>0.402038</td>\n",
       "      <td>0.565878</td>\n",
       "      <td>-0.678959</td>\n",
       "      <td>0.722195</td>\n",
       "      <td>-0.683991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.614791</td>\n",
       "      <td>0.276938</td>\n",
       "      <td>0.442164</td>\n",
       "      <td>0.011055</td>\n",
       "      <td>0.051128</td>\n",
       "      <td>-0.683991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   waktu  arah  koridor  target_kecepatan     jarak  target_waktu_tempuh  \\\n",
       "0    0.0   5.0     16.0         -0.614791  1.511478             1.665935   \n",
       "1    0.0   5.0      0.0         -0.614791 -0.549380            -0.377027   \n",
       "2    0.0   5.0     25.0         -0.614791 -1.283520            -1.103711   \n",
       "3    0.0   5.0     29.0         -0.614791  0.402038             0.565878   \n",
       "4    0.0   5.0     26.0         -0.614791  0.276938             0.442164   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                         -0.587928                 1.718837   \n",
       "1                         -0.090900                -0.282108   \n",
       "2                          0.293251                -1.103322   \n",
       "3                         -0.678959                 0.722195   \n",
       "4                          0.011055                 0.051128   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0             -0.683991  \n",
       "1             -0.683991  \n",
       "2             -0.683991  \n",
       "3             -0.683991  \n",
       "4             -0.683991  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 9)\n",
      "Accuracy LR    =  69.1674%\n",
      "Cross val Mean =  0.4411\n",
      "Cross val Std  =  0.0369\n"
     ]
    }
   ],
   "source": [
    "# Standardization - Ordinal encoding\n",
    "stdz_x_ordinal = df_ordinal.iloc[:, :-df1_stdz.shape[1]]\n",
    "stdz_x_ordinal = stdz_x_ordinal.join(df1_stdz)\n",
    "\n",
    "# Display dataset\n",
    "display(stdz_x_ordinal.head())\n",
    "print(stdz_x_ordinal.shape)\n",
    "\n",
    "# Do linear regression\n",
    "stdz_x_ordinal_score = do_linear_regression(df=stdz_x_ordinal)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 6. Robust scaler - Ordinal encoding"
   ]
  },
  {
   "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>waktu</th>\n",
       "      <th>arah</th>\n",
       "      <th>koridor</th>\n",
       "      <th>target_kecepatan</th>\n",
       "      <th>jarak</th>\n",
       "      <th>target_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan_per_koridor</th>\n",
       "      <th>pencapaian_waktu_tempuh</th>\n",
       "      <th>pencapaian_kecepatan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>1.262755</td>\n",
       "      <td>1.394429</td>\n",
       "      <td>-0.353615</td>\n",
       "      <td>1.262146</td>\n",
       "      <td>-0.362663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.334184</td>\n",
       "      <td>-0.252471</td>\n",
       "      <td>0.037223</td>\n",
       "      <td>-0.132408</td>\n",
       "      <td>-0.362663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.903061</td>\n",
       "      <td>-0.838275</td>\n",
       "      <td>0.339298</td>\n",
       "      <td>-0.704752</td>\n",
       "      <td>-0.362663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.403061</td>\n",
       "      <td>0.507637</td>\n",
       "      <td>-0.425197</td>\n",
       "      <td>0.567539</td>\n",
       "      <td>-0.362663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>0.306122</td>\n",
       "      <td>0.407907</td>\n",
       "      <td>0.117394</td>\n",
       "      <td>0.099840</td>\n",
       "      <td>-0.362663</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   waktu  arah  koridor  target_kecepatan     jarak  target_waktu_tempuh  \\\n",
       "0    0.0   5.0     16.0         -0.333333  1.262755             1.394429   \n",
       "1    0.0   5.0      0.0         -0.333333 -0.334184            -0.252471   \n",
       "2    0.0   5.0     25.0         -0.333333 -0.903061            -0.838275   \n",
       "3    0.0   5.0     29.0         -0.333333  0.403061             0.507637   \n",
       "4    0.0   5.0     26.0         -0.333333  0.306122             0.407907   \n",
       "\n",
       "   pencapaian_kecepatan_per_koridor  pencapaian_waktu_tempuh  \\\n",
       "0                         -0.353615                 1.262146   \n",
       "1                          0.037223                -0.132408   \n",
       "2                          0.339298                -0.704752   \n",
       "3                         -0.425197                 0.567539   \n",
       "4                          0.117394                 0.099840   \n",
       "\n",
       "   pencapaian_kecepatan  \n",
       "0             -0.362663  \n",
       "1             -0.362663  \n",
       "2             -0.362663  \n",
       "3             -0.362663  \n",
       "4             -0.362663  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(943, 9)\n",
      "Accuracy LR    =  69.1674%\n",
      "Cross val Mean =  0.2911\n",
      "Cross val Std  =  0.0244\n"
     ]
    }
   ],
   "source": [
    "# Robust scaler - Ordinal encoding\n",
    "rs_x_ordinal = df_ordinal.iloc[:, :-df1_rs.shape[1]]\n",
    "rs_x_ordinal = rs_x_ordinal.join(df1_rs)\n",
    "\n",
    "# Display dataset\n",
    "display(rs_x_ordinal.head())\n",
    "print(rs_x_ordinal.shape)\n",
    "\n",
    "# Do linear regression\n",
    "rs_x_ordinal_score = do_linear_regression(df=rs_x_ordinal)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Bandingkan akurasi setiap skenario (dengan metrik MAE) / Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------------------------------------------------------------------------------------+\n",
      "|                                           Summary                                           |\n",
      "+------------------------------+----------------------------+----------------+----------------+\n",
      "|                              | Accuracy Linear Regression | cross_val mean | cross_vall std |\n",
      "+------------------------------+----------------------------+----------------+----------------+\n",
      "| Original score               |          64.0561%          |     2.0215     |     0.1775     |\n",
      "|                              |                            |                |                |\n",
      "| Normalization                |          64.0561%          |     0.0942     |     0.0083     |\n",
      "| Standardization              |          64.0561%          |     0.4687     |     0.0412     |\n",
      "| Robust Scaler                |          64.0561%          |     0.3093     |     0.0272     |\n",
      "|                              |                            |                |                |\n",
      "| One Hot Encoding             |          82.1780%          |     1.3533     |     0.1518     |\n",
      "| Ordinal Encoder              |          69.1674%          |     1.9023     |     0.1592     |\n",
      "|                              |                            |                |                |\n",
      "| A. ENCODING - SCALING        |                            |                |                |\n",
      "| OHE - Normalization          |          82.8621%          |     0.0620     |     0.0067     |\n",
      "| OHE - Standardization        |          81.9174%          |     0.3178     |     0.0330     |\n",
      "| OHE - Robust Scaler          |          82.9257%          |     0.2031     |     0.0227     |\n",
      "| Ordinal E. - Normalization   |          69.1674%          |     0.0887     |     0.0074     |\n",
      "| Ordinal E. - Standardization |          69.1674%          |     0.4411     |     0.0369     |\n",
      "| Ordinal E. - Robust Scaler   |          69.1674%          |     0.2911     |     0.0244     |\n",
      "|                              |                            |                |                |\n",
      "| B. SCALING - ENCODING        |                            |                |                |\n",
      "| Normalization - OHE          |          82.8621%          |     0.0620     |     0.0067     |\n",
      "| Standardization - OHE        |          82.8384%          |     0.3079     |     0.0322     |\n",
      "| Robust Scaler - OHE          |          82.9088%          |     0.2029     |     0.0228     |\n",
      "| Normalization - Ordinal E.   |          69.1674%          |     0.0887     |     0.0074     |\n",
      "| Standardization - Ordinal E. |          69.1674%          |     0.4411     |     0.0369     |\n",
      "| Robust Scaler - Ordinal E.   |          69.1674%          |     0.2911     |     0.0244     |\n",
      "+------------------------------+----------------------------+----------------+----------------+\n",
      "n_splits=15, random state=42, n_jobs=-1\n"
     ]
    }
   ],
   "source": [
    "# Print all the records into a table using PrettyTable\n",
    "def display_summary():\n",
    "    header = [\"\", \"Accuracy Linear Regression\", \"cross_val mean\", \"cross_vall std\"]\n",
    "    blank = [\"\" for i in range(len(header))]\n",
    "\n",
    "    row1 = 'Original score'\n",
    "    row2 = 'Normalization'\n",
    "    row3 = 'Standardization'\n",
    "    row4 = 'Robust Scaler'\n",
    "    row5 = 'One Hot Encoding'\n",
    "    row6 = 'Ordinal Encoder'\n",
    "    \n",
    "    row7 = 'OHE - Normalization'\n",
    "    row8 = 'OHE - Standardization'\n",
    "    row9 = 'OHE - Robust Scaler'\n",
    "    row10 = 'Ordinal E. - Normalization'\n",
    "    row11 = 'Ordinal E. - Standardization'\n",
    "    row12 = 'Ordinal E. - Robust Scaler'\n",
    "    \n",
    "    row13 = 'Normalization - OHE'\n",
    "    row14 = 'Standardization - OHE'\n",
    "    row15 = 'Robust Scaler - OHE'\n",
    "    row16 = 'Normalization - Ordinal E.'\n",
    "    row17 = 'Standardization - Ordinal E.'\n",
    "    row18 = 'Robust Scaler - Ordinal E.'\n",
    "\n",
    "    t = PrettyTable(header)\n",
    "    t.align[\"\"] = \"l\"\n",
    "    t.add_row([row1, f\"{pure_score[0]*100:^.4f}%\", f\"{pure_score[1]:^.4f}\", f\"{pure_score[2]:^.4f}\"])\n",
    "    t.add_row(blank)\n",
    "    t.add_row([row2, f\"{nrm_score[0]*100:^.4f}%\", f\"{nrm_score[1]:^.4f}\", f\"{nrm_score[2]:^.4f}\"])\n",
    "    t.add_row([row3, f\"{stdz_score[0]*100:^.4f}%\", f\"{stdz_score[1]:^.4f}\", f\"{stdz_score[2]:^.4f}\"])\n",
    "    t.add_row([row4, f\"{RS_score[0]*100:^.4f}%\", f\"{RS_score[1]:^.4f}\", f\"{RS_score[2]:^.4f}\"])\n",
    "    t.add_row(blank)\n",
    "    t.add_row([row5, f\"{one_hot_score[0]*100:^.4f}%\", f\"{one_hot_score[1]:^.4f}\", f\"{one_hot_score[2]:^.4f}\"])\n",
    "    t.add_row([row6, f\"{ordinal_score[0]*100:^.4f}%\", f\"{ordinal_score[1]:^.4f}\", f\"{ordinal_score[2]:^.4f}\"])\n",
    "    t.add_row(blank)\n",
    "    t.add_row([\"A. ENCODING - SCALING\", \"\", \"\", \"\"])\n",
    "    t.add_row([row7, f\"{ohe_x_norm_score[0]*100:^.4f}%\", f\"{ohe_x_norm_score[1]:^.4f}\", f\"{ohe_x_norm_score[2]:^.4f}\"])\n",
    "    t.add_row([row8, f\"{ohe_x_stdz_score[0]*100:^.4f}%\", f\"{ohe_x_stdz_score[1]:^.4f}\", f\"{ohe_x_stdz_score[2]:^.4f}\"])\n",
    "    t.add_row([row9, f\"{ohe_x_RS_score[0]*100:^.4f}%\", f\"{ohe_x_RS_score[1]:^.4f}\", f\"{ohe_x_RS_score[2]:^.4f}\"])\n",
    "    t.add_row([row10, f\"{ordinal_x_norm_score[0]*100:^.4f}%\", f\"{ordinal_x_norm_score[1]:^.4f}\", f\"{ordinal_x_norm_score[2]:^.4f}\"])\n",
    "    t.add_row([row11, f\"{ordinal_x_stdz_score[0]*100:^.4f}%\", f\"{ordinal_x_stdz_score[1]:^.4f}\", f\"{ordinal_x_stdz_score[2]:^.4f}\"])\n",
    "    t.add_row([row12, f\"{ordinal_x_RS_score[0]*100:^.4f}%\", f\"{ordinal_x_RS_score[1]:^.4f}\", f\"{ordinal_x_RS_score[2]:^.4f}\"])\n",
    "    t.add_row(blank)\n",
    "    t.add_row([\"B. SCALING - ENCODING\", \"\", \"\", \"\"])\n",
    "    t.add_row([row13, f\"{norm_x_ohe_score[0]*100:^.4f}%\", f\"{norm_x_ohe_score[1]:^.4f}\", f\"{norm_x_ohe_score[2]:^.4f}\"])\n",
    "    t.add_row([row14, f\"{stdz_x_ohe_score[0]*100:^.4f}%\", f\"{stdz_x_ohe_score[1]:^.4f}\", f\"{stdz_x_ohe_score[2]:^.4f}\"])\n",
    "    t.add_row([row15, f\"{rs_x_ohe_score[0]*100:^.4f}%\", f\"{rs_x_ohe_score[1]:^.4f}\", f\"{rs_x_ohe_score[2]:^.4f}\"])\n",
    "    t.add_row([row16, f\"{norm_x_ordinal_score[0]*100:^.4f}%\", f\"{norm_x_ordinal_score[1]:^.4f}\", f\"{norm_x_ordinal_score[2]:^.4f}\"])\n",
    "    t.add_row([row17, f\"{stdz_x_ordinal_score[0]*100:^.4f}%\", f\"{stdz_x_ordinal_score[1]:^.4f}\", f\"{stdz_x_ordinal_score[2]:^.4f}\"])\n",
    "    t.add_row([row18, f\"{rs_x_ordinal_score[0]*100:^.4f}%\", f\"{rs_x_ordinal_score[1]:^.4f}\", f\"{rs_x_ordinal_score[2]:^.4f}\"])\n",
    "\n",
    "    print(t.get_string(title=f\"Summary\"))\n",
    "    print(f\"n_splits=15, random state=42, n_jobs=-1\")\n",
    "\n",
    "display_summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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