diff --git a/Klasifikasi Tingkat Kemiskinan/Perbandingan_3_Algoritma.ipynb b/Klasifikasi Tingkat Kemiskinan/Perbandingan_3_Algoritma.ipynb
new file mode 100644
index 0000000..50297af
--- /dev/null
+++ b/Klasifikasi Tingkat Kemiskinan/Perbandingan_3_Algoritma.ipynb	
@@ -0,0 +1,1212 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "lvG2Ug4RqOg7"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_csv('/content/Klasifikasi Tingkat Kemiskinan di Indonesia.csv', sep=';')\n",
+        "df.head(25)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "LYW6BVyeqgo5",
+        "outputId": "0fd74086-3128-4a05-daac-6a0e41173838"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          Provinsi           Kab/Kota  \\\n",
+              "0             ACEH           Simeulue   \n",
+              "1             ACEH       Aceh Singkil   \n",
+              "2             ACEH       Aceh Selatan   \n",
+              "3             ACEH      Aceh Tenggara   \n",
+              "4             ACEH         Aceh Timur   \n",
+              "5             ACEH        Aceh Tengah   \n",
+              "6             ACEH         Aceh Barat   \n",
+              "7             ACEH         Aceh Besar   \n",
+              "8             ACEH              Pidie   \n",
+              "9             ACEH            Bireuen   \n",
+              "10            ACEH         Aceh Utara   \n",
+              "11            ACEH    Aceh Barat Daya   \n",
+              "12            ACEH          Gayo Lues   \n",
+              "13            ACEH       Aceh Tamiang   \n",
+              "14            ACEH         Nagan Raya   \n",
+              "15            ACEH          Aceh Jaya   \n",
+              "16            ACEH       Bener Meriah   \n",
+              "17            ACEH         Pidie Jaya   \n",
+              "18            ACEH    Kota Banda Aceh   \n",
+              "19            ACEH        Kota Sabang   \n",
+              "20            ACEH        Kota Langsa   \n",
+              "21            ACEH   Kota Lhokseumawe   \n",
+              "22            ACEH  Kota Subulussalam   \n",
+              "23  SUMATERA UTARA               Nias   \n",
+              "24  SUMATERA UTARA   Mandailing Natal   \n",
+              "\n",
+              "   Persentase Penduduk Miskin (P0) Menurut Kabupaten/Kota (Persen)  \\\n",
+              "0                                               18,98                \n",
+              "1                                               20,36                \n",
+              "2                                               13,18                \n",
+              "3                                               13,41                \n",
+              "4                                               14,45                \n",
+              "5                                               15,26                \n",
+              "6                                               18,81                \n",
+              "7                                               14,05                \n",
+              "8                                               19,59                \n",
+              "9                                               13,25                \n",
+              "10                                              17,43                \n",
+              "11                                              16,34                \n",
+              "12                                              19,64                \n",
+              "13                                              13,34                \n",
+              "14                                              18,23                \n",
+              "15                                              13,23                \n",
+              "16                                              19,16                \n",
+              "17                                              19,55                \n",
+              "18                                               7,61                \n",
+              "19                                              15,32                \n",
+              "20                                              10,96                \n",
+              "21                                              11,16                \n",
+              "22                                              17,65                \n",
+              "23                                              16,82                \n",
+              "24                                               9,49                \n",
+              "\n",
+              "   Rata-rata Lama Sekolah Penduduk 15+ (Tahun)  \\\n",
+              "0                                         9,48   \n",
+              "1                                         8,68   \n",
+              "2                                         8,88   \n",
+              "3                                         9,67   \n",
+              "4                                         8,21   \n",
+              "5                                         9,86   \n",
+              "6                                         9,55   \n",
+              "7                                        10,33   \n",
+              "8                                            9   \n",
+              "9                                         9,29   \n",
+              "10                                        8,64   \n",
+              "11                                        8,67   \n",
+              "12                                         8,4   \n",
+              "13                                        8,91   \n",
+              "14                                        8,69   \n",
+              "15                                        8,71   \n",
+              "16                                          10   \n",
+              "17                                        9,34   \n",
+              "18                                       12,83   \n",
+              "19                                       11,18   \n",
+              "20                                       11,12   \n",
+              "21                                       11,11   \n",
+              "22                                        8,03   \n",
+              "23                                        5,64   \n",
+              "24                                        8,63   \n",
+              "\n",
+              "    Pengeluaran per Kapita Disesuaikan (Ribu Rupiah/Orang/Tahun)  \\\n",
+              "0                                              7148.0              \n",
+              "1                                              8776.0              \n",
+              "2                                              8180.0              \n",
+              "3                                              8030.0              \n",
+              "4                                              8577.0              \n",
+              "5                                             10780.0              \n",
+              "6                                              9593.0              \n",
+              "7                                              9644.0              \n",
+              "8                                              9860.0              \n",
+              "9                                              8867.0              \n",
+              "10                                             8201.0              \n",
+              "11                                             8428.0              \n",
+              "12                                             8856.0              \n",
+              "13                                             8367.0              \n",
+              "14                                             8292.0              \n",
+              "15                                             9666.0              \n",
+              "16                                            11118.0              \n",
+              "17                                            10290.0              \n",
+              "18                                            16891.0              \n",
+              "19                                            11378.0              \n",
+              "20                                            12067.0              \n",
+              "21                                            11390.0              \n",
+              "22                                             7385.0              \n",
+              "23                                             6995.0              \n",
+              "24                                             9771.0              \n",
+              "\n",
+              "   Indeks Pembangunan Manusia Umur Harapan Hidup (Tahun)  \\\n",
+              "0                       66,41                      65,28   \n",
+              "1                       69,22                      67,43   \n",
+              "2                       67,44                       64,4   \n",
+              "3                       69,44                      68,22   \n",
+              "4                       67,83                      68,74   \n",
+              "5                       73,37                      68,86   \n",
+              "6                       71,67                      67,99   \n",
+              "7                       73,58                      69,79   \n",
+              "8                        70,7                      66,95   \n",
+              "9                       72,33                      71,26   \n",
+              "10                      69,46                      68,81   \n",
+              "11                      66,99                      65,06   \n",
+              "12                      67,56                      65,53   \n",
+              "13                      69,48                      69,63   \n",
+              "14                      69,31                      69,24   \n",
+              "15                      69,84                      67,19   \n",
+              "16                      73,27                      69,26   \n",
+              "17                       73,6                      70,18   \n",
+              "18                      85,71                      71,52   \n",
+              "19                      76,11                      70,56   \n",
+              "20                      77,44                      69,43   \n",
+              "21                      77,57                      71,64   \n",
+              "22                      65,27                      64,07   \n",
+              "23                      62,74                      69,78   \n",
+              "24                      67,19                      62,65   \n",
+              "\n",
+              "   Persentase rumah tangga yang memiliki akses terhadap sanitasi layak  \\\n",
+              "0                                               71,56                    \n",
+              "1                                               69,56                    \n",
+              "2                                               62,55                    \n",
+              "3                                               62,71                    \n",
+              "4                                               66,75                    \n",
+              "5                                               90,58                    \n",
+              "6                                               89,60                    \n",
+              "7                                               87,40                    \n",
+              "8                                               54,10                    \n",
+              "9                                               81,89                    \n",
+              "10                                              79,97                    \n",
+              "11                                              65,71                    \n",
+              "12                                              47,63                    \n",
+              "13                                              87,45                    \n",
+              "14                                              74,86                    \n",
+              "15                                              81,60                    \n",
+              "16                                              86,69                    \n",
+              "17                                              74,30                    \n",
+              "18                                              99,88                    \n",
+              "19                                              92,25                    \n",
+              "20                                              91,81                    \n",
+              "21                                              93,10                    \n",
+              "22                                              69,71                    \n",
+              "23                                              19,93                    \n",
+              "24                                              35,73                    \n",
+              "\n",
+              "   Persentase rumah tangga yang memiliki akses terhadap air minum layak  \\\n",
+              "0                                               87,45                     \n",
+              "1                                               78,58                     \n",
+              "2                                               79,65                     \n",
+              "3                                               86,71                     \n",
+              "4                                               83,16                     \n",
+              "5                                               90,10                     \n",
+              "6                                               94,22                     \n",
+              "7                                               82,36                     \n",
+              "8                                               89,24                     \n",
+              "9                                               93,53                     \n",
+              "10                                              91,09                     \n",
+              "11                                              95,34                     \n",
+              "12                                              84,68                     \n",
+              "13                                              83,12                     \n",
+              "14                                              90,13                     \n",
+              "15                                              86,36                     \n",
+              "16                                              89,71                     \n",
+              "17                                              93,46                     \n",
+              "18                                              99,37                     \n",
+              "19                                              96,18                     \n",
+              "20                                              97,14                     \n",
+              "21                                              94,44                     \n",
+              "22                                              60,87                     \n",
+              "23                                              47,79                     \n",
+              "24                                              73,78                     \n",
+              "\n",
+              "   Tingkat Pengangguran Terbuka Tingkat Partisipasi Angkatan Kerja  \\\n",
+              "0                          5,71                             71,15    \n",
+              "1                          8,36                             62,85    \n",
+              "2                          6,46                             60,85    \n",
+              "3                          6,43                             69,62    \n",
+              "4                          7,13                             59,48    \n",
+              "5                          2,61                             76,30    \n",
+              "6                          7,09                             60,05    \n",
+              "7                          7,70                             61,67    \n",
+              "8                          7,28                             60,29    \n",
+              "9                          4,32                             65,91    \n",
+              "10                         8,31                             58,47    \n",
+              "11                         4,04                             57,91    \n",
+              "12                         1,84                             78,99    \n",
+              "13                         5,87                             66,43    \n",
+              "14                         4,99                             64,99    \n",
+              "15                         3,47                             72,59    \n",
+              "16                         1,24                             77,53    \n",
+              "17                         3,57                             57,77    \n",
+              "18                         8,94                             63,00    \n",
+              "19                         3,56                             63,71    \n",
+              "20                         7,21                             67,04    \n",
+              "21                        11,16                             63,91    \n",
+              "22                         6,26                             63,78    \n",
+              "23                         3,12                             81,79    \n",
+              "24                         6,12                             69,79    \n",
+              "\n",
+              "    PDRB atas Dasar Harga Konstan menurut Pengeluaran (Rupiah)  \\\n",
+              "0                                           1648096.0            \n",
+              "1                                           1780419.0            \n",
+              "2                                           4345784.0            \n",
+              "3                                           3487157.0            \n",
+              "4                                           8433526.0            \n",
+              "5                                           5953118.0            \n",
+              "6                                           7485861.0            \n",
+              "7                                          10261585.0            \n",
+              "8                                           7975099.0            \n",
+              "9                                          10374480.0            \n",
+              "10                                         16924103.0            \n",
+              "11                                          3069805.0            \n",
+              "12                                          1981879.0            \n",
+              "13                                          6062520.0            \n",
+              "14                                          7110421.0            \n",
+              "15                                          2033844.0            \n",
+              "16                                          3744095.0            \n",
+              "17                                          2635517.0            \n",
+              "18                                         15454371.0            \n",
+              "19                                          1152875.0            \n",
+              "20                                          3974614.0            \n",
+              "21                                          7252905.0            \n",
+              "22                                          1438997.0            \n",
+              "23                                          2666855.0            \n",
+              "24                                          9585899.0            \n",
+              "\n",
+              "    Klasifikasi Kemiskinan  \n",
+              "0                      0.0  \n",
+              "1                      1.0  \n",
+              "2                      0.0  \n",
+              "3                      0.0  \n",
+              "4                      0.0  \n",
+              "5                      0.0  \n",
+              "6                      0.0  \n",
+              "7                      0.0  \n",
+              "8                      0.0  \n",
+              "9                      0.0  \n",
+              "10                     0.0  \n",
+              "11                     0.0  \n",
+              "12                     0.0  \n",
+              "13                     0.0  \n",
+              "14                     0.0  \n",
+              "15                     0.0  \n",
+              "16                     0.0  \n",
+              "17                     0.0  \n",
+              "18                     0.0  \n",
+              "19                     0.0  \n",
+              "20                     0.0  \n",
+              "21                     0.0  \n",
+              "22                     0.0  \n",
+              "23                     0.0  \n",
+              "24                     0.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-355d6490-0dc5-488f-8f19-de7f4ed9db48\" class=\"colab-df-container\">\n",
+              "    <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>Provinsi</th>\n",
+              "      <th>Kab/Kota</th>\n",
+              "      <th>Persentase Penduduk Miskin (P0) Menurut Kabupaten/Kota (Persen)</th>\n",
+              "      <th>Rata-rata Lama Sekolah Penduduk 15+ (Tahun)</th>\n",
+              "      <th>Pengeluaran per Kapita Disesuaikan (Ribu Rupiah/Orang/Tahun)</th>\n",
+              "      <th>Indeks Pembangunan Manusia</th>\n",
+              "      <th>Umur Harapan Hidup (Tahun)</th>\n",
+              "      <th>Persentase rumah tangga yang memiliki akses terhadap sanitasi layak</th>\n",
+              "      <th>Persentase rumah tangga yang memiliki akses terhadap air minum layak</th>\n",
+              "      <th>Tingkat Pengangguran Terbuka</th>\n",
+              "      <th>Tingkat Partisipasi Angkatan Kerja</th>\n",
+              "      <th>PDRB atas Dasar Harga Konstan menurut Pengeluaran (Rupiah)</th>\n",
+              "      <th>Klasifikasi Kemiskinan</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Simeulue</td>\n",
+              "      <td>18,98</td>\n",
+              "      <td>9,48</td>\n",
+              "      <td>7148.0</td>\n",
+              "      <td>66,41</td>\n",
+              "      <td>65,28</td>\n",
+              "      <td>71,56</td>\n",
+              "      <td>87,45</td>\n",
+              "      <td>5,71</td>\n",
+              "      <td>71,15</td>\n",
+              "      <td>1648096.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Singkil</td>\n",
+              "      <td>20,36</td>\n",
+              "      <td>8,68</td>\n",
+              "      <td>8776.0</td>\n",
+              "      <td>69,22</td>\n",
+              "      <td>67,43</td>\n",
+              "      <td>69,56</td>\n",
+              "      <td>78,58</td>\n",
+              "      <td>8,36</td>\n",
+              "      <td>62,85</td>\n",
+              "      <td>1780419.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Selatan</td>\n",
+              "      <td>13,18</td>\n",
+              "      <td>8,88</td>\n",
+              "      <td>8180.0</td>\n",
+              "      <td>67,44</td>\n",
+              "      <td>64,4</td>\n",
+              "      <td>62,55</td>\n",
+              "      <td>79,65</td>\n",
+              "      <td>6,46</td>\n",
+              "      <td>60,85</td>\n",
+              "      <td>4345784.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Tenggara</td>\n",
+              "      <td>13,41</td>\n",
+              "      <td>9,67</td>\n",
+              "      <td>8030.0</td>\n",
+              "      <td>69,44</td>\n",
+              "      <td>68,22</td>\n",
+              "      <td>62,71</td>\n",
+              "      <td>86,71</td>\n",
+              "      <td>6,43</td>\n",
+              "      <td>69,62</td>\n",
+              "      <td>3487157.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Timur</td>\n",
+              "      <td>14,45</td>\n",
+              "      <td>8,21</td>\n",
+              "      <td>8577.0</td>\n",
+              "      <td>67,83</td>\n",
+              "      <td>68,74</td>\n",
+              "      <td>66,75</td>\n",
+              "      <td>83,16</td>\n",
+              "      <td>7,13</td>\n",
+              "      <td>59,48</td>\n",
+              "      <td>8433526.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Tengah</td>\n",
+              "      <td>15,26</td>\n",
+              "      <td>9,86</td>\n",
+              "      <td>10780.0</td>\n",
+              "      <td>73,37</td>\n",
+              "      <td>68,86</td>\n",
+              "      <td>90,58</td>\n",
+              "      <td>90,10</td>\n",
+              "      <td>2,61</td>\n",
+              "      <td>76,30</td>\n",
+              "      <td>5953118.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Barat</td>\n",
+              "      <td>18,81</td>\n",
+              "      <td>9,55</td>\n",
+              "      <td>9593.0</td>\n",
+              "      <td>71,67</td>\n",
+              "      <td>67,99</td>\n",
+              "      <td>89,60</td>\n",
+              "      <td>94,22</td>\n",
+              "      <td>7,09</td>\n",
+              "      <td>60,05</td>\n",
+              "      <td>7485861.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Besar</td>\n",
+              "      <td>14,05</td>\n",
+              "      <td>10,33</td>\n",
+              "      <td>9644.0</td>\n",
+              "      <td>73,58</td>\n",
+              "      <td>69,79</td>\n",
+              "      <td>87,40</td>\n",
+              "      <td>82,36</td>\n",
+              "      <td>7,70</td>\n",
+              "      <td>61,67</td>\n",
+              "      <td>10261585.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Pidie</td>\n",
+              "      <td>19,59</td>\n",
+              "      <td>9</td>\n",
+              "      <td>9860.0</td>\n",
+              "      <td>70,7</td>\n",
+              "      <td>66,95</td>\n",
+              "      <td>54,10</td>\n",
+              "      <td>89,24</td>\n",
+              "      <td>7,28</td>\n",
+              "      <td>60,29</td>\n",
+              "      <td>7975099.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Bireuen</td>\n",
+              "      <td>13,25</td>\n",
+              "      <td>9,29</td>\n",
+              "      <td>8867.0</td>\n",
+              "      <td>72,33</td>\n",
+              "      <td>71,26</td>\n",
+              "      <td>81,89</td>\n",
+              "      <td>93,53</td>\n",
+              "      <td>4,32</td>\n",
+              "      <td>65,91</td>\n",
+              "      <td>10374480.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Utara</td>\n",
+              "      <td>17,43</td>\n",
+              "      <td>8,64</td>\n",
+              "      <td>8201.0</td>\n",
+              "      <td>69,46</td>\n",
+              "      <td>68,81</td>\n",
+              "      <td>79,97</td>\n",
+              "      <td>91,09</td>\n",
+              "      <td>8,31</td>\n",
+              "      <td>58,47</td>\n",
+              "      <td>16924103.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Barat Daya</td>\n",
+              "      <td>16,34</td>\n",
+              "      <td>8,67</td>\n",
+              "      <td>8428.0</td>\n",
+              "      <td>66,99</td>\n",
+              "      <td>65,06</td>\n",
+              "      <td>65,71</td>\n",
+              "      <td>95,34</td>\n",
+              "      <td>4,04</td>\n",
+              "      <td>57,91</td>\n",
+              "      <td>3069805.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Gayo Lues</td>\n",
+              "      <td>19,64</td>\n",
+              "      <td>8,4</td>\n",
+              "      <td>8856.0</td>\n",
+              "      <td>67,56</td>\n",
+              "      <td>65,53</td>\n",
+              "      <td>47,63</td>\n",
+              "      <td>84,68</td>\n",
+              "      <td>1,84</td>\n",
+              "      <td>78,99</td>\n",
+              "      <td>1981879.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Tamiang</td>\n",
+              "      <td>13,34</td>\n",
+              "      <td>8,91</td>\n",
+              "      <td>8367.0</td>\n",
+              "      <td>69,48</td>\n",
+              "      <td>69,63</td>\n",
+              "      <td>87,45</td>\n",
+              "      <td>83,12</td>\n",
+              "      <td>5,87</td>\n",
+              "      <td>66,43</td>\n",
+              "      <td>6062520.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Nagan Raya</td>\n",
+              "      <td>18,23</td>\n",
+              "      <td>8,69</td>\n",
+              "      <td>8292.0</td>\n",
+              "      <td>69,31</td>\n",
+              "      <td>69,24</td>\n",
+              "      <td>74,86</td>\n",
+              "      <td>90,13</td>\n",
+              "      <td>4,99</td>\n",
+              "      <td>64,99</td>\n",
+              "      <td>7110421.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Jaya</td>\n",
+              "      <td>13,23</td>\n",
+              "      <td>8,71</td>\n",
+              "      <td>9666.0</td>\n",
+              "      <td>69,84</td>\n",
+              "      <td>67,19</td>\n",
+              "      <td>81,60</td>\n",
+              "      <td>86,36</td>\n",
+              "      <td>3,47</td>\n",
+              "      <td>72,59</td>\n",
+              "      <td>2033844.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Bener Meriah</td>\n",
+              "      <td>19,16</td>\n",
+              "      <td>10</td>\n",
+              "      <td>11118.0</td>\n",
+              "      <td>73,27</td>\n",
+              "      <td>69,26</td>\n",
+              "      <td>86,69</td>\n",
+              "      <td>89,71</td>\n",
+              "      <td>1,24</td>\n",
+              "      <td>77,53</td>\n",
+              "      <td>3744095.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Pidie Jaya</td>\n",
+              "      <td>19,55</td>\n",
+              "      <td>9,34</td>\n",
+              "      <td>10290.0</td>\n",
+              "      <td>73,6</td>\n",
+              "      <td>70,18</td>\n",
+              "      <td>74,30</td>\n",
+              "      <td>93,46</td>\n",
+              "      <td>3,57</td>\n",
+              "      <td>57,77</td>\n",
+              "      <td>2635517.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Kota Banda Aceh</td>\n",
+              "      <td>7,61</td>\n",
+              "      <td>12,83</td>\n",
+              "      <td>16891.0</td>\n",
+              "      <td>85,71</td>\n",
+              "      <td>71,52</td>\n",
+              "      <td>99,88</td>\n",
+              "      <td>99,37</td>\n",
+              "      <td>8,94</td>\n",
+              "      <td>63,00</td>\n",
+              "      <td>15454371.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Kota Sabang</td>\n",
+              "      <td>15,32</td>\n",
+              "      <td>11,18</td>\n",
+              "      <td>11378.0</td>\n",
+              "      <td>76,11</td>\n",
+              "      <td>70,56</td>\n",
+              "      <td>92,25</td>\n",
+              "      <td>96,18</td>\n",
+              "      <td>3,56</td>\n",
+              "      <td>63,71</td>\n",
+              "      <td>1152875.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Kota Langsa</td>\n",
+              "      <td>10,96</td>\n",
+              "      <td>11,12</td>\n",
+              "      <td>12067.0</td>\n",
+              "      <td>77,44</td>\n",
+              "      <td>69,43</td>\n",
+              "      <td>91,81</td>\n",
+              "      <td>97,14</td>\n",
+              "      <td>7,21</td>\n",
+              "      <td>67,04</td>\n",
+              "      <td>3974614.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Kota Lhokseumawe</td>\n",
+              "      <td>11,16</td>\n",
+              "      <td>11,11</td>\n",
+              "      <td>11390.0</td>\n",
+              "      <td>77,57</td>\n",
+              "      <td>71,64</td>\n",
+              "      <td>93,10</td>\n",
+              "      <td>94,44</td>\n",
+              "      <td>11,16</td>\n",
+              "      <td>63,91</td>\n",
+              "      <td>7252905.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Kota Subulussalam</td>\n",
+              "      <td>17,65</td>\n",
+              "      <td>8,03</td>\n",
+              "      <td>7385.0</td>\n",
+              "      <td>65,27</td>\n",
+              "      <td>64,07</td>\n",
+              "      <td>69,71</td>\n",
+              "      <td>60,87</td>\n",
+              "      <td>6,26</td>\n",
+              "      <td>63,78</td>\n",
+              "      <td>1438997.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>SUMATERA UTARA</td>\n",
+              "      <td>Nias</td>\n",
+              "      <td>16,82</td>\n",
+              "      <td>5,64</td>\n",
+              "      <td>6995.0</td>\n",
+              "      <td>62,74</td>\n",
+              "      <td>69,78</td>\n",
+              "      <td>19,93</td>\n",
+              "      <td>47,79</td>\n",
+              "      <td>3,12</td>\n",
+              "      <td>81,79</td>\n",
+              "      <td>2666855.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>SUMATERA UTARA</td>\n",
+              "      <td>Mandailing Natal</td>\n",
+              "      <td>9,49</td>\n",
+              "      <td>8,63</td>\n",
+              "      <td>9771.0</td>\n",
+              "      <td>67,19</td>\n",
+              "      <td>62,65</td>\n",
+              "      <td>35,73</td>\n",
+              "      <td>73,78</td>\n",
+              "      <td>6,12</td>\n",
+              "      <td>69,79</td>\n",
+              "      <td>9585899.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-355d6490-0dc5-488f-8f19-de7f4ed9db48')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-355d6490-0dc5-488f-8f19-de7f4ed9db48 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-355d6490-0dc5-488f-8f19-de7f4ed9db48');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-c06fcf73-c9c9-4b30-a462-3670508d27f6\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c06fcf73-c9c9-4b30-a462-3670508d27f6')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c06fcf73-c9c9-4b30-a462-3670508d27f6 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 999,\n  \"fields\": [\n    {\n      \"column\": \"Provinsi\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 34,\n        \"samples\": [\n          \"BANTEN\",\n          \"KALIMANTAN BARAT\",\n          \"SULAWESI TENGGARA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kab/Kota\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 514,\n        \"samples\": [\n          \"Manggarai\",\n          \"Yahukimo\",\n          \"Gorontalo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Persentase Penduduk Miskin (P0) Menurut Kabupaten/Kota (Persen)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 452,\n        \"samples\": [\n          \"5,91\",\n          \"8,3\",\n          \"12,85\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rata-rata Lama Sekolah Penduduk 15+ (Tahun)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 377,\n        \"samples\": [\n          \"10,91\",\n          \"7,12\",\n          \"6,02\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pengeluaran per Kapita Disesuaikan (Ribu Rupiah/Orang/Tahun)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2717.144185738409,\n        \"min\": 3976.0,\n        \"max\": 23888.0,\n        \"num_unique_values\": 498,\n        \"samples\": [\n          5708.0,\n          13317.0,\n          9410.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Indeks Pembangunan Manusia\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 452,\n        \"samples\": [\n          \"65,87\",\n          \"70,83\",\n          \"80,59\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Umur Harapan Hidup (Tahun)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 411,\n        \"samples\": [\n          \"73,8\",\n          \"71,58\",\n          \"70,38\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Persentase rumah tangga yang memiliki akses terhadap sanitasi layak\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 487,\n        \"samples\": [\n          \"89,49\",\n          \"91,56\",\n          \"90,56\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Persentase rumah tangga yang memiliki akses terhadap air minum layak\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 465,\n        \"samples\": [\n          \"74,11\",\n          \"85,78\",\n          \"91,43\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tingkat Pengangguran Terbuka\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 373,\n        \"samples\": [\n          \"4,56\",\n          \"1,95\",\n          \"3,47\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tingkat Partisipasi Angkatan Kerja\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 448,\n        \"samples\": [\n          \" 77,57 \",\n          \" 70,16 \",\n          \" 70,13 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PDRB atas Dasar Harga Konstan menurut Pengeluaran (Rupiah)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47904920.44381927,\n        \"min\": 147485.0,\n        \"max\": 460081046.0,\n        \"num_unique_values\": 514,\n        \"samples\": [\n          3025880.0,\n          1556231.0,\n          9082312.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Klasifikasi Kemiskinan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3260054197106571,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "# DATA DARI HASIL MODEL\n",
+        "\n",
+        "# --- Accuracy ---\n",
+        "acc_rf = 0.99\n",
+        "acc_lr = 0.985\n",
+        "acc_dt = 0.99\n",
+        "\n",
+        "# --- Cross Validation ---\n",
+        "cv_rf = [0.99, 1.0, 0.97, 0.99, 1.0]\n",
+        "cv_lr = [0.975, 0.98, 0.965, 0.99, 1.0]\n",
+        "cv_dt = [0.985, 0.995, 0.975, 0.99, 0.98]\n",
+        "\n",
+        "# --- Classification Metrics ---\n",
+        "precision_rf = [0.99, 1.00]\n",
+        "recall_rf = [1.00, 0.80]\n",
+        "f1_rf = [0.99, 0.89]\n",
+        "\n",
+        "precision_lr = [0.98, 1.00]\n",
+        "recall_lr = [1.00, 0.70]\n",
+        "f1_lr = [0.99, 0.82]\n",
+        "\n",
+        "precision_dt = [0.99, 1.00]\n",
+        "recall_dt = [1.00, 0.83]\n",
+        "f1_dt = [0.99, 0.91]\n",
+        "\n",
+        "\n",
+        "classes = [\"0\", \"1\"]\n",
+        "\n",
+        "# 1. PERBANDINGAN AKURASI\n",
+        "plt.figure()\n",
+        "plt.bar([\"Random Forest\", \"Logistic Regression\", \"Decision Tree\"], [acc_rf, acc_lr, acc_dt])\n",
+        "plt.title(\"Perbandingan Akurasi\")\n",
+        "plt.ylabel(\"Accuracy\")\n",
+        "plt.ylim(0, 1)\n",
+        "plt.show()\n",
+        "\n",
+        "\n",
+        "# 2. PERBANDINGAN CROSS VALIDATION\n",
+        "plt.figure()\n",
+        "plt.plot(cv_rf, marker=\"o\", label=\"Random Forest\")\n",
+        "plt.plot(cv_lr, marker=\"o\", label=\"Logistic Regression\")\n",
+        "plt.plot(cv_dt, marker=\"o\", label=\"Decision Tree\") # FIX: Changed cv_lr to cv_dt\n",
+        "plt.title(\"Perbandingan Cross Validation\")\n",
+        "plt.xlabel(\"Fold\")\n",
+        "plt.ylabel(\"Score\")\n",
+        "plt.ylim(0, 1)\n",
+        "plt.legend()\n",
+        "plt.show()\n",
+        "\n",
+        "# 3. PERBANDINGAN METRIK KELAS\n",
+        "x = np.arange(len(classes))\n",
+        "# FIX: Define new bar_width and offsets for three bars to prevent overlap\n",
+        "bar_width_for_group = 0.2\n",
+        "offset_rf = -bar_width_for_group * 1.1\n",
+        "offset_lr = 0\n",
+        "offset_dt = bar_width_for_group * 1.1\n",
+        "\n",
+        "\n",
+        "# Precision\n",
+        "plt.figure(figsize=(8, 5)) # Add figsize for better readability\n",
+        "plt.bar(x + offset_rf, precision_rf, bar_width_for_group, label=\"RF Precision\")\n",
+        "plt.bar(x + offset_lr, precision_lr, bar_width_for_group, label=\"LR Precision\")\n",
+        "plt.bar(x + offset_dt, precision_dt, bar_width_for_group, label=\"DT Precision\")\n",
+        "plt.xticks(x, classes)\n",
+        "plt.title(\"Precision per Class\")\n",
+        "plt.ylabel(\"Score\")\n",
+        "plt.ylim(0, 1)\n",
+        "plt.legend()\n",
+        "plt.tight_layout() # Adjust layout\n",
+        "plt.show()\n",
+        "\n",
+        "# Recall\n",
+        "plt.figure(figsize=(8, 5)) # Add figsize for better readability\n",
+        "plt.bar(x + offset_rf, recall_rf, bar_width_for_group, label=\"RF Recall\")\n",
+        "plt.bar(x + offset_lr, recall_lr, bar_width_for_group, label=\"LR Recall\")\n",
+        "plt.bar(x + offset_dt, recall_dt, bar_width_for_group, label=\"DT Recall\")\n",
+        "plt.xticks(x, classes)\n",
+        "plt.title(\"Recall per Class\")\n",
+        "plt.ylabel(\"Score\")\n",
+        "plt.ylim(0, 1)\n",
+        "plt.legend()\n",
+        "plt.tight_layout() # Adjust layout\n",
+        "plt.show()\n",
+        "\n",
+        "# F1-Score\n",
+        "plt.figure(figsize=(8, 5)) # Add figsize for better readability\n",
+        "plt.bar(x + offset_rf, f1_rf, bar_width_for_group, label=\"RF F1\")\n",
+        "plt.bar(x + offset_lr, f1_lr, bar_width_for_group, label=\"LR F1\")\n",
+        "plt.bar(x + offset_dt, f1_dt, bar_width_for_group, label=\"DT F1\")\n",
+        "plt.xticks(x, classes)\n",
+        "plt.title(\"F1-Score per Class\")\n",
+        "plt.ylabel(\"Score\")\n",
+        "plt.ylim(0, 1)\n",
+        "plt.legend()\n",
+        "plt.tight_layout() # Adjust layout\n",
+        "plt.show()\n",
+        "\n",
+        "# 4. CONFUSION MATRIX RF vs LR vs DT\n",
+        "conf_rf = np.array([[190, 0],\n",
+        "                    [2, 8]])\n",
+        "\n",
+        "conf_lr = np.array([[190, 0],\n",
+        "                    [3, 7]])\n",
+        "\n",
+        "conf_dt = np.array([[188, 0],\n",
+        "                    [2, 10]])\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.imshow(conf_rf)\n",
+        "plt.title(\"Confusion Matrix - Random Forest\")\n",
+        "plt.colorbar()\n",
+        "plt.xticks([0, 1], [\"Pred 0\", \"Pred 1\"])\n",
+        "plt.yticks([0, 1], [\"Actual 0\", \"Actual 1\"])\n",
+        "plt.show()\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.imshow(conf_lr)\n",
+        "plt.title(\"Confusion Matrix - Logistic Regression\")\n",
+        "plt.colorbar()\n",
+        "plt.xticks([0, 1], [\"Pred 0\", \"Pred 1\"])\n",
+        "plt.yticks([0, 1], [\"Actual 0\", \"Actual 1\"])\n",
+        "plt.show()\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.imshow(conf_dt) # FIX: Changed conf_lr to conf_dt\n",
+        "plt.title(\"Confusion Matrix - Decision Tree\") # FIX: Changed title\n",
+        "plt.colorbar()\n",
+        "plt.xticks([0, 1], [\"Pred 0\", \"Pred 1\"])\n",
+        "plt.yticks([0, 1], [\"Actual 0\", \"Actual 1\"])\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "xQXMPuutqlT1",
+        "outputId": "f149755c-d548-48a1-d9f0-51ca8fce4281"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAxYAAAHqCAYAAACZcdjsAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAMnNJREFUeJzt3X+Yl2WdL/D3DDgDiOAPZEYIxSSXUAMCRbRS11E006XUzH6AqFQmHpXWBFMQPYla/kyMzUQ8tR7RNrjcNMxFWVNpSQxXy99JuMYPOSoYuIwy3/NHh6k5DCA8A18GXq/r+l4Xz/3c93N/nunK4c393M+3olQqlQIAAFBAZbkLAAAAWj/BAgAAKEywAAAAChMsAACAwgQLAACgMMECAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIACpg9e3YqKioye/bscpcCUFaCBcAWMnXq1FRUVDT7GTNmTGO/X/7ylznrrLNy4IEHpk2bNunZs+cmz/XMM8/klFNOyT777JN27dqle/fuOeaYY/L973+/Be9oxzN9+vQcf/zx6dKlS6qqqtKtW7d8/vOfz8MPP1zu0gC2OW3LXQDA9u6KK67Ivvvu26TtwAMPbPzzXXfdlWnTpuXjH/94unXrtsnXf+KJJ3LUUUdl7733zsiRI1NbW5vXXnstv/71r3PTTTflvPPOK3wPO5pSqZQzzzwzU6dOTf/+/TN69OjU1tZm0aJFmT59eo4++ug8/vjjOeyww8pdKsA2Q7AA2MKOP/74DBw4cL3nr7rqqtx2223Zaaed8pnPfCbPPvvsJl3/O9/5Tjp37pzf/OY32XXXXZucW7p06eaUvNlWrVqVDh06bNU5N0dDQ0Pq6+vTrl27Zs9fd911mTp1ai644IJcf/31qaioaDz37W9/Oz/+8Y/Ttq1foQB/y6NQAGXWrVu37LTTTps9/pVXXskBBxywTqhIkq5du67T9pOf/CSHHHJIOnTokN122y2f+tSn8stf/rJJn1tvvTUHHHBAqqur061bt5x77rl5++23m/Q58sgjc+CBB2bevHn51Kc+lQ4dOuSSSy5JkqxevTrjx49Pr169Ul1dnR49euRb3/pWVq9evdH7+dvrHnbYYWnfvn323XffTJ48eZ2+H3SeioqKjBo1Kv/8z//ceF8zZ85sdv533303EydOTO/evfO9732vSahY6ytf+UoOOeSQ9d7Dr371q5x66qnZe++9G+u68MIL8+677zbpt3jx4owYMSIf+tCHUl1dnb322iv/8A//kAULFjT2efLJJzNkyJB06dKl8Wdx5plnbuhHCFAW/rkFYAtbvnx5li1b1qStS5cuLXb9ffbZJ3PmzMmzzz7b5BGr5kyYMCGXX355DjvssFxxxRWpqqrKf/zHf+Thhx/OsccemyS5/PLLM2HChNTV1eWcc87JCy+8kB/84Af5zW9+k8cff7xJCPo//+f/5Pjjj88XvvCFfPnLX05NTU0aGhpy0kkn5bHHHstXv/rVfPSjH80zzzyTG264IS+++GJmzJix0Xt666238ulPfzqf//znc/rpp+eee+7JOeeck6qqqsa/VG/qPA8//HDuueeejBo1Kl26dFnvXpbHHnssb775Zi644IK0adNmo7U25957782qVatyzjnnZI899sjcuXPz/e9/P//1X/+Ve++9t7HfySefnN/97nc577zz0rNnzyxdujQPPfRQFi5c2Hh87LHHZs8998yYMWOy6667ZsGCBfnZz362WXUBbFElALaIO+64o5Sk2c/6nHDCCaV99tlnk+b55S9/WWrTpk2pTZs2pcGDB5e+9a1vlR588MFSfX19k34vvfRSqbKysvTZz362tGbNmibnGhoaSqVSqbR06dJSVVVV6dhjj23S55ZbbiklKU2ZMqWx7YgjjiglKU2ePLnJtX784x+XKisrS7/61a+atE+ePLmUpPT4449v8H7WXve6665rbFu9enWpX79+pa5duzbe16bMk6RUWVlZ+t3vfrfBuUulUummm24qJSlNnz59o31LpVLpkUceKSUpPfLII41tq1atWqffxIkTSxUVFaU//vGPpVKpVHrrrbdKSUrf/e5313vt6dOnl5KUfvOb33ygWgDKyaNQAFvYpEmT8tBDDzX5tKRjjjkmc+bMyUknnZSnn3461157bYYMGZLu3bvnvvvua+w3Y8aMNDQ0ZNy4camsbPqf/7WP+/zbv/1b6uvrc8EFFzTpM3LkyHTq1Cn3339/k3HV1dUZMWJEk7Z77703H/3oR9O7d+8sW7as8fP3f//3SZJHHnlko/fUtm3bfO1rX2s8rqqqyte+9rUsXbo08+bN26x5jjjiiPTp02ejc69YsSJJsssuu2y07/q0b9++8c8rV67MsmXLcthhh6VUKuW3v/1tY5+qqqrMnj07b731VrPXWft4289//vO89957m10PwNbgUSiALeyQQw7Z4ObtD2LNmjV54403mrTtvvvuqaqqSpIcfPDB+dnPfpb6+vo8/fTTmT59em644YaccsopmT9/fvr06ZNXXnkllZWVG/zL9R//+Mckyd/93d81aa+qqsqHP/zhxvNrde/evbGGtV566aU899xz2XPPPZud44NsKO/WrVt23nnnJm37779/kmTBggU59NBDN3me///NXOvTqVOnJMk777zzgfo3Z+HChRk3blzuu+++dULD8uXLk/wllF1zzTX55je/mZqamhx66KH5zGc+k2HDhqW2tjbJX8LQySefnAkTJuSGG27IkUcemaFDh+aLX/xiqqurN7s+gC1BsABoBV577bV1/mL8yCOP5Mgjj2zSVlVVlYMPPjgHH3xw9t9//4wYMSL33ntvxo8fv0Xq+tt/mV+roaEhBx10UK6//vpmx/To0aNF5t7UeZqrtTm9e/dO8pfvBhk6dOgm17VmzZocc8wxefPNN3PxxRend+/e2XnnnfP666/njDPOSENDQ2PfCy64ICeeeGJmzJiRBx98MJdddlkmTpyYhx9+OP37909FRUV++tOf5te//nX+9V//NQ8++GDOPPPMXHfddfn1r3+djh07bnJ9AFuKYAHQCtTW1q7zCFXfvn03OGbtKsmiRYuSJPvtt18aGhry+9//Pv369Wt2zD777JMkeeGFF/LhD3+4sb2+vj6vvvpq6urqNlrrfvvtl6effjpHH310s29U+iD+9Kc/ZeXKlU1WLV588cUkadx03RLzNOcTn/hEdtttt/zv//2/c8kll2zyBu5nnnkmL774Yu68884MGzassX19j8Dtt99++eY3v5lvfvObeemll9KvX79cd911+clPftLY59BDD82hhx6a73znO7nrrrvypS99KXfffXfOPvvszbtJgC3AHguAVqBdu3apq6tr8tltt92S/GXlolQqrTPmgQceSPLXx5qGDh2aysrKXHHFFU3+1TxJ4/i6urpUVVXl5ptvbnLN22+/PcuXL88JJ5yw0Vo///nP5/XXX89tt922zrl33303K1eu3Og13n///fzTP/1T43F9fX3+6Z/+KXvuuWcGDBjQYvM0p0OHDrn44ovz3HPP5eKLL272Z/uTn/wkc+fObXb82iDyt+NKpVJuuummJv1WrVqV//7v/27Stt9++2WXXXZpfF3uW2+9tc78a0PhB3l1L8DWZMUCoMz+8z//s3GT9csvv5zly5fnf/7P/5nkL6sSJ5544gbHn3feeVm1alU++9nPpnfv3qmvr88TTzyRadOmpWfPno2bq3v16pVvf/vbufLKK/PJT34yn/vc51JdXZ3f/OY36datWyZOnJg999wzY8eOzYQJE3LcccflpJNOygsvvJBbb701Bx98cL785S9v9H6+8pWv5J577snXv/71PPLIIzn88MOzZs2aPP/887nnnnvy4IMPbnTPSbdu3XLNNddkwYIF2X///TNt2rTMnz8/P/zhDxtfd9sS86zPRRddlN/97ne57rrr8sgjj+SUU05JbW1tFi9enBkzZmTu3Ll54oknmh3bu3fv7LfffvnHf/zHvP766+nUqVP+5V/+ZZ29Fi+++GKOPvrofP7zn0+fPn3Stm3bTJ8+PUuWLMkXvvCFJMmdd96ZW2+9NZ/97Gez33775Z133sltt92WTp065dOf/vRm3RvAFlO+F1IBbN/Wvm52Y68K3dBraYcPH77ReX7xi1+UzjzzzFLv3r1LHTt2LFVVVZV69epVOu+880pLlixZp/+UKVNK/fv3L1VXV5d222230hFHHFF66KGHmvS55ZZbSr179y7ttNNOpZqamtI555xTeuutt5r0OeKII0oHHHBAszXV19eXrrnmmtIBBxzQOM+AAQNKEyZMKC1fvnyD97P2uk8++WRp8ODBpXbt2pX22Wef0i233LLZ8yQpnXvuuRuctzk//elPS8cee2xp9913L7Vt27a01157lU477bTS7NmzG/s097rZ3//+96W6urpSx44dS126dCmNHDmy9PTTT5eSlO64445SqVQqLVu2rHTuueeWevfuXdp5551LnTt3Lg0aNKh0zz33NF7nqaeeKp1++umlvffeu1RdXV3q2rVr6TOf+UzpySef3OR7AdjSKkqlZtZ4AaBMjjzyyCxbtizPPvtsuUsBYBPYYwEAABQmWAAAAIUJFgAAQGFlDRaPPvpoTjzxxHTr1i0VFRWZMWPGRsfMnj07H//4x1NdXZ1evXpl6tSpW7xOALae2bNn218B0AqVNVisXLkyffv2zaRJkz5Q/1dffTUnnHBCjjrqqMyfPz8XXHBBzj777Dz44INbuFIAAGBDtpm3QlVUVGT69OkZOnToevtcfPHFuf/++5v8S9YXvvCFvP3225k5c+ZWqBIAAGhOq/qCvDlz5qSurq5J25AhQ3LBBResd8zq1aubfDtpQ0ND3nzzzeyxxx6pqKjYUqUCAECrVyqV8s4776Rbt26prNzww06tKlgsXrw4NTU1TdpqamqyYsWKvPvuu2nfvv06YyZOnJgJEyZsrRIBAGC789prr+VDH/rQBvu0qmCxOcaOHZvRo0c3Hi9fvjx77713XnvttXTq1KmMlQEAwLZtxYoV6dGjR3bZZZeN9m1VwaK2tjZLlixp0rZkyZJ06tSp2dWKJKmurk51dfU67Z06dRIsAADgA/ggWwha1fdYDB48OLNmzWrS9tBDD2Xw4MFlqggAAEjKHCz+/Oc/Z/78+Zk/f36Sv7xOdv78+Vm4cGGSvzzGNGzYsMb+X//61/OHP/wh3/rWt/L888/n1ltvzT333JMLL7ywHOUDAAD/T1kfhXryySdz1FFHNR6v3QsxfPjwTJ06NYsWLWoMGUmy77775v7778+FF16Ym266KR/60Ifyox/9KEOGDNnqtRfVc8z95S6hVVtw9Qkb7nB5561TyPbo8uUbPH3QnQdtpUK2T88Mf6bcJQDAFlHWYHHkkUdmQ1+j0dy3ah955JH57W9/uwWrAgAANlWr2mMBAABsmwQLAACgMMECAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIAAChMsAAAAAoTLAAAgMIECwAAoDDBAgAAKEywAAAAChMsAACAwgQLAACgMMECAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIAAChMsAAAAAoTLAAAgMIECwAAoDDBAgAAKEywAAAAChMsAACAwgQLAACgMMECAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIAACisbbkLAABg+3bQnQeVu4RW65nhz5S7hA/MigUAAFCYYAEAABQmWAAAAIUJFgAAQGGCBQAAUJhgAQAAFCZYAAAAhQkWAABAYb4gDwBagZ5j7i93Ca3agqtPKHcJsN2zYgEAABQmWAAAAIUJFgAAQGGCBQAAUJhgAQAAFCZYAAAAhQkWAABAYYIFAABQmGABAAAUJlgAAACFCRYAAEBhggUAAFCYYAEAABQmWAAAAIUJFgAAQGGCBQAAUJhgAQAAFCZYAAAAhQkWAABAYYIFAABQmGABAAAUJlgAAACFCRYAAEBhggUAAFCYYAEAABQmWAAAAIUJFgAAQGGCBQAAUJhgAQAAFNa23AUAAJTd5Z3LXUHrdvnyclfANsCKBQAAUJhgAQAAFCZYAAAAhZU9WEyaNCk9e/ZMu3btMmjQoMydO3eD/W+88cb83d/9Xdq3b58ePXrkwgsvzH//939vpWoBAIDmlDVYTJs2LaNHj8748ePz1FNPpW/fvhkyZEiWLl3abP+77rorY8aMyfjx4/Pcc8/l9ttvz7Rp03LJJZds5coBAIC/VdZgcf3112fkyJEZMWJE+vTpk8mTJ6dDhw6ZMmVKs/2feOKJHH744fniF7+Ynj175thjj83pp5++0VUOAABgyypbsKivr8+8efNSV1f312IqK1NXV5c5c+Y0O+awww7LvHnzGoPEH/7whzzwwAP59Kc/vd55Vq9enRUrVjT5AAAALats32OxbNmyrFmzJjU1NU3aa2pq8vzzzzc75otf/GKWLVuWT3ziEymVSnn//ffz9a9/fYOPQk2cODETJkxo0doBAICmyr55e1PMnj07V111VW699dY89dRT+dnPfpb7778/V1555XrHjB07NsuXL2/8vPbaa1uxYgAA2DGUbcWiS5cuadOmTZYsWdKkfcmSJamtrW12zGWXXZavfOUrOfvss5MkBx10UFauXJmvfvWr+fa3v53KynVzUnV1daqrq1v+BgAAgEZlW7GoqqrKgAEDMmvWrMa2hoaGzJo1K4MHD252zKpVq9YJD23atEmSlEqlLVcsAACwQWVbsUiS0aNHZ/jw4Rk4cGAOOeSQ3HjjjVm5cmVGjBiRJBk2bFi6d++eiRMnJklOPPHEXH/99enfv38GDRqUl19+OZdddllOPPHExoABAABsfWUNFqeddlreeOONjBs3LosXL06/fv0yc+bMxg3dCxcubLJCcemll6aioiKXXnppXn/99ey555458cQT853vfKdctwAAAKTMwSJJRo0alVGjRjV7bvbs2U2O27Ztm/Hjx2f8+PFboTIAAOCDalVvhQIAALZNggUAAFCYYAEAABQmWAAAAIUJFgAAQGGCBQAAUJhgAQAAFCZYAAAAhQkWAABAYYIFAABQmGABAAAUJlgAAACFCRYAAEBhggUAAFCYYAEAABQmWAAAAIUJFgAAQGGCBQAAUJhgAQAAFCZYAAAAhQkWAABAYYIFAABQmGABAAAUJlgAAACFCRYAAEBhggUAAFCYYAEAABQmWAAAAIUJFgAAQGGCBQAAUJhgAQAAFCZYAAAAhQkWAABAYYIFAABQmGABAAAUJlgAAACFCRYAAEBhggUAAFCYYAEAABQmWAAAAIUJFgAAQGGCBQAAUJhgAQAAFCZYAAAAhQkWAABAYYIFAABQmGABAAAUJlgAAACFCRYAAEBhggUAAFCYYAEAABQmWAAAAIUJFgAAQGGCBQAAUJhgAQAAFCZYAAAAhQkWAABAYYIFAABQmGABAAAUJlgAAACFCRYAAEBhggUAAFCYYAEAABQmWAAAAIUJFgAAQGGCBQAAUJhgAQAAFCZYAAAAhQkWAABAYYIFAABQmGABAAAUJlgAAACFCRYAAEBhZQ8WkyZNSs+ePdOuXbsMGjQoc+fO3WD/t99+O+eee2722muvVFdXZ//9988DDzywlaoFAACa07ack0+bNi2jR4/O5MmTM2jQoNx4440ZMmRIXnjhhXTt2nWd/vX19TnmmGPStWvX/PSnP0337t3zxz/+MbvuuuvWLx4AAGhU1mBx/fXXZ+TIkRkxYkSSZPLkybn//vszZcqUjBkzZp3+U6ZMyZtvvpknnngiO+20U5KkZ8+eW7NkAACgGWV7FKq+vj7z5s1LXV3dX4uprExdXV3mzJnT7Jj77rsvgwcPzrnnnpuampoceOCBueqqq7JmzZqtVTYAANCMsq1YLFu2LGvWrElNTU2T9pqamjz//PPNjvnDH/6Qhx9+OF/60pfywAMP5OWXX843vvGNvPfeexk/fnyzY1avXp3Vq1c3Hq9YsaLlbgIAAEiyDWze3hQNDQ3p2rVrfvjDH2bAgAE57bTT8u1vfzuTJ09e75iJEyemc+fOjZ8ePXpsxYoBAGDHULZg0aVLl7Rp0yZLlixp0r5kyZLU1tY2O2avvfbK/vvvnzZt2jS2ffSjH83ixYtTX1/f7JixY8dm+fLljZ/XXnut5W4CAABIUsZgUVVVlQEDBmTWrFmNbQ0NDZk1a1YGDx7c7JjDDz88L7/8choaGhrbXnzxxey1116pqqpqdkx1dXU6derU5AMAALSssj4KNXr06Nx22225884789xzz+Wcc87JypUrG98SNWzYsIwdO7ax/znnnJM333wz559/fl588cXcf//9ueqqq3LuueeW6xYAAICU+XWzp512Wt54442MGzcuixcvTr9+/TJz5szGDd0LFy5MZeVfs0+PHj3y4IMP5sILL8zHPvaxdO/ePeeff34uvvjict0CAACQMgeLJBk1alRGjRrV7LnZs2ev0zZ48OD8+te/3sJVAQAAm6JVvRUKAADYNgkWAABAYYIFAABQmGABAAAUJlgAAACFCRYAAEBhggUAAFCYYAEAABQmWAAAAIUJFgAAQGGCBQAAUJhgAQAAFCZYAAAAhQkWAABAYYIFAABQmGABAAAUJlgAAACFCRYAAEBhggUAAFBYoWBRX1+fF154Ie+//35L1QMAALRCmxUsVq1albPOOisdOnTIAQcckIULFyZJzjvvvFx99dUtWiAAALDt26xgMXbs2Dz99NOZPXt22rVr19heV1eXadOmtVhxAABA69B2cwbNmDEj06ZNy6GHHpqKiorG9gMOOCCvvPJKixUHAAC0Dpu1YvHGG2+ka9eu67SvXLmySdAAAAB2DJsVLAYOHJj777+/8XhtmPjRj36UwYMHt0xlAABAq7FZj0JdddVVOf744/P73/8+77//fm666ab8/ve/zxNPPJF///d/b+kaAQCAbdxmrVh84hOfyNNPP533338/Bx10UH75y1+ma9eumTNnTgYMGNDSNQIAANu4TV6xeO+99/K1r30tl112WW677bYtURMAANDKbPKKxU477ZR/+Zd/2RK1AAAArdRmPQo1dOjQzJgxo4VLAQAAWqvN2rz9kY98JFdccUUef/zxDBgwIDvvvHOT8//jf/yPFikOAABoHTYrWNx+++3ZddddM2/evMybN6/JuYqKCsECAAB2MJsVLF599dWWrgMAAGjFNmuPxd8qlUoplUotUQsAANBKbXaw+F//63/loIMOSvv27dO+fft87GMfy49//OOWrA0AAGglNutRqOuvvz6XXXZZRo0alcMPPzxJ8thjj+XrX/96li1blgsvvLBFiwQAALZtmxUsvv/97+cHP/hBhg0b1th20kkn5YADDsjll18uWAAAwA5msx6FWrRoUQ477LB12g877LAsWrSocFEAAEDrslnBolevXrnnnnvWaZ82bVo+8pGPFC4KAABoXTbrUagJEybktNNOy6OPPtq4x+Lxxx/PrFmzmg0cAADA9m2zVixOPvnk/Md//Ee6dOmSGTNmZMaMGenSpUvmzp2bz372sy1dIwAAsI3brBWLJBkwYEB+8pOftGQtAABAK7VZKxYPPPBAHnzwwXXaH3zwwfziF78oXBQAANC6bFawGDNmTNasWbNOe6lUypgxYwoXBQAAtC6bFSxeeuml9OnTZ5323r175+WXXy5cFAAA0LpsVrDo3Llz/vCHP6zT/vLLL2fnnXcuXBQAANC6bFaw+Id/+IdccMEFeeWVVxrbXn755Xzzm9/MSSed1GLFAQAArcNmBYtrr702O++8c3r37p199903++67b3r37p099tgj3/ve91q6RgAAYBu3Wa+b7dy5c5544ok89NBDefrpp9O+ffv07ds3n/zkJ1u6PgAAoBXYpBWLOXPm5Oc//3mSpKKiIscee2y6du2a733vezn55JPz1a9+NatXr94ihQIAANuuTQoWV1xxRX73u981Hj/zzDMZOXJkjjnmmIwZMyb/+q//mokTJ7Z4kQAAwLZtk4LF/Pnzc/TRRzce33333TnkkENy2223ZfTo0bn55ptzzz33tHiRAADAtm2TgsVbb72VmpqaxuN///d/z/HHH994fPDBB+e1115rueoAAIBWYZOCRU1NTV599dUkSX19fZ566qkceuihjeffeeed7LTTTi1bIQAAsM3bpGDx6U9/OmPGjMmvfvWrjB07Nh06dGjyJqj//M//zH777dfiRQIAANu2TXrd7JVXXpnPfe5zOeKII9KxY8fceeedqaqqajw/ZcqUHHvssS1eJAAAsG3bpGDRpUuXPProo1m+fHk6duyYNm3aNDl/7733pmPHji1aIAAAsO3b7C/Ia87uu+9eqBgAAKB12qQ9FgAAAM0RLAAAgMIECwAAoDDBAgAAKEywAAAAChMsAACAwgQLAACgMMECAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIAAChMsAAAAAoTLAAAgMIECwAAoDDBAgAAKGybCBaTJk1Kz549065duwwaNChz5879QOPuvvvuVFRUZOjQoVu2QAAAYIPKHiymTZuW0aNHZ/z48XnqqafSt2/fDBkyJEuXLt3guAULFuQf//Ef88lPfnIrVQoAAKxP2YPF9ddfn5EjR2bEiBHp06dPJk+enA4dOmTKlCnrHbNmzZp86UtfyoQJE/LhD394K1YLAAA0p6zBor6+PvPmzUtdXV1jW2VlZerq6jJnzpz1jrviiivStWvXnHXWWVujTAAAYCPalnPyZcuWZc2aNampqWnSXlNTk+eff77ZMY899lhuv/32zJ8//wPNsXr16qxevbrxeMWKFZtdLwAA0LyyPwq1Kd5555185StfyW233ZYuXbp8oDETJ05M586dGz89evTYwlUCAMCOp6wrFl26dEmbNm2yZMmSJu1LlixJbW3tOv1feeWVLFiwICeeeGJjW0NDQ5Kkbdu2eeGFF7Lffvs1GTN27NiMHj268XjFihXCBQAAtLCyBouqqqoMGDAgs2bNanxlbENDQ2bNmpVRo0at079379555plnmrRdeumleeedd3LTTTc1Gxiqq6tTXV29ReoHAAD+oqzBIklGjx6d4cOHZ+DAgTnkkENy4403ZuXKlRkxYkSSZNiwYenevXsmTpyYdu3a5cADD2wyftddd02SddoBAICtp+zB4rTTTssbb7yRcePGZfHixenXr19mzpzZuKF74cKFqaxsVVtBAABgh1P2YJEko0aNavbRpySZPXv2BsdOnTq15QsCAAA2iaUAAACgMMECAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIAAChMsAAAAAoTLAAAgMIECwAAoDDBAgAAKEywAAAAChMsAACAwgQLAACgMMECAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIAAChMsAAAAAoTLAAAgMIECwAAoDDBAgAAKEywAAAAChMsAACAwgQLAACgMMECAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIAAChMsAAAAAoTLAAAgMIECwAAoDDBAgAAKEywAAAAChMsAACAwgQLAACgMMECAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIAAChMsAAAAAoTLAAAgMIECwAAoDDBAgAAKEywAAAAChMsAACAwgQLAACgMMECAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIAAChMsAAAAAoTLAAAgMIECwAAoDDBAgAAKEywAAAAChMsAACAwgQLAACgMMECAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIAAChMsAAAAAoTLAAAgMIECwAAoDDBAgAAKEywAAAAChMsAACAwgQLAACgMMECAAAobJsIFpMmTUrPnj3Trl27DBo0KHPnzl1v39tuuy2f/OQns9tuu2W33XZLXV3dBvsDAABbXtmDxbRp0zJ69OiMHz8+Tz31VPr27ZshQ4Zk6dKlzfafPXt2Tj/99DzyyCOZM2dOevTokWOPPTavv/76Vq4cAABYq+zB4vrrr8/IkSMzYsSI9OnTJ5MnT06HDh0yZcqUZvv/8z//c77xjW+kX79+6d27d370ox+loaEhs2bN2sqVAwAAa5U1WNTX12fevHmpq6trbKusrExdXV3mzJnzga6xatWqvPfee9l9992bPb969eqsWLGiyQcAAGhZZQ0Wy5Yty5o1a1JTU9OkvaamJosXL/5A17j44ovTrVu3JuHkb02cODGdO3du/PTo0aNw3QAAQFNlfxSqiKuvvjp33313pk+fnnbt2jXbZ+zYsVm+fHnj57XXXtvKVQIAwPavbTkn79KlS9q0aZMlS5Y0aV+yZElqa2s3OPZ73/terr766vzbv/1bPvaxj623X3V1daqrq1ukXgAAoHllXbGoqqrKgAEDmmy8XrsRe/Dgwesdd+211+bKK6/MzJkzM3DgwK1RKgAAsAFlXbFIktGjR2f48OEZOHBgDjnkkNx4441ZuXJlRowYkSQZNmxYunfvnokTJyZJrrnmmowbNy533XVXevbs2bgXo2PHjunYsWPZ7gMAAHZkZQ8Wp512Wt54442MGzcuixcvTr9+/TJz5szGDd0LFy5MZeVfF1Z+8IMfpL6+PqecckqT64wfPz6XX3751iwdAAD4f8oeLJJk1KhRGTVqVLPnZs+e3eR4wYIFW74gAABgk7Tqt0IBAADbBsECAAAoTLAAAAAK2yb2WACwZaxZsybvvfdeucvYLu20005p06ZNucsA2GYIFgDboVKplMWLF+ftt98udynbtV133TW1tbWpqKgodykAZSdYAGyH1oaKrl27pkOHDv7i28JKpVJWrVqVpUuXJkn22muvMlcEUH6CBcB2Zs2aNY2hYo899ih3Odut9u3bJ0mWLl2arl27eiwK2OHZvA2wnVm7p6JDhw5lrmT7t/ZnbB8LgGABsN3y+NOW52cM8FeCBQAAUJhgAQAAFGbzNsAOpOeY+7fqfAuuPmGT+p9xxhm58847kyRt27bNhz70oZx66qm54oor0q5du8Z+zT2CdPjhh+exxx7b6HX/1ksvvZRevXrl0UcfzXe/+93MmzcvixYtyvTp0zN06NBNqh1gRydYALBNOe6443LHHXfkvffey7x58zJ8+PBUVFTkmmuuadLvjjvuyHHHHdd4XFVV9YGu+7f23HPPJMnKlSvTt2/fnHnmmfnc5z7XQncCsGMRLADYplRXV6e2tjZJ0qNHj9TV1eWhhx5aJ1is/XK6zbnu/+/444/P8ccfv/lFA2CPBQDbrmeffTZPPPHERlcjACg/wQKAbcrPf/7zdOzYMe3atctBBx2UpUuX5qKLLlqn3+mnn56OHTs2fmbMmPGBrrv2c+qpp26hOwDYMXkUCoBtylFHHZUf/OAHWblyZW644Ya0bds2J5988jr9brjhhtTV1TUe77XXXh/oumvtvPPOLVc0AIIFANuWnXfeOb169UqSTJkyJX379s3tt9+es846q0m/2traxn6bel0AWp5HoQDYZlVWVuaSSy7JpZdemnfffbfc5QCwAYIFANu0U089NW3atMmkSZO22Bx//vOfM3/+/MyfPz9J8uqrr2b+/PlZuHDhFpsTYHsjWACwTWvbtm1GjRqVa6+9NitXrtwiczz55JPp379/+vfvnyQZPXp0+vfvn3Hjxm2R+QC2R/ZYAOxANvWbsLe2qVOnNts+ZsyYjBkzpvG4VCq1yHXXOvLIIzf5mgA0ZcUCAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIAAChMsAAAAAoTLAAAgMIECwAAoDDBAgAAKEywAAAACmtb7gIA2Iou77yV51u+Sd3POOOMvP3225kxY0az53v27Jk//vGPSZL27dtnv/32y/nnn5+zzz57g9f923Frde/ePf/1X/+VJPnhD3+Yu+66K0899VTeeeedvPXWW9l11103qXaAHZ0VCwBalSuuuCKLFi3Ks88+my9/+csZOXJkfvGLX3zgcWs/v/3tbxvPrVq1Kscdd1wuueSSLVk6wHbNigUArcouu+yS2traJMnFF1+ca6+9Ng899FCOP/74Dzzu/3fBBRckSWbPnt2SpQLsUAQLAFqlhoaGTJ8+PW+99VaqqqrKXQ7ADs+jUAC0KhdffHE6duyY6urqnHLKKdltt902usfib8et/dx8881boVqAHYcVCwBalYsuuihnnHFGFi1alIsuuijf+MY30qtXrw88bq0uXbpswSoBdjyCBQCtSpcuXdKrV6/06tUr9957bw466KAMHDgwffr0+UDjANgyPAoFQKvVo0ePnHbaaRk7dmy5SwHY4VmxAGCbsnz58syfP79J2x577JEePXo02//888/PgQcemCeffDIDBw7crDkXL16cxYsX5+WXX06SPPPMM9lll12y9957Z/fdd9+sawLsaKxYALBNmT17dvr379/kM2HChPX279OnT4499tiMGzdus+ecPHly+vfvn5EjRyZJPvWpT6V///657777NvuaADsaKxYAO5JN/CbsrW3q1KmZOnXqes8vWLCg2faZM2du8LrrG7fW5Zdfnssvv3zDxQGwQVYsAACAwgQLAACgMMECAAAoTLAAAAAKEywAAIDCBAsAAKAwwQIAAChMsAAAAAoTLAAAgMIECwAAoLC25S4AgK3noDsP2qrzPTP8mU3qf8YZZ+TOO+9MkrRt2za77757Pvaxj+X000/PGWeckcrKysyePTtHHXXUBq/zyCOP5Mgjj1ynvaKiYp22ww8/PI899liS5Dvf+U7uv//+zJ8/P1VVVXn77bc3qX6AHZlgAcA25bjjjssdd9yRNWvWZMmSJZk5c2bOP//8/PSnP819992Xww47LIsWLWrsf/7552fFihW54447Gtt233339V7/jjvuyHHHHdd4XFVV1fjn+vr6nHrqqRk8eHBuv/32Fr4zgO2bYAHANqW6ujq1tbVJku7du+fjH/94Dj300Bx99NGZOnVqzj777MbzSdK+ffusXr26SduG7LrrruvtO2HChCTJ1KlTi90EwA7IHgsAtnl///d/n759++ZnP/tZuUsBYD0ECwBahd69e2fBggWFr3P66aenY8eOjZ8ZM2YUviYAHoUCoJUolUrNbr7eVDfccEPq6uoaj/faa6/C1wRAsACglXjuueey7777Fr5ObW1tevXq1QIVAfC3PAoFwDbv4YcfzjPPPJOTTz653KUAsB5WLADYpqxevTqLFy9u8rrZiRMn5jOf+UyGDRu2RedeuHBh3nzzzSxcuDBr1qzJ/PnzkyS9evVKx44dt+jcAK2dYAHANmXmzJnZa6+90rZt2+y2227p27dvbr755gwfPjyVlVt2oX3cuHGNX9CXJP3790+y/i/cA+CvBAuAHcimfhP21jZ16tRN/g6JTelfKpVafH4A/sIeCwAAoDDBAgAAKEywAAAAChMsAACAwgQLAACgMMECYDvV0NBQ7hK2e37GAH/ldbMA25mqqqpUVlbmT3/6U/bcc89UVVWloqKi3GVtV0qlUurr6/PGG2+ksrIyVVVV5S4JoOwEC4DtTGVlZfbdd98sWrQof/rTn8pdznatQ4cO2Xvvvbf4F/cBtAaCBcB2qKqqKnvvvXfef//9rFmzptzlbJfatGmTtm3bWg0C+H8EC4DtVEVFRXbaaafstNNO5S4FgB3ANrF2O2nSpPTs2TPt2rXLoEGDMnfu3A32v/fee9O7d++0a9cuBx10UB544IGtVCkAANCcsgeLadOmZfTo0Rk/fnyeeuqp9O3bN0OGDMnSpUub7f/EE0/k9NNPz1lnnZXf/va3GTp0aIYOHZpnn312K1cOAACsVfZgcf3112fkyJEZMWJE+vTpk8mTJ6dDhw6ZMmVKs/1vuummHHfccbnooovy0Y9+NFdeeWU+/vGP55ZbbtnKlQMAAGuVdY9FfX195s2bl7Fjxza2VVZWpq6uLnPmzGl2zJw5czJ69OgmbUOGDMmMGTOa7b969eqsXr268Xj58uVJkhUrVhSsvpiG1avKOn9rt9H//VaXtk4h26ON/GzXvGsjcBHl/m8PrZffG8X4vbGF+d2xxZT798ba+Uuljf9/pKzBYtmyZVmzZk1qamqatNfU1OT5559vdszixYub7b948eJm+0+cODETJkxYp71Hjx6bWTXbgs43lruC7djVnctdwXat8zl+vlAOfm9sYX53bDHbyu+Nd955J507b7iW7f6tUGPHjm2ywtHQ0JA333wze+yxh1cEstWtWLEiPXr0yGuvvZZOnTqVuxwAWgG/OyinUqmUd955J926ddto37IGiy5duqRNmzZZsmRJk/YlS5aktra22TG1tbWb1L+6ujrV1dVN2nbdddfNLxpaQKdOnfxyAGCT+N1BuWxspWKtsm7erqqqyoABAzJr1qzGtoaGhsyaNSuDBw9udszgwYOb9E+Shx56aL39AQCALa/sj0KNHj06w4cPz8CBA3PIIYfkxhtvzMqVKzNixIgkybBhw9K9e/dMnDgxSXL++efniCOOyHXXXZcTTjghd999d5588sn88Ic/LOdtAADADq3sweK0007LG2+8kXHjxmXx4sXp169fZs6c2bhBe+HChams/OvCymGHHZa77rorl156aS655JJ85CMfyYwZM3LggQeW6xbgA6uurs748ePXeTwPANbH7w5ai4rSB3l3FAAAwAaU/QvyAACA1k+wAAAAChMsAACAwgQLAACgMMECtqJJkyalZ8+eadeuXQYNGpS5c+eWuyQAtlGPPvpoTjzxxHTr1i0VFRWZMWNGuUuCDRIsYCuZNm1aRo8enfHjx+epp55K3759M2TIkCxdurTcpQGwDVq5cmX69u2bSZMmlbsU+EC8bha2kkGDBuXggw/OLbfckuQv3zLfo0ePnHfeeRkzZkyZqwNgW1ZRUZHp06dn6NCh5S4F1suKBWwF9fX1mTdvXurq6hrbKisrU1dXlzlz5pSxMgCAliFYwFawbNmyrFmzpvEb5deqqanJ4sWLy1QVAEDLESwAAIDCBAvYCrp06ZI2bdpkyZIlTdqXLFmS2traMlUFANByBAvYCqqqqjJgwIDMmjWrsa2hoSGzZs3K4MGDy1gZAEDLaFvuAmBHMXr06AwfPjwDBw7MIYcckhtvvDErV67MiBEjyl0aANugP//5z3n55Zcbj1999dXMnz8/u+++e/bee+8yVgbN87pZ2IpuueWWfPe7383ixYvTr1+/3HzzzRk0aFC5ywJgGzR79uwcddRR67QPHz48U6dO3foFwUYIFgAAQGH2WAAAAIUJFgAAQGGCBQAAUJhgAQAAFCZYAAAAhQkWAABAYYIFAABQmGABAAAUJlgAAACFCRYAAEBhggUAAFCYYAEAABT2fwEvVJkSmzNDRgAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Klasifikasi Tingkat Kemiskinan/Revisi_Klasifikasi_Tingkat_Kemiskinan_di_Indonesia.ipynb b/Klasifikasi Tingkat Kemiskinan/Revisi_Klasifikasi_Tingkat_Kemiskinan_di_Indonesia.ipynb
new file mode 100644
index 0000000..7fe8a3e
--- /dev/null
+++ b/Klasifikasi Tingkat Kemiskinan/Revisi_Klasifikasi_Tingkat_Kemiskinan_di_Indonesia.ipynb	
@@ -0,0 +1,1773 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# One-Hot Encoding\n",
+        "X_encoded = pd.get_dummies(X, drop_first=True)\n"
+      ],
+      "metadata": {
+        "id": "i4e7z6erwlmV"
+      },
+      "execution_count": 241,
+      "outputs": []
+    },
+    {
+      "source": [
+        "df = pd.read_csv('/content/Klasifikasi Tingkat Kemiskinan di Indonesia.csv', sep=';')\n",
+        "df.head(100)"
+      ],
+      "cell_type": "code",
+      "execution_count": 242,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Provinsi            Kab/Kota  \\\n",
+              "0               ACEH            Simeulue   \n",
+              "1               ACEH        Aceh Singkil   \n",
+              "2               ACEH        Aceh Selatan   \n",
+              "3               ACEH       Aceh Tenggara   \n",
+              "4               ACEH          Aceh Timur   \n",
+              "..               ...                 ...   \n",
+              "95             JAMBI               Bungo   \n",
+              "96             JAMBI          Kota Jambi   \n",
+              "97             JAMBI   Kota Sungai Penuh   \n",
+              "98  SUMATERA SELATAN   Ogan Komering Ulu   \n",
+              "99  SUMATERA SELATAN  Ogan Komering Ilir   \n",
+              "\n",
+              "   Persentase Penduduk Miskin (P0) Menurut Kabupaten/Kota (Persen)  \\\n",
+              "0                                               18,98                \n",
+              "1                                               20,36                \n",
+              "2                                               13,18                \n",
+              "3                                               13,41                \n",
+              "4                                               14,45                \n",
+              "..                                                ...                \n",
+              "95                                               6,23                \n",
+              "96                                               9,02                \n",
+              "97                                               3,41                \n",
+              "98                                              12,62                \n",
+              "99                                              14,68                \n",
+              "\n",
+              "   Rata-rata Lama Sekolah Penduduk 15+ (Tahun)  \\\n",
+              "0                                         9,48   \n",
+              "1                                         8,68   \n",
+              "2                                         8,88   \n",
+              "3                                         9,67   \n",
+              "4                                         8,21   \n",
+              "..                                         ...   \n",
+              "95                                        8,28   \n",
+              "96                                        11,2   \n",
+              "97                                       10,33   \n",
+              "98                                        8,71   \n",
+              "99                                        7,05   \n",
+              "\n",
+              "    Pengeluaran per Kapita Disesuaikan (Ribu Rupiah/Orang/Tahun)  \\\n",
+              "0                                              7148.0              \n",
+              "1                                              8776.0              \n",
+              "2                                              8180.0              \n",
+              "3                                              8030.0              \n",
+              "4                                              8577.0              \n",
+              "..                                                ...              \n",
+              "95                                            11670.0              \n",
+              "96                                            12240.0              \n",
+              "97                                            10454.0              \n",
+              "98                                            10040.0              \n",
+              "99                                            10755.0              \n",
+              "\n",
+              "   Indeks Pembangunan Manusia Umur Harapan Hidup (Tahun)  \\\n",
+              "0                       66,41                      65,28   \n",
+              "1                       69,22                      67,43   \n",
+              "2                       67,44                       64,4   \n",
+              "3                       69,44                      68,22   \n",
+              "4                       67,83                      68,74   \n",
+              "..                        ...                        ...   \n",
+              "95                      70,15                      67,83   \n",
+              "96                      79,12                      72,71   \n",
+              "97                       75,7                      72,21   \n",
+              "98                       69,6                      68,24   \n",
+              "99                      67,17                      68,67   \n",
+              "\n",
+              "   Persentase rumah tangga yang memiliki akses terhadap sanitasi layak  \\\n",
+              "0                                               71,56                    \n",
+              "1                                               69,56                    \n",
+              "2                                               62,55                    \n",
+              "3                                               62,71                    \n",
+              "4                                               66,75                    \n",
+              "..                                                ...                    \n",
+              "95                                              77,58                    \n",
+              "96                                              93,22                    \n",
+              "97                                              74,04                    \n",
+              "98                                              82,72                    \n",
+              "99                                              68,09                    \n",
+              "\n",
+              "   Persentase rumah tangga yang memiliki akses terhadap air minum layak  \\\n",
+              "0                                               87,45                     \n",
+              "1                                               78,58                     \n",
+              "2                                               79,65                     \n",
+              "3                                               86,71                     \n",
+              "4                                               83,16                     \n",
+              "..                                                ...                     \n",
+              "95                                              73,63                     \n",
+              "96                                              95,83                     \n",
+              "97                                              90,55                     \n",
+              "98                                              81,78                     \n",
+              "99                                              79,02                     \n",
+              "\n",
+              "   Tingkat Pengangguran Terbuka Tingkat Partisipasi Angkatan Kerja  \\\n",
+              "0                          5,71                             71,15    \n",
+              "1                          8,36                             62,85    \n",
+              "2                          6,46                             60,85    \n",
+              "3                          6,43                             69,62    \n",
+              "4                          7,13                             59,48    \n",
+              "..                          ...                                ...   \n",
+              "95                         5,86                             63,58    \n",
+              "96                        10,66                             63,12    \n",
+              "97                         3,00                             64,92    \n",
+              "98                         4,57                             69,96    \n",
+              "99                         3,01                             69,68    \n",
+              "\n",
+              "    PDRB atas Dasar Harga Konstan menurut Pengeluaran (Rupiah)  \\\n",
+              "0                                           1648096.0            \n",
+              "1                                           1780419.0            \n",
+              "2                                           4345784.0            \n",
+              "3                                           3487157.0            \n",
+              "4                                           8433526.0            \n",
+              "..                                                ...            \n",
+              "95                                         13133523.0            \n",
+              "96                                         19515486.0            \n",
+              "97                                          4768840.0            \n",
+              "98                                         10114558.0            \n",
+              "99                                         20909479.0            \n",
+              "\n",
+              "    Klasifikasi Kemiskinan  \n",
+              "0                      0.0  \n",
+              "1                      1.0  \n",
+              "2                      0.0  \n",
+              "3                      0.0  \n",
+              "4                      0.0  \n",
+              "..                     ...  \n",
+              "95                     0.0  \n",
+              "96                     0.0  \n",
+              "97                     0.0  \n",
+              "98                     0.0  \n",
+              "99                     0.0  \n",
+              "\n",
+              "[100 rows x 13 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fcb1097a-09cc-413b-bd5a-fe6af659d1ae\" class=\"colab-df-container\">\n",
+              "    <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>Provinsi</th>\n",
+              "      <th>Kab/Kota</th>\n",
+              "      <th>Persentase Penduduk Miskin (P0) Menurut Kabupaten/Kota (Persen)</th>\n",
+              "      <th>Rata-rata Lama Sekolah Penduduk 15+ (Tahun)</th>\n",
+              "      <th>Pengeluaran per Kapita Disesuaikan (Ribu Rupiah/Orang/Tahun)</th>\n",
+              "      <th>Indeks Pembangunan Manusia</th>\n",
+              "      <th>Umur Harapan Hidup (Tahun)</th>\n",
+              "      <th>Persentase rumah tangga yang memiliki akses terhadap sanitasi layak</th>\n",
+              "      <th>Persentase rumah tangga yang memiliki akses terhadap air minum layak</th>\n",
+              "      <th>Tingkat Pengangguran Terbuka</th>\n",
+              "      <th>Tingkat Partisipasi Angkatan Kerja</th>\n",
+              "      <th>PDRB atas Dasar Harga Konstan menurut Pengeluaran (Rupiah)</th>\n",
+              "      <th>Klasifikasi Kemiskinan</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Simeulue</td>\n",
+              "      <td>18,98</td>\n",
+              "      <td>9,48</td>\n",
+              "      <td>7148.0</td>\n",
+              "      <td>66,41</td>\n",
+              "      <td>65,28</td>\n",
+              "      <td>71,56</td>\n",
+              "      <td>87,45</td>\n",
+              "      <td>5,71</td>\n",
+              "      <td>71,15</td>\n",
+              "      <td>1648096.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Singkil</td>\n",
+              "      <td>20,36</td>\n",
+              "      <td>8,68</td>\n",
+              "      <td>8776.0</td>\n",
+              "      <td>69,22</td>\n",
+              "      <td>67,43</td>\n",
+              "      <td>69,56</td>\n",
+              "      <td>78,58</td>\n",
+              "      <td>8,36</td>\n",
+              "      <td>62,85</td>\n",
+              "      <td>1780419.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Selatan</td>\n",
+              "      <td>13,18</td>\n",
+              "      <td>8,88</td>\n",
+              "      <td>8180.0</td>\n",
+              "      <td>67,44</td>\n",
+              "      <td>64,4</td>\n",
+              "      <td>62,55</td>\n",
+              "      <td>79,65</td>\n",
+              "      <td>6,46</td>\n",
+              "      <td>60,85</td>\n",
+              "      <td>4345784.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Tenggara</td>\n",
+              "      <td>13,41</td>\n",
+              "      <td>9,67</td>\n",
+              "      <td>8030.0</td>\n",
+              "      <td>69,44</td>\n",
+              "      <td>68,22</td>\n",
+              "      <td>62,71</td>\n",
+              "      <td>86,71</td>\n",
+              "      <td>6,43</td>\n",
+              "      <td>69,62</td>\n",
+              "      <td>3487157.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>Aceh Timur</td>\n",
+              "      <td>14,45</td>\n",
+              "      <td>8,21</td>\n",
+              "      <td>8577.0</td>\n",
+              "      <td>67,83</td>\n",
+              "      <td>68,74</td>\n",
+              "      <td>66,75</td>\n",
+              "      <td>83,16</td>\n",
+              "      <td>7,13</td>\n",
+              "      <td>59,48</td>\n",
+              "      <td>8433526.0</td>\n",
+              "      <td>0.0</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",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>95</th>\n",
+              "      <td>JAMBI</td>\n",
+              "      <td>Bungo</td>\n",
+              "      <td>6,23</td>\n",
+              "      <td>8,28</td>\n",
+              "      <td>11670.0</td>\n",
+              "      <td>70,15</td>\n",
+              "      <td>67,83</td>\n",
+              "      <td>77,58</td>\n",
+              "      <td>73,63</td>\n",
+              "      <td>5,86</td>\n",
+              "      <td>63,58</td>\n",
+              "      <td>13133523.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>96</th>\n",
+              "      <td>JAMBI</td>\n",
+              "      <td>Kota Jambi</td>\n",
+              "      <td>9,02</td>\n",
+              "      <td>11,2</td>\n",
+              "      <td>12240.0</td>\n",
+              "      <td>79,12</td>\n",
+              "      <td>72,71</td>\n",
+              "      <td>93,22</td>\n",
+              "      <td>95,83</td>\n",
+              "      <td>10,66</td>\n",
+              "      <td>63,12</td>\n",
+              "      <td>19515486.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97</th>\n",
+              "      <td>JAMBI</td>\n",
+              "      <td>Kota Sungai Penuh</td>\n",
+              "      <td>3,41</td>\n",
+              "      <td>10,33</td>\n",
+              "      <td>10454.0</td>\n",
+              "      <td>75,7</td>\n",
+              "      <td>72,21</td>\n",
+              "      <td>74,04</td>\n",
+              "      <td>90,55</td>\n",
+              "      <td>3,00</td>\n",
+              "      <td>64,92</td>\n",
+              "      <td>4768840.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>98</th>\n",
+              "      <td>SUMATERA SELATAN</td>\n",
+              "      <td>Ogan Komering Ulu</td>\n",
+              "      <td>12,62</td>\n",
+              "      <td>8,71</td>\n",
+              "      <td>10040.0</td>\n",
+              "      <td>69,6</td>\n",
+              "      <td>68,24</td>\n",
+              "      <td>82,72</td>\n",
+              "      <td>81,78</td>\n",
+              "      <td>4,57</td>\n",
+              "      <td>69,96</td>\n",
+              "      <td>10114558.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>99</th>\n",
+              "      <td>SUMATERA SELATAN</td>\n",
+              "      <td>Ogan Komering Ilir</td>\n",
+              "      <td>14,68</td>\n",
+              "      <td>7,05</td>\n",
+              "      <td>10755.0</td>\n",
+              "      <td>67,17</td>\n",
+              "      <td>68,67</td>\n",
+              "      <td>68,09</td>\n",
+              "      <td>79,02</td>\n",
+              "      <td>3,01</td>\n",
+              "      <td>69,68</td>\n",
+              "      <td>20909479.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>100 rows × 13 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fcb1097a-09cc-413b-bd5a-fe6af659d1ae')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fcb1097a-09cc-413b-bd5a-fe6af659d1ae button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fcb1097a-09cc-413b-bd5a-fe6af659d1ae');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-caba88ae-daeb-4415-9876-0df7cee1305e\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-caba88ae-daeb-4415-9876-0df7cee1305e')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-caba88ae-daeb-4415-9876-0df7cee1305e button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 999,\n  \"fields\": [\n    {\n      \"column\": \"Provinsi\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 34,\n        \"samples\": [\n          \"BANTEN\",\n          \"KALIMANTAN BARAT\",\n          \"SULAWESI TENGGARA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Kab/Kota\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 514,\n        \"samples\": [\n          \"Manggarai\",\n          \"Yahukimo\",\n          \"Gorontalo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Persentase Penduduk Miskin (P0) Menurut Kabupaten/Kota (Persen)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 452,\n        \"samples\": [\n          \"5,91\",\n          \"8,3\",\n          \"12,85\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rata-rata Lama Sekolah Penduduk 15+ (Tahun)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 377,\n        \"samples\": [\n          \"10,91\",\n          \"7,12\",\n          \"6,02\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pengeluaran per Kapita Disesuaikan (Ribu Rupiah/Orang/Tahun)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2717.144185738409,\n        \"min\": 3976.0,\n        \"max\": 23888.0,\n        \"num_unique_values\": 498,\n        \"samples\": [\n          5708.0,\n          13317.0,\n          9410.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Indeks Pembangunan Manusia\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 452,\n        \"samples\": [\n          \"65,87\",\n          \"70,83\",\n          \"80,59\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Umur Harapan Hidup (Tahun)\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 411,\n        \"samples\": [\n          \"73,8\",\n          \"71,58\",\n          \"70,38\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Persentase rumah tangga yang memiliki akses terhadap sanitasi layak\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 487,\n        \"samples\": [\n          \"89,49\",\n          \"91,56\",\n          \"90,56\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Persentase rumah tangga yang memiliki akses terhadap air minum layak\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 465,\n        \"samples\": [\n          \"74,11\",\n          \"85,78\",\n          \"91,43\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tingkat Pengangguran Terbuka\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 373,\n        \"samples\": [\n          \"4,56\",\n          \"1,95\",\n          \"3,47\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tingkat Partisipasi Angkatan Kerja\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 448,\n        \"samples\": [\n          \" 77,57 \",\n          \" 70,16 \",\n          \" 70,13 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PDRB atas Dasar Harga Konstan menurut Pengeluaran (Rupiah)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47904920.44381927,\n        \"min\": 147485.0,\n        \"max\": 460081046.0,\n        \"num_unique_values\": 514,\n        \"samples\": [\n          3025880.0,\n          1556231.0,\n          9082312.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Klasifikasi Kemiskinan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3260054197106571,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 242
+        }
+      ],
+      "metadata": {
+        "cellView": "form",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 774
+        },
+        "id": "jpmStKaasKmD",
+        "outputId": "c7d076a5-b771-421a-9308-d94f3d61bd05"
+      }
+    },
+    {
+      "source": [
+        "df_subset = df[[\"Provinsi\", \"Klasifikasi Kemiskinan\"]]\n",
+        "df_subset.head(100)"
+      ],
+      "cell_type": "code",
+      "execution_count": 243,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Provinsi  Klasifikasi Kemiskinan\n",
+              "0               ACEH                     0.0\n",
+              "1               ACEH                     1.0\n",
+              "2               ACEH                     0.0\n",
+              "3               ACEH                     0.0\n",
+              "4               ACEH                     0.0\n",
+              "..               ...                     ...\n",
+              "95             JAMBI                     0.0\n",
+              "96             JAMBI                     0.0\n",
+              "97             JAMBI                     0.0\n",
+              "98  SUMATERA SELATAN                     0.0\n",
+              "99  SUMATERA SELATAN                     0.0\n",
+              "\n",
+              "[100 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ebc80c7d-c73d-42b9-847d-11730a6527f4\" class=\"colab-df-container\">\n",
+              "    <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>Provinsi</th>\n",
+              "      <th>Klasifikasi Kemiskinan</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>ACEH</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>95</th>\n",
+              "      <td>JAMBI</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>96</th>\n",
+              "      <td>JAMBI</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97</th>\n",
+              "      <td>JAMBI</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>98</th>\n",
+              "      <td>SUMATERA SELATAN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>99</th>\n",
+              "      <td>SUMATERA SELATAN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>100 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ebc80c7d-c73d-42b9-847d-11730a6527f4')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ebc80c7d-c73d-42b9-847d-11730a6527f4 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ebc80c7d-c73d-42b9-847d-11730a6527f4');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-16b13a1f-5473-4437-83e1-6bbd9a8f6a8c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-16b13a1f-5473-4437-83e1-6bbd9a8f6a8c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-16b13a1f-5473-4437-83e1-6bbd9a8f6a8c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_subset",
+              "summary": "{\n  \"name\": \"df_subset\",\n  \"rows\": 999,\n  \"fields\": [\n    {\n      \"column\": \"Provinsi\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 34,\n        \"samples\": [\n          \"BANTEN\",\n          \"KALIMANTAN BARAT\",\n          \"SULAWESI TENGGARA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Klasifikasi Kemiskinan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3260054197106571,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 243
+        }
+      ],
+      "metadata": {
+        "cellView": "form",
+        "id": "0Np7uHb3s1vm",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "outputId": "d6f28af5-16e3-4bbe-934a-acc7e27fef6d"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_subset = df[[\"Provinsi\", \"Klasifikasi Kemiskinan\"]]\n",
+        "df_subset = df_subset.dropna()\n",
+        "\n",
+        "plt.figure(figsize=(12, 4))\n",
+        "sns.lineplot(data=df_subset, x=\"Provinsi\", y=\"Klasifikasi Kemiskinan\", marker=\"o\")\n",
+        "\n",
+        "plt.xticks(rotation=90)\n",
+        "plt.title(\"Line Plot Klasifikasi Kemiskinan di setiap Provinsi\")\n",
+        "plt.xlabel(\"Provinsi\")\n",
+        "plt.ylabel(\"Klasifikasi Kemiskinan\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 398
+        },
+        "id": "9cbPJC3aKczY",
+        "outputId": "0ca834f4-13c0-49cf-de11-c16566bc1076"
+      },
+      "execution_count": 244,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.model_selection import train_test_split, cross_val_score\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "LIvFHTtMw5mI"
+      },
+      "execution_count": 245,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y = pd.to_numeric(df[\"Klasifikasi Kemiskinan\"], errors='coerce')\n",
+        "y = y.fillna(0).astype(int)\n",
+        "\n",
+        "X = df.drop(\"Klasifikasi Kemiskinan\", axis=1)"
+      ],
+      "metadata": {
+        "id": "LPcA1dxyxERN"
+      },
+      "execution_count": 246,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X = X.apply(lambda s: s.astype(str).str.replace(\",\", \".\").str.replace(\" \", \"\", regex=True))\n",
+        "\n",
+        "for col in X.columns:\n",
+        "    X[col] = pd.to_numeric(X[col], errors='coerce')\n",
+        "\n",
+        "label_enc = LabelEncoder()\n",
+        "X[\"Provinsi\"] = label_enc.fit_transform(X[\"Provinsi\"].astype(str))\n",
+        "X[\"Kab/Kota\"] = label_enc.fit_transform(X[\"Kab/Kota\"].astype(str))\n",
+        "\n",
+        "imputer = SimpleImputer(strategy='mean')\n",
+        "X[X.columns] = imputer.fit_transform(X)"
+      ],
+      "metadata": {
+        "id": "g_oElrw_xSFf"
+      },
+      "execution_count": 247,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X, y, test_size=0.2, random_state=42\n",
+        ")\n"
+      ],
+      "metadata": {
+        "id": "QubEOuCOxVE7"
+      },
+      "execution_count": 248,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rf = RandomForestClassifier(n_estimators=200, random_state=42)\n",
+        "rf.fit(X_train, y_train)\n",
+        "\n",
+        "pred_rf = rf.predict(X_test)\n",
+        "\n",
+        "print(\"=== Random Forest Accuracy ===\")\n",
+        "print(accuracy_score(y_test, pred_rf))\n",
+        "print(\"\\n=== Classification Report ===\")\n",
+        "print(classification_report(y_test, pred_rf))\n",
+        "print(\"\\n=== Confusion Matrix ===\")\n",
+        "print(confusion_matrix(y_test, pred_rf))\n",
+        "\n",
+        "cv_rf = cross_val_score(rf, X, y, cv=5)\n",
+        "print(\"\\n=== Cross Validation (Random Forest) ===\")\n",
+        "print(cv_rf)\n",
+        "print(\"Mean CV Score:\", cv_rf.mean())\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "aEaCyi8XxXql",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0c297472-00b2-4e18-f3da-b29a050a8a9e"
+      },
+      "execution_count": 249,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "=== Random Forest Accuracy ===\n",
+            "0.99\n",
+            "\n",
+            "=== Classification Report ===\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.99      1.00      0.99       190\n",
+            "           1       1.00      0.80      0.89        10\n",
+            "\n",
+            "    accuracy                           0.99       200\n",
+            "   macro avg       0.99      0.90      0.94       200\n",
+            "weighted avg       0.99      0.99      0.99       200\n",
+            "\n",
+            "\n",
+            "=== Confusion Matrix ===\n",
+            "[[190   0]\n",
+            " [  2   8]]\n",
+            "\n",
+            "=== Cross Validation (Random Forest) ===\n",
+            "[0.99 1.   0.97 0.99 1.  ]\n",
+            "Mean CV Score: 0.99\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n"
+      ],
+      "metadata": {
+        "id": "l_Bms2snyjks"
+      },
+      "execution_count": 250,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "accuracy = 0.99\n",
+        "\n",
+        "conf_matrix = np.array([\n",
+        "    [190, 0],\n",
+        "    [2,   8]\n",
+        "])"
+      ],
+      "metadata": {
+        "id": "isQJoC9Tyq5G"
+      },
+      "execution_count": 251,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "classes = [\"0\", \"1\"]\n",
+        "precision = [0.99, 1.00]\n",
+        "recall = [1.00, 0.80]\n",
+        "f1 = [0.99, 0.89]\n",
+        "\n",
+        "cv_scores = np.array([0.99, 1.00, 0.97, 0.99, 1.00])"
+      ],
+      "metadata": {
+        "id": "ZmNBm0wyzKA8"
+      },
+      "execution_count": 252,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure()\n",
+        "plt.bar([\"Accuracy\"], [accuracy])\n",
+        "plt.title(\"Random Forest Accuracy\")\n",
+        "plt.ylabel(\"Score\")\n",
+        "plt.ylim(0, 1)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "kTf7LP5my2ja",
+        "outputId": "5dd533a5-59e8-40e1-a666-885fa6511458"
+      },
+      "execution_count": 253,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGzCAYAAADT4Tb9AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAL/xJREFUeJzt3XlYVdX+x/HPAeWgmDigIMoVQ8tMBS8O4ZD5i+Kq2cUpxa44l6VW0iCWcyXeTK+lluVcZpkTWZoT2Shlapheh+tEmr9AcQClhID9+8Mf53oCBxA9uHq/nuc8T2fttdb+nq1P5+Paw7FZlmUJAADAEG6uLgAAAKAkEW4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgDD9O3bV4GBga4uAwBchnADFNOCBQtks9kcrzJlyqhmzZrq27evjh075urySo0/HqeLX7Gxsa4ur1ATJ05UfHx8kcft2bNHNptNnp6eOnPmTInXBeDqlHF1AcDNbsKECapTp47Onz+vb7/9VgsWLNDXX3+tXbt2ydPT09XllRr5x+liDRs2dFE1lzdx4kR169ZNkZGRRRq3aNEi+fn56fTp01q2bJkGDhx4fQoEcFmEG+AatW/fXk2bNpUkDRw4UD4+PvrnP/+pVatW6aGHHnJxdaXHxcepJGVmZsrLy6vE5y0qy7K0ePFi9erVS4cPH9Z7771XasNNaTlmwPXCaSmghLVp00aSdPDgQUdbdna2xowZo9DQUHl7e8vLy0tt2rTRpk2bnMYmJyfLZrPp1Vdf1dtvv62goCDZ7XY1a9ZM33//fYF9xcfHq2HDhvL09FTDhg21cuXKQmvKzMzU008/rYCAANntdt1+++169dVXZVmWUz+bzaahQ4dq6dKlatCggcqVK6ewsDDt3LlTkvTWW2+pbt268vT01D333KPk5ORrOVROPvvsM7Vp00ZeXl6qVKmS/v73v2vPnj1OfcaNGyebzabdu3erV69eqly5slq3bu3YvmjRIoWGhqpcuXKqUqWKevbsqaNHjzrNsX//fnXt2lV+fn7y9PRUrVq11LNnT6WnpzuOQWZmphYuXOg4fda3b98r1v/NN98oOTlZPXv2VM+ePfXll1/q559/LtAvLy9Pr732mho1aiRPT09Vq1ZNf/vb37R161anfosWLVLz5s1Vvnx5Va5cWXfffbfWr1/v2G6z2TRu3LgC8wcGBjrVm39a8IsvvtDjjz+u6tWrq1atWpKkn376SY8//rhuv/12lStXTlWrVlX37t0L/XM9c+aMhg8frsDAQNntdtWqVUvR0dFKS0vTuXPn5OXlpSeffLLAuJ9//lnu7u6Ki4u74jEESgorN0AJy/9iqFy5sqMtIyNDc+bMUVRUlAYNGqSzZ89q7ty5ioiI0JYtWxQSEuI0x+LFi3X27Fk9+uijstlseuWVV9SlSxcdOnRIZcuWlSStX79eXbt2VYMGDRQXF6eTJ0+qX79+ji+ufJZl6cEHH9SmTZs0YMAAhYSEaN26dXr22Wd17Ngx/etf/3Lq/9VXX2nVqlUaMmSIJCkuLk4PPPCAnnvuOb3xxht6/PHHdfr0ab3yyivq37+/Pvvss6s6Lunp6UpLS3Nq8/HxkSRt3LhR7du316233qpx48bpt99+0/Tp09WqVStt3769wAXS3bt3V7169TRx4kRHQHv55Zc1evRoPfTQQxo4cKBOnDih6dOn6+6779YPP/ygSpUqKTs7WxEREcrKytKwYcPk5+enY8eO6ZNPPtGZM2fk7e2td999VwMHDlTz5s31yCOPSJKCgoKu+Pnee+89BQUFqVmzZmrYsKHKly+v999/X88++6xTvwEDBmjBggVq3769Bg4cqJycHH311Vf69ttvHStb48eP17hx49SyZUtNmDBBHh4e+u677/TZZ5/p/vvvv6rj/UePP/64qlWrpjFjxigzM1OS9P3332vz5s3q2bOnatWqpeTkZL355pu65557tHv3bpUvX16SdO7cObVp00Z79uxR//799de//lVpaWlatWqVfv75Z4WEhKhz585asmSJpk6dKnd3d8d+33//fVmWpYcffrhYdQPFYgEolvnz51uSrI0bN1onTpywjh49ai1btsyqVq2aZbfbraNHjzr65uTkWFlZWU7jT58+bfn6+lr9+/d3tB0+fNiSZFWtWtU6deqUo/2jjz6yJFkff/yxoy0kJMSqUaOGdebMGUfb+vXrLUlW7dq1HW3x8fGWJOull15y2n+3bt0sm81mHThwwNEmybLb7dbhw4cdbW+99ZYlyfLz87MyMjIc7SNHjrQkOfW93HEq7HXxZ6levbp18uRJR9uOHTssNzc3Kzo62tE2duxYS5IVFRXltI/k5GTL3d3devnll53ad+7caZUpU8bR/sMPP1iSrKVLl162Zi8vL6tPnz6X7XOx7Oxsq2rVqtYLL7zgaOvVq5cVHBzs1O+zzz6zJFlPPPFEgTny8vIsy7Ks/fv3W25ublbnzp2t3NzcQvtY1oU/q7FjxxaYp3bt2k615x//1q1bWzk5OU59f/311wLjExMTLUnWO++842gbM2aMJclasWLFJetet26dJcn69NNPnbY3btzYatu2bYFxwPXEaSngGoWHh6tatWoKCAhQt27d5OXlpVWrVjmtoLi7u8vDw0PShdMSp06dUk5Ojpo2bart27cXmLNHjx5OKz/5p7oOHTokSfrll1+UlJSkPn36yNvb29HvvvvuU4MGDZzmWrNmjdzd3fXEE084tT/99NOyLEuffvqpU/u9997rtFLSokULSVLXrl11yy23FGjPr+lKZs6cqQ0bNji9Lv4sffv2VZUqVRz9GzdurPvuu09r1qwpMNfgwYOd3q9YsUJ5eXl66KGHlJaW5nj5+fmpXr16jtN/+cdq3bp1+vXXX6+q7qvx6aef6uTJk4qKinK0RUVFaceOHfr3v//taFu+fLlsNpvGjh1bYA6bzSbpwqnGvLw8jRkzRm5uboX2KY5BgwY5rahIUrly5Rz//fvvv+vkyZOqW7euKlWq5PT3cvny5QoODlbnzp0vWXd4eLj8/f313nvvObbt2rVLP/74o/7xj38Uu26gOAg3wDXK/9JetmyZOnTooLS0NNnt9gL9Fi5cqMaNG8vT01NVq1ZVtWrVtHr1ase1Hhf7y1/+4vQ+P+icPn1a0oVrJSSpXr16BcbefvvtTu9/+ukn+fv7OwUTSbrjjjuc5rrUvvMDQUBAQKHt+TVdSfPmzRUeHu70unj/f6w7v8a0tDTHaZR8f7zrav/+/bIsS/Xq1VO1atWcXnv27NHx48cd42JiYjRnzhz5+PgoIiJCM2fOLPTPoCgWLVqkOnXqyG6368CBAzpw4ICCgoJUvnx5py/7gwcPyt/f3ynE/dHBgwfl5uZWIKReqz8eM0n67bffNGbMGMe1WD4+PqpWrZrOnDnjdEwOHjx4xTvb3Nzc9PDDDys+Pt4RHN977z15enqqe/fuJfpZgCvhmhvgGjVv3txxrURkZKRat26tXr16ad++fapQoYKkC19+ffv2VWRkpJ599llVr17dcZHlxRce5/vjv7DzWX+4APh6uNS+XVnTH1284iBdWA2z2Wz69NNPC60z/89BkqZMmaK+ffvqo48+0vr16/XEE08oLi5O3377bYHrla5GRkaGPv74Y50/f77QsLl48WK9/PLL17TqUhS5ubmFtv/xmEnSsGHDNH/+fD311FMKCwuTt7e3bDabevbsqby8vCLvOzo6WpMnT1Z8fLyioqK0ePFiPfDAA06ri8CNQLgBSlB+YGnXrp1mzJjheEjdsmXLdOutt2rFihVOX3KFnZ64GrVr15Z0YcXij/bt21eg78aNG3X27Fmn1Zu9e/c6zeUq+fv/Y93ShRp9fHyueNtyUFCQLMtSnTp1dNttt11xn40aNVKjRo00atQobd68Wa1atdKsWbP00ksvSSra6Z8VK1bo/PnzevPNNx0XSOfbt2+fRo0apW+++UatW7dWUFCQ1q1bp1OnTl1y9SYoKEh5eXnavXt3gQvNL1a5cuUCDwrMzs7WL7/8ctW1L1u2TH369NGUKVMcbefPny8wb1BQkHbt2nXF+Ro2bKgmTZrovffeU61atXTkyBFNnz79qusBSgqnpYASds8996h58+aaNm2azp8/L+m/qx4Xr3J89913SkxMLNY+atSooZCQEC1cuNDp9MGGDRu0e/dup74dOnRQbm6uZsyY4dT+r3/9SzabTe3bty9WDSXl4s9y8Zfqrl27tH79enXo0OGKc3Tp0kXu7u4aP358gZUky7J08uRJSRdWWXJycpy2N2rUSG5ubsrKynK0eXl5XfUThhctWqRbb71VgwcPVrdu3ZxezzzzjCpUqOA4NdW1a1dZlqXx48cXmCe/7sjISLm5uWnChAkFVk8u/mxBQUH68ssvnba//fbbl1y5KYy7u3uB4zV9+vQCc3Tt2lU7duwo9FEDfxzfu3dvrV+/XtOmTVPVqlVd/vcLf06s3ADXwbPPPqvu3btrwYIFGjx4sB544AGtWLFCnTt3VseOHXX48GHNmjVLDRo00Llz54q1j7i4OHXs2FGtW7dW//79derUKU2fPl133nmn05ydOnVSu3bt9MILLyg5OVnBwcFav369PvroIz311FNXdZvz9TZ58mS1b99eYWFhGjBggONWcG9v70Kf5fJHQUFBeumllzRy5EglJycrMjJSt9xyiw4fPqyVK1fqkUce0TPPPKPPPvtMQ4cOVffu3XXbbbcpJydH7777rtzd3dW1a1fHfKGhodq4caOmTp0qf39/1alTx3EB9cX+93//V5s2bSpwsXY+u92uiIgILV26VK+//rratWun3r176/XXX9f+/fv1t7/9TXl5efrqq6/Url07DR06VHXr1tULL7ygF198UW3atFGXLl1kt9v1/fffy9/f3/G8mIEDB2rw4MHq2rWr7rvvPu3YsUPr1q0rsHp0OQ888IDeffddeXt7q0GDBkpMTNTGjRtVtWpVp37PPvusli1bpu7du6t///4KDQ3VqVOntGrVKs2aNUvBwcGOvr169dJzzz2nlStX6rHHHnM8ugC4oVxyjxZggPxbbL///vsC23Jzc62goCArKCjIysnJsfLy8qyJEydatWvXtux2u9WkSRPrk08+sfr06eN023b+reCTJ08uMKcKufV3+fLl1h133GHZ7XarQYMG1ooVKwrMaVmWdfbsWWv48OGWv7+/VbZsWatevXrW5MmTnW4tzt/HkCFDnNouVdOmTZuu6rbqyx2ni23cuNFq1aqVVa5cOatixYpWp06drN27dzv1yb8V/MSJE4XOsXz5cqt169aWl5eX5eXlZdWvX98aMmSItW/fPsuyLOvQoUNW//79raCgIMvT09OqUqWK1a5dO2vjxo1O8+zdu9e6++67rXLlylmSLnlb+JQpUyxJVkJCwiU/14IFCyxJ1kcffWRZ1oXHAkyePNmqX7++5eHhYVWrVs1q3769tW3bNqdx8+bNs5o0aWLZ7XarcuXKVtu2ba0NGzY4tufm5lojRoywfHx8rPLly1sRERHWgQMHLnkreGHH//Tp01a/fv0sHx8fq0KFClZERIS1d+/eAnNYlmWdPHnSGjp0qFWzZk3Lw8PDqlWrltWnTx8rLS2twLwdOnSwJFmbN2++5HEBriebZbngakAAgLE6d+6snTt36sCBA64uBX9SXHMDACgxv/zyi1avXq3evXu7uhT8iXHNDQDgmh0+fFjffPON5syZo7Jly+rRRx91dUn4E2PlBgBwzb744gv17t1bhw8f1sKFC+Xn5+fqkvAn5tJw8+WXX6pTp07y9/eXzWZTfHz8Fcd8/vnn+utf/yq73a66detqwYIF171OAMDl9e3bV5Zl6aefflK3bt1cXQ7+5FwabjIzMxUcHKyZM2deVf/Dhw+rY8eOateunZKSkvTUU09p4MCBWrdu3XWuFAAA3CxKzd1SNptNK1euVGRk5CX7jBgxQqtXr3Z6UmbPnj115swZrV279gZUCQAASrub6oLixMREx4/t5YuIiNBTTz11yTFZWVlOTx7N/0XmqlWr3rDfegEAANfGsiydPXtW/v7+cnO7/ImnmyrcpKSkyNfX16nN19dXGRkZ+u233wr9Ybi4uLhCH3UOAABuPkePHr3ij9zeVOGmOEaOHKmYmBjH+/T0dP3lL3/R0aNHVbFiRRdWBgAArlZGRoYCAgKcfgD4Um6qcOPn56fU1FSnttTUVFWsWLHQVRvpwm+72O32Au0VK1Yk3AAAcJO5mktKbqpwExYWpjVr1ji1bdiwQWFhYS6qqKDA2NWuLgEAAJdKntTRpft36a3g586dU1JSkpKSkiRduNU7KSlJR44ckXThlFJ0dLSj/+DBg3Xo0CE999xz2rt3r9544w19+OGHGj58uCvKBwAApZBLw83WrVvVpEkTNWnSRJIUExOjJk2aaMyYMZIu/EZJftCRpDp16mj16tXasGGDgoODNWXKFM2ZM0cREREuqR8AAJQ+peY5NzdKRkaGvL29lZ6efl2uueG0FADgz+56nJYqyvc3vy0FAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMIrLw83MmTMVGBgoT09PtWjRQlu2bLls/2nTpun2229XuXLlFBAQoOHDh+v8+fM3qFoAAFDauTTcLFmyRDExMRo7dqy2b9+u4OBgRURE6Pjx44X2X7x4sWJjYzV27Fjt2bNHc+fO1ZIlS/T888/f4MoBAEBp5dJwM3XqVA0aNEj9+vVTgwYNNGvWLJUvX17z5s0rtP/mzZvVqlUr9erVS4GBgbr//vsVFRV1xdUeAADw5+GycJOdna1t27YpPDz8v8W4uSk8PFyJiYmFjmnZsqW2bdvmCDOHDh3SmjVr1KFDh0vuJysrSxkZGU4vAABgrjKu2nFaWppyc3Pl6+vr1O7r66u9e/cWOqZXr15KS0tT69atZVmWcnJyNHjw4MueloqLi9P48eNLtHYAAFB6ufyC4qL4/PPPNXHiRL3xxhvavn27VqxYodWrV+vFF1+85JiRI0cqPT3d8Tp69OgNrBgAANxoLlu58fHxkbu7u1JTU53aU1NT5efnV+iY0aNHq3fv3ho4cKAkqVGjRsrMzNQjjzyiF154QW5uBbOa3W6X3W4v+Q8AAABKJZet3Hh4eCg0NFQJCQmOtry8PCUkJCgsLKzQMb/++muBAOPu7i5Jsizr+hULAABuGi5buZGkmJgY9enTR02bNlXz5s01bdo0ZWZmql+/fpKk6Oho1axZU3FxcZKkTp06aerUqWrSpIlatGihAwcOaPTo0erUqZMj5AAAgD83l4abHj166MSJExozZoxSUlIUEhKitWvXOi4yPnLkiNNKzahRo2Sz2TRq1CgdO3ZM1apVU6dOnfTyyy+76iMAAIBSxmb9yc7nZGRkyNvbW+np6apYsWKJzx8Yu7rE5wQA4GaSPKljic9ZlO/vm+puKQAAgCsh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYxeXhZubMmQoMDJSnp6datGihLVu2XLb/mTNnNGTIENWoUUN2u1233Xab1qxZc4OqBQAApV0ZV+58yZIliomJ0axZs9SiRQtNmzZNERER2rdvn6pXr16gf3Z2tu677z5Vr15dy5YtU82aNfXTTz+pUqVKN754AABQKrk03EydOlWDBg1Sv379JEmzZs3S6tWrNW/ePMXGxhboP2/ePJ06dUqbN29W2bJlJUmBgYE3smQAAFDKuey0VHZ2trZt26bw8PD/FuPmpvDwcCUmJhY6ZtWqVQoLC9OQIUPk6+urhg0bauLEicrNzb3kfrKyspSRkeH0AgAA5nJZuElLS1Nubq58fX2d2n19fZWSklLomEOHDmnZsmXKzc3VmjVrNHr0aE2ZMkUvvfTSJfcTFxcnb29vxysgIKBEPwcAAChdXH5BcVHk5eWpevXqevvttxUaGqoePXrohRde0KxZsy45ZuTIkUpPT3e8jh49egMrBgAAN5rLrrnx8fGRu7u7UlNTndpTU1Pl5+dX6JgaNWqobNmycnd3d7TdcccdSklJUXZ2tjw8PAqMsdvtstvtJVs8AAAotVy2cuPh4aHQ0FAlJCQ42vLy8pSQkKCwsLBCx7Rq1UoHDhxQXl6eo+0///mPatSoUWiwAQAAfz4uPS0VExOj2bNna+HChdqzZ48ee+wxZWZmOu6eio6O1siRIx39H3vsMZ06dUpPPvmk/vOf/2j16tWaOHGihgwZ4qqPAAAAShmX3greo0cPnThxQmPGjFFKSopCQkK0du1ax0XGR44ckZvbf/NXQECA1q1bp+HDh6tx48aqWbOmnnzySY0YMcJVHwEAAJQyNsuyLFcXcSNlZGTI29tb6enpqlixYonPHxi7usTnBADgZpI8qWOJz1mU7++b6m4pAACAKyHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGCUawo32dnZ2rdvn3JyckqqHgAAgGtSrHDz66+/asCAASpfvrzuvPNOHTlyRJI0bNgwTZo0qUQLBAAAKIpihZuRI0dqx44d+vzzz+Xp6eloDw8P15IlS0qsOAAAgKIqU5xB8fHxWrJkie666y7ZbDZH+5133qmDBw+WWHEAAABFVayVmxMnTqh69eoF2jMzM53CDgAAwI1WrHDTtGlTrV692vE+P9DMmTNHYWFhJVMZAABAMRTrtNTEiRPVvn177d69Wzk5OXrttde0e/dubd68WV988UVJ1wgAAHDVirVy07p1a+3YsUM5OTlq1KiR1q9fr+rVqysxMVGhoaElXSMAAMBVK/LKze+//65HH31Uo0eP1uzZs69HTQAAAMVW5JWbsmXLavny5dejFgAAgGtWrNNSkZGRio+PL+FSAAAArl2xLiiuV6+eJkyYoG+++UahoaHy8vJy2v7EE0+USHEAAABFVaxwM3fuXFWqVEnbtm3Ttm3bnLbZbDbCDQAAcJlihZvDhw+XdB0AAAAl4pp+FVySLMuSZVklUQsAAMA1K3a4eeedd9SoUSOVK1dO5cqVU+PGjfXuu++WZG0AAABFVqzTUlOnTtXo0aM1dOhQtWrVSpL09ddfa/DgwUpLS9Pw4cNLtEgAAICrVaxwM336dL355puKjo52tD344IO68847NW7cOMINAABwmWKdlvrll1/UsmXLAu0tW7bUL7/8cs1FAQAAFFexwk3dunX14YcfFmhfsmSJ6tWrd81FAQAAFFexTkuNHz9ePXr00Jdffum45uabb75RQkJCoaEHAADgRinWyk3Xrl313XffycfHR/Hx8YqPj5ePj4+2bNmizp07l3SNAAAAV61YKzeSFBoaqkWLFpVkLQAAANesWCs3a9as0bp16wq0r1u3Tp9++uk1FwUAAFBcxQo3sbGxys3NLdBuWZZiY2OvuSgAAIDiKla42b9/vxo0aFCgvX79+jpw4MA1FwUAAFBcxQo33t7eOnToUIH2AwcOyMvL65qLAgAAKK5ihZu///3veuqpp3Tw4EFH24EDB/T000/rwQcfLLHiAAAAiqpY4eaVV16Rl5eX6tevrzp16qhOnTqqX7++qlatqldffbWkawQAALhqxboV3NvbW5s3b9aGDRu0Y8cOlStXTsHBwWrTpk1J1wcAAFAkRVq5SUxM1CeffCJJstlsuv/++1W9enW9+uqr6tq1qx555BFlZWVdl0IBAACuRpHCzYQJE/Tvf//b8X7nzp0aNGiQ7rvvPsXGxurjjz9WXFxciRcJAABwtYoUbpKSknTvvfc63n/wwQdq3ry5Zs+erZiYGL3++uv8thQAAHCpIoWb06dPy9fX1/H+iy++UPv27R3vmzVrpqNHj5ZcdQAAAEVUpHDj6+urw4cPS5Kys7O1fft23XXXXY7tZ8+eVdmyZUu2QgAAgCIoUrjp0KGDYmNj9dVXX2nkyJEqX7680x1SP/74o4KCgkq8SAAAgKtVpFvBX3zxRXXp0kVt27ZVhQoVtHDhQnl4eDi2z5s3T/fff3+JFwkAAHC1ihRufHx89OWXXyo9PV0VKlSQu7u70/alS5eqQoUKJVogAABAURT7IX6FqVKlyjUVAwAAcK2K9fMLAAAApRXhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMUirCzcyZMxUYGChPT0+1aNFCW7ZsuapxH3zwgWw2myIjI69vgQAA4Kbh8nCzZMkSxcTEaOzYsdq+fbuCg4MVERGh48ePX3ZccnKynnnmGbVp0+YGVQoAAG4GLg83U6dO1aBBg9SvXz81aNBAs2bNUvny5TVv3rxLjsnNzdXDDz+s8ePH69Zbb73s/FlZWcrIyHB6AQAAc7k03GRnZ2vbtm0KDw93tLm5uSk8PFyJiYmXHDdhwgRVr15dAwYMuOI+4uLi5O3t7XgFBASUSO0AAKB0cmm4SUtLU25urnx9fZ3afX19lZKSUuiYr7/+WnPnztXs2bOvah8jR45Uenq643X06NFrrhsAAJReZVxdQFGcPXtWvXv31uzZs+Xj43NVY+x2u+x2+3WuDAAAlBYuDTc+Pj5yd3dXamqqU3tqaqr8/PwK9D948KCSk5PVqVMnR1teXp4kqUyZMtq3b5+CgoKub9EAAKBUc+lpKQ8PD4WGhiohIcHRlpeXp4SEBIWFhRXoX79+fe3cuVNJSUmO14MPPqh27dopKSmJ62kAAIDrT0vFxMSoT58+atq0qZo3b65p06YpMzNT/fr1kyRFR0erZs2aiouLk6enpxo2bOg0vlKlSpJUoB0AAPw5uTzc9OjRQydOnNCYMWOUkpKikJAQrV271nGR8ZEjR+Tm5vI71gEAwE3CZlmW5eoibqSMjAx5e3srPT1dFStWLPH5A2NXl/icAADcTJIndSzxOYvy/c2SCAAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAopSLczJw5U4GBgfL09FSLFi20ZcuWS/adPXu22rRpo8qVK6ty5coKDw+/bH8AAPDn4vJws2TJEsXExGjs2LHavn27goODFRERoePHjxfa//PPP1dUVJQ2bdqkxMREBQQE6P7779exY8ducOUAAKA0slmWZbmygBYtWqhZs2aaMWOGJCkvL08BAQEaNmyYYmNjrzg+NzdXlStX1owZMxQdHV1ge1ZWlrKyshzvMzIyFBAQoPT0dFWsWLHkPsj/C4xdXeJzAgBwM0me1LHE58zIyJC3t/dVfX+7dOUmOztb27ZtU3h4uKPNzc1N4eHhSkxMvKo5fv31V/3++++qUqVKodvj4uLk7e3teAUEBJRI7QAAoHRyabhJS0tTbm6ufH19ndp9fX2VkpJyVXOMGDFC/v7+TgHpYiNHjlR6errjdfTo0WuuGwAAlF5lXF3AtZg0aZI++OADff755/L09Cy0j91ul91uv8GVAQAAV3FpuPHx8ZG7u7tSU1Od2lNTU+Xn53fZsa+++qomTZqkjRs3qnHjxtezTAAAcBNx6WkpDw8PhYaGKiEhwdGWl5enhIQEhYWFXXLcK6+8ohdffFFr165V06ZNb0SpAADgJuHy01IxMTHq06ePmjZtqubNm2vatGnKzMxUv379JEnR0dGqWbOm4uLiJEn//Oc/NWbMGC1evFiBgYGOa3MqVKigChUquOxzAACA0sHl4aZHjx46ceKExowZo5SUFIWEhGjt2rWOi4yPHDkiN7f/LjC9+eabys7OVrdu3ZzmGTt2rMaNG3cjSwcAAKWQy59zc6MV5T754uA5NwCAP7s/9XNuAAAAShrhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRSkW4mTlzpgIDA+Xp6akWLVpoy5Ytl+2/dOlS1a9fX56enmrUqJHWrFlzgyoFAAClncvDzZIlSxQTE6OxY8dq+/btCg4OVkREhI4fP15o/82bNysqKkoDBgzQDz/8oMjISEVGRmrXrl03uHIAAFAa2SzLslxZQIsWLdSsWTPNmDFDkpSXl6eAgAANGzZMsbGxBfr36NFDmZmZ+uSTTxxtd911l0JCQjRr1qwr7i8jI0Pe3t5KT09XxYoVS+6D/L/A2NUlPicAADeT5EkdS3zOonx/lynxvRdBdna2tm3bppEjRzra3NzcFB4ersTExELHJCYmKiYmxqktIiJC8fHxhfbPyspSVlaW4316erqkCwfpesjL+vW6zAsAwM3ienzH5s95NWsyLg03aWlpys3Nla+vr1O7r6+v9u7dW+iYlJSUQvunpKQU2j8uLk7jx48v0B4QEFDMqgEAwOV4T7t+c589e1be3t6X7ePScHMjjBw50mmlJy8vT6dOnVLVqlVls9lcWBmAkpaRkaGAgAAdPXr0upx2BuA6lmXp7Nmz8vf3v2Jfl4YbHx8fubu7KzU11ak9NTVVfn5+hY7x8/MrUn+73S673e7UVqlSpeIXDaDUq1ixIuEGMNCVVmzyufRuKQ8PD4WGhiohIcHRlpeXp4SEBIWFhRU6JiwszKm/JG3YsOGS/QEAwJ+Ly09LxcTEqE+fPmratKmaN2+uadOmKTMzU/369ZMkRUdHq2bNmoqLi5MkPfnkk2rbtq2mTJmijh076oMPPtDWrVv19ttvu/JjAACAUsLl4aZHjx46ceKExowZo5SUFIWEhGjt2rWOi4aPHDkiN7f/LjC1bNlSixcv1qhRo/T888+rXr16io+PV8OGDV31EQCUEna7XWPHji1wKhrAn4vLn3MDAABQklz+hGIAAICSRLgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsA11ViYqLc3d3VsWPJ/0owABSGW8EBXFcDBw5UhQoVNHfuXO3bt++qfhfmesjOzpaHh4dL9g3gxmLlBsB1c+7cOS1ZskSPPfaYOnbsqAULFjht//jjj9WsWTN5enrKx8dHnTt3dmzLysrSiBEjFBAQILvdrrp162ru3LmSpAULFhT4jbj4+HinH8MdN26cQkJCNGfOHNWpU0eenp6SpLVr16p169aqVKmSqlatqgceeEAHDx50muvnn39WVFSUqlSpIi8vLzVt2lTfffedkpOT5ebmpq1btzr1nzZtmmrXrq28vLxrPWQASgDhBsB18+GHH6p+/fq6/fbb9Y9//EPz5s1T/mLx6tWr1blzZ3Xo0EE//PCDEhIS1Lx5c8fY6Ohovf/++3r99de1Z88evfXWW6pQoUKR9n/gwAEtX75cK1asUFJSkiQpMzNTMTEx2rp1qxISEuTm5qbOnTs7gsm5c+fUtm1bHTt2TKtWrdKOHTv03HPPKS8vT4GBgQoPD9f8+fOd9jN//nz17dvX6WnqAFzIAoDrpGXLlta0adMsy7Ks33//3fLx8bE2bdpkWZZlhYWFWQ8//HCh4/bt22dJsjZs2FDo9vnz51ve3t5ObStXrrQu/l/a2LFjrbJly1rHjx+/bI0nTpywJFk7d+60LMuy3nrrLeuWW26xTp48WWj/JUuWWJUrV7bOnz9vWZZlbdu2zbLZbNbhw4cvux8ANw7/zABwXezbt09btmxRVFSUJKlMmTLq0aOH49RSUlKS7r333kLHJiUlyd3dXW3btr2mGmrXrq1q1ao5te3fv19RUVG69dZbVbFiRQUGBkq68Dt2+ftu0qSJqlSpUuickZGRcnd318qVKyVdOEXWrl07xzwAXM/lP5wJwExz585VTk6O0wXElmXJbrdrxowZKleu3CXHXm6bJLm5uTlOb+X7/fffC/Tz8vIq0NapUyfVrl1bs2fPlr+/v/Ly8tSwYUNlZ2df1b49PDwUHR2t+fPnq0uXLlq8eLFee+21y44BcGOxcgOgxOXk5Oidd97RlClTlJSU5Hjt2LFD/v7+ev/999W4cWMlJCQUOr5Ro0bKy8vTF198Uej2atWq6ezZs8rMzHS05V9TczknT57Uvn37NGrUKN1777264447dPr0aac+jRs3VlJSkk6dOnXJeQYOHKiNGzfqjTfeUE5Ojrp06XLFfQO4cVi5AVDiPvnkE50+fVoDBgyQt7e307auXbtq7ty5mjx5su69914FBQWpZ8+eysnJ0Zo1azRixAgFBgaqT58+6t+/v15//XUFBwfrp59+0vHjx/XQQw+pRYsWKl++vJ5//nk98cQT+u677wrciVWYypUrq2rVqnr77bdVo0YNHTlyRLGxsU59oqKiNHHiREVGRiouLk41atTQDz/8IH9/f4WFhUmS7rjjDt11110aMWKE+vfvf8XVHgA3Fis3AErc3LlzFR4eXiDYSBfCzdatW1WlShUtXbpUq1atUkhIiP7nf/5HW7ZscfR788031a1bNz3++OOqX7++Bg0a5FipqVKlihYtWqQ1a9aoUaNGev/99zVu3Lgr1uXm5qYPPvhA27ZtU8OGDTV8+HBNnjzZqY+Hh4fWr1+v6tWrq0OHDmrUqJEmTZokd3d3p34DBgxQdna2+vfvX4wjBOB64iF+AFAML774opYuXaoff/zR1aUA+ANWbgCgCM6dO6ddu3ZpxowZGjZsmKvLAVAIwg0AFMHQoUMVGhqqe+65h1NSQCnFaSkAAGAUVm4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKP8H+5MGmKMjUGLAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "conf_matrix = np.array([[190, 0],\n",
+        "                        [2, 8]])\n",
+        "\n",
+        "plt.figure(figsize=(6,4))\n",
+        "plt.imshow(conf_matrix)\n",
+        "plt.title(\"Confusion Matrix Random Forest\")\n",
+        "plt.colorbar()\n",
+        "plt.xticks([0,1], [\"Pred 0\", \"Pred 1\"])\n",
+        "plt.yticks([0,1], [\"Actual 0\", \"Actual 1\"])\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 391
+        },
+        "id": "mV2_uYR1y5zj",
+        "outputId": "d70dbefb-0f3e-4d2b-b57d-50ee0959e3b1"
+      },
+      "execution_count": 254,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x400 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.arange(len(classes))\n",
+        "width = 0.25\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.bar(x - width, precision, width, label=\"Precision\")\n",
+        "plt.bar(x, recall, width, label=\"Recall\")\n",
+        "plt.bar(x + width, f1, width, label=\"F1-Score\")\n",
+        "\n",
+        "plt.xticks(x, classes)\n",
+        "plt.ylabel(\"Score\")\n",
+        "plt.title(\"Classification Metrics per Class\")\n",
+        "plt.legend()\n",
+        "plt.ylim(0, 1)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "OgLQduE6y85d",
+        "outputId": "28b910bf-caed-497a-cf1e-8e22ef8bf04f"
+      },
+      "execution_count": 255,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cv_scores = [0.99, 1.00, 0.97, 0.99, 1.00]\n",
+        "\n",
+        "plt.figure(figsize=(7,4))\n",
+        "plt.plot(cv_scores, marker='o')\n",
+        "plt.title(\"Cross Validation Scores (Random Forest)\")\n",
+        "plt.xlabel(\"Fold\")\n",
+        "plt.ylabel(\"Score\")\n",
+        "plt.ylim(0,1)\n",
+        "plt.grid(True)\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "EhayQ2eQyyIL",
+        "outputId": "d603d162-79b2-43a2-9ea7-883d79872f7d"
+      },
+      "execution_count": 256,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 700x400 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "logreg = LogisticRegression(max_iter=2000)\n",
+        "logreg.fit(X_train, y_train)\n",
+        "\n",
+        "pred_lr = logreg.predict(X_test)\n",
+        "\n",
+        "print(\"\\n=== Logistic Regression Accuracy ===\")\n",
+        "print(accuracy_score(y_test, pred_lr))\n",
+        "print(\"\\n=== Classification Report ===\")\n",
+        "print(classification_report(y_test, pred_lr))\n",
+        "print(\"\\n=== Confusion Matrix ===\")\n",
+        "print(confusion_matrix(y_test, pred_lr))\n",
+        "\n",
+        "cv_lr = cross_val_score(logreg, X, y, cv=5)\n",
+        "print(\"\\n=== Cross Validation (Logistic Regression) ===\")\n",
+        "print(cv_lr)\n",
+        "print(\"Mean CV Score:\", cv_lr.mean())\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8pScuIEBxcuQ",
+        "outputId": "5b2f8e01-fd44-45c4-993a-a1e2ac452270"
+      },
+      "execution_count": 257,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "=== Logistic Regression Accuracy ===\n",
+            "0.985\n",
+            "\n",
+            "=== Classification Report ===\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.98      1.00      0.99       190\n",
+            "           1       1.00      0.70      0.82        10\n",
+            "\n",
+            "    accuracy                           0.98       200\n",
+            "   macro avg       0.99      0.85      0.91       200\n",
+            "weighted avg       0.99      0.98      0.98       200\n",
+            "\n",
+            "\n",
+            "=== Confusion Matrix ===\n",
+            "[[190   0]\n",
+            " [  3   7]]\n",
+            "\n",
+            "=== Cross Validation (Logistic Regression) ===\n",
+            "[0.975 0.98  0.965 0.99  1.   ]\n",
+            "Mean CV Score: 0.982\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure()\n",
+        "plt.bar([\"Accuracy\"], [accuracy])\n",
+        "plt.title(\"Logistic Regression Accuracy\")\n",
+        "plt.ylabel(\"Score\")\n",
+        "plt.ylim(0, 1)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "dLeeMyia2oWm",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "outputId": "28b7a674-314d-4508-fc5f-a39ae49a3e2f"
+      },
+      "execution_count": 258,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure()\n",
+        "plt.imshow(conf_matrix)\n",
+        "plt.title(\"Confusion Matrix Logistic Regression\")\n",
+        "plt.colorbar()\n",
+        "plt.xticks([0,1], [\"Pred 0\", \"Pred 1\"])\n",
+        "plt.yticks([0,1], [\"Actual 0\", \"Actual 1\"])\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "uqY9L5R82sCw",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "outputId": "5cf3860f-533e-465f-f627-e03d4f812796"
+      },
+      "execution_count": 259,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.arange(len(classes))\n",
+        "width = 0.25\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.bar(x - width, precision, width, label=\"Precision\")\n",
+        "plt.bar(x, recall, width, label=\"Recall\")\n",
+        "plt.bar(x + width, f1, width, label=\"F1-Score\")\n",
+        "plt.xticks(x, classes)\n",
+        "plt.ylabel(\"Score\")\n",
+        "plt.title(\"Classification Metrics per Class (Logistic Regression)\")\n",
+        "plt.legend()\n",
+        "plt.ylim(0, 1)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "5XsjRubu21tt",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "outputId": "ea06da93-3ca1-4c85-9de7-d9c02fef861d"
+      },
+      "execution_count": 260,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAGzCAYAAADT4Tb9AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAR+dJREFUeJzt3XlYFeX///HXAdkRUBFwQUBTUSMXXDI1MxdccslMzUqk1BZxoyytXMssSzOXcs8WTVM/mbmmFFlqWS7lkuYCaiW477LP749+nG9HUAGRg+PzcV1el+eee2bec87M4XVm7jnHYhiGIQAAAJNwsHcBAAAABYlwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwcxsIDg5Wr1697Lb+Xr16KTg42Kbt4sWL6t27twICAmSxWDRo0CAlJCTIYrFo3rx5hV7jAw88oAceeKDQ11tUxcXFyWKxKC4uzt6lFEn23Fdzo23bturTp4+9y8jx2L8ZHKcFrzD+PnTv3l1du3a9pesoaIQbOzp48KCeeeYZVaxYUa6urvLy8lKjRo30/vvv68qVK/Yu77refPNNzZs3T88995w+/fRTPfnkk7d8nXv27NGoUaOUkJBwy9eVW1khwmKx6LPPPsuxT6NGjWSxWHT33Xfnax0LFizQpEmTbqLKO0tcXJw6d+6sgIAAOTs7y8/PT+3bt9f//vc/e5eWKxs3btQ333yjl19+2dqWtZ8tWbLEjpXlTmEdpw888ID12LNYLHJzc9M999yjSZMmKTMz85au+07z8ssva+nSpfrtt9/sXUruGbCLFStWGG5uboaPj48xYMAAY+bMmcbUqVON7t27G05OTkafPn2sfYOCgozIyEi71ZqammokJyfbtDVo0MBo1KiRTVtmZqZx5coVIz09/ZbUsXjxYkOS8d1332WblpKSYqSkpNyS9V7Pd999Z0gyXF1djTZt2mSbHh8fb51eo0aNfK2jXbt2RlBQUJ7mycjIMK5cuWJkZGTka523qxEjRhiSjMqVKxsjRoww5syZY4wfP9544IEHDEnG/PnzDcP4v9flo48+sm/BOejYsaPRqlUrm7as/Wzx4sWFWktOx/6NFNZx2rRpU6N8+fLGp59+anz66afGe++9Z9SrV8+QZLzyyisFso7bQXJyspGamnrL11O/fn3jySefvOXrKSjF7Jaq7mDx8fHq3r27goKC9O2336pMmTLWaf369dOBAwe0cuVKO1Zoy8nJKVvb8ePHVb16dZs2i8UiV1fXwirLhrOzs13Wm6Vt27Zavny5Tp48KV9fX2v7ggUL5O/vr8qVK+vMmTO3vI7k5GQ5OzvLwcHBbq/FrWIYhpKTk+Xm5pbj9CVLlmjMmDHq0qWLFixYYLPfDhkyRGvXrlVaWlphlZsvx48f18qVKzV9+nR7lyIp52P/ZhT0cert7a0nnnjC+vjZZ59VaGiopkyZojFjxsjR0bFA13c9/z32CpOLi0uhrKdr164aOXKkPvjgA3l6ehbKOm8Gl6XsYPz48bp48aLmzJljE2yy3HXXXRo4cOA15z99+rRefPFFhYWFydPTU15eXmrTpk2OpwynTJmiGjVqyN3dXSVKlFDdunW1YMEC6/QLFy5o0KBBCg4OlouLi/z8/NSyZUtt27bN2ue/192zTo/Hx8dr5cqV1lPCCQkJ1xzHsHfvXnXt2lWlS5eWm5ubqlatqldffdU6/fDhw3r++edVtWpVubm5qVSpUnr00UdtTmvPmzdPjz76qCSpWbNm1vVmjSnJ6Vr+8ePH9fTTT8vf31+urq6qWbOmPv74Y5s+WTW/++67mjlzpipVqiQXFxfVq1dPv/zyyzVfg6t17NhRLi4uWrx4sU37ggUL1LVr12u+yX722WcKDw+Xm5ubSpYsqe7du+vo0aPW6Q888IBWrlypw4cPW7f56tdi4cKFeu2111SuXDm5u7vr/Pnz1xxz8/PPP6tt27YqUaKEPDw8dM899+j999+3Tk9MTFRUVJTKly8vFxcXlSlTRh07drzhJYZevXrJ09NThw4dUkREhDw8PFS2bFmNGTNGhmHY9M3MzNSkSZNUo0YNubq6yt/fX88880y28BccHKyHHnpIa9euVd26deXm5qYZM2Zcs4bhw4erZMmSmjt3bo5/lCMiIvTQQw9dc/7ff/9dvXr1sl4mDggI0FNPPaVTp07Z9MvNMbN//3498sgjCggIkKurq8qXL6/u3bvr3Llz130eV65cqfT0dLVo0eK6/a7l0KFDevTRR1WyZEm5u7vr3nvvzfGD0uHDh9WhQwd5eHjIz89PgwcP1tq1a7PtMzmNuVm4cKHCw8NVvHhxeXl5KSwszLoP5ec4TU5O1qhRo1SlShW5urqqTJky6ty5sw4ePJjn7Xd1dVW9evV04cIFHT9+3GbajY61LNOmTVPFihXl5uam+vXr64cffshW9/WOPenf46x169by9vaWu7u7mjZtqo0bN9qsp6D2o5zG3ORmP8jahi+++EJjx45V+fLl5erqqubNm+vAgQPZnpeWLVvq0qVLWrdu3fVfhCKCMzd28PXXX6tixYq677778jX/oUOHtGzZMj366KMKCQlRUlKSZsyYoaZNm2rPnj0qW7asJGnWrFkaMGCAunTpooEDByo5OVm///67fv75Z/Xo0UPSv590lixZoujoaFWvXl2nTp3Sjz/+qD/++EN16tTJtu5q1arp008/1eDBg1W+fHm98MILkqTSpUvrxIkT2fr//vvvatKkiZycnNS3b18FBwfr4MGD+vrrrzV27FhJ0i+//KJNmzape/fuKl++vBISEvThhx/qgQce0J49e+Tu7q77779fAwYM0OTJk/XKK6+oWrVq1npycuXKFT3wwAM6cOCAoqOjFRISosWLF6tXr146e/ZstvC4YMECXbhwQc8884wsFovGjx+vzp0769ChQ7n69Oru7q6OHTvq888/13PPPSdJ+u2337R7927Nnj1bv//+e7Z5xo4dq+HDh6tr167q3bu3Tpw4oSlTpuj+++/X9u3b5ePjo1dffVXnzp3TX3/9pffee0+Ssn1qev311+Xs7KwXX3xRKSkp1/x0vG7dOj300EMqU6aMBg4cqICAAP3xxx9asWKF9fl45JFHtHv3bvXv31/BwcE6fvy41q1bpyNHjtxwYGlGRoZat26te++9V+PHj9eaNWs0cuRIpaena8yYMdZ+zzzzjObNm6eoqCgNGDBA8fHxmjp1qrZv366NGzfaPN/79u3TY489pmeeeUZ9+vRR1apVc1z3/v37tXfvXj311FMqXrz4deu8lnXr1unQoUOKiopSQECAdu/erZkzZ2r37t366aefZLFYJN34mElNTVVERIRSUlLUv39/BQQE6O+//9aKFSt09uxZeXt7X7OGTZs2qVSpUgoKCspz/UlJSbrvvvt0+fJlDRgwQKVKldLHH3+sDh06aMmSJXr44YclSZcuXdKDDz6oY8eOWfeDBQsW6LvvvsvVc/TYY4+pefPmevvttyVJf/zxhzZu3KiBAwfm+TjNyMjQQw89pNjYWHXv3l0DBw7UhQsXtG7dOu3atUuVKlXK8/OQ9YHFx8fH2pabY02SPvzwQ0VHR6tJkyYaPHiwEhIS1KlTJ5UoUULly5fPtq6cjr1vv/1Wbdq0UXh4uEaOHCkHBwd99NFHevDBB/XDDz+ofv36km7dfpTb/SDLW2+9JQcHB7344os6d+6cxo8fr8cff1w///yzTb/q1avLzc1NGzduzLaMIsne18XuNOfOnTMkGR07dsz1PFePuUlOTs42liI+Pt5wcXExxowZY23r2LHjDcd5eHt7G/369btun8jIyGxjPoKCgox27dplq0FXjWO4//77jeLFixuHDx+26ZuZmWn9/+XLl7Otc/PmzYYk45NPPrG2Xe9aftOmTY2mTZtaH0+aNMmQZHz22WfWttTUVKNhw4aGp6encf78eZuaS5UqZZw+fdra96uvvjIkGV9//XX2J+Q//jsWYsWKFYbFYjGOHDliGIZhDBkyxKhYsaK1vv++FgkJCYajo6MxduxYm+Xt3LnTKFasmE37tcbcZK27YsWK2Z7DrGlZz1V6eroREhJiBAUFGWfOnLHpm/VanDlzxpBkvPPOO9fd5pxERkYakoz+/fvbLLddu3aGs7OzceLECcMwDOOHH36wGfuSZc2aNdnag4KCDEnGmjVrbrj+rNfrvffey1W9Oe2rOe2Hn3/+uSHJ2LBhg7XtRsfM9u3b8z0+pnHjxkZ4eHi29tyMuRk0aJAhyfjhhx+sbRcuXDBCQkKM4OBg63vGhAkTDEnGsmXLrP2uXLlihIaGZju+rj72Bw4caHh5eV13XF1ejtO5c+cakoyJEydm6/vf94icNG3a1AgNDTVOnDhhnDhxwti7d68xZMgQQ5LNe1Nuj7WUlBSjVKlSRr169Yy0tDRrv3nz5hmSbOq+1rGXmZlpVK5c2YiIiMj2HhcSEmK0bNnS2lZQ+9HVfx9yux9kbUO1atVsxkG9//77hiRj586d2dZVpUqVHMcWFkVclipkWact8/vpUvr3GmvWdd2MjAydOnVKnp6eqlq1qs0pTR8fH/3111/Xvbzi4+Ojn3/+Wf/880++67mWEydOaMOGDXrqqadUoUIFm2lZn4Il2YyhSEtL06lTp3TXXXfJx8fHZnvyYtWqVQoICNBjjz1mbXNyctKAAQN08eJFff/99zb9u3XrphIlSlgfN2nSRNK/Z8lyq1WrVipZsqQWLlwowzC0cOFCm/X/1//+9z9lZmaqa9euOnnypPVfQECAKleunKtP0VkiIyOvOQ4ly/bt2xUfH69BgwbZfKKV/u+1cHNzk7Ozs+Li4vI9Pig6OtpmudHR0UpNTdX69eslSYsXL5a3t7datmxps93h4eHy9PTMtt0hISGKiIi44XoL4rj673OYnJyskydP6t5775WkbMfV9Y6ZrE/Ua9eu1eXLl/NUw6lTp2z2w7xYtWqV6tevr8aNG1vbPD091bdvXyUkJGjPnj2SpDVr1qhcuXLq0KGDtZ+rq2uubj338fEp0EsTS5cula+vr/r3759t2n/fI65l7969Kl26tEqXLq3Q0FC988476tChg82l8dwea7/++qtOnTqlPn36qFix/7uo8fjjj1/zNbn62NuxY4f279+vHj166NSpU9Z1Xbp0Sc2bN9eGDRusd3Ldqv0ot/tBlqioKJuzvdd77ytRooROnjyZ61rsiXBTyLy8vCT9e701vzIzM/Xee++pcuXKcnFxka+vr0qXLq3ff//d5lrsyy+/LE9PT9WvX1+VK1dWv379sl33HT9+vHbt2qXAwEDVr19fo0aNytMf9OvJWs6NboG+cuWKRowYocDAQJvtOXv27A3HKFzL4cOHVbly5WyD+7JOjx8+fNim/erwlfVmlpc/8k5OTnr00Ue1YMECbdiwQUePHrVe/rva/v37ZRiGKleubH1zzvr3xx9/ZBsvcD0hISE37JM1fuF6r4WLi4vefvttrV69Wv7+/rr//vs1fvx4JSYm5qoOBwcHVaxY0aatSpUqkmQds7N//36dO3dOfn5+2bb74sWL2bY7N9smFcxxdfr0aQ0cOFD+/v5yc3NT6dKlrev/7354o2MmJCREMTExmj17tnx9fRUREaFp06blel82rhqjlFuHDx/O8bLd1fv84cOHValSpWzh4a677rrhOp5//nlVqVJFbdq0Ufny5fXUU09pzZo1+apX+ne/rFq1qk2YyIvg4GCtW7dOa9eu1QcffKBy5crpxIkTNoPpc3usZT0/Vz8PxYoVu+Yl2av3z/3790v6N/Rcva7Zs2crJSXFuh/cqv0ot/tBlry89xmGkavQWRQw5qaQeXl5qWzZstq1a1e+l/Hmm29q+PDheuqpp/T666+rZMmScnBw0KBBg2y+36FatWrat2+fVqxYoTVr1mjp0qX64IMPNGLECI0ePVrSvyPgmzRpoi+//FLffPON3nnnHb399tv63//+pzZt2tz09uZG//799dFHH2nQoEFq2LChvL29ZbFY1L1790L7voprDfjN6x+aHj16aPr06Ro1apRq1qyZ7Y6yLJmZmbJYLFq9enWO687L3Qg3OmuTF4MGDVL79u21bNkyrV27VsOHD9e4ceP07bffqnbt2je9/MzMTPn5+Wn+/Pk5Ti9durTN49xuW2hoqCRp586d+a6ta9eu2rRpk4YMGaJatWrJ09NTmZmZat26tc1+mJtjZsKECerVq5e++uorffPNNxowYIDGjRunn376KcexG1lKlSpVKHfV5Zefn5927NihtWvXavXq1Vq9erU++ugj9ezZM9tg/cLg4eFhM/i6UaNGqlOnjl555RVNnjxZUsEea1e7ev/M2k/eeecd1apVK8d5stZ3K/ejvMjLe9+ZM2dUuXLlAlnvrUa4sYOHHnpIM2fO1ObNm9WwYcM8z79kyRI1a9ZMc+bMsWk/e/aszW3I0r8Hf7du3dStWzelpqaqc+fOGjt2rIYNG2b9dFOmTBk9//zzev7553X8+HHVqVNHY8eOvelwk/Up/kZBbsmSJYqMjNSECROsbcnJyTp79qxNv7x8YggKCtLvv/+uzMxMm7M3e/futU6/FRo3bqwKFSooLi7OOuAyJ5UqVZJhGAoJCbGe3biWgviklDUwc9euXTe8E6dSpUp64YUX9MILL2j//v2qVauWJkyYcM0vKcySmZmpQ4cO2WzPn3/+KUnWT76VKlXS+vXr1ahRowINZVWqVFHVqlX11Vdf6f3338/zH6wzZ84oNjZWo0eP1ogRI6ztWZ/Er5abYyYsLExhYWF67bXXtGnTJjVq1EjTp0/XG2+8cc06QkNDtXTp0jzVniUoKEj79u3L1n71Ph8UFKQ9e/Zk+xSe0x0yOXF2dlb79u3Vvn17ZWZm6vnnn9eMGTM0fPhw3XXXXXnaXytVqqSff/5ZaWlpBXLb+T333KMnnnhCM2bM0IsvvqgKFSrk+ljLen4OHDigZs2aWdvT09OVkJCge+65J1fbI/37ITY3d7zdiv0ot/tBXqWnp+vo0aM2lzOLMi5L2cFLL70kDw8P9e7dW0lJSdmmHzx40Ob23Ks5OjpmS9WLFy/W33//bdN29S2szs7Oql69ugzDUFpamjIyMrKd4vTz81PZsmWVkpKS183KpnTp0rr//vs1d+5cHTlyxGbaf+vPaXumTJmijIwMmzYPDw9JyhZ6ctK2bVslJiZq0aJF1rb09HRNmTJFnp6eatq0aV43J1csFosmT56skSNHXvdbmzt37ixHR0eNHj0627YbhmHz2nl4eOT78lyWOnXqKCQkRJMmTcr2/GWt//Lly0pOTraZVqlSJRUvXjzX+8PUqVNtljt16lQ5OTmpefPmkv79tJqRkaHXX38927zp6em5em2vZfTo0Tp16pR69+6t9PT0bNO/+eYbrVixIsd5sz69Xv1aXP3N0Lk5Zs6fP59t/WFhYXJwcLjh89iwYUOdOXMmX5eG27Ztqy1btmjz5s3WtkuXLmnmzJkKDg62nkWMiIjQ33//reXLl1v7JScna9asWTdcx9XvKQ4ODtY/+lnblpfj9JFHHtHJkydt9pss+b0899JLLyktLU0TJ06UlPtjrW7duipVqpRmzZpl8/rNnz8/12fTwsPDValSJb377ru6ePFitulZd5Teyv0ot/tBXu3Zs0fJycn5vsu3sHHmxg4qVaqkBQsWqFu3bqpWrZp69uypu+++W6mpqdq0aZP1luVreeihhzRmzBhFRUXpvvvu086dOzV//vxs4x1atWqlgIAANWrUSP7+/vrjjz80depUtWvXTsWLF9fZs2dVvnx5denSRTVr1pSnp6fWr1+vX375xeYsys2YPHmyGjdurDp16qhv374KCQlRQkKCVq5cqR07dli359NPP5W3t7eqV6+uzZs3a/369SpVqpTNsmrVqiVHR0e9/fbbOnfunFxcXPTggw/Kz88v23r79u2rGTNmqFevXtq6dauCg4O1ZMkSbdy4UZMmTbqpgac30rFjR3Xs2PG6fSpVqqQ33nhDw4YNs95uWrx4ccXHx+vLL79U37599eKLL0r69w1z0aJFiomJUb169eTp6an27dvnqSYHBwd9+OGHat++vWrVqqWoqCiVKVNGe/fu1e7du7V27Vr9+eefat68ubp27arq1aurWLFi+vLLL5WUlKTu3bvfcB2urq5as2aNIiMj1aBBA61evVorV67UK6+8Yr3c1LRpUz3zzDMaN26cduzYoVatWsnJyUn79+/X4sWL9f7776tLly552rYs3bp1086dOzV27Fht375djz32mIKCgnTq1CmtWbNGsbGxNt/x9F9eXl7WMUZpaWkqV66cvvnmG8XHx9v0u3Dhwg2PmW+//VbR0dF69NFHVaVKFaWnp+vTTz+Vo6OjHnnkketuQ7t27VSsWDGtX79effv2zTZ96dKl1k/g/xUZGamhQ4fq888/V5s2bTRgwACVLFlSH3/8seLj47V06VLrGcxnnnlGU6dO1WOPPaaBAweqTJkymj9/vvVM7vXOvPTu3VunT5/Wgw8+qPLly+vw4cOaMmWKatWqZR3TkZfjtGfPnvrkk08UExOjLVu2qEmTJrp06ZLWr1+v559//obHUU6qV6+utm3bavbs2Ro+fHiujzVnZ2eNGjVK/fv314MPPqiuXbsqISFB8+bNy3GMUk4cHBw0e/ZstWnTRjVq1FBUVJTKlSunv//+W9999528vLz09ddf39L9KLf7QV6tW7dO7u7uatmyZb7mL3SFeWsWbP35559Gnz59jODgYMPZ2dkoXry40ahRI2PKlCk2X3me063gL7zwglGmTBnDzc3NaNSokbF58+Zst1nOmDHDuP/++41SpUoZLi4uRqVKlYwhQ4YY586dMwzj31sfhwwZYtSsWdMoXry44eHhYdSsWdP44IMPbOq8mVvBDcMwdu3aZTz88MOGj4+P4erqalStWtUYPny4dfqZM2eMqKgow9fX1/D09DQiIiKMvXv35vizE7NmzTIqVqxoODo62txuevW2G4ZhJCUlWZfr7OxshIWFZastq+acbn+WZIwcOTJb+3/l9mvxr74VPMvSpUuNxo0bGx4eHoaHh4cRGhpq9OvXz9i3b5+1z8WLF40ePXoYPj4+hiTra3G9dV99K3iWH3/80WjZsqX19b7nnnuMKVOmGIZhGCdPnjT69etnhIaGGh4eHoa3t7fRoEED44svvrjuthnGv/uIh4eHcfDgQaNVq1aGu7u74e/vb4wcOTLHn4CYOXOmER4ebri5uRnFixc3wsLCjJdeesn4559/rH1y2sdyIzY21ujYsaPh5+dnFCtWzChdurTRvn1746uvvrL2yWlf/euvv6z7qbe3t/Hoo48a//zzj81+kJtj5tChQ8ZTTz1lVKpUyXB1dTVKlixpNGvWzFi/fn2u6u/QoYPRvHlzm7as1/Na/7Ju+z148KDRpUsX67FWv359Y8WKFdnWcejQIaNdu3aGm5ubUbp0aeOFF14wli5dakgyfvrpJ2u/q4/9JUuWGK1atTL8/PwMZ2dno0KFCsYzzzxjHDt2zGb5eTlOL1++bLz66qtGSEiI4eTkZAQEBBhdunQxDh48eN3n6VrHlGEYRlxcXLbjNzfHmmEYxuTJk42goCDDxcXFqF+/vrFx40YjPDzcaN26tbXPjY777du3G507d7a+9wYFBRldu3Y1YmNjDcMo2P0op/fJ3OwH19qGa72PN2jQwHjiiSdy3N6iyGIY+Tz3BwD/X69evbRkyZIcT8Ujb7K+EXfv3r2FOnhz0qRJGjx4sP766y+VK1eu0NZb1GVmZqp06dLq3Llzri7dmdGOHTtUp04dbdu27ZoDpYsaxtwAQBHSpEkTtWrVSuPHj79l67hy5YrN4+TkZM2YMUOVK1e+o4NNcnJytnE5n3zyiU6fPp3tZyPuJG+99Za6dOly2wQbiTE3AFDkrF69+pYuv3PnzqpQoYJq1aqlc+fO6bPPPtPevXuveYv+neKnn37S4MGD9eijj6pUqVLatm2b5syZo7vvvtv6m1l3ooULF9q7hDwj3ADAHSYiIkKzZ8/W/PnzlZGRoerVq2vhwoXq1q2bvUuzq+DgYAUGBmry5Mk6ffq0SpYsqZ49e+qtt94q8F80x61l1zE3GzZs0DvvvKOtW7fq2LFj+vLLL9WpU6frzhMXF6eYmBjt3r1bgYGBeu211657ZxEAALiz2HXMzaVLl1SzZk1NmzYtV/3j4+PVrl07NWvWTDt27NCgQYPUu3dvrV279hZXCgAAbhdF5m4pi8VywzM3L7/8slauXGnzjbfdu3fX2bNnb+r3TQAAgHncVmNuNm/enO0rrSMiIjRo0KBrzpOSkmLzbY6ZmZk6ffq0SpUqddv8ABgAAHc6wzB04cIFlS1b9oZfRnhbhZvExET5+/vbtPn7++v8+fO6cuVKjr9VM27cOOuPRAIAgNvb0aNHb/jDobdVuMmPYcOGKSYmxvr43LlzqlChgo4ePSovLy87VmZS4wrml2pva8P+sncFsKO7RzIGcNfoCHuXABM6f/68AgMDc/XzObdVuAkICMj2Q5NJSUny8vK65i8Mu7i4yMXFJVu7l5cX4eZWcOFSn9iv7mgOLu72LsHueG/FrZSbISW3Vbhp2LChVq1aZdO2bt06NWzY0E4VZRc8dKW9S7CrBFd7V2B/YR+H2bsEu9oZudPeJQC4w9n1VvCLFy9qx44d1l+Hjo+P144dO3TkyBFJ/15S6tmzp7X/s88+q0OHDumll17S3r179cEHH+iLL77Q4MGD7VE+AAAoguwabn799VfVrl1btWvXliTFxMSodu3aGjFihCTp2LFj1qAjSSEhIVq5cqXWrVunmjVrasKECZo9e7YiIri+CwAA/mXXy1IPPPBAth8p+6958+blOM/27dtvYVUAgNudYRhKT09XRkaGvUtBHjg5OcnR0fGml3NbjbkBAOBGUlNTdezYMV2+fNnepSCPLBaLypcvL09Pz5taDuEGAGAamZmZio+Pl6Ojo8qWLStnZ2e+sPU2YRiGTpw4ob/++kuVK1e+qTM4hBsAgGmkpqYqMzNTgYGBcnfntvzbTenSpZWQkKC0tLSbCjd2HVAMAMCtcKOv50fRVFBn2Xj1AQCAqRBuAACAqTDmBgBwRyjsb5BPeKtdoa4vPywWi7788kt16tSpQPvaG2duAAAoAnr16iWLxSKLxSJnZ2fdddddGjNmjNLT02/ZOo8dO6Y2bdoUeF9748wNAABFROvWrfXRRx8pJSVFq1atUr9+/eTk5KRhw4bZ9EtNTZWzs/NNry8gIOCW9LU3ztwAAFBEuLi4KCAgQEFBQXruuefUokULLV++XL169VKnTp00duxYlS1bVlWrVpUkHT16VF27dpWPj49Kliypjh07KiEhwWaZc+fOVY0aNeTi4qIyZcooOjraOs1isWjZsmWS/g1M0dHRKlOmjFxdXRUUFKRx48bl2FeSdu7cqQcffFBubm4qVaqU+vbtq4sXL1qnZ9X87rvvqkyZMipVqpT69euntLS0gn/irkK4AQCgiHJzc1NqaqokKTY2Vvv27dO6deu0YsUKpaWlKSIiQsWLF9cPP/ygjRs3ytPTU61bt7bO8+GHH6pfv37q27evdu7cqeXLl+uuu+7KcV2TJ0/W8uXL9cUXX2jfvn2aP3++goODc+x76dIlRUREqESJEvrll1+0ePFirV+/3iY4SdJ3332ngwcP6rvvvtPHH3+sefPm5fjTSgWNy1IAABQxhmEoNjZWa9euVf/+/XXixAl5eHho9uzZ1stRn332mTIzMzV79mzr98N89NFH8vHxUVxcnFq1aqU33nhDL7zwggYOHGhddr169XJc55EjR1S5cmU1btxYFotFQUFB16xvwYIFSk5O1ieffCIPDw9J0tSpU9W+fXu9/fbb8vf3lySVKFFCU6dOlaOjo0JDQ9WuXTvFxsaqT58+BfI8XQtnbgAAKCJWrFghT09Pubq6qk2bNurWrZtGjRolSQoLC7MZZ/Pbb7/pwIEDKl68uDw9PeXp6amSJUsqOTlZBw8e1PHjx/XPP/+oefPmuVp3r169tGPHDlWtWlUDBgzQN998c82+f/zxh2rWrGkNNpLUqFEjZWZmat++fda2GjVq2HzTcJkyZXT8+PHcPh35xpkbAACKiGbNmunDDz+Us7OzypYtq2LF/u/P9H+DhCRdvHhR4eHhmj9/frbllC5dOs/f0lynTh3Fx8dr9erVWr9+vbp27aoWLVpoyZIl+dsY/fsr3/9lsViUmZmZ7+XlFuEGAIAiwsPD45pjYq5Wp04dLVq0SH5+fvLy8sqxT3BwsGJjY9WsWbNcLdPLy0vdunVTt27d1KVLF7Vu3VqnT59WyZIlbfpVq1ZN8+bN06VLl6yha+PGjXJwcLAOdrYnLksBAHAbevzxx+Xr66uOHTvqhx9+UHx8vOLi4jRgwAD99ddfkqRRo0ZpwoQJmjx5svbv369t27ZpypQpOS5v4sSJ+vzzz7V37179+eefWrx4sQICAuTj45Pjul1dXRUZGaldu3bpu+++U//+/fXkk09ax9vYE2duAAB3hNvhG4Pzwt3dXRs2bNDLL7+szp0768KFCypXrpyaN29uPZMTGRmp5ORkvffee3rxxRfl6+urLl265Li84sWLa/z48dq/f78cHR1Vr149rVq1KsfLW+7u7lq7dq0GDhyoevXqyd3dXY888ogmTpx4S7c5tyyGYRj2LqIwnT9/Xt7e3jp37tw1T+PdjML+eu+iJsG1h71LsLuwkAr2LsGudkbutHcJdnWnvwdI9g0RycnJio+PV0hIiFxdXe1WB/Lneq9fXv5+c1kKAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCuEGAACYCj+/AAC4M4zyLuT1nSvc9RUAi8WiL7/8Up06dVJCQoJCQkK0fft21apVy96l5QlnbgAAKAJ69eoli8Uii8UiJycnhYSE6KWXXlJycrK9S7vtcOYGAIAionXr1vroo4+UlpamrVu3KjIyUhaLRW+//ba9S7utcOYGAIAiwsXFRQEBAQoMDFSnTp3UokULrVu3TpKUmZmpcePGKSQkRG5ubqpZs6aWLFliM//u3bv10EMPycvLS8WLF1eTJk108OBBSdIvv/yili1bytfXV97e3mratKm2bdtW6NtYGAg3AAAUQbt27dKmTZvk7OwsSRo3bpw++eQTTZ8+Xbt379bgwYP1xBNP6Pvvv5ck/f3337r//vvl4uKib7/9Vlu3btVTTz2l9PR0SdKFCxcUGRmpH3/8UT/99JMqV66stm3b6sKFC3bbxluFy1IAABQRK1askKenp9LT05WSkiIHBwdNnTpVKSkpevPNN7V+/Xo1bNhQklSxYkX9+OOPmjFjhpo2bapp06bJ29tbCxculJOTkySpSpUq1mU/+OCDNuuaOXOmfHx89P333+uhhx4qvI0sBIQbAABugd0nd+ep/9nks6rXuJ5GjB+hy5cv69Ppn8qxmKNCm4ZqzS9rdPnyZTVv0dxmnrS0NFULq6bdJ3frxy0/KqxemP4892eOyz95/KSmjJuiXzb+otMnTysjI0PJV5L1856fFXJviLXfkfNHtPvkbv19+m9J0sEzB+V00ilP21LDt0ae+hc0wg0AAEWEu7u7KlSsIEl6ffLreuSBR7T0s6WqXK2yJOmDBR/Iv4y/zTxOLv8GDxdXl+su+9XoV3X2zFkNHTtUZQPLytnZWY+3fVxpqWm3YEvsi3ADAEAR5ODgoD6D+mj88PFa+fNKObs469jfx1SvUb0c+1epUUXLFy1XWlqa9bLUf23fsl2vjX9N97e8X5J07O9jOnPqzC3dBnthQDEAAEVUqw6t5OjoqC8+/kK9nu+l8cPH66uFX+lI/BHt+W2P5s+ar68WfiVJ6vF0D128cFFD+gzRrh27dPjgYS3/YrniD8RLkoIqBunrxV/r4J8H9fvW3zX02aFydXO15+bdMpy5AQDcGQrpG4PzOtbmeooVK6bHnn5MH039SGt+XaMSpUpo9vuzdfTwUXl5e6laWDX1GdRHkuRT0kdz/jdHE0ZNUFTHKDk4OCj07lDVrl9bkjRm0hiNemGUujbvqoCyARr46kC9O+rdAqu1KLEYhmHYu4jCdP78eXl7e+vcuXPy8vIq8OUHD11Z4Mu8nSS49rB3CXYXFlLB3iXY1c7InfYuwa7u9PcASUp4q53d1p2cnKz4+HiFhITI1dU+ZyUKMtzcrvI7oPh6r19e/n5zWQoAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgK4QYAAJgKP78AALgjhH0cVqjrW9huYaGuD/+HcAMAQBHwavSr+mrRV9naV/28SscTj+ujaR9pz297dCLphN7/+H01b9v8hsvcu2uvpr41Vb9v/V0XL1yUr5+vwuqE6ZVxr6hU6VK3YjOKBMINAABFROMHG+uNyW/YtJXwLaHDhw6rao2qerjHwxrUa1CulnX65Gn1fqS3mrZsqhmLZqi4d3H9c/QffbfmO125fOUWVP+vtLS0W7bs3GLMDQAARYSzi7N8/X1t/jk6OqpJiyYa8MoAtWjXItfL2r5luy6ev6jRk0ar2j3VVD6ovOo3rq+X33hZ5YPKW/sd2HtAz/d4Xg1CGqh+cH31fKinjsQfkSRlZmbqw3c/VPN7mqt2udp65IFH9GPsj9Z5/z7yt+4ufbdWf7lavTr0Up3ydbRyyb8/Hjt79mxVq1ZNrq6uCg0N1QcffFBAz9KNceYGAAAT8vXzVXp6umJXxqpVh1ayWCzZ+iQdS1Jkh0jVu6+e5nw5R57FPbX95+3KyMiQJH028zN9/MHHGjFhhKqFVdOXC75U9JPR+uqHrxRUKci6nElvTNKLo1/UG2FvyMXFRfPnz9eIESM0depU1a5dW9u3b1efPn3k4eGhyMjIW77thBsAAIqI77/5XvWC6lkfN2neRBPnTszXsmrWrak+g/ro5Wdf1pghYxRWO0z1m9RXh64d5OvnK0n6fM7nKu5VXO/MekdOTk6SpOBKwdZlzJs2T0/1f0ptH24rSYoZEaMtP27RpzM+1WvjX7P2e6LvE2r5UEvr496demvChAnq3LmzJCkkJER79uzRjBkzCDcAANxJ6jWupxHjR1gfu7m75Wq+me/N1KxJs6yPl29crjLly2jgqwMV+Vykfv7hZ/2+7Xd98fEXmj1ptuYtn6cq1ato3659qtOgjjXY/NfFCxd1PPG4atevbdNeu35t7du9z6atRq0a1v9fvnRZBw8e1NNPP60+ffpY29PT0+Xt7Z2r7blZhBsAAIoId3d3VahYIc/zdevVTa07trY+Lh1Q2vp/n5I+iugYoYiOERr06iB1ebCL5k2bpzenvSkXV5cCqzvL5UuXJUmzZs1SgwYNbPo5OjoWyPpuhHADAMBtzruEt7xL3PisiJOzkwKDA613S1WpUUXLFy1XWlpatrM3nsU95Rfgp+1btqteo/+7VLZ9y3aF1b72dwb5+vmqbNmyOnTokB5//PF8btHNIdwAAFDEXb542XoHk/TvXUp7d+6VdwlvlSlfJsd54r6J0+ovV6vNw20UXClYhmEobm2cflj/g16f/LokqcfTPbRg9gIN6TNEvQf1VvHixfXb1t8UVidMIXeFKKpflKaNn6bA4ECFhoVq2YJl2rtrr97+8O3r1jt69GgNGDBA3t7eat26tVJSUvTrr7/qzJkziomJKbgn5hoINwCAO8LOyJ2Fsp7dJ3cX+DJ3/bZLT3V6yvp4/PDxkqSO3Tpq7NSxOc5TqUolubm56d0R7yrxn0Q5OzurQsUKGv3eaHXo2kHSv5es5vxvjiaMmqCojlFycHBQ6N2h1nE2j/d9XBcuXNC7I9/VqZOnVKlKJU39dKrNnVI56d27t9zd3fXOO+9oyJAh8vDwUFhYmAYNGlQAz8aNWQzDMAplTUXE+fPn5e3trXPnzsnLy6vAlx88dGWBL/N2kuDaw94l2F1YSN6vl5tJYf0BKaru9PcASUp4q53d1p2cnKz4+HiFhITI1dXVLjXcinBzu6nhW+PGnXJwvdcvL3+/+RI/AABgKoQbAABgKoQbAABgKnYPN9OmTVNwcLBcXV3VoEEDbdmy5br9J02apKpVq8rNzU2BgYEaPHiwkpOTC6laAABQ1Nk13CxatEgxMTEaOXKktm3bppo1ayoiIkLHjx/Psf+CBQs0dOhQjRw5Un/88YfmzJmjRYsW6ZVXXinkygEARdkddq+MaRTU62bXcDNx4kT16dNHUVFRql69uqZPny53d3fNnTs3x/6bNm1So0aN1KNHDwUHB6tVq1Z67LHHbni2BwBwZ8j6IrrLly/buRLkR2pqqqSb/yZju33PTWpqqrZu3aphw4ZZ2xwcHNSiRQtt3rw5x3nuu+8+ffbZZ9qyZYvq16+vQ4cOadWqVXryySevuZ6UlBSlpKRYH58/f77gNgIAUKQ4OjrKx8fHegXA3d09x1/DvpUy0zILdX1FUX6Gi2RmZurEiRNyd3dXsWI3F0/sFm5OnjypjIwM+fv727T7+/tr7969Oc7To0cPnTx5Uo0bN5ZhGEpPT9ezzz573ctS48aN0+jRowu0dgDAdYwqnB9HvJYAWaTKPXQ8qI3k6Fzo6z9+k3+YzaDY2fw9Bw4ODqpQocJNB9Lb6hWIi4vTm2++qQ8++EANGjTQgQMHNHDgQL3++usaPnx4jvMMGzbM5quez58/r8DAwMIqGQBQyCwyVGb/fPkd+p/SXEtJhXzmZmC5soW6vqJo+cPL8zWfs7OzHBxufsSM3cKNr6+vHB0dlZSUZNOelJSkgICAHOcZPny4nnzySfXu3VuSFBYWpkuXLqlv37569dVXc3xCXFxc5OJSML96CgC4fThmXJHjpb8Kfb3HUgs3TBVF9vp26Cx2G1Ds7Oys8PBwxcbGWtsyMzMVGxurhg0b5jjP5cuXswWYrEFHjIwHAACSnS9LxcTEKDIyUnXr1lX9+vU1adIkXbp0SVFRUZKknj17qly5cho3bpwkqX379po4caJq165tvSw1fPhwtW/f/qZHVgMAAHOwa7jp1q2bTpw4oREjRigxMVG1atXSmjVrrIOMjxw5YnOm5rXXXpPFYtFrr72mv//+W6VLl1b79u01dmzOv4gKAADuPHYfUBwdHa3o6Ogcp8XFxdk8LlasmEaOHKmRI0cWQmUAAOB2ZPefXwAAAChIhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqdg8306ZNU3BwsFxdXdWgQQNt2bLluv3Pnj2rfv36qUyZMnJxcVGVKlW0atWqQqoWAAAUdcXsufJFixYpJiZG06dPV4MGDTRp0iRFRERo37598vPzy9Y/NTVVLVu2lJ+fn5YsWaJy5crp8OHD8vHxKfziAQBAkWTXcDNx4kT16dNHUVFRkqTp06dr5cqVmjt3roYOHZqt/9y5c3X69Glt2rRJTk5OkqTg4ODCLBkAABRxdrsslZqaqq1bt6pFixb/V4yDg1q0aKHNmzfnOM/y5cvVsGFD9evXT/7+/rr77rv15ptvKiMj45rrSUlJ0fnz523+AQAA87JbuDl58qQyMjLk7+9v0+7v76/ExMQc5zl06JCWLFmijIwMrVq1SsOHD9eECRP0xhtvXHM948aNk7e3t/VfYGBggW4HAAAoWuw+oDgvMjMz5efnp5kzZyo8PFzdunXTq6++qunTp19znmHDhuncuXPWf0ePHi3EigEAQGGz25gbX19fOTo6KikpyaY9KSlJAQEBOc5TpkwZOTk5ydHR0dpWrVo1JSYmKjU1Vc7OztnmcXFxkYuLS8EWDwAAiiy7nblxdnZWeHi4YmNjrW2ZmZmKjY1Vw4YNc5ynUaNGOnDggDIzM61tf/75p8qUKZNjsAEAAHceu16WiomJ0axZs/Txxx/rjz/+0HPPPadLly5Z757q2bOnhg0bZu3/3HPP6fTp0xo4cKD+/PNPrVy5Um+++ab69etnr00AAABFjF1vBe/WrZtOnDihESNGKDExUbVq1dKaNWusg4yPHDkiB4f/y1+BgYFau3atBg8erHvuuUflypXTwIED9fLLL9trEwAAQBFj13AjSdHR0YqOjs5xWlxcXLa2hg0b6qeffrrFVQEAgNvVbXW3FAAAwI0QbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKkQbgAAgKncVLhJTU3Vvn37lJ6eXlD1AAAA3JR8hZvLly/r6aeflru7u2rUqKEjR45Ikvr376+33nqrQAsEAADIi3yFm2HDhum3335TXFycXF1dre0tWrTQokWLCqw4AACAvCqWn5mWLVumRYsW6d5775XFYrG216hRQwcPHiyw4gAAAPIqX2duTpw4IT8/v2ztly5dsgk7AAAAhS1f4aZu3bpauXKl9XFWoJk9e7YaNmxYMJUBAADkQ74uS7355ptq06aN9uzZo/T0dL3//vvas2ePNm3apO+//76gawQAAMi1fJ25ady4sX777Telp6crLCxM33zzjfz8/LR582aFh4cXdI0AAAC5luczN2lpaXrmmWc0fPhwzZo161bUBAAAkG95PnPj5OSkpUuX3opaAAAAblq+Lkt16tRJy5YtK+BSAAAAbl6+BhRXrlxZY8aM0caNGxUeHi4PDw+b6QMGDCiQ4gAAAPIqX+Fmzpw58vHx0datW7V161abaRaLhXADAADsJl/hJj4+vqDrAAAAKBA39avgkmQYhgzDKIhaAAAAblq+w80nn3yisLAwubm5yc3NTffcc48+/fTTgqwNAAAgz/J1WWrixIkaPny4oqOj1ahRI0nSjz/+qGeffVYnT57U4MGDC7RIAACA3MpXuJkyZYo+/PBD9ezZ09rWoUMH1ahRQ6NGjSLcAAAAu8nXZaljx47pvvvuy9Z+33336dixYzddFAAAQH7lK9zcdddd+uKLL7K1L1q0SJUrV77pogAAAPIrX5elRo8erW7dumnDhg3WMTcbN25UbGxsjqEHAACgsOTrzM0jjzyin3/+Wb6+vlq2bJmWLVsmX19fbdmyRQ8//HBB1wgAAJBr+TpzI0nh4eH67LPPCrIWAACAm5avMzerVq3S2rVrs7WvXbtWq1evvumiAAAA8itf4Wbo0KHKyMjI1m4YhoYOHXrTRQEAAORXvsLN/v37Vb169WztoaGhOnDgwE0XBQAAkF/5Cjfe3t46dOhQtvYDBw7Iw8PjposCAADIr3yFm44dO2rQoEE6ePCgte3AgQN64YUX1KFDhwIrDgAAIK/yFW7Gjx8vDw8PhYaGKiQkRCEhIQoNDVWpUqX07rvvFnSNAAAAuZavW8G9vb21adMmrVu3Tr/99pvc3NxUs2ZNNWnSpKDrAwAAyJM8nbnZvHmzVqxYIUmyWCxq1aqV/Pz89O677+qRRx5R3759lZKScksKBQAAyI08hZsxY8Zo9+7d1sc7d+5Unz591LJlSw0dOlRff/21xo0bV+BFAgAA5Faews2OHTvUvHlz6+OFCxeqfv36mjVrlmJiYjR58mR+WwoAANhVnsLNmTNn5O/vb338/fffq02bNtbH9erV09GjRwuuOgAAgDzKU7jx9/dXfHy8JCk1NVXbtm3Tvffea51+4cIFOTk5FWyFAAAAeZCncNO2bVsNHTpUP/zwg4YNGyZ3d3ebO6R+//13VapUqcCLBAAAyK083Qr++uuvq3PnzmratKk8PT318ccfy9nZ2Tp97ty5atWqVYEXCQAAkFt5Cje+vr7asGGDzp07J09PTzk6OtpMX7x4sTw9PQu0QAAAgLzI95f45aRkyZI3VQwAAMDNytfPLwAAABRVhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqhBsAAGAqRSLcTJs2TcHBwXJ1dVWDBg20ZcuWXM23cOFCWSwWderU6dYWCAAAbht2DzeLFi1STEyMRo4cqW3btqlmzZqKiIjQ8ePHrztfQkKCXnzxRTVp0qSQKgUAALcDu4ebiRMnqk+fPoqKilL16tU1ffp0ubu7a+7cudecJyMjQ48//rhGjx6tihUrXnf5KSkpOn/+vM0/AABgXnYNN6mpqdq6datatGhhbXNwcFCLFi20efPma843ZswY+fn56emnn77hOsaNGydvb2/rv8DAwAKpHQAAFE12DTcnT55URkaG/P39bdr9/f2VmJiY4zw//vij5syZo1mzZuVqHcOGDdO5c+es/44ePXrTdQMAgKKrmL0LyIsLFy7oySef1KxZs+Tr65ureVxcXOTi4nKLKwMAAEWFXcONr6+vHB0dlZSUZNOelJSkgICAbP0PHjyohIQEtW/f3tqWmZkpSSpWrJj27dunSpUq3dqiAQBAkWbXy1LOzs4KDw9XbGystS0zM1OxsbFq2LBhtv6hoaHauXOnduzYYf3XoUMHNWvWTDt27GA8DQAAsP9lqZiYGEVGRqpu3bqqX7++Jk2apEuXLikqKkqS1LNnT5UrV07jxo2Tq6ur7r77bpv5fXx8JClbOwAAuDPZPdx069ZNJ06c0IgRI5SYmKhatWppzZo11kHGR44ckYOD3e9YBwAAtwm7hxtJio6OVnR0dI7T4uLirjvvvHnzCr4gAABw2+KUCAAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMBXCDQAAMJUiEW6mTZum4OBgubq6qkGDBtqyZcs1+86aNUtNmjRRiRIlVKJECbVo0eK6/QEAwJ3F7uFm0aJFiomJ0ciRI7Vt2zbVrFlTEREROn78eI794+Li9Nhjj+m7777T5s2bFRgYqFatWunvv/8u5MoBAEBRZPdwM3HiRPXp00dRUVGqXr26pk+fLnd3d82dOzfH/vPnz9fzzz+vWrVqKTQ0VLNnz1ZmZqZiY2Nz7J+SkqLz58/b/AMAAOZl13CTmpqqrVu3qkWLFtY2BwcHtWjRQps3b87VMi5fvqy0tDSVLFkyx+njxo2Tt7e39V9gYGCB1A4AAIomu4abkydPKiMjQ/7+/jbt/v7+SkxMzNUyXn75ZZUtW9YmIP3XsGHDdO7cOeu/o0eP3nTdAACg6Cpm7wJuxltvvaWFCxcqLi5Orq6uOfZxcXGRi4tLIVcGAADsxa7hxtfXV46OjkpKSrJpT0pKUkBAwHXnfffdd/XWW29p/fr1uueee25lmQAA4DZi18tSzs7OCg8PtxkMnDU4uGHDhtecb/z48Xr99de1Zs0a1a1btzBKBQAAtwm7X5aKiYlRZGSk6tatq/r162vSpEm6dOmSoqKiJEk9e/ZUuXLlNG7cOEnS22+/rREjRmjBggUKDg62js3x9PSUp6en3bYDAAAUDXYPN926ddOJEyc0YsQIJSYmqlatWlqzZo11kPGRI0fk4PB/J5g+/PBDpaamqkuXLjbLGTlypEaNGlWYpQMAgCLI7uFGkqKjoxUdHZ3jtLi4OJvHCQkJt74gAABw27L7l/gBAAAUJMINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwFcINAAAwlSIRbqZNm6bg4GC5urqqQYMG2rJly3X7L168WKGhoXJ1dVVYWJhWrVpVSJUCAICizu7hZtGiRYqJidHIkSO1bds21axZUxERETp+/HiO/Tdt2qTHHntMTz/9tLZv365OnTqpU6dO2rVrVyFXDgAAiiK7h5uJEyeqT58+ioqKUvXq1TV9+nS5u7tr7ty5OfZ///331bp1aw0ZMkTVqlXT66+/rjp16mjq1KmFXDkAACiKitlz5ampqdq6dauGDRtmbXNwcFCLFi20efPmHOfZvHmzYmJibNoiIiK0bNmyHPunpKQoJSXF+vjcuXOSpPPnz99k9TnLTLl8S5Z7uzhvMexdgt1lXMmwdwl2dauOrdvFnf4eIPE+cKe/B0i35n0ga5mGceP9y67h5uTJk8rIyJC/v79Nu7+/v/bu3ZvjPImJiTn2T0xMzLH/uHHjNHr06GztgYGB+awa1+Nt7wKKhD/sXYBdeT/HXnCnYw+4s98DpFv7PnDhwgV5e19/+XYNN4Vh2LBhNmd6MjMzdfr0aZUqVUoWi8WOlaGgnT9/XoGBgTp69Ki8vLzsXQ4AO+B9wLwMw9CFCxdUtmzZG/a1a7jx9fWVo6OjkpKSbNqTkpIUEBCQ4zwBAQF56u/i4iIXFxebNh8fn/wXjSLPy8uLNzXgDsf7gDnd6IxNFrsOKHZ2dlZ4eLhiY2OtbZmZmYqNjVXDhg1znKdhw4Y2/SVp3bp11+wPAADuLHa/LBUTE6PIyEjVrVtX9evX16RJk3Tp0iVFRUVJknr27Kly5cpp3LhxkqSBAweqadOmmjBhgtq1a6eFCxfq119/1cyZM+25GQAAoIiwe7jp1q2bTpw4oREjRigxMVG1atXSmjVrrIOGjxw5IgeH/zvBdN9992nBggV67bXX9Morr6hy5cpatmyZ7r77bnttAooIFxcXjRw5MttlSAB3Dt4HIEkWIzf3VAEAANwm7P4lfgAAAAWJcAMAAEyFcAMAAEyFcAMAAEyFcAMAAEyFcAPTmDZtmoKDg+Xq6qoGDRpoy5Yt9i4JQCHZsGGD2rdvr7Jly8pisVzzx5RxZyDcwBQWLVqkmJgYjRw5Utu2bVPNmjUVERGh48eP27s0AIXg0qVLqlmzpqZNm2bvUlAE8D03MIUGDRqoXr16mjp1qqR/f8YjMDBQ/fv319ChQ+1cHYDCZLFY9OWXX6pTp072LgV2wpkb3PZSU1O1detWtWjRwtrm4OCgFi1aaPPmzXasDABgD4Qb3PZOnjypjIwM6092ZPH391diYqKdqgIA2AvhBgAAmArhBrc9X19fOTo6KikpyaY9KSlJAQEBdqoKAGAvhBvc9pydnRUeHq7Y2FhrW2ZmpmJjY9WwYUM7VgYAsIdi9i4AKAgxMTGKjIxU3bp1Vb9+fU2aNEmXLl1SVFSUvUsDUAguXryoAwcOWB/Hx8drx44dKlmypCpUqGDHymAP3AoO05g6dareeecdJSYmqlatWpo8ebIaNGhg77IAFIK4uDg1a9YsW3tkZKTmzZtX+AXBrgg3AADAVBhzAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATIVwAwAATOX/AX/l1y+dj/m+AAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure()\n",
+        "plt.plot(cv_scores, marker='o')\n",
+        "plt.title(\"Cross Validation Scores (Logistic Regression)\")\n",
+        "plt.xlabel(\"Fold\")\n",
+        "plt.ylabel(\"Score\")\n",
+        "plt.ylim(0, 1)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "sF-dgugW28Dt",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "6687fe34-170c-4e89-8068-85bd11e99ce9"
+      },
+      "execution_count": 261,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns"
+      ],
+      "metadata": {
+        "id": "ddHu3Ditf64F"
+      },
+      "execution_count": 262,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y = pd.to_numeric(df[\"Klasifikasi Kemiskinan\"], errors='coerce')\n",
+        "y = y.fillna(0).astype(int)\n",
+        "\n",
+        "X = df.drop(\"Klasifikasi Kemiskinan\", axis=1)"
+      ],
+      "metadata": {
+        "id": "mJFidT4pkw_h"
+      },
+      "execution_count": 263,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X,\n",
+        "    y,\n",
+        "    test_size=0.2,\n",
+        "    random_state=42,\n",
+        "    stratify=y\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "LxqkKvx8lDJh"
+      },
+      "execution_count": 264,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "\n",
+        "# Jika belum ada y_test dan y_pred\n",
+        "cm = confusion_matrix(y_test, y_pred)\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.imshow(cm)\n",
+        "plt.title(\"Confusion Matrix - Decision Tree\")\n",
+        "plt.xlabel(\"Predicted Label\")\n",
+        "plt.ylabel(\"True Label\")\n",
+        "plt.xticks([0, 1], ['Kelas 0', 'Kelas 1'])\n",
+        "plt.yticks([0, 1], ['Kelas 0', 'Kelas 1'])\n",
+        "\n",
+        "# Menampilkan nilai di dalam matriks\n",
+        "for i in range(cm.shape[0]):\n",
+        "    for j in range(cm.shape[1]):\n",
+        "        plt.text(j, i, cm[i, j], ha='center', va='center')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "MrObTJ_GlwwV",
+        "outputId": "2ee51d51-69fb-4a86-8cba-bd357a482693"
+      },
+      "execution_count": 265,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "accuracy = accuracy_score(y_test, y_pred)\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.bar(['Decision Tree'], [accuracy])\n",
+        "plt.title('Accuracy Model Decision Tree')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.ylim(0, 1)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "r6NBUXRJmHQJ",
+        "outputId": "54fc77ca-5230-4f94-d1ff-1dba931b08c8"
+      },
+      "execution_count": 266,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import classification_report\n",
+        "\n",
+        "report = classification_report(y_test, y_pred, output_dict=True)\n",
+        "\n",
+        "metrics = ['precision', 'recall', 'f1-score']\n",
+        "class_0 = [report['0'][m] for m in metrics]\n",
+        "class_1 = [report['1'][m] for m in metrics]\n",
+        "\n",
+        "x = np.arange(len(metrics))\n",
+        "width = 0.35\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.bar(x - width/2, class_0, width, label='Kelas 0')\n",
+        "plt.bar(x + width/2, class_1, width, label='Kelas 1')\n",
+        "\n",
+        "plt.xticks(x, metrics)\n",
+        "plt.ylabel('Score')\n",
+        "plt.title('Precision, Recall, dan F1-Score per Kelas')\n",
+        "plt.legend()\n",
+        "plt.ylim(0, 1)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "ZU8PYqK2mKs7",
+        "outputId": "545583af-bd93-4c8c-950f-d827c6198945"
+      },
+      "execution_count": 267,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "\n",
+        "dt = DecisionTreeClassifier(random_state=42)\n",
+        "\n",
+        "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
+        "\n",
+        "cv_scores = cross_val_score(\n",
+        "    estimator=dt,\n",
+        "    X=X_encoded,\n",
+        "    y=y,\n",
+        "    cv=kfold,\n",
+        "    scoring='accuracy'\n",
+        ")\n",
+        "\n",
+        "print(\"Hasil Cross Validation per fold:\", cv_scores)\n",
+        "print(\"Rata-rata CV Accuracy:\", cv_scores.mean())\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "H7FNZpqhlXRJ",
+        "outputId": "1639036f-c48c-47f0-cfb1-7a1ae90acc98"
+      },
+      "execution_count": 268,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Hasil Cross Validation per fold: [0.98      0.965     0.955     0.93      0.9798995]\n",
+            "Rata-rata CV Accuracy: 0.9619798994974875\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure()\n",
+        "plt.plot(cv_scores, marker='o')\n",
+        "plt.title(\"Cross Validation Scores (Decision Tree)\")\n",
+        "plt.xlabel(\"Fold\")\n",
+        "plt.ylabel(\"Score\")\n",
+        "plt.ylim(0.9, 1.0)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "vbOQrFNdxHjm",
+        "outputId": "482136fa-2b75-49e3-a70e-477e6c987fb5"
+      },
+      "execution_count": 269,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file