2106515_Fachri_UAS_Datmin.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ba1903f8",
   "metadata": {},
   "source": [
    "Nama : Fachri Najm Noer Kartiman <br>\n",
    "NIM : 2106515 <br>\n",
    "UAS DATA MINING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "1f8e48a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "import xgboost as xgb\n",
    "import joblib\n",
    "import pickle\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import tree\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5d1e9d79",
   "metadata": {},
   "source": [
    "IPM (Indeks Pembangunan Manusia) adalah ukuran kualitas sumber daya manusia. PMI memiliki tiga dimensi: <br>(1) umur panjang dan hidup sehat (a long and healthy life), <br>(2) pengetahuan (knowledge), dan <br>(3) standar hidup layak (decent standard of living).  PMI dihitung menggunakan data harapan hidup, rata-rata lama sekolah, pengeluaran per kapita."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7f8c33be",
   "metadata": {},
   "source": [
    "# PRAPROSES"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a0e04711",
   "metadata": {},
   "source": [
    "## import dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "068ab8a5",
   "metadata": {},
   "source": [
    "dengan adanya tiga dimensi dari indeks pembangunan manusia maka diputuskan saya mengimport 5 dataset ini: <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "87f9bb94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# hidup sehat\n",
    "dfPangan = pd.read_csv(\"C:\\\\Users\\\\Fachri Najm\\\\Data Mining\\\\UAS\\\\data\\\\ketidak_cukupan_pangan_prov.csv\")\n",
    "# pengetahuan\n",
    "dfMelek = pd.read_csv(\"C:\\\\Users\\\\Fachri Najm\\\\Data Mining\\\\UAS\\\\data\\\\melek_huruf_diatas15.csv\")\n",
    "# dataset yang perlu diprediksi\n",
    "dfPMI = pd.read_csv(\"C:\\\\Users\\\\Fachri Najm\\\\Data Mining\\\\UAS\\\\data\\\\pmi.csv\")\n",
    "# standar hidup layak\n",
    "dfNikah = pd.read_csv(\"C:\\\\Users\\\\Fachri Najm\\\\Data Mining\\\\UAS\\\\data\\\\proporsi_perempuan_18_24_nikah_prov.csv\")\n",
    "# hidup sehat\n",
    "dfKesehatan = pd.read_csv(\"C:\\\\Users\\\\Fachri Najm\\\\Data Mining\\\\UAS\\\\data\\\\persen_unmet_need_pelayanan_kesehatan.csv\")\n",
    "# standar hidup layak\n",
    "dflayanan = pd.read_csv(\"C:\\\\Users\\\\Fachri Najm\\\\Data Mining\\\\UAS\\\\data\\\\proporsi_rt_akses_layanan_dasar.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "3b819633",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge data ketidak cukuoan pangan dna melek huruf diatas 15 tahun\n",
    "df = pd.merge(dfPMI, dfMelek,how = 'left', on = ['prov', 'tahun'])\n",
    "df = pd.merge(df, dfPangan,how = 'left', on = ['prov', 'tahun'])\n",
    "df = pd.merge(df, dfNikah,how = 'left', on = ['prov', 'tahun'])\n",
    "df = pd.merge(df, dfKesehatan,how = 'left', on = ['prov', 'tahun'])\n",
    "df = pd.merge(df, dflayanan,how = 'left', on = ['prov', 'tahun'])\n",
    "# mengecek ada berapa provinsi 'indonesia'\n",
    "len(df[df['prov'] == 'INDONESIA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "28f9d150",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['tahun'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "f2003ca5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 386,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['prov'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "2bbf9f7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['prov'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "18f140f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</th>\n",
       "      <th>tahun</th>\n",
       "      <th>kategori_pmi</th>\n",
       "      <th>melek_huruf_diatas15</th>\n",
       "      <th>pct_tdk_cukup_pangan</th>\n",
       "      <th>pct_perempuan_nikah</th>\n",
       "      <th>persen_unmet_need_pelayanan_kesehatan</th>\n",
       "      <th>proporsi_rt_akses_layanan_dasar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACEH</td>\n",
       "      <td>2022</td>\n",
       "      <td>TINGGI</td>\n",
       "      <td>98.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.83</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SUMATERA UTARA</td>\n",
       "      <td>2022</td>\n",
       "      <td>TINGGI</td>\n",
       "      <td>99.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.96</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SUMATERA BARAT</td>\n",
       "      <td>2022</td>\n",
       "      <td>TINGGI</td>\n",
       "      <td>99.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.31</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RIAU</td>\n",
       "      <td>2022</td>\n",
       "      <td>TINGGI</td>\n",
       "      <td>99.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.82</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JAMBI</td>\n",
       "      <td>2022</td>\n",
       "      <td>TINGGI</td>\n",
       "      <td>98.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.56</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>SULAWESI BARAT</td>\n",
       "      <td>2010</td>\n",
       "      <td>SANGAT RENDAH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>MALUKU</td>\n",
       "      <td>2010</td>\n",
       "      <td>SEDANG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>MALUKU UTARA</td>\n",
       "      <td>2010</td>\n",
       "      <td>SEDANG</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>PAPUA BARAT</td>\n",
       "      <td>2010</td>\n",
       "      <td>SANGAT RENDAH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>PAPUA</td>\n",
       "      <td>2010</td>\n",
       "      <td>SANGAT RENDAH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               prov  tahun   kategori_pmi melek_huruf_diatas15  \\\n",
       "0              ACEH   2022         TINGGI                98.25   \n",
       "1    SUMATERA UTARA   2022         TINGGI                99.11   \n",
       "2    SUMATERA BARAT   2022         TINGGI                99.29   \n",
       "3              RIAU   2022         TINGGI                99.18   \n",
       "4             JAMBI   2022         TINGGI                 98.1   \n",
       "..              ...    ...            ...                  ...   \n",
       "437  SULAWESI BARAT   2010  SANGAT RENDAH                  NaN   \n",
       "438          MALUKU   2010         SEDANG                  NaN   \n",
       "439    MALUKU UTARA   2010         SEDANG                  NaN   \n",
       "440     PAPUA BARAT   2010  SANGAT RENDAH                  NaN   \n",
       "441           PAPUA   2010  SANGAT RENDAH                  NaN   \n",
       "\n",
       "     pct_tdk_cukup_pangan  pct_perempuan_nikah  \\\n",
       "0                     NaN                  NaN   \n",
       "1                     NaN                  NaN   \n",
       "2                     NaN                  NaN   \n",
       "3                     NaN                  NaN   \n",
       "4                     NaN                  NaN   \n",
       "..                    ...                  ...   \n",
       "437                   NaN                  NaN   \n",
       "438                   NaN                  NaN   \n",
       "439                   NaN                  NaN   \n",
       "440                   NaN                  NaN   \n",
       "441                   NaN                  NaN   \n",
       "\n",
       "     persen_unmet_need_pelayanan_kesehatan  proporsi_rt_akses_layanan_dasar  \n",
       "0                                     6.83                              NaN  \n",
       "1                                     5.96                              NaN  \n",
       "2                                     4.31                              NaN  \n",
       "3                                     5.82                              NaN  \n",
       "4                                     6.56                              NaN  \n",
       "..                                     ...                              ...  \n",
       "437                                    NaN                              NaN  \n",
       "438                                    NaN                              NaN  \n",
       "439                                    NaN                              NaN  \n",
       "440                                    NaN                              NaN  \n",
       "441                                    NaN                              NaN  \n",
       "\n",
       "[442 rows x 8 columns]"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "1d679269",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 442 entries, 0 to 441\n",
      "Data columns (total 8 columns):\n",
      " #   Column                                 Non-Null Count  Dtype  \n",
      "---  ------                                 --------------  -----  \n",
      " 0   prov                                   442 non-null    object \n",
      " 1   tahun                                  442 non-null    int64  \n",
      " 2   kategori_pmi                           442 non-null    object \n",
      " 3   melek_huruf_diatas15                   306 non-null    object \n",
      " 4   pct_tdk_cukup_pangan                   170 non-null    float64\n",
      " 5   pct_perempuan_nikah                    238 non-null    float64\n",
      " 6   persen_unmet_need_pelayanan_kesehatan  272 non-null    float64\n",
      " 7   proporsi_rt_akses_layanan_dasar        102 non-null    float64\n",
      "dtypes: float64(4), int64(1), object(3)\n",
      "memory usage: 31.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "id": "bfad6cb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['prov', 'kategori_pmi', 'tahun']] = df[['prov', 'kategori_pmi','tahun']].astype('category')\n",
    "df['melek_huruf_diatas15'] = pd.to_numeric(df['melek_huruf_diatas15'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "86555643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>melek_huruf_diatas15</th>\n",
       "      <th>pct_tdk_cukup_pangan</th>\n",
       "      <th>pct_perempuan_nikah</th>\n",
       "      <th>persen_unmet_need_pelayanan_kesehatan</th>\n",
       "      <th>proporsi_rt_akses_layanan_dasar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>305.000000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>96.014787</td>\n",
       "      <td>10.813706</td>\n",
       "      <td>27.543992</td>\n",
       "      <td>5.142206</td>\n",
       "      <td>77.222255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.668504</td>\n",
       "      <td>8.447757</td>\n",
       "      <td>6.812265</td>\n",
       "      <td>1.646468</td>\n",
       "      <td>10.079431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>70.780000</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>10.250000</td>\n",
       "      <td>1.560000</td>\n",
       "      <td>35.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>94.100000</td>\n",
       "      <td>5.982500</td>\n",
       "      <td>23.205000</td>\n",
       "      <td>3.952500</td>\n",
       "      <td>73.927500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>97.900000</td>\n",
       "      <td>8.845000</td>\n",
       "      <td>27.135000</td>\n",
       "      <td>4.825000</td>\n",
       "      <td>79.110000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>98.770000</td>\n",
       "      <td>11.477500</td>\n",
       "      <td>31.852500</td>\n",
       "      <td>6.207500</td>\n",
       "      <td>83.330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.870000</td>\n",
       "      <td>38.350000</td>\n",
       "      <td>50.430000</td>\n",
       "      <td>10.210000</td>\n",
       "      <td>91.300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       melek_huruf_diatas15  pct_tdk_cukup_pangan  pct_perempuan_nikah  \\\n",
       "count            305.000000            170.000000           238.000000   \n",
       "mean              96.014787             10.813706            27.543992   \n",
       "std                4.668504              8.447757             6.812265   \n",
       "min               70.780000              1.430000            10.250000   \n",
       "25%               94.100000              5.982500            23.205000   \n",
       "50%               97.900000              8.845000            27.135000   \n",
       "75%               98.770000             11.477500            31.852500   \n",
       "max               99.870000             38.350000            50.430000   \n",
       "\n",
       "       persen_unmet_need_pelayanan_kesehatan  proporsi_rt_akses_layanan_dasar  \n",
       "count                             272.000000                       102.000000  \n",
       "mean                                5.142206                        77.222255  \n",
       "std                                 1.646468                        10.079431  \n",
       "min                                 1.560000                        35.550000  \n",
       "25%                                 3.952500                        73.927500  \n",
       "50%                                 4.825000                        79.110000  \n",
       "75%                                 6.207500                        83.330000  \n",
       "max                                10.210000                        91.300000  "
      ]
     },
     "execution_count": 391,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3387946d",
   "metadata": {},
   "source": [
    "Melihat apakah data NULL di dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "id": "e9254ea7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prov                                       0\n",
       "tahun                                      0\n",
       "kategori_pmi                               0\n",
       "melek_huruf_diatas15                     137\n",
       "pct_tdk_cukup_pangan                     272\n",
       "pct_perempuan_nikah                      204\n",
       "persen_unmet_need_pelayanan_kesehatan    170\n",
       "proporsi_rt_akses_layanan_dasar          340\n",
       "dtype: int64"
      ]
     },
     "execution_count": 392,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "196f070f",
   "metadata": {},
   "source": [
    "mengisi data NULL dengan rata - rata tiap provinsi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "id": "528c75ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['melek_huruf_diatas15'] = df.groupby(['prov'])['melek_huruf_diatas15'].transform('mean')\n",
    "df['pct_tdk_cukup_pangan'] = df.groupby(['prov'])['pct_tdk_cukup_pangan'].transform('mean')\n",
    "df['pct_perempuan_nikah'] = df.groupby(['prov'])['pct_perempuan_nikah'].transform('mean')\n",
    "df['persen_unmet_need_pelayanan_kesehatan'] = df.groupby(['prov'])['persen_unmet_need_pelayanan_kesehatan'].transform('mean')\n",
    "df['proporsi_rt_akses_layanan_dasar'] = df.groupby(['prov'])['proporsi_rt_akses_layanan_dasar'].transform('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "f3aa5312",
   "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>prov</th>\n",
       "      <th>tahun</th>\n",
       "      <th>kategori_pmi</th>\n",
       "      <th>melek_huruf_diatas15</th>\n",
       "      <th>pct_tdk_cukup_pangan</th>\n",
       "      <th>pct_perempuan_nikah</th>\n",
       "      <th>persen_unmet_need_pelayanan_kesehatan</th>\n",
       "      <th>proporsi_rt_akses_layanan_dasar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACEH</td>\n",
       "      <td>2022</td>\n",
       "      <td>TINGGI</td>\n",
       "      <td>97.967778</td>\n",
       "      <td>8.394</td>\n",
       "      <td>23.478571</td>\n",
       "      <td>4.23875</td>\n",
       "      <td>67.096667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SUMATERA UTARA</td>\n",
       "      <td>2022</td>\n",
       "      <td>TINGGI</td>\n",
       "      <td>98.966667</td>\n",
       "      <td>6.208</td>\n",
       "      <td>28.145714</td>\n",
       "      <td>4.17375</td>\n",
       "      <td>68.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SUMATERA BARAT</td>\n",
       "      <td>2022</td>\n",
       "      <td>TINGGI</td>\n",
       "      <td>98.957778</td>\n",
       "      <td>5.552</td>\n",
       "      <td>33.188571</td>\n",
       "      <td>3.64875</td>\n",
       "      <td>81.193333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RIAU</td>\n",
       "      <td>2022</td>\n",
       "      <td>TINGGI</td>\n",
       "      <td>99.097778</td>\n",
       "      <td>9.272</td>\n",
       "      <td>26.217143</td>\n",
       "      <td>5.17250</td>\n",
       "      <td>73.363333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JAMBI</td>\n",
       "      <td>2022</td>\n",
       "      <td>TINGGI</td>\n",
       "      <td>98.047778</td>\n",
       "      <td>9.662</td>\n",
       "      <td>25.830000</td>\n",
       "      <td>4.74625</td>\n",
       "      <td>72.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>SULAWESI BARAT</td>\n",
       "      <td>2010</td>\n",
       "      <td>SANGAT RENDAH</td>\n",
       "      <td>92.996667</td>\n",
       "      <td>9.516</td>\n",
       "      <td>30.260000</td>\n",
       "      <td>6.84375</td>\n",
       "      <td>78.846667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>MALUKU</td>\n",
       "      <td>2010</td>\n",
       "      <td>SEDANG</td>\n",
       "      <td>99.171111</td>\n",
       "      <td>32.006</td>\n",
       "      <td>32.210000</td>\n",
       "      <td>5.04875</td>\n",
       "      <td>78.190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>MALUKU UTARA</td>\n",
       "      <td>2010</td>\n",
       "      <td>SEDANG</td>\n",
       "      <td>98.667778</td>\n",
       "      <td>32.990</td>\n",
       "      <td>27.794286</td>\n",
       "      <td>4.83250</td>\n",
       "      <td>78.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>PAPUA BARAT</td>\n",
       "      <td>2010</td>\n",
       "      <td>SANGAT RENDAH</td>\n",
       "      <td>97.333333</td>\n",
       "      <td>23.060</td>\n",
       "      <td>25.160000</td>\n",
       "      <td>4.02000</td>\n",
       "      <td>72.236667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>PAPUA</td>\n",
       "      <td>2010</td>\n",
       "      <td>SANGAT RENDAH</td>\n",
       "      <td>75.476667</td>\n",
       "      <td>35.938</td>\n",
       "      <td>22.128571</td>\n",
       "      <td>2.74000</td>\n",
       "      <td>36.320000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               prov tahun   kategori_pmi  melek_huruf_diatas15  \\\n",
       "0              ACEH  2022         TINGGI             97.967778   \n",
       "1    SUMATERA UTARA  2022         TINGGI             98.966667   \n",
       "2    SUMATERA BARAT  2022         TINGGI             98.957778   \n",
       "3              RIAU  2022         TINGGI             99.097778   \n",
       "4             JAMBI  2022         TINGGI             98.047778   \n",
       "..              ...   ...            ...                   ...   \n",
       "437  SULAWESI BARAT  2010  SANGAT RENDAH             92.996667   \n",
       "438          MALUKU  2010         SEDANG             99.171111   \n",
       "439    MALUKU UTARA  2010         SEDANG             98.667778   \n",
       "440     PAPUA BARAT  2010  SANGAT RENDAH             97.333333   \n",
       "441           PAPUA  2010  SANGAT RENDAH             75.476667   \n",
       "\n",
       "     pct_tdk_cukup_pangan  pct_perempuan_nikah  \\\n",
       "0                   8.394            23.478571   \n",
       "1                   6.208            28.145714   \n",
       "2                   5.552            33.188571   \n",
       "3                   9.272            26.217143   \n",
       "4                   9.662            25.830000   \n",
       "..                    ...                  ...   \n",
       "437                 9.516            30.260000   \n",
       "438                32.006            32.210000   \n",
       "439                32.990            27.794286   \n",
       "440                23.060            25.160000   \n",
       "441                35.938            22.128571   \n",
       "\n",
       "     persen_unmet_need_pelayanan_kesehatan  proporsi_rt_akses_layanan_dasar  \n",
       "0                                  4.23875                        67.096667  \n",
       "1                                  4.17375                        68.983333  \n",
       "2                                  3.64875                        81.193333  \n",
       "3                                  5.17250                        73.363333  \n",
       "4                                  4.74625                        72.666667  \n",
       "..                                     ...                              ...  \n",
       "437                                6.84375                        78.846667  \n",
       "438                                5.04875                        78.190000  \n",
       "439                                4.83250                        78.180000  \n",
       "440                                4.02000                        72.236667  \n",
       "441                                2.74000                        36.320000  \n",
       "\n",
       "[442 rows x 8 columns]"
      ]
     },
     "execution_count": 394,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "97412ae9",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "593a2cbb",
   "metadata": {},
   "source": [
    "relasi antar atribut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "db080335",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Temp\\ipykernel_10668\\2511265636.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(df.corr(), annot=True, vmin=0, vmax=1, cmap='plasma')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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eU6dOnVStWjVJ0vbt2zVnzhwNGjRIFy5c0IcffigXFxe9++67BqeFGSxevFiNGjVS+/btNW/ePP36669q2LCh2rdvr8jISKPjwURatWol6c4NSDp16mRzB8Hk5GTt3btXtWrVMioeTGzYsGEqV66cTp06pdatW1uvLUdHR7399tsGpwPMhcIVAMBGQkKCvLy8JEkrV65Uq1at5ODgoBo1aujkyZMGp4MZzZkzR+PHj1ebNm2sY88++6zCwsL06aefavXq1QoKCtKoUaMoXCFD3NzctHTpUtWvX19t2rTRL7/8og4dOuiDDz4wOhpMxsfHR9KdGVdeXl5yc3OzbnN2dlaNGjX0yiuvGBUPJvf888+nGevYsaMBSQBzozk7AMBG+fLl1a1bN7Vs2VLlypXTTz/9pJo1a2rnzp1q2rSpYmNjjY4Ik3Fzc9PevXsVGhpqM3748GFVqFBBCQkJOn78uMqWLWvTXw34p/QaGZ89e1ZPPPGEnnnmGY0dO9Y6frfG7cDdDB8+XP369WNZILLV9evXtX79esXExKTp6di7d2+DUgHmQ+EKAGBj4cKFevHFF5WcnKyGDRtam7KPGTNGv/zyi5YvX25wQphNiRIl1KpVK5vCgiS9/fbb+u6773To0CFFRUWpefPmOnPmjEEpYe8cHBzS7UH016+yFovF2qcoOTn5QccDABu7du1SkyZNlJCQoOvXrytv3ry6ePGi3N3d5e/vr2PHjhkdETANlgoCAGw8//zzeuyxx3T27FlVqFDBOt6wYUO1bNnSwGQwqw8//FCtW7fW8uXLVbVqVUlSVFSUDh48qIULF0qSduzYobZt2xoZE3Zu7dq1RkdALrdw4UItWLAg3dkx0dHRBqWCWfXt21fPPvuspk2bJh8fH23dulVOTk566aWX1KdPH6PjAabCjCsAAJDjjh8/rk8//VS///67JKlkyZLq3r27QkJCjA0GAJImT55svYnE9OnT1blzZx09elQ7duxQz549NWrUKKMjwmR8fX21bds2lSxZUr6+vtqyZYtKly6tbdu2qWPHjjp48KDREQHTYMYVACCNqKiou37qvHjxYoNSwcyKFi2aZqkgkBl79+5VuXLl5ODgoL17995z3/Llyz+gVMgtpk6dqunTp6tdu3aaPXu2BgwYoGLFimnIkCGKi4szOh5MyMnJSQ4ODpIkf39/xcTEqHTp0vLx8dGpU6cMTgeYC4UrAICNefPmqUOHDnryySe1cuVKNW7cWL///rvOnTvHUkFk2eXLl7V9+3adP39eKSkpNts6dOhgUCqYScWKFRUbGyt/f39VrFjR2tPqf9HjClkRExOjWrVqSbpzQ4lr165Jkl5++WXVqFFDU6ZMMTIeTKhSpUrasWOHQkNDVa9ePQ0ZMkQXL17UF198oXLlyhkdDzAVClcAABujR4/WhAkT1LNnT3l5eWnSpEkqWrSounfvrsDAQKPjwYT++9//qn379oqPj5e3t7dNg22LxULhChly/PhxFShQwPo1kJ0CAgIUFxen4OBgBQUFaevWrapQoYKOHz+eboEUuJ/Ro0dbC6CjRo1Shw4d1KNHD4WGhmrWrFkGpwPMhR5XAAAbHh4e+vXXXxUSEqJ8+fJp3bp1CgsL04EDB/T444/r7NmzRkeEyZQoUUJNmjTR6NGj5e7ubnQcAEijW7duKlKkiIYOHaqPP/5Y/fv3V+3atRUVFaVWrVrps88+MzoiADy0mHEFALDh5+dn/YSwcOHC2r9/v8LCwnT58mUlJCQYnA5mdObMGfXu3ZuiFbLV4cOHtXbt2nSXnw4ZMsSgVDCr6dOnW6+jnj17Kl++fNq8ebOaNWum7t27G5wOAB5uFK4AADbq1q2rVatWKSwsTK1bt1afPn20Zs0arVq1Sg0bNjQ6HkzoySefVFRUlIoVK2Z0FOQSM2bMUI8ePZQ/f34FBASkWX5K4QqZ5eDgYG2kLUkvvPCCXnjhBQMTwYwqVapk8350L9HR0TmcBsg9KFwBAGxMmTJFN2/elCS99957cnJy0ubNm/Xcc89p0KBBBqeDGTVt2lT9+/fXb7/9prCwMDk5Odlsb9asmUHJYFbvv/++Ro0apYEDBxodBbkIN5HAv9WiRQvr1zdv3tTUqVNVpkwZ1axZU5K0detW/frrr3r99dcNSgiYEz2uAABAjvrnLIb/xR3gkBXe3t7avXs3s/iQbe53E4m4uDgD08GMunXrpsDAQI0cOdJmfOjQoTp16hQN2oFMoHAFALDh6Oios2fPyt/f32b80qVL8vf3p8gAwHBdu3ZV1apV9dprrxkdBbkEN5FAdvPx8VFUVJRCQ0Ntxg8fPqzw8HBduXLFoGSA+bBUEABg426fZ9y6dUvOzs4POA0ApFW8eHENHjxYW7duTXf5ae/evQ1KBrPiJhLIbm5ubtq0aVOawtWmTZvk6upqUCrAnChcAQAkSZMnT5Z0Z0nEzJkz5enpad2WnJysX375RaVKlTIqHkzu+vXrWr9+vWJiYpSYmGizjSIDMmv69Ony9PTU+vXrtX79epttFouFawqZxk0kkN3efPNN9ejRQ9HR0apWrZokadu2bZo1a5YGDx5scDrAXFgqCACQJBUtWlSSdPLkST3yyCNydHS0bnN2dlZISIhGjBih6tWrGxURJrVr1y41adJECQkJun79uvLmzauLFy/K3d1d/v7+OnbsmNERATyEfvzxR+vXFy5c0IgRI9S5c2duIoFss2DBAk2aNEkHDhyQJJUuXVp9+vRRmzZtDE4GmAuFKwCAjQYNGmjx4sXy8/MzOgpyifr166tEiRKaNm2afHx8tGfPHjk5Oemll15Snz591KpVK6MjIpeiiTvu5V43jvgnbiKBnPTNN9+oWbNm8vDwMDoKYLcoXAEAgBzl6+urbdu2qWTJkvL19dWWLVtUunRpbdu2TR07dtTBgweNjohcysvLS3v27KFwBcBuUWAH7o8eVwCANE6fPq0ff/wx3X5EkZGRBqWCWTk5OVlnNvj7+ysmJkalS5eWj4+PTp06ZXA6ALB18+ZNmmfjgWEeCXB/FK4AADZWr16tZs2aqVixYjp48KDKlSunEydOKDU1VZUrVzY6HkyoUqVK2rFjh0JDQ1WvXj0NGTJEFy9e1BdffKFy5coZHQ8AlJycrNGjR2vatGk6d+6cfv/9dxUrVkyDBw9WSEiIunbtanREAHhoZWxhNwDgofHOO++oX79+2rdvn1xdXbVo0SKdOnVK9erVU+vWrY2OBxMaPXq0AgMDJUmjRo2Sn5+fevTooQsXLmj69OkGpwOAO+9Ns2fP1rhx4+Ts7GwdL1eunGbOnGlgMgAAPa4AADa8vLy0e/duPfroo/Lz89PGjRtVtmxZ7dmzR82bN9eJEyeMjggAGULvGGRU8eLF9emnn6phw4Y2vdEOHjyomjVr6s8//zQ6InIpevEB98eMKwCADQ8PD2tfq8DAQB09etS67eLFi0bFQi5w/vx5bdiwQRs2bNCFCxeMjoOHAJ/PIqPOnDmj4sWLpxlPSUlRUlKSAYkAAH+hcAUAsFGjRg1t3LhRktSkSRO99dZbGjVqlLp06aIaNWoYnA5mdO3aNb388ssqXLiw6tWrp3r16qlQoUJ66aWXdOXKFaPjIRdbvny5ChcubHQMmECZMmW0YcOGNOMLFy5UpUqVDEiEh0VwcLCcnJyMjgHYNZqzAwBsREZGKj4+XpI0fPhwxcfHa/78+QoNDeWOgsiSbt26adeuXVqyZIlq1qwpSdqyZYv69Omj7t27a968eQYnhNkkJydr9uzZWr16tc6fP6+UlBSb7WvWrJEkPfbYY0bEgwkNGTJEHTt21JkzZ5SSkqLFixfr0KFDmjt3rpYsWWJ0PJhYYmJiuu9TQUFBkqT9+/cbEQswFXpcAQCAHOXh4aEVK1akKSJs2LBBTz31lK5fv25QMpjVG2+8odmzZ6tp06YKDAyUxWKx2T5hwgSDksHMNmzYoBEjRmjPnj2Kj49X5cqVNWTIEDVu3NjoaDChw4cPq0uXLtq8ebPNeGpqqiwWi5KTkw1KBpgPM64AAECOypcvn3x8fNKM+/j4yM/Pz4BEMLt58+ZpwYIFatKkidFRkIvUqVNHq1atMjoGcolOnTopT548WrJkSboFdgAZR+EKAKC8efPq999/V/78+eXn53fPX67i4uIeYDLkBoMGDVJERIS++OILBQQESJJiY2PVv39/DR482OB0MCNnZ+d0G2kDWXXq1ClZLBY98sgjkqTt27fr66+/VpkyZfTqq68anA5mtHv3bu3cuVOlSpUyOgpgehSuAACaMGGCvLy8JEkTJ040NgxynU8++URHjhxRUFCQtadHTEyMXFxcdOHCBX366afWfaOjo42KCRN56623NGnSJE2ZMoVZDMgWL774ol599VW9/PLLio2NVaNGjVSuXDl99dVXio2N1ZAhQ4yOCJMpU6YMd2MGsgk9rgAAQI4aPnx4hvcdOnRoDiZBbtGyZUutXbtWefPmVdmyZdPckWvx4sUGJYNZ+fn5aevWrSpZsqQmT56s+fPna9OmTVq5cqVee+01HTt2zOiIMJk1a9Zo0KBBGj16tMLCwtK8T3l7exuUDDAfZlwBAHT16tUM78svWsisjBajvvnmG12/fl0eHh45nAhm5+vrq5YtWxodA7lIUlKSXFxcJEk///yzmjVrJkkqVaqUzp49a2Q0mFSjRo0kSQ0bNrQZpzk7kHnMuAIAyMHBIcPLbfhFCznF29tbu3fvVrFixYyOAuAhU716dTVo0EBNmzZV48aNtXXrVlWoUEFbt27V888/r9OnTxsdESazfv36e26vV6/eA0oCmB8zrgAAWrt2rfXrEydO6O2331anTp1Us2ZNSdKWLVs0Z84cjRkzxqiIeAjwWRoAo/znP/9Ry5Yt9cEHH6hjx46qUKGCJOnHH39UtWrVDE4HM6IwBWQfZlwBAGw0bNhQ3bp1U7t27WzGv/76a02fPl3r1q0zJhhyPS8vL+3Zs4cZV8iQhQsXasGCBYqJiVFiYqLNNpr8IyuSk5N19epV+fn5WcdOnDghd3d3+fv7G5gMZpaQkJDu+1T58uUNSgSYj4PRAQAA9mXLli0KDw9PMx4eHq7t27cbkAgAbE2ePFmdO3dWwYIFtWvXLlWrVk358uXTsWPH9PTTTxsdDyaVmpqqnTt36tNPP9W1a9ckSc7OznJ3dzc4GczowoULeuaZZ+Tl5aWyZcuqUqVKNg8AGUfhCgBgo0iRIpoxY0aa8ZkzZ6pIkSIGJAIAW1OnTtX06dP10UcfydnZWQMGDNCqVavUu3dvXblyxeh4MKGTJ08qLCxMzZs3V8+ePXXhwgVJd5YQ9uvXz+B0MKM333xTly9f1rZt2+Tm5qaffvpJc+bMUWhoqH788Uej4wGmQo8rAICNCRMm6LnnntPy5ctVvXp1SdL27dt1+PBhLVq0yOB0ACDFxMSoVq1akiQ3Nzfr7JiXX35ZNWrU0JQpU4yMBxPq06ePwsPDtWfPHuXLl8863rJlS73yyisGJoNZrVmzRj/88IPCw8Pl4OCg4OBgPfHEE/L29taYMWPUtGlToyMCpsGMKwCAjSZNmujw4cNq1qyZ4uLiFBcXp2effVa///67mjRpYnQ85GLBwcFycnIyOgZMICAgQHFxcZKkoKAgbd26VZJ0/PhxmvwjSzZs2KBBgwbJ2dnZZjwkJERnzpwxKBXM7Pr169beaH5+ftZZfGFhYfThAzKJGVcAgDQeeeQRjRo16p77vP766xoxYoTy58//gFLB7KKionTgwAFJUunSpdP0Utu/f78RsWBCjz/+uH788UdVqlRJnTt3Vt++fbVw4UJFRUWpVatWRseDCaWkpCg5OTnN+OnTp+Xl5WVAIphdyZIldejQIYWEhKhChQr69NNPFRISomnTpikwMNDoeICpcFdBAECWeHt7a/fu3dwBDvd1+vRptWvXTps2bZKvr68k6fLly6pVq5bmzZunRx55xNiAMJ2UlBSlpKQoT547n8HOmzdPmzdvVmhoqLp3755m1gxwP23btpWPj4+mT58uLy8v7d27VwUKFFDz5s0VFBSkzz//3OiIMJkvv/xSt2/fVqdOnbRz50499dRTiouLk7Ozs2bPnq22bdsaHREwDQpXAIAs8fLy0p49eyhc4b6eeuopXb58WXPmzFHJkiUlSYcOHVLnzp3l7e2tn376yeCEAB52p0+f1pNPPqnU1FQdPnxY4eHhOnz4sPLnz69ffvnFuuQLyKqEhAQdPHhQQUFBzFYHMonCFQAgSyhcIaPc3Ny0efPmNLf/3rlzp+rUqaOEhASDksGsfvnll3tur1u37gNKgtzk9u3bmj9/vvbs2aP4+HhVrlxZ7du3l5ubm9HRAOChRo8rAACQo4oUKaKkpKQ048nJySpUqJABiWB29evXTzNmsVisX6fXqwi4l2+++Ubt2rVT+/bt1b59e5tt/fv31wcffGBQMphVcnKyZs+erdWrV+v8+fNKSUmx2b5mzRqDkgHmw10FAQBAjvrggw/Uq1cvRUVFWceioqLUp08fffjhhwYmg1n9+eefNo/z58/rp59+UtWqVbVy5Uqj48GEevTooeXLl6cZ79u3r7788ksDEsHs+vTpoz59+ig5OVnlypVThQoVbB4AMo6lggCALGGpIDLKz89PCQkJun37trWZ9l9fe3h42OwbFxdnRETkEuvXr1dERIR27txpdBSYzNKlS9W+fXstWbJEjz32mCSpV69eWrx4sVavXq1SpUoZnBBmkz9/fs2dO1dNmjQxOgpgeiwVBACoVatWmj17try9vTV37ly1bdtWLi4u93zNSy+9JG9v7weUEGY2ceJEoyPgIVGwYEEdOnTI6BgwoaZNm2rq1Klq1qyZVq1apc8++0w//PCD1q5dqxIlShgdDybk7Oys4sWLGx0DyBWYcQUAkLOzs06ePKnAwEA5Ojrq7Nmz3EEJgN3au3evzfPU1FSdPXtWY8eO1e3bt7Vx40aDksHspk6dqoiICBUoUEBr166l8IAsGz9+vI4dO6YpU6bY9OADkHnMuAIAqFSpUnrnnXfUoEEDpaamasGCBXedTdWhQ4cHnA5mFxMTc8/tQUFBDygJcouKFSvKYrHofz9/rVGjhmbNmmVQKphNREREuuMFChRQ5cqVNXXqVOtYZGTkg4qFXGLjxo1au3atli9frrJly8rJyclm++LFiw1KBpgPM64AANq8ebMiIiJ09OhRxcXFycvLK91PBy0WCz2IkGkODg73/LSZO8Ahs06ePGnz3MHBQQUKFJCrq6tBiWBGDRo0yNB+FouFO8Ah0zp37nzP7Z9//vkDSgKYH4UrAIANBwcHxcbGslQQ2WbPnj02z5OSkrRr1y5FRkZq1KhRatWqlUHJACBzTp8+rUKFCsnBgZuzA8CDQuEKAGDj5MmTCgoKoh8DctzSpUv1wQcfaN26dUZHgQmtXr1aEyZM0IEDByRJpUuX1ptvvqlGjRoZnAy5mbe3t3bv3s0ddQHgAaLHFQDAxsmTJ9Msw/mnunXrPsA0yM1KliypHTt2GB0DJjR16lT16dNHzz//vPr06SNJ2rp1q5o0aaIJEyaoZ8+eBidEbsVn/siMhQsXasGCBYqJiVFiYqLNtujoaINSAeZD4QoAYKN+/fppxv45+4p+RMisq1ev2jz/6w5ww4YNU2hoqEGpYGajR4/WhAkT9MYbb1jHevfurdq1a2v06NEUrgAYbvLkyXrvvffUqVMn/fDDD+rcubOOHj2qHTt28B4FZBKLswEANv7880+bx/nz5/XTTz+patWqWrlypdHxYEK+vr7y8/OzPvLmzasyZcpoy5YtNnftAjLq8uXLeuqpp9KMN27cWFeuXDEgEQDYmjp1qqZPn66PPvpIzs7OGjBggFatWqXevXvzPgVkEjOuAAA2fHx80ow98cQTcnZ2VkREhHbu3GlAKpjZ2rVrbZ7/dQe44sWLK08efhVB5jVr1kzfffed+vfvbzP+ww8/6JlnnjEoFQD8LSYmRrVq1ZIkubm56dq1a5Kkl19+WTVq1NCUKVOMjAeYCr8tAgAypGDBgjp06JDRMWBCKSkpd73t/Mcff8ySCWRamTJlNGrUKK1bt041a9aUdKfH1aZNm/TWW29p8uTJ1n179+5tVEzkQty4BBkVEBCguLg4BQcHKygoSFu3blWFChV0/PhxeqUBmcRdBQEANvbu3Wvz/K9+RGPHjtXt27e1ceNGg5LBrPz8/PTzzz+rSpUqNuOTJk3S4MGD0/TAAu6naNGiGdrPYrHo2LFjOZwGDxMvLy/t2bOHuwrivrp166YiRYpo6NCh+vjjj9W/f3/Vrl1bUVFRatWqlT777DOjIwKmQeEKAGDDwcFBFoslzaeBNWrU0KxZs1SqVCmDksGsZs6cqXfffVe//PKL9foZP368RowYoSVLlqhOnToGJwSAjDl16pQKFSokR0dHo6PAzqWkpCglJcW6JH7evHnavHmzQkND1b17dzk7OxucEDAPClcAABsnT560ef5XPyJXV1eDEiE3GDdunCZPnqyNGzdq/vz5Gj16tJYtW6batWsbHQ0AdP36dY0dO1arV6/W+fPnlZKSYrOdmXsAYBx6XAEArJKSktSlSxdNmzZNoaGhRsdBLjJgwABdunRJ4eHhSk5O1ooVK1SjRg2jY8GkUlNTtXDhQq1duzbdIsPixYsNSgaz6tatm9avX6+XX35ZgYGB9LLCvxYSEqIuXbqoU6dOCgoKMjoOYGoUrgAAVk5OTml6XAFZ8c/m2H8pXLiw3N3dVbduXW3fvl3bt2+XRPNsZN6bb76pTz/9VA0aNFDBggUpMuBfW758uZYuXcosUGSbN998U7Nnz9aIESPUoEEDde3aVS1btpSLi4vR0QDTYakgAMBG37595eLiorFjxxodBSZG82zkpLx58+rLL79UkyZNjI6CXKJo0aJatmyZSpcubXQU5DLR0dGaPXu2vvnmGyUnJ+vFF19Uly5dVLlyZaOjAaZB4QoAYKNXr16aO3euQkNDVaVKFXl4eNhsj4yMNCgZANxRtGhRLV++nJtFINt8+eWX+uGHHzRnzhy5u7sbHQe5UFJSkqZOnaqBAwcqKSlJYWFh6t27tzp37sysUeA+KFwBAGw0aNDgrtssFovWrFnzANMAQFpz5szRTz/9pFmzZsnNzc3oOMgFKlWqpKNHjyo1NVUhISFycnKy2R4dHW1QMphdUlKSvvvuO33++edatWqVatSooa5du+r06dP6+OOP9fjjj+vrr782OiZg1+hxBQCwsXbtWqMjIJfp0qXLPbfPmjXrASVBbtGmTRt988038vf3p8iAbNGiRQujIyCXiY6O1ueff65vvvlGDg4O6tChgyZMmGAzU7Rly5aqWrWqgSkBc6BwBQAActSff/5p8zwpKUn79+/X5cuX9fjjjxuUCmbWsWNH7dy5Uy+99BLN2ZEthg4danQE5DJVq1bVE088oU8++UQtWrRIU2CX7ix7fuGFFwxIB5gLSwUBADYaNGhwzz8CWSqI7JCSkqIePXro0Ucf1YABA4yOA5Px8PDQihUr9NhjjxkdBQDSdfLkSQUHBxsdA8gVmHEFALBRsWJFm+dJSUnavXu39u/fr44dOxoTCrmOg4ODIiIiVL9+fQpXyLQiRYrI29vb6BjIRZKTkzVhwgQtWLBAMTExSkxMtNkeFxdnUDKYFUUrIPtQuAIA2JgwYUK648OGDVN8fPwDToPc7OjRo7p9+7bRMWBC48eP14ABAzRt2jSFhIQYHQe5wPDhwzVz5ky99dZbGjRokN577z2dOHFC33//vYYMGWJ0PJgQxVAg+7BUEACQIUeOHFG1atX4RQuZFhERYfM8NTVVZ8+e1dKlS9WxY0dNmTLFoGQwKz8/PyUkJOj27dtyd3dP0zuG9ylk1qOPPqrJkyeradOm8vLy0u7du61jW7du5a5vyLQhQ4bcsxjau3dvoyMCpsGMKwBAhmzZskWurq5Gx4AJ7dq1y+a5g4ODChQooPHjx9/3joNAeiZOnGh0BOQysbGxCgsLkyR5enrqypUrkqRnnnlGgwcPNjIaTOqrr77SjBkz1LRpUw0bNkzt2rXTo48+qvLly2vr1q0UroBMoHAFALDRqlUrm+d/zY6Jioril3dkydq1a42OgFyGfnvIbo888ojOnj2roKAgPfroo1q5cqUqV66sHTt2yMXFxeh4MCGKoUD2cTA6AADAvvj4+Ng88ubNq/r162vZsmXcLhxZcvz4cR0+fDjN+OHDh3XixIkHHwi5wtGjRzVo0CC1a9dO58+flyQtX75cv/76q8HJYEYtW7bU6tWrJUm9evXS4MGDFRoaqg4dOjAzFFnyVzFUkrUYKoliKJAF9LgCAAA5ql69eurSpUuaWTJffvmlZs6cqXXr1hkTDKa1fv16Pf3006pdu7Z++eUXHThwQMWKFdPYsWMVFRWlhQsXGh0RJrdlyxZt2bJFoaGhevbZZ42OAxN6++235e3trXfffVfz58/XSy+9pJCQEMXExKhv374aO3as0REB06BwBQBIV2Jios6fP6+UlBSb8aCgIIMSway8vb0VHR2t4sWL24wfOXJE4eHhunz5sjHBYFo1a9ZU69atFRERIS8vL+3Zs0fFihXT9u3b1apVK50+fdroiABgg2IokHX0uAIA2Pj999/VtWtXbd682WY8NTVVFotFycnJBiWDWVksFl27di3N+JUrV7iekCX79u1L9y5v/v7+unjxogGJkBscPnxYa9euTfdDmyFDhhiUCrlFzZo1VbNmTaNjAKZE4QoAYKNz587KkyePlixZosDAQFksFqMjweTq1q2rMWPG6JtvvpGjo6MkKTk5WWPGjNFjjz1mcDqYka+vr86ePauiRYvajO/atUuFCxc2KBXMbMaMGerRo4fy58+vgIAAm//3WSwWClfIkB9//DHD+zZr1iwHkwC5C0sFAQA2PDw8tHPnTpUqVcroKMglfvvtN9WtW1e+vr6qU6eOJGnDhg26evWq1qxZo3LlyhmcEGbTr18/bdu2Td9++61KlCih6OhonTt3Th06dFCHDh24kQQyLTg4WK+//roGDhxodBSYmINDxu59xgx2IHO4qyAAwEaZMmVYaoNsVaZMGe3du1dt2rTR+fPnde3aNXXo0EEHDx6kaIUsGT16tEqVKqUiRYooPj5eZcqUUd26dVWrVi0NGjTI6HgwoT///FOtW7c2OgZMLiUlJUMPilZA5jDjCgCgq1evWr+OiorSoEGDNHr0aIWFhcnJyclmX29v7wcdDw+J119/XSNGjFD+/PmNjgI7lpqaqlOnTqlAgQK6ePGi9u3bp/j4eFWqVEmhoaFGx4NJde3aVVWrVtVrr71mdBQ8ZMLCwrRs2TIVKVLE6CiA3aJwBQCQg4ODTT+Pvxqx/xPN2ZHTvL29tXv3bhUrVszoKLBjKSkpcnV11a+//kqhCtlmzJgxioyMVNOmTdP90KZ3794GJUNu9887owJIH83ZAQBau3at0REA8VkaMsLBwUGhoaG6dOkShStkm+nTp8vT01Pr16/X+vXrbbZZLBYKVwBgIApXAADVq1cv069hWRcAo4wdO1b9+/fXJ598Qp80ZIvjx48bHQEAcBcsFQQAZAnLupDdWC6BjPLz81NCQoJu374tZ2dnubm52WyPi4szKBkAZA7/7wPujxlXAIAs4XMPAEaZOHGi0RGQC50+fVo//vijYmJilJiYaLMtMjLSoFQAAApXAAAAMJWOHTsaHQG5zOrVq9WsWTMVK1ZMBw8eVLly5XTixAmlpqaqcuXKRscDgIeag9EBAABA7paUlHTXbRcvXrR+/dJLL8nb2/tBREIucPToUQ0aNEjt2rXT+fPnJUnLly/Xr7/+anAymNE777yjfv36ad++fXJ1ddWiRYt06tQp1atXT61btzY6HnKJy5cvpxn79NNPVbBgwQcfBjARClcAACBHvfDCC+kuLT137pzq169vff7JJ5/Q7B8Zsn79eoWFhWnbtm1avHix4uPjJUl79uzR0KFDDU4HMzpw4IA6dOggScqTJ49u3LghT09PjRgxQv/5z38MTgcz+s9//qP58+dbn7dp00b58uVT4cKFtWfPHuv4iy++KA8PDyMiAqZB4QoAAOSomJgYdevWzWYsNjZW9evXV6lSpQxKBTN7++239f7772vVqlVydna2jj/++OPaunWrgclgVh4eHta+VoGBgTp69Kh12z9nhgIZNW3aNBUpUkSStGrVKq1atUrLly/X008/rf79+xucDjAXClcAABss60J2W7ZsmTZv3qyIiAhJ0h9//KF69eopLCxMCxYsMDgdzGjfvn1q2bJlmnF/f3+KDMiSGjVqaOPGjZKkJk2a6K233tKoUaPUpUsX1ahRw+B0MKPY2Fhr4WrJkiVq06aNGjdurAEDBmjHjh0GpwPMhcIVAMAGy7qQ3QoUKKCVK1dq0aJFioiIUP369VWpUiV98803cnDgVxFknq+vr86ePZtmfNeuXSpcuLABiWB2kZGRql69uiRp+PDhatiwoebPn6+QkBB99tlnBqeDGfn5+enUqVOSpJ9++kmNGjWSdOeuzMnJyUZGA0yHuwoCAGz8tazrn7+ox8bGqkGDBipbtqyByWBmRYoU0apVq1SnTh098cQT+uKLL2SxWIyOBZN64YUXNHDgQH377beyWCxKSUnRpk2b1K9fP2ufIiAzihUrZv3aw8ND06ZNMzANcoNWrVrpxRdfVGhoqC5duqSnn35a0p0Ce/HixQ1OB5iLJTW9j9UBAA+tCxcuqG7dunr66acVGRmpP/74Qw0aNFCFChU0b948ZsggQ/z8/NItTCUkJMjFxUWOjo7Wsbi4uAcZDblAYmKievbsqdmzZys5OVl58uRRcnKyXnzxRc2ePdvm+gIyomPHjuratavq1q1rdBTkEklJSZo0aZJOnTqlTp06qVKlSpKkCRMmyMvLK03vRwB3R+EKAJDGqVOn9Nhjj+m5557TkiVLVLlyZX311Vf8MYgMmzNnTob37dixYw4mQW4WExOj/fv3Kz4+XpUqVVJoaKjRkWBSLVq00LJlyxQcHKzOnTurY8eOLDsFADtB4QoAkK7ff/+dZV0A7N5fv8ryHoV/68KFC/riiy80Z84c/fbbb2rUqJG6du2q5s2by8nJyeh4MKEvvvhCn376qY4dO6YtW7YoODhYEydOVNGiRdW8eXOj4wGmwXoPAID8/PyUN29em0eNGjV05coV/fe//1W+fPms40BGXL16NcMPICs+++wzlStXTq6urnJ1dVW5cuU0c+ZMo2PBxAoUKKCIiAjt2bNH27ZtU/HixfXyyy+rUKFC6tu3rw4fPmx0RJjIJ598ooiICD399NO6fPmytSG7r6+vJk6caGw4wGRozg4A4BcoZDtfX98Mz4Dh7krIrCFDhigyMlK9evVSzZo1JUlbtmxR3759FRMToxEjRhicEGZ29uxZrVq1SqtWrZKjo6OaNGmiffv2qUyZMho3bpz69u1rdESYwEcffaQZM2aoRYsWGjt2rHU8PDxc/fr1MzAZYD4sFQQAANlu/fr11q9PnDiht99+W506dbIpMsyZM0djxoyhxxUyrUCBApo8ebLatWtnM/7NN9+oV69eunjxokHJYFZJSUn68ccf9fnnn2vlypUqX768unXrphdffFHe3t6SpO+++05dunTRn3/+aXBamIGbm5sOHjyo4OBgeXl5ac+ePSpWrJgOHz6s8uXL68aNG0ZHBEyDGVcAgDSOHj2qzz//XEePHtWkSZPk7++v5cuXKygoSGXLljU6HkygXr161q9HjBihyMhImyJDs2bNFBYWpunTp1O4QqYlJSUpPDw8zXiVKlV0+/ZtAxLB7AIDA5WSkqJ27dpp+/btqlixYpp9GjRoIF9f3weeDeZUtGhR7d69W8HBwTbjP/30k0qXLm1QKsCc6HEFALCxfv16hYWFadu2bVq8eLHi4+MlSXv27NHQoUMNTgcz2rJlS7pFhvDwcG3fvt2ARDC7l19+WZ988kma8enTp6t9+/YGJILZTZgwQX/88Yc+/vjjdItW0p0l0MePH3+wwWBaERER6tmzp+bPn6/U1FRt375do0aN0jvvvKMBAwYYHQ8wFZYKAgBs1KxZU61bt1ZERITN1Pbt27erVatWOn36tNERYTIlS5ZU8+bNNW7cOJvxAQMG6IcfftChQ4cMSgaz6tWrl+bOnasiRYqoRo0akqRt27YpJiZGHTp0sLkDXGRkpFExATzkvvrqKw0bNkxHjx6VJBUqVEjDhw9X165dDU4GmAuFKwCADU9PT+3bt09Fixa1KVydOHFCpUqV0s2bN42OCJNZtmyZnnvuORUvXlzVq1eXJG3fvl2HDx/WokWL1KRJE4MTwmwaNGiQof0sFovWrFmTw2mQW0RFRWnBggWKiYlRYmKizbbFixcblAq5QUJCguLj4+Xv7290FMCU6HEFALDh6+urs2fPqmjRojbju3btUuHChQ1KBTNr0qSJDh8+rE8++UQHDhyQJD377LN67bXXVKRIEYPTwYzWrl2bof1Onz6tlJQUOTjQHQP3Nm/ePHXo0EFPPvmkVq5cqcaNG+v333/XuXPn1LJlS6PjwYRu3Lih1NRUubu7y93dXRcuXNDEiRNVpkwZNW7c2Oh4gKkw4woAYKNfv37atm2bvv32W5UoUULR0dE6d+6cOnTooA4dOtDnCpkWExOjIkWKyGKxpLstKCjIgFR4GHh7e2v37t0qVqyY0VFg58qXL6/u3burZ8+e1tnGRYsWVffu3RUYGKjhw4cbHREm07hxY7Vq1UqvvfaaLl++rJIlS8rZ2VkXL15UZGSkevToYXREwDT4+AkAYGP06NEqVaqUihQpovj4eJUpU0Z169ZVrVq1NGjQIKPjwYSKFi2qCxcupBm/dOlSmpl9QHbi81lk1NGjR9W0aVNJkrOzs65fvy6LxaK+fftq+vTpBqeDGUVHR6tOnTqSpIULFyogIEAnT57U3LlzNXnyZIPTAebCUkEAgA1nZ2fNmDFDgwcP1v79+xUfH69KlSopNDTU6GgwqdTU1HRnW8XHx8vV1dWARABgy8/PT9euXZMkFS5cWPv371dYWJguX76shIQEg9PBjBISEuTl5SVJWrlypVq1aiUHBwfVqFFDJ0+eNDgdYC4UrgAA6QoKCmIJF/6ViIgISXcaZA8ePFju7u7WbcnJydq2bdtdbzsPAA9S3bp1tWrVKoWFhal169bq06eP1qxZo1WrVqlhw4ZGx4MJFS9eXN9//71atmypFStWqG/fvpKk8+fPy9vb2+B0gLlQuAIAWAsMGcGt5ZFRu3btknRnxtW+ffvk7Oxs3ebs7KwKFSqoX79+RsUDAKspU6ZY75r73nvvycnJSZs3b9Zzzz3HMnlkyZAhQ/Tiiy+qb9++atiwoWrWrCnpzuyrSpUqGZwOMBeaswMAuLU8clTnzp01adKk+37CfPr0aRUqVIg7wCHb0JwdgJFiY2N19uxZVahQwfr/tu3bt8vb21ulSpUyOB1gHhSuAACAXaDIgOz2193huKaQnqtXr2Z4X5Z2IbPWrl171w8GP/74Y/Xs2fMBJwLMi480AQDpOnLkiFasWKEbN25I4u5cyHlcY8ioLl26WBtp/9P169fVpUsX6/PffvtNwcHBDzIaTMTX11d+fn73fPy1D5BZrVq10s6dO9OMT5o0Se+8844BiQDzYsYVAMDGpUuX1KZNG61du1YWi0WHDx9WsWLF1KVLF/n5+Wn8+PFGR0QuxewYZJSjo6POnj0rf39/m/GLFy8qICBAt2/fNigZzGT9+vUZ3rdevXo5mAS50cyZM/Xuu+/ql19+sS4LHD9+vEaMGKElS5aoTp06BicEzIPm7AAAG3379pWTk5NiYmJUunRp63jbtm0VERFB4QqAYa5evarU1FSlpqbq2rVrcnV1tW5LTk7WsmXL0hSzgLuhGIWc1K1bN8XFxalRo0bauHGj5s+fr9GjR2vZsmWqXbu20fEAU6FwBQCwsXLlSq1YsUKPPPKIzXhoaKhOnjxpUCoAuLO0y2KxyGKxqESJEmm2WywWDR8+3IBkyA02bNigTz/9VMeOHdO3336rwoUL64svvlDRokX12GOPGR0PJjRgwABdunRJ4eHhSk5O1ooVK1SjRg2jYwGmQ+EKAGDj+vXrcnd3TzMeFxcnFxcXAxLhYWGxWIyOADu3du1apaam6vHHH9eiRYuUN29e6zZnZ2cFBwerUKFCBiaEWS1atEgvv/yy2rdvr+joaN26dUuSdOXKFessGeB+Jk+enGascOHCcnd3V926dbV9+3Zt375dktS7d+8HHQ8wLXpcAQBsNGnSRFWqVNHIkSPl5eWlvXv3Kjg4WC+88IJSUlK0cOFCoyMil6LHFTLq5MmTCgoKotiJbFOpUiX17dtXHTp0sHkv2rVrl55++mnFxsYaHREmULRo0QztZ7FYdOzYsRxOA+QezLgCANgYN26cGjZsqKioKCUmJmrAgAH69ddfFRcXp02bNhkdDybUpUsXTZo0SV5eXjbj169fV69evTRr1ixJd+4Ax2wZZMSaNWvk6emp1q1b24x/++23SkhIUMeOHQ1KBrM6dOiQ6tatm2bcx8dHly9ffvCBYErHjx83OgKQKzkYHQAAYF/KlSunQ4cO6bHHHlPz5s11/fp1tWrVSrt27dKjjz5qdDyY0Jw5c3Tjxo004zdu3NDcuXOtz4sUKSJHR8cHGQ0mNWbMGOXPnz/NuL+/v0aPHm1AIphdQECAjhw5kmZ848aNzAIFAIMx4woAkIarq6ueeOIJVahQQSkpKZKkHTt2SJKaNWtmZDSYCHeAQ06JiYlJd0lOcHCwYmJiDEgEs3vllVfUp08fzZo1SxaLRX/88Ye2bNmifv36afDgwUbHg0mdPn1aP/74o2JiYpSYmGizLTIy0qBUgPlQuAIA2Pjpp5/08ssvKy4uTv/bBtFisSg5OdmgZDAb7gCHnOLv76+9e/cqJCTEZnzPnj3Kly+fMaFgam+//bZSUlLUsGFDJSQkqG7dunJxcVG/fv3Uq1cvo+PBhFavXq1mzZqpWLFiOnjwoMqVK6cTJ04oNTVVlStXNjoeYCo0ZwcA2AgNDVXjxo01ZMgQFSxY0Og4MLH169dzBzjkiIEDB2r+/Pn6/PPPrX2J1q9fry5duuj555/Xhx9+aHBCmFViYqKOHDmi+Ph4lSlTRp6enkZHgklVq1ZNTz/9tIYPH25t+O/v76/27dvrqaeeUo8ePYyOCJgGhSsAgA1vb2/6WSFbcQc4ZLfExES9/PLL+vbbb5Unz50FBMnJyerYsaOmTZsmZ2dngxPCzE6dOiXpTt89IKu8vLy0e/duPfroo/Lz89PGjRtVtmxZ7dmzR82bN9eJEyeMjgiYBs3ZAQA2nn/+ea1bt87oGMhF1qxZo4ULF6YZ//bbbzVnzhwDEsHsnJ2dNX/+fB06dEhffvmlFi1apGPHjmnWrFkUrZAlt2/f1uDBg+Xj46OQkBCFhITIx8dHgwYNUlJSktHxYEIeHh7WvlaBgYE6evSoddvFixeNigWYEj2uAAA2pkyZotatW2vDhg0KCwuTk5OTzfbevXsblAxmNWbMGH366adpxv39/fXqq6+qY8eOBqSC2X322WeaMGGCDh8+LOnOMuc333xT3bp1MzgZzKhXr15avHixxo0bp5o1a0qStmzZomHDhunSpUv65JNPDE4Is6lRo4Y2btyo0qVLq0mTJnrrrbe0b98+LV68WDVq1DA6HmAqLBUEANj47LPP9Nprr8nV1VX58uWzWd5lsVh07NgxA9PBjFxdXXXw4ME0jbRPnDih0qVL68aNG8YEg2kNGTJEkZGR6tWrl02RYcqUKerbt69GjBhhcEKYjY+Pj+bNm6enn37aZnzZsmVq166drly5YlAymNWxY8cUHx+v8uXL6/r163rrrbe0efNmhYaGKjIyUsHBwUZHBEyDwhUAwEZAQIB69+6tt99+Ww4OrCjHvxcUFKQpU6aoWbNmNuM//PCDevbsqdOnTxuUDGZVoEABTZ48We3atbMZ/+abb9SrVy+W4SDT/P39tX79epUuXdpm/MCBA6pbt64uXLhgUDLkdt98842aNWsmDw8Po6MAdou/SAAANhITE9W2bVuKVsg27dq1U+/evbV27VolJycrOTlZa9asUZ8+ffTCCy8YHQ8mlJSUpPDw8DTjVapU0e3btw1IBLN74403NHLkSN26dcs6duvWLY0aNUpvvPGGgcmQ23Xv3l3nzp0zOgZg15hxBQCw0bdvXxUoUEDvvvuu0VGQS3AHOGS3Xr16ycnJSZGRkTbj/fr1040bN/Txxx8blAxm1bJlS61evVouLi6qUKGCJGnPnj1KTExUw4YNbfZdvHixERGRS3l5eWnPnj0qVqyY0VEAu0XhCgBgo3fv3po7d64qVKig8uXLp2nO/r9/KAIZdfjwYe3atUtubm4qX748/T2QZb169dLcuXNVpEgRa5Pjbdu2KSYmRh06dLB53+I9CxnRuXPnDO/7+eef52ASPGwoXAH3R+EKAGCjQYMGd91msVi0Zs2aB5gGuQV3gEN2utf71D/xnoXstmnTJoWHh8vFxcXoKMglKFwB90fhCgAA5CjuAAcgt/D29tbu3bspMiDbULgC7o/CFQAAyFHcAQ5AbkGRAdmNawq4P24ZBQAAchR3gAMAIH3BwcFp+okCsEXhCgAA5KiXX35Zn3zySZrx6dOnq3379gYkAgAgZxUrVkyXLl1KM3758mWb2VX79+9XkSJFHmQ0wHTyGB0AAADkfp999plWrlyZ7h3gIiIirPtxBzgAQG5w4sQJJScnpxm/deuWzpw5Y0AiwLwoXAEAgBy1f/9+Va5cWZJ09OhRSVL+/PmVP39+7d+/37qfxWIxJB8AZBTvU7ifH3/80fr1ihUr5OPjY32enJys1atXKyQkxIBkgHnRnB0AAAAAMoBG2rgfB4e7d+NxcnJSSEiIxo8fr2eeeeYBpgLMjcIVAAAAAADZqGjRooqKilK+fPmMjgKYHksFAQAAADx0KlWqlOGlf9HR0TmcBrlJUlKSihUrpri4OApXQDagcAUAAADgodOiRQvr1zdv3tTUqVNVpkwZ1axZU5K0detW/frrr3r99dcNSgizcnJy0t69e42OAeQaLBUEAAAA8FDr1q2bAgMDNXLkSJvxoUOH6tSpU5o1a5ZByWBWffv2lYuLi8aOHWt0FMD0KFwBAAAAeKj5+PgoKipKoaGhNuOHDx9WeHi4rly5YlAymFWvXr00d+5chYaGqkqVKvLw8LDZHhkZaVAywHxYKggAAADgoebm5qZNmzalKVxt2rRJrq6uBqWCme3fv1+VK1eWJP3+++822zLaWw3AHRSuAAAAADzU3nzzTfXo0UPR0dGqVq2aJGnbtm2aNWuWBg8ebHA6mNHatWsztN/p06dVqFAhOTg45HAiwLxYKggAAADgobdgwQJNmjRJBw4ckCSVLl1affr0UZs2bQxOhtzM29tbu3fvVrFixYyOAtgtClcAAAAAABjAy8tLe/bsoXAF3APzEQEAAAA89C5fvqyZM2fq3XffVVxcnCQpOjpaZ86cMTgZADzc6HEFAAAA4KG2d+9eNWrUSD4+Pjpx4oS6deumvHnzavHixYqJidHcuXONjggADy1mXAEAAAB4qEVERKhTp046fPiwzV0EmzRpol9++cXAZAAAClcAAAAAHmo7duxQ9+7d04wXLlxYsbGxBiTCw8JisRgdAbB7FK4AAAAAPNRcXFx09erVNOO///67ChQoYEAiPCy4VxpwfxSuAAAAADzUmjVrphEjRigpKUnSnVkwMTExGjhwoJ577jmD0yE3++233xQcHGx0DMCuWVIp8QIAAAB4iF25ckXPP/+8oqKidO3aNRUqVEixsbGqWbOmli1bJg8PD6MjwgRatWql2bNny9vbW61atbrnvosXL35AqQDz466CAAAAAB5qPj4+WrVqlTZu3Ki9e/cqPj5elStXVqNGjYyOBhPx8fGx9qzy8fExOA2QezDjCgAAAAD+382bN+Xi4kLTbACwE/S4AgAAAPBQS0lJ0ciRI1W4cGF5enrq+PHjkqTBgwfrs88+MzgdzOjGjRtKSEiwPj958qQmTpyolStXGpgKMCcKVwAAAAAeau+//75mz56tcePGydnZ2Tperlw5zZw508BkMKvmzZtr7ty5kqTLly+rWrVqGj9+vJo3b65PPvnE4HSAuVC4AgAAAPBQmzt3rqZPn6727dvL0dHROl6hQgUdPHjQwGQwq+joaNWpU0eStHDhQgUEBOjkyZOaO3euJk+ebHA6wFwoXAEAAAB4qJ05c0bFixdPM56SkqKkpCQDEsHsEhIS5OXlJUlauXKlWrVqJQcHB9WoUUMnT540OB1gLhSuAAAAADzUypQpow0bNqQZX7hwoSpVqmRAIphd8eLF9f333+vUqVNasWKFGjduLEk6f/68vL29DU4HmEseowMAAAAAgJGGDBmijh076syZM0pJSdHixYt16NAhzZ07V0uWLDE6HkxoyJAhevHFF9W3b181bNhQNWvWlHRn9hXFUCBzLKmpqalGhwAAAAAAI23YsEEjRozQnj17FB8fr8qVK2vIkCHWmTJAZsXGxurs2bOqUKGCHBzuLHbavn27vL29VapUKYPTAeZB4QoAAAAAgGySlJQkNzc37d69W+XKlTM6DmB6LBUEAAAAAEmJiYk6f/68UlJSbMaDgoIMSgQzcnJyUlBQkJKTk42OAuQKzLgCAAAA8FA7fPiwunTpos2bN9uMp6amymKxUIBApn322WdavHixvvjiC+XNm9foOICpUbgCAAAA8FCrXbu28uTJo7fffluBgYGyWCw22ytUqGBQMphVpUqVdOTIESUlJSk4OFgeHh4226Ojow1KBpgPSwUBAAAAPNR2796tnTt30jAb2aZFixZGRwByDQpXAAAAAB5qZcqU0cWLF42OgVxk6NChRkcAcg2WCgIAAAB4qK1Zs0aDBg3S6NGjFRYWJicnJ5vt3t7eBiWD2e3cuVMHDhyQJJUtW1aVKlUyOBFgPhSuAAAAADzUHBwcJClNbyuasyOrzp8/rxdeeEHr/q+9uwvSsi74OP67wUXARFaFcpAZeVknNCDESmRqQhhiiGH0wDFCYEpmctTlRaSYCqo1HeNgLcQoJmZwswimNHuZNMGXIVLIAQkxG4OpJVxjkEFcyBF29znocccV7eHp8fHa2+vzmeFg/9cefA844cd9/e/HHkv//v2TJIcPH86ECRPyk5/8JAMGDCg2EKqIVwUBAIBSe/TRR4tO4D2mvr4+r7zySnbv3p0RI0YkSZ599tnMmTMn8+bNy7p16wouhOrhE1cAAACn4IYbbkhDQ0POPffcolPo5s4666xs3LgxH/nIR7qcb9u2LZMnT87hw4eLCYMq1KPoAAAAgGpw77335siRI0VnUAXa29tPuistSWpqatLe3l5AEVQvwxUAAMAp8LIKp+qKK67I/Pnz88ILL3Se7d+/PwsXLszEiRMLLIPqY7gCAACAd9DKlStz5MiRXHDBBRk2bFiGDRuWIUOG5MiRI7nrrruKzoOq4nJ2AAAAeAcNHjw427dvz8aNG/Pcc88lSUaMGJFJkyYVXAbVx+XsAAAAp+DMM8/Mzp07M3To0KJTAErDq4IAAADwDtu0aVOmTZvW+argtGnTsnHjxqKzoOoYrgAAgFJrbm5+y4vXOzo60tzc3Pnztddem379+r2baVSp7373u5kyZUrOPPPMzJ8/P/Pnz0+/fv0yderU3H333UXnQVXxqiAAAFBqPXv2TEtLSwYOHNjl/KWXXsrAgQPT1tZWUBnV6vzzz8+SJUty0003dTm/++67c/vtt2f//v0FlUH18YkrAACg1Do6OlKpVE46b21tTe/evQsootodPnw4U6ZMOel88uTJefnllwsogurlWwUBAIBSuvnmm5MklUolS5cuTd++fTuftbW1ZevWrfnwhz9cUB3VbPr06bn//vuzePHiLucPPPBApk2bVlAVVCfDFQAAUEo7duxI8q9PXO3atSu9evXqfNarV6+MHj06t9xyS1F5VLGLLroot912Wx577LGMGzcuSfLkk09my5YtWbRoUVasWNH5u/PmzSsqE6qCO64AAIBS+9znPpfvfOc7Ll7nHTNkyJBT+r1KpZK9e/f+P9dAdTNcAQAAJPnLX/6SPXv25BOf+ET69OnztndfAfDucTk7AABQaocOHcrEiRNz4YUXZurUqWlpaUmSXHfddVm0aFHBdVS7jo6O+LwI/OcMVwAAQKktWLAgNTU1aW5u7nJB+zXXXJMHH3ywwDKqWVNTU0aOHJk+ffqkT58+GTVqVH74wx8WnQVVx+XsAABAqf32t7/NQw89lPPPP7/LeV1dXf72t78VVEU1a2xszNKlS3PTTTdl/PjxSZLf/e53uf7663Pw4MEsXLiw4EKoHoYrAACg1I4ePdrlk1avO3ToUE4//fQCiqh2d911V1atWpXZs2d3nk2fPj0XX3xxvv71rxuu4H/Bq4IAAECpffzjH09TU1Pnz5VKJe3t7Vm+fHkmTJhQYBnVqqWlJZdffvlJ55dffnnnHWrAqfGJKwAAoNSWL1+eiRMn5qmnnsprr72WL37xi9m9e3cOHTqULVu2FJ1HFRo+fHg2bNiQL3/5y13O169fn7q6uoKqoDpVOny9AQAAUHIvv/xyVq5cmZ07d6a1tTWXXHJJbrzxxpx33nlFp1GFfvazn+Waa67JpEmTOu+42rJlSzZt2pQNGzbkqquuKrgQqofhCgAAAN5h27dvT2NjY/70pz8lSUaMGJFFixZlzJgxBZdBdTFcAQAApffqq6/mj3/8Yw4cOJD29vYuz6ZPn15QFdXo+PHj+cIXvpClS5dmyJAhRedA1TNcAQAApfbggw9m9uzZOXjw4EnPKpVK2traCqiimp111ll5+umnDVfwDvCtggAAQKnV19fn6quvTktLS9rb27v8MVrxn7jyyivz85//vOgMeE/wrYIAAECp/eMf/8jNN9+c97///UWn8B5RV1eXhoaGbNmyJWPHjs0ZZ5zR5fm8efMKKoPq41VBAACg1D7/+c9n/Pjxue6664pO4T3i370iWKlUsnfv3nexBqqb4QoAACi1Y8eO5eqrr86AAQMycuTI1NTUdHnu0zH8X7z+T+5KpVJwCVQnwxUAAFBqa9asyfXXX5/evXvnnHPO6TIw+HQM/6k1a9bkzjvvzPPPP5/kX68PLliwIHPnzi24DKqL4QoAACi1D3zgA5k3b16WLFmSHj18fxX/d8uWLUtjY2Pq6+szbty4JMkTTzyRlStXZuHChWloaCi4EKqH4QoAACi1s88+O3/4wx8ybNiwolN4jxgwYEBWrFiRGTNmdDlft25d6uvrc/DgwYLKoPr47wQAAKDU5syZk/Xr1xedwXvI8ePHc+mll550Pnbs2Jw4caKAIqhepxUdAAAAUKS2trYsX748Dz30UEaNGnXS5eyNjY0FlVGtZs2alVWrVp30d2f16tWZOXNmQVVQnbwqCAAAlNqECRPe9lmlUskjjzzyLtbwXlBfX5+mpqYMHjw4l112WZJk69ataW5uzuzZs7uMo4ZR+PcMVwAAAPAO+ndj6BsZRuF/ZrgCAAAAoFtyxxUAAFBqR48ezR133JFNmzblwIEDaW9v7/J87969BZUBYLgCAABKbe7cuXn88ccza9asnHfeealUKkUnAfDfvCoIAACUWv/+/fPrX/8648ePLzoFgDfpUXQAAABAkWpra3P22WcXnQHAWzBcAQAApXbrrbdm2bJlOXbsWNEpALyJVwUBAIBSGzNmTPbs2ZOOjo5ccMEFqamp6fJ8+/btBZUB4HJ2AACg1K688sqiEwB4Gz5xBQAAAEC35I4rAAAAALolrwoCAACl1qNHj1Qqlbd93tbW9i7WAPBGhisAAKDU7r///i4/Hz9+PDt27Mg999yTb3zjGwVVAZC44woAAOAt/fjHP8769evzwAMPFJ0CUFqGKwAAgLewd+/ejBo1Kq2trUWnAJSWy9kBAADe5J///GdWrFiRQYMGFZ0CUGruuAIAAEqttra2y+XsHR0deeWVV9K3b9/ce++9BZYB4FVBAACg1O65554uP/fo0SMDBgzIxz72sdTW1hZUBUBiuAIAADglN9xwQxoaGnLuuecWnQJQGoYrAACAU9CvX788/fTTGTp0aNEpAKXhcnYAAIBT4P/8Ad59hisAAAAAuiXDFQAAAADdkuEKAAAAgG7JcAUAAABAt2S4AgAAOAXXXntt+vXrV3QGQKlUOnw1BgAAUHKHDx/Otm3bcuDAgbS3t3d5Nnv27IKqADBcAQAApfbLX/4yM2fOTGtra/r165dKpdL5rFKp5NChQwXWAZSb4QoAACi1Cy+8MFOnTs3tt9+evn37Fp0DwBsYrgAAgFI744wzsmvXrgwdOrToFADexOXsAABAqX3qU5/KU089VXQGAG/htKIDAAAAivTpT386ixcvzrPPPpuRI0empqamy/Pp06cXVAaAVwUBAIBS69Hj7V9EqVQqaWtrexdrAHgjwxUAAAAA3ZI7rgAAAP7bq6++WnQCAG9guAIAAEqtra0tt956awYNGpT3ve992bt3b5Jk6dKlWbNmTcF1AOVmuAIAAErttttuy9q1a7N8+fL06tWr8/xDH/pQfvCDHxRYBoDhCgAAKLWmpqasXr06M2fOTM+ePTvPR48eneeee67AMgAMVwAAQKnt378/w4cPP+m8vb09x48fL6AIgNcZrgAAgFK76KKLsnnz5pPOf/rTn2bMmDEFFAHwutOKDgAAACjSsmXLMmfOnOzfvz/t7e2577778uc//zlNTU351a9+VXQeQKlVOjo6OoqOAAAAKNLmzZvT0NCQnTt3prW1NZdcckmWLVuWyZMnF50GUGqGKwAAAAC6JXdcAQAApbZv3778/e9/7/x527ZtWbBgQVavXl1gFQCJ4QoAACi5z372s3n00UeTJC+++GImTZqUbdu25Stf+UoaGhoKrgMoN8MVAABQas8880w++tGPJkk2bNiQkSNH5ve//31+9KMfZe3atcXGAZSc4QoAACi148eP5/TTT0+SbNy4MdOnT0+SfPCDH0xLS0uRaQClZ7gCAABK7eKLL873vve9bN68OQ8//HCmTJmSJHnhhRdyzjnnFFwHUG6GKwAAoNS+9a1v5fvf/34++clPZsaMGRk9enSS5Be/+EXnK4QAFKPS0dHRUXQEAABAETo6OrJv377U1tbmxIkTqa2t7Xz217/+NX379s3AgQMLLAQoN8MVAABQWu3t7endu3d2796durq6onMAeBOvCgIAAKXVo0eP1NXV5aWXXio6BYC3YLgCAABK7Y477sjixYvzzDPPFJ0CwJt4VRAAACi12traHDt2LCdOnEivXr3Sp0+fLs8PHTpUUBkApxUdAAAAUKRvf/vbRScA8DZ84goAAACAbskdVwAAQOnt2bMnX/3qVzNjxowcOHAgSfKb3/wmu3fvLrgMoNwMVwAAQKk9/vjjGTlyZLZu3Zr77rsvra2tSZKdO3fma1/7WsF1AOVmuAIAAEptyZIl+eY3v5mHH344vXr16jy/4oor8uSTTxZYBoDhCgAAKLVdu3blqquuOul84MCBOXjwYAFFALzOcAUAAJRa//7909LSctL5jh07MmjQoAKKAHid4QoAACi1z3zmM/nSl76UF198MZVKJe3t7dmyZUtuueWWzJ49u+g8gFKrdHR0dBQdAQAAUJTXXnstN954Y9auXZu2tracdtppOXHiRGbOnJm1a9emZ8+eRScClJbhCgAAIMm+ffuya9euHD16NGPGjMnw4cOLTgIovdOKDgAAACjamjVrcuedd+b5559PktTV1WXBggWZO3duwWUA5Wa4AgAASm3ZsmVpbGxMfX19xo0blyR54oknsnDhwjQ3N6ehoaHgQoDy8qogAABQagMGDMiKFSsyY8aMLufr1q1LfX19Dh48WFAZAL5VEAAAKLXjx4/n0ksvPel87NixOXHiRAFFALzOcAUAAJTarFmzsmrVqpPOV69enZkzZxZQBMDrvCoIAACUWn19fZqamjJ48OBcdtllSZKtW7emubk5s2fPTk1NTefvNjY2FpUJUEqGKwAAoNQmTJhwSr9XqVTyyCOP/D/XAPBGhisAAAAAuiV3XAEAAADQLRmuAAAAAOiWDFcAAAAAdEuGKwAAAAC6JcMVAAAAAN2S4QoAAACAbslwBQAAAEC3ZLgCAAAAoFv6L7VuvMBD3x9+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "sns.heatmap(df.corr(), annot=True, vmin=0, vmax=1, cmap='plasma')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ae810bed",
   "metadata": {},
   "source": [
    "membuat group berdasarkan kategori_pmi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "2c751e3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Temp\\ipykernel_10668\\1619729414.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  grouped = df.groupby(by='kategori_pmi').mean().reset_index()\n"
     ]
    },
    {
     "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>kategori_pmi</th>\n",
       "      <th>melek_huruf_diatas15</th>\n",
       "      <th>pct_tdk_cukup_pangan</th>\n",
       "      <th>pct_perempuan_nikah</th>\n",
       "      <th>persen_unmet_need_pelayanan_kesehatan</th>\n",
       "      <th>proporsi_rt_akses_layanan_dasar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SANGAT RENDAH</td>\n",
       "      <td>84.792120</td>\n",
       "      <td>26.141733</td>\n",
       "      <td>22.973714</td>\n",
       "      <td>3.931500</td>\n",
       "      <td>55.083778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SANGAT TINGGI</td>\n",
       "      <td>98.456944</td>\n",
       "      <td>3.573000</td>\n",
       "      <td>30.417857</td>\n",
       "      <td>3.411250</td>\n",
       "      <td>78.471667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SEDANG</td>\n",
       "      <td>95.801456</td>\n",
       "      <td>12.564635</td>\n",
       "      <td>27.732817</td>\n",
       "      <td>5.639091</td>\n",
       "      <td>76.626359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TINGGI</td>\n",
       "      <td>97.202087</td>\n",
       "      <td>7.237376</td>\n",
       "      <td>27.535437</td>\n",
       "      <td>4.602699</td>\n",
       "      <td>79.989667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    kategori_pmi  melek_huruf_diatas15  pct_tdk_cukup_pangan  \\\n",
       "0  SANGAT RENDAH             84.792120             26.141733   \n",
       "1  SANGAT TINGGI             98.456944              3.573000   \n",
       "2         SEDANG             95.801456             12.564635   \n",
       "3         TINGGI             97.202087              7.237376   \n",
       "\n",
       "   pct_perempuan_nikah  persen_unmet_need_pelayanan_kesehatan  \\\n",
       "0            22.973714                               3.931500   \n",
       "1            30.417857                               3.411250   \n",
       "2            27.732817                               5.639091   \n",
       "3            27.535437                               4.602699   \n",
       "\n",
       "   proporsi_rt_akses_layanan_dasar  \n",
       "0                        55.083778  \n",
       "1                        78.471667  \n",
       "2                        76.626359  \n",
       "3                        79.989667  "
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby(by='kategori_pmi').mean().reset_index()\n",
    "grouped"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a301c848",
   "metadata": {},
   "source": [
    "Membandingkan nilai rata tiap column dengan kategori_PMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "5b4c0816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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11WlXrxdffDHHei/dpaV+/fpWUlJSjv1mDlB+fn45/iN76tSpHJ8zs7S0NKtnz552nyNGjMix7aW7nQUGBlp//fVXju2HDh1qtw0JCcmx3ZAhQ5y+LGNjY3Ns+8ILLzjVUNABVpJ1991357iNpaSkOP3TMnr06GzbnTx50ipVqpRTeM0pFP3zzz9Wu3btnGpwN8B+8cUXTkGzV69eee4S5eq2smjRIrvfihUr5rh74qVf2JKsDz74IMd+161b5/QP9y+//OJSPbkpzBoy93m5AdbVdX/27Fnruuuus/ucMmVKjm0LO8BKsh588EGXHnfmzBmXf5DIfDjCnXfemWM7d8NLYXwmuuPSz5uuXbvm+ENrZqmpqS7vTjp58mS7/xYtWuTYzt0AWxiva34UdoB1dVvZs2eP5e/vb/f5+++/59i2sL7r86Ow/y9wN8Dm533g7vd6Qa1vAmzOOIkTjJP5jMLZXRM287T8nn14wYIF+vPPPyVJtWvX1rvvvpvtgfAZKlSooLfeesu+P3XqVJee58MPP5SPj0+282rWrKmOHTva91NTU/XCCy+oTp062bYvVqyY00kmVq1a5VINkjRt2rQcd7UtVqyYPvroIxUrVkySlJSUpHnz5mXb1tXdbD08PPTGG2/Y97/66iuXa504cWKuu0wNHz7c/nv//v3ZXmrpzJkzTicAe+KJJ9S8efMc+xwzZozCwsJcrjG//P39NW3atBy3MR8fHw0aNMi+n9NrO2fOHHsXsxIlSmjatGk57pLk7e2t6dOn57pduyIyMlJ9+/bVuXPnJEkPPPCAPvvsMxUvXjzXx7m6rdx+++26/vrrJUkJCQn2rod56dixox5++OEc57do0cLuV8rf+8VVV0MN2XF13ZcoUUKvvvqqfT8/79OCVqFCBb399tsutS1ZsqTLu+K9/PLL9m6G3333ndPhBAXhSnwm5lfx4sX10UcfubQ7frFixfJ8L2d4/PHH7c/m2NhYe7fQgnI1va6FwdVtpVq1aho6dKh9f/HixS4/R0F91+fH1fgekPL3PnBXUazv/xoCLIzTsWNHVa5cWdLFYxkzjjOQpD///NM+trRx48Zq3LhxvvrOHG6GDBni0gdcjx49VKpUKUnSunXr8jx7Z/369dWqVatc22Q+xsbhcGjgwIG5ts/cX3x8fF4lS7q4fjL/E52dmjVrqkuXLvb9RYsWudR3Xn2WL19e0sVjURITE/N8jK+vr+66665c29SoUUPBwcH2/eyOZYmOjraPe3E4HHr00Udz7dPLyyvXIHK57rrrrlyP1ZUuXsM4Q07H53z33Xf237feemu2x7NkVrduXd1www35qNTZm2++qUGDBtnHbY0YMUL/93//J09PT7f7zE7m7drVAPvQQw/l2caVdXo5roYaLlfmdR8bG1tkddx9992Fchy6j4+P6tevL+niD1t5nUm3MLnzmeiObt265fnZ4A6Hw+H0nRUTE1Pgz+Gqq+l1LQzufCYW1Xd9flyp94BUeO+DDCas738DTuIE43h4eKhfv36aMGGCUlNTNW/ePDuIXM7oq2VZTiMhmUdBc1OsWDGFhYUpLi5OaWlp2rRpk9q2bZtj+7zCqyQFBQXZf9eoUUPXXHONy+1d/eDP/OGZm65du+rrr7+W5NoX5t9//60ff/xRmzZtUkJCglJSUnT+/HmnNhkh37IsHTp0SP7+/rn22bx5c/vXytxUqlRJhw4dkpT9eshcf7169ewfQnLTtWtXPfXUU3m2c0fr1q3zbJP5izan1zbzcmW+VEVuOnTo4HQqfVeNHDlSr7/+un3/tdde06hRo/Ldz/nz57V06VKtW7dOu3btUnJyss6ePet0Wapdu3bZf2c+iUtuCmqdXo6roYa87NmzR7/88os2b96so0ePKiUlJcfRqpMnT+rMmTP2D3VXUuagnx+JiYn68ccftXHjRh04cEDJyclKTU11apP5Ek8HDx7M9w+eriqMz0R3uLsuz5w5o59++kkbNmzQ3r17lZycrHPnzjm9V3///Xf7b1ffq+64ml7XwrBu3TqtWrVKf/zxh06ePKnTp087XTon8w/2rq7nwvquz4+r5T0guf8+cNXVsL7/CwiwMNJ9992nCRMmSLoYWh999FFZlmWPoHp6euqee+7JV58HDhxw+kdy2LBhLu9i8tdff9l/Z5zFMyeunNkt8z+KgYGB+WqfsStpXho0aOBSu4xfs6WL12JMSkqSn59flnYHDx7Uk08+qQULFtgjc65w5dqtmQN6bvJaD5kDkavLHxYWpmLFimX5si0IrixXXsv0zz//OF1DL6ddzS9Vt25dl9pl9swzz9hftB4eHpo6dWq+R6jT0tL0zjvvaPz48Xm+VzJz9Rq/BbFOL9fVUENOtm3bpmHDhmnJkiUuXcM6Q1JSUpEE2Jo1a+ar/cmTJzVq1ChFRUXZu7e7Ir/XkHZFYX4muiO/6/Ls2bN6+eWXNWXKFKezV+elMOq/ml7XwvDdd9/pqaeeytfeGK4uW0F/1+fH1fYekPL/Psivolzf/yUEWBipbt26atGihWJjY7V27Vrt2LFDf//9t3386s0335zvU4BnPt27dPGyNO7I64PX1eOK3G3vqoCAALfaJSYmZvmQ/eOPP9ShQ4c8L0aeHVf+GXFnHWT3z3nGaeMl15ff09NTfn5++QpbriqI1/bS0TtXf7V254vy0pFeV3aVzezChQvq3bu3W7tLufpPa37XaX5CnKuuhhqys2LFCnXt2tWtwJyf0FCQypQp43LbhIQEtW/f3unyL64q6OUr6M/E/v376++//871cW+88YYaNWqU4/z8rMuUlBTdfPPNWrNmjcuPyVDQ6/Jqel0Lw8SJE/X000/n+3GuLltBftfnR2H/X+Cu/LwP3FFU6/u/hmNgYazM13edPXv2ZV/79fTp0wVSV+bdfa5mrv6T7e3t7XT/0i+WtLQ09enTx/6S8vb21gMPPKCFCxdq27ZtSkpKUmpqqn29QMuyVLVq1YJZiHzKvKtZfkLGpevg38Cd68517drV/vuXX36x93xw1cSJE53Ca+vWrTVt2jTFxsbqyJEj9i7EGbcxY8bku0ZkLzk5Wb1797bDa5kyZTRs2DB9++232rlzp70Lceb1fzXIz3Y6cOBAO+R4eHioT58+mjt3rn2t6IzdXjNuOV2H9HIVxmfi8uXL9eOPP+Z6y7x7aXbysy6ffvppp/DapUsXzZw5U5s2bdKxY8f0zz//ONXvzneuq66W17UwrFmzxim8Vq1aVa+++qpWrFih/fv36/Tp00pLS7OXLTo6Ot/PUVDf9flxNf9fUNjXXC2K9f1fxAgsjBUREaEnn3xS58+f1+zZs+2RTz8/P91+++357u/SX76OHj1qn1Tg38jVXcIubXfpSYe+/vpr+0QZxYoV09KlS9WmTZsCee6Clrn2/NRQVPW64tLt1tVdrzKPRrvqmWeeUcuWLTVu3DhJ0gcffCBPT09NmTIlz8empaVp4sSJ9v3HHntM7733Xq6PuZrXu2lmzJihI0eOSJLKli2rtWvXKjQ0NMf2pq37uLg4/fDDD/b9zz77THfeeWeujymsZTTpMzE7x48f1//93//Z9998802NGDEi18cUVv1X0+taGDIOhZKk6667TkuWLMnxCgWSe8tWUN/1+WH6e+ByFMX6zpCf3bRNxwgsjBUQEKBbbrlF0sXLpmScXbZ3794qUaJEvvu7dJfjjH/2/q327NmT73ZeXl4qV66c0/wlS5bYf99zzz15fkmdOXOmSE5aIzkfT7x3716XHnPixAl727oalSxZ0umHFlePoXL3zLdjx47VCy+8YN9///33NWzYsDwft2HDBns3/VKlSjmdCConhXkymP+azO/TYcOG5RpeJfPWfebl69ChQ54hRyq8ZSyMz8S9e/c6jVZld+vQoUMBVC8tXbrU/ke4evXqLp3E7kqsy6J+XQuaZVn6+eef7fsTJkzINbxK7i1bQX3X54dJ/xcUtIJc35lHc105D8e/ZR26ggALo2V3puH8nn04wzXXXKMaNWrY99059sckrl7qIHO7+vXrZ9k9Zt++ffbfLVq0yLO/tWvXFtlu1k2bNrX/3rBhg0u/VhblJSFclXm9u7qLmTtnIM7w8ssv67nnnrPvv/vuu3riiSdyfUzm7aRevXounRBo9erVbtcIZ/l9n5q27vO7fHv27MnzmFLJeXdDV3erNukzMTuZ62/evHme14w+e/as4uLi8uz3SqxLV1/X/HCnblecOHHC6dClwnpfFtR3fX4U1nugsF6LglSQ6zvz8bqu7DW1ZcsWl57bhPWYFwIsjHbrrbc6/WpVvXr1yzpFenh4uP135mNq/42+/fbbPK9Zm56ers8//9y+n926ze/ZeaOiovLVviBlvjbbyZMnnX4lzokJFxjP2BNBunht5ISEhFzbb9u27bICrCS9+uqrevbZZ+37kydPznU3w/xuJ9HR0U7/BOHymPQ+dUdhLV/ma9CePXu2SGu5UvJb/7x581w6fs/UdelO3a7I77IlJydr4cKF+X6egvquz4+r6f14pRXk+q5SpYr99+bNm/N87q+++sqlGk1Yj3khwMJoxYsX1/Hjx+1dqP788888fy3OzeOPP27/MrVixQp98sknBVXqVSclJcXp+JvsREZG2md2lqQBAwZkaVOxYkX778zX0c3O2rVri3Sd1q1bV82bN7fvv/jii7mOwv7xxx+aM2fOlSjtsvTr108lS5aUdPHLaPDgwTn+mp2amqqHH364QEZ8JkyY4BRa33rrLadQm1nm7WTLli25Hqt7/vx5Pfnkk5ddH/4nP+/Tzz//XCtWrCjskgpUfpZvz549euutt1zqN/Mlkf7880+XRitM+kzMTub6165dm+tnZGJiokaPHu1Sv5nX5bFjx1w6Xr+wXtf8cGcbcEVAQIDT9c3zWr5Ro0blGYyyU1Df9flRWO+BzK9F5sviXU0Kcn1n3mts4cKFuX5vx8TEaPHixS7VaMJ6zAsBFsikTp06TpcGeeCBB/TBBx/k+YV1+PBhvfzyyxo6dGhhl1igxo8fr7lz52Y7Lzo62unYxk6dOmW7K1DmMz7Onz8/x8sPbdiwQbfddluR7yo3cuRI++9169bp0Ucf1YULF7K0O3DggO64445s511typYt6/RP5FdffaVevXpp//79Tu127Nihrl27asWKFQV2grI333zTaffhN954w2n34gwtW7a0Q/Y///yjJ598Mtv31alTp9SnTx+XdkmE6zK/T6dMmaLff/8923Y//vjjZf/zWhQyL9/q1atz3INmz549Cg8Pd/ms8w0bNrSvB378+HHNnj07X7WY8Jl4qRtuuMH+e//+/XrllVeybXf06FHdcsstLh+XWalSJfs8BJZl6Z133snzMYX1uuaHO9uAK4oVK+Z0bOgzzzyT7fkWLMvSa6+9pqlTp7r9XAXxXZ8fhfUeaNasmf13XFycW2dlvhIKan336NHD/js+Pt7pRIiZ/fHHH+rVq5fLJ3HKvB7nz5+vQ4cOufS4qwlnIQYuMXnyZMXFxWnNmjVKTU3V4MGD9e6776p3795q1qyZypUrp3PnzunYsWPavHmzVq5cqVWrVik9PV19+/Yt6vJd1qdPH82fP19333235s+frz59+qhSpUo6evSovv76a3388cf2l0rp0qU1bdq0bPvp27evRo0apcOHDystLU1du3bVoEGD1KVLF5UtW1YJCQn67rvvNGfOHF24cEHh4eH6448/soSrK+XOO+9U9+7d9fXXX0uSpk+frpiYGD344IOqW7euzp49q5UrV2ratGlKTExUmzZttG/fPh04cKBI6nXV008/rejoaHu36EWLFumrr75SgwYNVL58eR0+fFjx8fGSLgbeSZMm2ceLX+71aCdNmqS0tDS9++67ki5+eXt6eurll1+225QsWVIPPvig3WbGjBnatm2bBg0apFq1aun06dOKiYnRRx99pAMHDsjHx0e33nqrEbtwm+Chhx7SG2+8obNnzyolJUVt2rTR4MGDdeONN6p06dLat2+fvvzyS3sXxUGDBjmdifZq1759ezVu3FibNm2SJN1///366aef1LNnT1WoUEHHjh3Tzz//rJkzZ+rMmTNq1KiRSpQokefxar6+vrr11lvtyz8NGDBAr732mmrWrOn0vnnllVfUoEEDSeZ9Jl6qevXquu222+zRnLFjxyomJkb33HOPQkJClJSUpJUrV+qjjz7SiRMnFBwcrCZNmui7777Ls++7775bkydPliSNGTNGkZGRqlu3rtOJFx9//HF17NhRUuG9rvnhzjbgqmHDhtmHc2zatEmNGjXSY489pubNm8uyLMXHx2vWrFn2NbjdeV8W1Hd9fhTWe6Bu3bpq0qSJ4uLiZFmWOnbsqEaNGikkJMT+kUG6+L2e+aSNV1JBru+GDRvq5ptv1k8//SRJevbZZxUbG6u+ffsqMDBQR44c0U8//aSoqCilpqaqX79+Lo1m9+zZU0888YTOnTungwcPqkaNGmrWrJnKly9v74UYGBio6dOnF8AaKSQWcJXq37+/JcmSZPXq1euy+2vfvr3d31NPPZVr21OnTlk9e/a027t669u3b57LMmbMmDxrnTlzpt2+ffv2ebbfs2ePUx05qVq1qt0mOjra6tevX57LVKJECeuXX37J9fl/+eUXq3jx4nn2Va9ePevIkSNZ6sjOmDFj7Db9+/fPcx1YlvNrPHPmzBzbpaSkWNddd12e9VauXNnau3evS/W6+pq50ldmrr62lmVZp0+ftnr16pXrMlWtWtWKjY21vv/+e3taixYtCqTeIUOGOD3Xpdv6qVOnrMaNG+e53r29va0FCxa4tA3kZ/1kyO/7Ky+FWUPmfvfs2XNZfc2ePdtyOBx5rv/rr7/eOnv2rEvP7c77NC+uPG92tmzZYvn5+eW5fJUqVbK2b9/u8ufFnj17rMqVK+fa56XvjcL4THSHu/0eOnTICgkJybN+Pz8/a+XKlS5/xyUmJloNGjTItc9LX4vCel3zw51twNV18sADD+S5bJKsESNGWNHR0U6f5TkprO/6/Cis98C6dessf3//XPu89HPD3feBu9/rBbm+//zzTysoKCjP/vr06WPt3r3baVpuPvzwQ8vDwyPH/nLbvq4G7EIMZKN06dJasGCBFi9erNatW+d6XK2np6fatGmjSZMm5Xldy6vNxx9/rDfeeEMBAQHZzr/++uu1fv16+9fwnHTs2FHLli1Tw4YNs51fqlQpPfLII4qJidE111xz2XVfLh8fH0VHR+vZZ5+1d2vNzNPTU3fccYfWr19/xS6uXhBKlSqlL774Qt9//7169+6typUrq3jx4goMDNR1112nSZMmKS4uTs2bN3e6TFRB7U48ZcoUDR482L4/btw4p1HY0qVLa8WKFerfv788PT2z7aN169ZavXq1evbsWSA14X/uvfdeffXVV6pevXq288uWLavnn39eS5cudetSZEWtfv36WrNmTY4nRClWrJj69u2ruLg4hYWFudxvtWrVtGnTJo0fP1433HCDAgMD89xrwbTPxEtVrFhRa9euVffu3bOd7+HhofDwcG3cuFFt27Z1uV8/Pz/FxMTovffe00033aSKFSvmua0V1uuaH+5sA6766KOPNH78+ByvA1qjRg19+umnevPNN91+joL6rs+PwnoPtGjRQlu2bNHzzz+v6667TuXKlXMafb0aFOT6rl69ulauXKlOnTplOz8gIEBvvvmm5s2b53R24bw89NBDiomJ0UMPPaQGDRrI19c3X48vag7LMvT8ycAVdPToUa1atUqHDh3SyZMnVbx4cQUEBCg0NFSNGzcukAtQF6XU1FRFR0frzz//VFJSkipUqKC2bdvm+58By7IUGxur2NhYnTx5UmXLllVISIg6dOiQ5/XtikpKSop+/vln7dmzR5ZlqXLlymrXrp0qVapU1KUVqqFDh2rKlCmSLu6WlNdJJwraoUOHFB0drQMHDsjLy0vBwcG69tprVatWrStax3/RhQsXtHr1am3atEnJyckqX768qlWrpg4dOhTYP+VFbevWrfrtt9907NgxlSlTRpUqVVL79u0v69qW7jDxM/FSf/75p1asWKGEhASVLFlSlSpVUps2bYrkM/JqeV0LQ0pKipYtW6adO3cqNTVVQUFBqlu3rlq1apXvvqpVq6a//vpL0sVjLjOuEVxQ3/X58W94D7iroNf39u3btWrVKv3999/y9fVVjRo11KlTp3/N53Z+EGAB4D/m7NmzqlKlio4dOybp4kmfbrvttiKuCgBQEHIKsMC/hTljxQCAXLnye6RlWXrsscfs8BoYGKiuXbsWdmkAAAAFggALAP8SPXr00IsvvpjjJVLWr1+vbt26acaMGfa0UaNGOV2LEAAA4Gp2dR31DABw2+HDh/XVV1/p5Zdflr+/v0JDQ+Xn56fTp09r9+7dTidukqRbbrnF6Xp0AAAAVzsCLAD8S2Q+g2BiYqJ97cBLeXp66qGHHtI777yT6xm2AQAArjYEWAD4l/j+++/11VdfaenSpdq0aZP279+v5ORkORwOlStXTjVq1FCHDh3Uv3//Qj3rJAAAQGHhLMQAAAAAACNwEicAAAAAgBEIsAAAAAAAIxBgAQAAAABGIMACAAAAAIxAgAUAAAAAGIEACwAAAAAwAgEWAAAAAGAEAiwAAAAAwAgEWAAAAACAEQiwAAAAAAAjEGABAAAAAEYgwAIAAAAAjECABQAAAAAYgQALAAAAADACARYAAAAAYAQCLAAAAADACARYAAAAAIARCLAAAAAAACMQYAEAAAAARiDAAgAAAACMQIAFAAAAABiBAAsAAAAAMAIBFgAAAABgBAIsAAAAAMAIBFgAAAAAgBEIsAAAAAAAIxBgAQAAAABGIMACAAAAAIxAgAUAAAAAGIEACwAAAAAwAgEWAAAAAGAEAiwAAAAAwAgEWAAAAACAEQiwAAAAAAAjEGABAAAAAEYgwAIAAAAAjECABQAAAAAYgQALAAAAADACARYAAAAAYAQCLAAAAADACARYAAAAAIARCLAAAAAAACMQYAEAAAAARiDAAgAAAACMQIAFAAAAABiBAAsAAAAAMAIBFgAAAABgBAIsAAAAAMAIBFgAAAAAgBEIsAAAAAAAIxBgAQAAAABGIMACAAAAAIxAgAUAAAAAGIEACwAAAAAwAgEWAAAAAGAEAiwAAAAAwAgEWAAAAACAEQiwAAAAAAAjEGABAAAAAEYgwAIAAAAAjECABQAAAAAYgQALAAAAADACARYAAAAAYAQCLAAAAADACARYAAAAAIARCLAAAAAAACMQYAEAAAAARiDAAgAAAACMQIAFAAAAABiBAAsAAAAAMAIBFgAAAABgBAIsAAAAAMAIBFgAAAAAgBEIsAAAAAAAIxBgAQAAAABGIMACAAAAAIxAgAUAAAAAGIEACwAAAAAwAgEWAAAAAGAEAiwAAAAAwAgEWAAAAACAEQiwAAAAAAAjEGABAAAAAEYgwAIAAAAAjECABQAAAAAYgQALAAAAADACARYAAAAAYAQCLAAAAADACARYAAAAAIARCLAAAAAAACMQYAEAAAAARiDAAgAAAACMQIAFAAAAABiBAAsAAAAAMAIBFgAAAABgBAIsAAAAAMAIBFgAAAAAgBEIsAAAAAAAIxBgAQAAAABGIMACAAAAAIxAgAUAAAAAGIEACwAAAAAwAgEWAAAAAGAEAiwAAAAAwAgEWAAAAACAEQiwAAAAAAAjEGABAAAAAEYgwAIAAAAAjECABQAAAAAYgQALAAAAADACARYAAAAAYAQCLAAAAADACARYAAAAAIARCLAAAAAAACMQYAEAAAAARiDAAgAAAACMQIAFAAAAABiBAAsAAAAAMAIBFgAAAABgBAIsAAAAAMAIBFgAAAAAgBEIsAAAAAAAIxBgAQAAAABGIMACAAAAAIxAgAUAAAAAGIEACwAAAAAwAgEWAAAAAGAEAiwAAAAAwAgEWAA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",
      "text/plain": [
       "<Figure size 1000x3000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(5,1,figsize=(10,30))\n",
    "for index,i in enumerate(grouped.columns[1:]):\n",
    "    sns.barplot(data=grouped,y='kategori_pmi', x=i, ax=ax[index], \n",
    "                order=grouped.sort_values(i, ascending = False).kategori_pmi)\n",
    "    plt.suptitle(\"Membandingkan nilai rata-rata tiap atribut\", size=25)\n",
    "    plt.xlabel(\"\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "41e3895f",
   "metadata": {},
   "source": [
    "dapat dilihat dari plot diatas bahwa <br>\n",
    "1. melek huruf diatas 15 tahun kategori PMI nya menyebarlebih dari 80% <br>\n",
    "2. banyak orang yang tidak cukup pangan dengan kategori PMI sangat rendah <br>\n",
    "3. lebih dari 20% peremmpuan yang sudah menikkah dengan kondisi PMI yang hampir sama<br>\n",
    "5. dengan meningkat nya akses kela yanan daasar menigkat pula kategori PMI nya<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "b6e0ce76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(30,12), bins=30, ec='black')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c4f2fabb",
   "metadata": {},
   "source": [
    "menampilkan keseluruhan persen 5 atribut yang ternyata ada yang menarik:\n",
    "1. hampir semua melek huruf<br>\n",
    "2. 50% dari keseluruhan masih kekurangan pangan<br>\n",
    "3. perempuan dibawah 50% yang belum nikah dibawah 18 tahun<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "a6ccec0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x225f199a1d0>"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720.125x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y = \"pct_tdk_cukup_pangan\", x = \"tahun\", hue = \"prov\", data = df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "644ee358",
   "metadata": {},
   "source": [
    "dari plot diatas disimpulkan kalo tiap tahun bahkan kekurangan pangan belum teratasi dengan baik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "9ea9d59f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 442 entries, 0 to 441\n",
      "Data columns (total 8 columns):\n",
      " #   Column                                 Non-Null Count  Dtype   \n",
      "---  ------                                 --------------  -----   \n",
      " 0   prov                                   442 non-null    category\n",
      " 1   tahun                                  442 non-null    category\n",
      " 2   kategori_pmi                           442 non-null    category\n",
      " 3   melek_huruf_diatas15                   442 non-null    float64 \n",
      " 4   pct_tdk_cukup_pangan                   442 non-null    float64 \n",
      " 5   pct_perempuan_nikah                    442 non-null    float64 \n",
      " 6   persen_unmet_need_pelayanan_kesehatan  442 non-null    float64 \n",
      " 7   proporsi_rt_akses_layanan_dasar        442 non-null    float64 \n",
      "dtypes: category(3), float64(5)\n",
      "memory usage: 24.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "8869b206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x225f1f8ba90>"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 585.25x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y = \"persen_unmet_need_pelayanan_kesehatan\", x = \"kategori_pmi\", hue = \"tahun\", data = df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8ee3b1ef",
   "metadata": {},
   "source": [
    "yang menarik ada di outer di sebelah kiri ternyata walaupun persen mendapatkan pelayaan kesehatan tinggi tapi masih tergolong sangat rendah"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "707e149d",
   "metadata": {},
   "source": [
    "# TRAINING DATA"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a21306e7",
   "metadata": {},
   "source": [
    "One hot encoding data dengan tipe categori"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "2843836f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfHot = pd.get_dummies(data=df, columns=['prov', 'tahun'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c4fad053",
   "metadata": {},
   "source": [
    "Label encoding data yang akan diprediksi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "id": "57abd988",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = preprocessing.LabelEncoder()\n",
    "target.fit(dfHot.kategori_pmi)\n",
    "Y = target.transform(dfHot.kategori_pmi)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b85eb3e9",
   "metadata": {},
   "source": [
    "Drop kategori_PMI dan masukkan ke X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "1722ace2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfHot.drop(\"kategori_pmi\",axis=1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c76a89a6",
   "metadata": {},
   "source": [
    "split data menjadi data train dan data test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "id": "f082d6cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=123)\n",
    "with open('xTrainColumn.pickle', 'wb') as fp:\n",
    "    pickle.dump(X_train.columns, fp)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "509f5c42",
   "metadata": {},
   "source": [
    "## Random Forest Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "id": "04a5f684",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Akurasi 0.797752808988764\n",
      "[[ 1  0  1  0]\n",
      " [ 0  0  0  1]\n",
      " [ 2  0 48  4]\n",
      " [ 0  2  8 22]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.33      0.50      0.40         2\n",
      "           1       0.00      0.00      0.00         1\n",
      "           2       0.84      0.89      0.86        54\n",
      "           3       0.81      0.69      0.75        32\n",
      "\n",
      "    accuracy                           0.80        89\n",
      "   macro avg       0.50      0.52      0.50        89\n",
      "weighted avg       0.81      0.80      0.80        89\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_estimators=200, random_state=12, max_depth = 20)\n",
    "clf.fit(X_train, Y_train)\n",
    "Y_pred = clf.predict(X_test)\n",
    "acc = accuracy_score(Y_test, Y_pred)\n",
    "print(\"Akurasi {}\".format(acc))\n",
    "print(confusion_matrix(Y_test, Y_pred))\n",
    "print(classification_report(Y_test, Y_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0c03091b",
   "metadata": {},
   "source": [
    "## XGboost Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "id": "a6256948",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Akurasi 0.797752808988764\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.33      0.50      0.40         2\n",
      "           1       0.00      0.00      0.00         1\n",
      "           2       0.86      0.89      0.87        54\n",
      "           3       0.81      0.69      0.75        32\n",
      "\n",
      "    accuracy                           0.80        89\n",
      "   macro avg       0.50      0.52      0.50        89\n",
      "weighted avg       0.82      0.80      0.81        89\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clf = xgb.XGBClassifier(objective = \"multi:softprob\", num_class = 2, eval_metric = \"mlogloss\", max_depth = 24, gamma=0.1, subsample = 0.90,learning_rate=0.01, n_estimators = 10, nthread=-1)\n",
    "# num_class adalah jumlah kelas\n",
    "clf.fit(X_train, Y_train)\n",
    "Y_pred = clf.predict(X_test)\n",
    "acc = accuracy_score(Y_test, Y_pred)\n",
    "print(\"Akurasi {}\".format(acc))\n",
    "print(classification_report(Y_test, Y_pred))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "36f56d1c",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "7e3358db",
   "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>attr</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pct_tdk_cukup_pangan</td>\n",
       "      <td>0.084571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>persen_unmet_need_pelayanan_kesehatan</td>\n",
       "      <td>0.081504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>tahun_2013</td>\n",
       "      <td>0.062483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>tahun_2014</td>\n",
       "      <td>0.057533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>tahun_2012</td>\n",
       "      <td>0.057007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>tahun_2011</td>\n",
       "      <td>0.052171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>tahun_2018</td>\n",
       "      <td>0.051052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>prov_KALIMANTAN TIMUR</td>\n",
       "      <td>0.046498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>tahun_2019</td>\n",
       "      <td>0.043984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>tahun_2020</td>\n",
       "      <td>0.040634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>tahun_2021</td>\n",
       "      <td>0.037511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>prov_DI YOGYAKARTA</td>\n",
       "      <td>0.034851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>tahun_2022</td>\n",
       "      <td>0.034140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>tahun_2010</td>\n",
       "      <td>0.033959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>tahun_2017</td>\n",
       "      <td>0.033881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>tahun_2015</td>\n",
       "      <td>0.031820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>tahun_2016</td>\n",
       "      <td>0.031245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>prov_RIAU</td>\n",
       "      <td>0.028896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>melek_huruf_diatas15</td>\n",
       "      <td>0.028678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>prov_SUMATERA SELATAN</td>\n",
       "      <td>0.023830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>prov_JAMBI</td>\n",
       "      <td>0.022448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>prov_BENGKULU</td>\n",
       "      <td>0.021344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>proporsi_rt_akses_layanan_dasar</td>\n",
       "      <td>0.017348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>prov_JAWA TENGAH</td>\n",
       "      <td>0.015357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pct_perempuan_nikah</td>\n",
       "      <td>0.013619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>prov_PAPUA BARAT</td>\n",
       "      <td>0.008275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>prov_JAWA BARAT</td>\n",
       "      <td>0.002929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>prov_SULAWESI BARAT</td>\n",
       "      <td>0.002431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>prov_KALIMANTAN TENGAH</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>prov_JAWA TIMUR</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>prov_DKI JAKARTA</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>prov_GORONTALO</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>prov_BANTEN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>prov_BALI</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>prov_ACEH</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>prov_SUMATERA UTARA</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>prov_SULAWESI UTARA</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>prov_SUMATERA BARAT</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>prov_KALIMANTAN UTARA</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>prov_SULAWESI TENGGARA</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>prov_SULAWESI TENGAH</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>prov_SULAWESI SELATAN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>prov_KALIMANTAN BARAT</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>prov_PAPUA</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>prov_NUSA TENGGARA TIMUR</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>prov_KALIMANTAN SELATAN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>prov_MALUKU UTARA</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>prov_MALUKU</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>prov_LAMPUNG</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>prov_KEP. RIAU</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>prov_KEP. BANGKA BELITUNG</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>prov_NUSA TENGGARA BARAT</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     attr  importance\n",
       "1                    pct_tdk_cukup_pangan    0.084571\n",
       "3   persen_unmet_need_pelayanan_kesehatan    0.081504\n",
       "42                             tahun_2013    0.062483\n",
       "43                             tahun_2014    0.057533\n",
       "41                             tahun_2012    0.057007\n",
       "40                             tahun_2011    0.052171\n",
       "47                             tahun_2018    0.051052\n",
       "19                  prov_KALIMANTAN TIMUR    0.046498\n",
       "48                             tahun_2019    0.043984\n",
       "49                             tahun_2020    0.040634\n",
       "50                             tahun_2021    0.037511\n",
       "9                      prov_DI YOGYAKARTA    0.034851\n",
       "51                             tahun_2022    0.034140\n",
       "39                             tahun_2010    0.033959\n",
       "46                             tahun_2017    0.033881\n",
       "44                             tahun_2015    0.031820\n",
       "45                             tahun_2016    0.031245\n",
       "30                              prov_RIAU    0.028896\n",
       "0                    melek_huruf_diatas15    0.028678\n",
       "37                  prov_SUMATERA SELATAN    0.023830\n",
       "12                             prov_JAMBI    0.022448\n",
       "8                           prov_BENGKULU    0.021344\n",
       "4         proporsi_rt_akses_layanan_dasar    0.017348\n",
       "14                       prov_JAWA TENGAH    0.015357\n",
       "2                     pct_perempuan_nikah    0.013619\n",
       "29                       prov_PAPUA BARAT    0.008275\n",
       "13                        prov_JAWA BARAT    0.002929\n",
       "31                    prov_SULAWESI BARAT    0.002431\n",
       "18                 prov_KALIMANTAN TENGAH    0.000000\n",
       "15                        prov_JAWA TIMUR    0.000000\n",
       "10                       prov_DKI JAKARTA    0.000000\n",
       "11                         prov_GORONTALO    0.000000\n",
       "7                             prov_BANTEN    0.000000\n",
       "6                               prov_BALI    0.000000\n",
       "5                               prov_ACEH    0.000000\n",
       "38                    prov_SUMATERA UTARA    0.000000\n",
       "35                    prov_SULAWESI UTARA    0.000000\n",
       "36                    prov_SUMATERA BARAT    0.000000\n",
       "20                  prov_KALIMANTAN UTARA    0.000000\n",
       "34                 prov_SULAWESI TENGGARA    0.000000\n",
       "33                   prov_SULAWESI TENGAH    0.000000\n",
       "32                  prov_SULAWESI SELATAN    0.000000\n",
       "16                  prov_KALIMANTAN BARAT    0.000000\n",
       "28                             prov_PAPUA    0.000000\n",
       "27               prov_NUSA TENGGARA TIMUR    0.000000\n",
       "17                prov_KALIMANTAN SELATAN    0.000000\n",
       "25                      prov_MALUKU UTARA    0.000000\n",
       "24                            prov_MALUKU    0.000000\n",
       "23                           prov_LAMPUNG    0.000000\n",
       "22                         prov_KEP. RIAU    0.000000\n",
       "21              prov_KEP. BANGKA BELITUNG    0.000000\n",
       "26               prov_NUSA TENGGARA BARAT    0.000000"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = pd.DataFrame({\n",
    "    'attr' : X_train.columns, \n",
    "    'importance' :clf.feature_importances_\n",
    "}).sort_values('importance',ascending=False)\n",
    "# pd.set_option('display.max_rows', None)\n",
    "feature_importances"
   ]
  }
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