Latihan_statistical_test_&_dimensionality_reduction.ipynb

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      "name": "Latihan_statistical_test_&_dimensionality_reduction.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/riskydav/Belajar-Python/blob/main/Latihan_statistical_test_%26_dimensionality_reduction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cNzIDuWSmDgM"
      },
      "source": [
        "# 1) Contoh Kasus Uji Beda\n",
        "Sebuah lembaga pelatihan bahasa inggris ingin mengetahui apakah pelatihan mereka berhasil meningkatkan skor TOEFL pesertanya. Mereka lalu menganalisa skor TOEFL sebelum dan sesudah pelatihan. Dengan tingkat kepercayaan 95%, apakah pelatihan ini berhasil?\n",
        "\n",
        "![alt text](https://drive.google.com/uc?id=1QN1HbirK1jx5hwlFVEGJUHM64GNjuS1p)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JuDgnkw0l419"
      },
      "source": [
        "#Library\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy import stats\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "from sklearn.model_selection import train_test_split\n",
        "import time\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn import metrics\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline  "
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I3KhklpUTPtN",
        "outputId": "09a141b2-b45d-49c2-c5ad-0e7502040424"
      },
      "source": [
        "!wget -O skor_toefl.csv https://raw.githubusercontent.com/riskydav/Belajar-Python/main/Data/skor_toefl.csv"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-10-11 03:03:00--  https://raw.githubusercontent.com/riskydav/Belajar-Python/main/Data/skor_toefl.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 906 [text/plain]\n",
            "Saving to: ‘skor_toefl.csv’\n",
            "\n",
            "skor_toefl.csv      100%[===================>]     906  --.-KB/s    in 0s      \n",
            "\n",
            "2021-10-11 03:03:00 (33.0 MB/s) - ‘skor_toefl.csv’ saved [906/906]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "enm1JfVqwg2n"
      },
      "source": [
        "Membuat hipotesis\n",
        "\n",
        "Ho : Nilai TOEFL sesudah pelatihan <= sebelum pelatihan\n",
        "\n",
        "Ha : Nilai TOEFL sesudah pelatihan > sebelum pelatihan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "AgVflauDwvgS",
        "outputId": "13df67c9-00ec-4291-c491-6440b6d7f67b"
      },
      "source": [
        "toefl = pd.read_csv('skor_toefl.csv')\n",
        "toefl"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sebelum</th>\n",
              "      <th>Sesudah</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>429</td>\n",
              "      <td>569</td>\n",
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              "      <td>471</td>\n",
              "      <td>561</td>\n",
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              "      <td>443</td>\n",
              "      <td>588</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>99 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    Sebelum  Sesudah\n",
              "0       429      569\n",
              "1       443      599\n",
              "2       408      612\n",
              "3       459      536\n",
              "4       401      572\n",
              "..      ...      ...\n",
              "94      496      552\n",
              "95      413      544\n",
              "96      400      531\n",
              "97      471      561\n",
              "98      443      588\n",
              "\n",
              "[99 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4_pQVkuxfMk"
      },
      "source": [
        "Menentukan uji statistik\n",
        "\n",
        "![alt text](https://drive.google.com/uc?id=1asHY6TCNsltT7O8H6CmlEqQxQPgKH-6d)\n",
        "\n",
        "Meski datanya ada 2, namun masih dari orang yang sama sehingga termasuk uji beda 2 kelompok berhubungan. Datanya numerik dan jumlahnya juga cukup banyak sehingga menurut teorema limit pusat yang dibahas minggu lalu, distribusi datanya mendekati normal. Menurut tabel diatas, uji statistik yang sesuai adalah \"paired sample t-test\". Di python uji ini tersedia di scipy.stats dengan nama \"ttest_rel\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i20ZTsqdw6J6",
        "outputId": "85e53a4d-c8aa-4683-aabd-badb5a87ab6a"
      },
      "source": [
        "#Melakukan uji t\n",
        "uji_t = stats.ttest_rel(toefl['Sesudah'], toefl['Sebelum']) #hasilnya: (statistik hitungnya (t-hitung), pvalue)\n",
        "print('nilai t-hitung = ', uji_t[0])\n",
        "print('nilai p-value = ', uji_t[1])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nilai t-hitung =  30.255038012916643\n",
            "nilai p-value =  1.638101871655264e-51\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FydGBAmq0LyY"
      },
      "source": [
        "Berdasarkan hasil diatas, p-value = 1,6 x 10^(-51) < alpha (5%). Maka Ho ditolak, terdapat peningkatan yang signifikan dari TOEFL bahasa Inggris peserta pelatihan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uzzvacgS1kOu"
      },
      "source": [
        "# 2) Contoh dimensionality reduction (Latihan untuk student)\n",
        "Pada latihan ini kita akan mengklasifikasi data genre musik. Disini kita akan membuat beberapa model KNN dengan menggunakan data asli dan data yang dimensinya sudah direduksi. Tugas kalian adalah membandingkan akurasi dan lama training antara data asli dan data yang sudah direduksi."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVC7QUxITjWn",
        "outputId": "43db9543-b2c9-4080-b266-4423f9c5a1ed"
      },
      "source": [
        "!wget -O music_genre.csv https://raw.githubusercontent.com/riskydav/Belajar-Python/main/Data/music_genre.csv"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-10-11 03:04:46--  https://raw.githubusercontent.com/riskydav/Belajar-Python/main/Data/music_genre.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.111.133, ...\n",
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            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2082246 (2.0M) [text/plain]\n",
            "Saving to: ‘music_genre.csv’\n",
            "\n",
            "music_genre.csv     100%[===================>]   1.99M  --.-KB/s    in 0.07s   \n",
            "\n",
            "2021-10-11 03:04:46 (28.9 MB/s) - ‘music_genre.csv’ saved [2082246/2082246]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "EZe1xL54lVNR",
        "outputId": "f533309e-d890-4db6-e118-c7469064e0c8"
      },
      "source": [
        "music = pd.read_csv('music_genre.csv')\n",
        "music2 = music.copy()\n",
        "music2.dropna(inplace=True)\n",
        "del music2['Artist Name']\n",
        "del music2['Track Name']\n",
        "music2"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Popularity</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_in min/ms</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
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              "      <td>6.0</td>\n",
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              "      <td>0.167</td>\n",
              "      <td>0.975</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-4.279</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2160</td>\n",
              "      <td>0.000169</td>\n",
              "      <td>0.016100</td>\n",
              "      <td>0.1720</td>\n",
              "      <td>0.0918</td>\n",
              "      <td>199.060</td>\n",
              "      <td>229960.0</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>53.0</td>\n",
              "      <td>0.235</td>\n",
              "      <td>0.977</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.878</td>\n",
              "      <td>1</td>\n",
              "      <td>0.1070</td>\n",
              "      <td>0.003530</td>\n",
              "      <td>0.006040</td>\n",
              "      <td>0.1720</td>\n",
              "      <td>0.2410</td>\n",
              "      <td>152.952</td>\n",
              "      <td>208133.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>48.0</td>\n",
              "      <td>0.674</td>\n",
              "      <td>0.658</td>\n",
              "      <td>5.0</td>\n",
              "      <td>-9.647</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1040</td>\n",
              "      <td>0.404000</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>0.0981</td>\n",
              "      <td>0.6770</td>\n",
              "      <td>143.292</td>\n",
              "      <td>329387.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>17991</th>\n",
              "      <td>35.0</td>\n",
              "      <td>0.166</td>\n",
              "      <td>0.109</td>\n",
              "      <td>7.0</td>\n",
              "      <td>-17.100</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0413</td>\n",
              "      <td>0.993000</td>\n",
              "      <td>0.824000</td>\n",
              "      <td>0.0984</td>\n",
              "      <td>0.1770</td>\n",
              "      <td>171.587</td>\n",
              "      <td>193450.0</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>17992</th>\n",
              "      <td>27.0</td>\n",
              "      <td>0.638</td>\n",
              "      <td>0.223</td>\n",
              "      <td>11.0</td>\n",
              "      <td>-10.174</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0329</td>\n",
              "      <td>0.858000</td>\n",
              "      <td>0.000016</td>\n",
              "      <td>0.0705</td>\n",
              "      <td>0.3350</td>\n",
              "      <td>73.016</td>\n",
              "      <td>257067.0</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>17993</th>\n",
              "      <td>34.0</td>\n",
              "      <td>0.558</td>\n",
              "      <td>0.981</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-4.683</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0712</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>0.000136</td>\n",
              "      <td>0.6660</td>\n",
              "      <td>0.2620</td>\n",
              "      <td>105.000</td>\n",
              "      <td>216222.0</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
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              "      <th>17994</th>\n",
              "      <td>29.0</td>\n",
              "      <td>0.215</td>\n",
              "      <td>0.805</td>\n",
              "      <td>6.0</td>\n",
              "      <td>-12.757</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1340</td>\n",
              "      <td>0.001290</td>\n",
              "      <td>0.916000</td>\n",
              "      <td>0.2560</td>\n",
              "      <td>0.3550</td>\n",
              "      <td>131.363</td>\n",
              "      <td>219693.0</td>\n",
              "      <td>4</td>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>17995</th>\n",
              "      <td>43.0</td>\n",
              "      <td>0.400</td>\n",
              "      <td>0.853</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-5.320</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0591</td>\n",
              "      <td>0.006040</td>\n",
              "      <td>0.212000</td>\n",
              "      <td>0.3340</td>\n",
              "      <td>0.3770</td>\n",
              "      <td>138.102</td>\n",
              "      <td>182227.0</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11813 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Popularity  danceability  ...  time_signature  Class\n",
              "1            54.0         0.382  ...               4     10\n",
              "2            35.0         0.434  ...               4      6\n",
              "4            53.0         0.167  ...               4     10\n",
              "5            53.0         0.235  ...               4      6\n",
              "6            48.0         0.674  ...               4      2\n",
              "...           ...           ...  ...             ...    ...\n",
              "17991        35.0         0.166  ...               3      6\n",
              "17992        27.0         0.638  ...               4      2\n",
              "17993        34.0         0.558  ...               4      8\n",
              "17994        29.0         0.215  ...               4      8\n",
              "17995        43.0         0.400  ...               4     10\n",
              "\n",
              "[11813 rows x 15 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ueILPvBxqU-W"
      },
      "source": [
        "#Memisahkan Feature dan Label\n",
        "#Feature\n",
        "X = music2.iloc[:,:14].values\n",
        "Y = music2.iloc[:, 14].values"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kv_O-pOBt4jw",
        "outputId": "1f8a1650-6c28-4500-bc69-4f0001deb6b9"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "\n",
        "# transform data\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "print(X_scaled)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.6578634  -0.86108     0.59301271 ... -0.22000411  0.33481231\n",
            "   0.22582345]\n",
            " [-0.48893955 -0.54783734 -0.2323635  ...  0.8410691  -0.89146753\n",
            "   0.22582345]\n",
            " [ 0.59750535 -2.15621793  1.25744056 ...  2.5868942   0.14687296\n",
            "   0.22582345]\n",
            " ...\n",
            " [-0.5492976   0.19912594  1.28220184 ... -0.6092036   0.02828982\n",
            "   0.22582345]\n",
            " [-0.85108785 -1.86707086  0.55587078 ...  0.28659404  0.05825067\n",
            "   0.22582345]\n",
            " [-0.00607515 -0.75264985  0.75396107 ...  0.51558089 -0.26514691\n",
            "   0.22582345]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rvTSRUe-6MPv"
      },
      "source": [
        "## Feature Selection dengan uji statistik\n",
        "Disini kita akan memilih beberapa feature saja yang dianggap penting untuk nantinya digunakan untuk membuat model. Berbeda dengan PCA dan LDA, kita tidak mentransformasi data, hanya menggunakan sebagiannya saja. \n",
        "\n",
        "Karena labelnya kategorik, maka kita bisa menggunakan uji beda. Karena datanya banyak dan jumlah grupnya lebih dari 2, maka disini kita menggunakan ANOVA. Berikut contoh kodingan untuk feature selection dengan uji statistik yang tersedia di library 'sklearn.feature_selection'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJC9Geviwtxi"
      },
      "source": [
        "![select feature.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA34AAAB0CAYAAADaWuDvAAAgAElEQVR4nO29e1xV95nv/+kvHenGAQwriXZzE6tbSE/FOArukHM0KNNJThW8pGE2YiTDsWManO05RrLt0eGQlB2tr8OupGHKeEIadR9iTBTTsdOiqNOQJWiNmInC1oqby57YuBnYVFZxTl/5/UG/X9fa17UvXPO8Xy9eiay1nu91Lb7Pej7fZ33liy+++AIEQRAEQRAEQRDElOWrf/jDH8a7DgRBEARBEARBEMQo8v+NdwUIgiAIgiAIgiCI0YUcP4IgCIIgCIIgiCkOOX4EQRAEQRAEQRBTHHL8CIIgCIIgCIIgpjjk+BEEQRAEQRAEQUxxyPEjCIIgCIIgCIKY4pDjRxAEQRAEQRAEMcUhx48gCIIgCIIgCGKKQ44fQRAEQRAEQRDEFIccP4IgCIIgiEnIjRs3xrsKBEFMIsjxIwiCIAiCIAiCmOKQ40cQBEEQBEEQBDHFIcePIAiCIAiCIAhiijMmjp/T6cTq1auh0WiQlZUFm802FsVOaerr66HRaKDRaGA2m8e7OhMOp9OJjRs3ep1roijyvovUfLTZbMjKyoJGo8Hq1avhdDrDthkM7B4rLS2FJEkexyVJQmlpaUTnDCtTFMWwbXnDZrNh586dXtsz0ekd7IWuRoecQzlwDbsUx4oaiqDZo4FmjwaVzZVer69sroRmjwbHOo7x37mGXcg5lKP4XTAc6zgGXY0OvYO9IV0vt8PqP8syC62O1rDsfVk51nHM6/wIBl/zjP0+lLkS7jwLlkj0Q6TqEYn7Y7QI95kSiKKGokl5P3t7Vo627crmyjGZs5XNlV7HxN9cIEaH8bw/Il32mDh+giDgxIkT6Onpwfz58yNu32azYePGjWO+2A4XubMQrBNSUFAASZJQV1c3KnUTRdGrE2Gz2bBq1SpeT7b4Z46E3CEdDUefOTD+nA1JklBeXo6nn34aOp1OcczpdMJsNqOpqQmSJKGlpcXjnFDQ6XRoaWlBW1sbZsyYEba9SNPQ0AAA6OvrgyRJMJlM41qfC//2W8z+hxcR/+O/Ufz81REzXMMjc06n0yEmJgYWi2XM69fqaEX+u/lwDbtQ2VyJooaiiNk+mHcQUpmE9WnrI2ZzrOgd7EVZUxms+VZIZRI+M36GTG1mRGwXNRSFvGgdTVodrVj85uIJ6xAQxER+poTj2I/mS4ixfsExUWDtdnckWh2tmGWZpfhb5z52zOFkLxnYub5emrg/O32VrYbRfA5/2ebCV8e7Al9WnE4ntm/fDovFAr1eP97VCZna2lro9XqFI1FSUoK9e/dCo9Ggvr4e1dXV/N9jAXNy8vLyPI45nU7MmDEjIs7eRIK9XPFFZ2cnsrOzx2wMArHk69/Arb99XfG750/+AwAgNup+HY1GI3bs2AFRFMf8PkmISUBsVCwAIP2h9KCvtW0J/YXHzuyd2Jm9M+TrR4vewV7ERsVGzNkjwiPceUZMHkZ7rA/mHRw126PJaD4rx/s57Kv8cOZCu7MdsVGx2LRgE051nlI8y2OmxaB3sBe9g71IiElQXNc72IsnDz2JPTl7sGb+Gv7vyuZKPLfgOQAjztma+Wv4Nac6TyFdSOe2/JU90RnP+yPSZauO+LlLxdzlbHI5J1vwh2rbmwzNPZIkiiIvMyMjA++++y4SExO9RpnMZrPPCJTZbEZ9fb3C/mhJ1+SwOvhzQOR1ClY+GGg8fI0ni0Lm5OTgwIEDiI+P91m+KIpwOBwwGo0+67Fo0SIMDAxgaGhIVbv8zTOz2Yz4+HgcOHAAOTk5XtvmdDpx8uRJbsNbv/T396vuRzV9pgb38XCf43L5qbfx8jb/vR3zdu9IkgSHwxFCi4ODzR1fclN/9A72oaPPgS2P5Sp+r9FoYDAYYLVax0Tyyd78LTu4DG+2vQnNHg1e+fAVvPLhK/yNZmVzJY8Csjef8jeFTB4kfyOqFvkbVV9vReXnhCNNY/UMJsrWO9jr8819IAmovF/kx9nvj7YfxSsfvuLRd72DvVj85mKFPXl0kL2VPn3rNGZZZnn0OxvTYMeE9fOyg8vw6eefYu4bc722Td4u+XjI3yKzuRKoX9zHwlefuR8L1Cb2Nj+YyIu/eeb+5l9eb/Zm/oLjAj/HvVx53Q3HDT7L9dY2+TwLts/YvJGf4+stv7f7w5dt17AL+e/m88hHsPcmmyO+5kI4zxRmX95n8jazuRHq88TXeDC7huMGiL0iZlpm+rxH2LXyeVLUUISZlpkQe0UYjhs87Ad6Vrrblpcbrm132P2tJmLkLYomf77Jxzrc+94bpzpPIWZaDNamrUWro1VxX8Z9LQ7pD6XjZ1d+5nHdz678DFnaLO7YJcQkYE/OHm4vS5uF99vf5+e7hl041XkKa9PWqirbF+E+hwH/932gueDv/lBz38uv91Y/9/GWj2u496YvVDt+DQ0N0Gq1kCQJkiThxIkTEAQBwH1pncFggCRJ6OnpgdVqVe1AWSwWbruvrw8Oh0Ox8K2vr4fVakVPTw8vX6/X8yhHW1sbnnnmGX5cLt+rr6+Hw+HgErdt27Zh+/btigV7cXExOjs7uXRyLBaZrH7FxcVenQdRFBVtNhgMKC8vV1WvQOMhSRJ27NjhdTyZZLGpqQklJSW83+TjDYw4MlarFeXl5X6jSJcuXUJ6ejq/NlC7/M0zk8mEvr4+lJSUcKmmJEkoKCjg5dlsNsTFxSEpKUlRD+Yc5eTkoLGxkb8kULvXzV+fqaGhoQF1dXV8PERR5OPhdDpRU1ODtrY2n206efKkx/xn+JL9MmeTOcvFxcWjts/WZrOhqKgIFosF1dXVQUcWD1/9EPPjtVjy9W94HNPpdBgYGEB3d3ekquuT2KhYNG1owq4ndmHXE7tw23gb+gQ9rPlW2LbY+JvLVz58BekPpUMqk2DNt6KsqYw/lHdm74RUJmHXE7uCLp+9yb3xwg0kxiZ6PefVD1/FobxDuG28jcSYRK9/pANR2VyJt668hRsv3FD1Rpv9cTIcN6Db1c3/+Mr/QO04vQPnis5BKpNQ81QNNjRs4H3S6mhFbFQspDIJUpmErUu24uWml+EadvH+Wp+2Hrue2MXPCeYtp9grYssvtuA3f/MbnCs6h/O95/mi4Pv//H0kxCRAKpNw23ibO++BYGNxrugcvvnwN3HjhRse8tZjHcd4P0plEjYt2ISihiLFgsZw3MDnSm5qLqovVCvq3TvYC6lMwrmic6hrq+P19tdngPp51upoxeojq1HzVA2aNjTxKHYg2DyTyiRkabPwctPLAEYWO0UNRdi0YBOkMgk3XriBt668pVj09rh6sOrIKhzKO4QbL9xAz2APTt867bXPrPlWRbmvX3wdZzac4bZbHC3cdu9gL37Y/EM+z6QySTF/A/UZMHLvXrtzjfdd9YVqjwWot/sjkG3XsAtbfrEl5HvzaPtRXi9rvhVvXXkrIs+U3sFetDpaeb3dn1eZ2kx8ZvwM54rOqZ4bctu+xoPZteZboU/Q47bxNqQySfEcffuTt/Fazmv83gRGxh8YiXLIn7/u9gM9K1+/+Dq/793LDde2nKKGIvQO9uK28bYi2hUqbKzZMyFmWozX46HMBbkzlhCTgJ7BHrQ72xXnbFqwCdfuXPO4J67dueahfEmISUC3qxvtznasTVuLFkcLn1ftzna47rn4s1JN2d6I1HPY130faC4Euj/83feuYRdebnoZW5ds5fdeXFQcDuUdQkJMAlodrahrq+P3z/q09dAn6PGTv/qJqrJDJag9fizK5k53dzdiYmK4tE4QBBgMBpw9ezagTafTCYfDgc2bNwO4/4a/ubkZkiRxB8NkMqleZMttW61WGAwGvhDNy8tDSkqKYuFbUlLCo1beIlS+kEcSg93TxpxWvV7PnRC5o3z27FlFm3Nzc1UvfgONx+XLl2G323mfB8vvfvc75OTkwGAweB0TeaTQarUqylHTLl/zTA12ux1ardbD8WDOUVNTE3Jzc7kTpXavW7h99vzzz/M2C4IAvV4Pu93Oj/f39+PSpUs+r+/o6AjaWWNzjDnLzPGM1L5GxtWrV7nTJ3dI3aOUviLqvYN9aLh+0SPax4iOjkZcXNy47OEdvDcI1z2Xh+xlfdp6xR+G2KjYMdsDVvNUDS9zZepKXLtzLajr2aL2zIYzHu3yBVtsWPOtSIpN4n9gmXN2qvMUijOK+R/iFbNXIDEmkTsxmdpMvLj4RW5vZepKuO65MHhvMKi6+0L+xzRNSENSbBKXLPUO9uK1nNcAjDj2pUtKcarzVESSIrzf/j725Ozh/fjcgufguudSLGjkc2Vt2lpF1FT+R57V+1TnKQCR6bOW3hbu9AW7KGXzjNWbLeqYXIvVLSEmAZsWbFK87ZdfnxCTgCxtFl9MVl+oVvSZO+YnzfyY/FoGW0B6Q02fyfvc23Ff94ca2+Hcm+4Lvkg9UxJiEmB+8v4Lzkg/r/yNRyBeXPwin2OhPs/8IXdERoONJzaid7AXx585HtGF+Wghd8YSYhKQLqR7jJ02RgsA/EUNAAwOD6J3sNer4xf3tTgA4OPInvneZJ6Byg4VNc/hQPd9OPi679ud7eh2dWNl6koAnvfeqc5TSIpNQpqQBmDkOdsz2BOxevlCteNXUFCgcFLc5XVVVVV8sa/RaFBcXKzKrtPpRGtrK7fLojLy41988UXQTh9jxowZAa+V733S6XR4++23VZVnMpl4BIb9BLuoZjaampqQk5OD+vp6Ls2TyxkTExPR0dGhymag8bDb7UhJSUF0dLTqesp55JFHcOTIEdTU1HhdjMsjhSaTCU8//TRsNpuqdvmbZ2ro7OwMqU2BCLfP3KWcFRUV/JggCNi3bx+qqqq8Skh1Oh0sFgvvt1CklKPJiy++iJkzZ3rMe+Zsu/+479U7fPVDxEzTYH681qt9jUYDrVarcJRHCyatYPLOuW/Mxaeff4plB5f5jRK5hl2TIvlHt6sbb7a9iSxtlmqnTw3X7lxTyDSZdIbhLrtadnBZRLPRPfrQo/yPJ4vasn0oV+9c5RFKb9LCUGFjLpcHzX1jLnpcPYrz5FKnNfPX+Iy6xUbFKsYkEn324ws/jsieTPl+197BXvzy5i+5dI/JoeUkxiYqyjyYd1D1Xil36eDR9qOKehzKO4T9F/b7lF4F6rOVqSt5WzK1mbj4/EXe7/7uj9Gew6OJXJbubY6GSqDxCIS7DM59HoXDzuydyNJm8Xs/0omjxF4Rv7z5S8V8mui4O2Nr09Z6fQnGXo4xYqJikBCT4OGUs7FOiEngL2neb3/fp8xTTdnBovY57O++Hy0SYhLwla98hfel/EUoMJI74Oqdq9xBfb/9/Yj/bfZGUBE/5qQw6aB8US5f7LMftdGU+fPnK2RskiRxuZggCHjwwQeDa5WM/v5+xSJ6aGgoYgvIcCJ+7uj1euzevVvhuMjljME6lf7GIyUlJej6uTN//nw8/fTTAeWnCxcuRGZmpmIMArXL3zwLRGpqamgNCkA4fWaz2WA0GhXt3r17t+IcJrFl8ld3CbBer+fXarVa7NixY8I4f01NTdDr9R5zQU3Er3ewDwc//TW2PJarSOoih70wiMS8DUSmNhO2LTYu+5DLS/wtXN0X7ROVpNgk/EvRv6DF0RLxhZBcpsl+WJTp+//8fQDgcq9IS1f8kRibyCOU7CcYyWMg5PKgcDKdsgUMe6seiT6z5luRpc3ykD0FS+9gL2KnxXLZmVy6F4o01xetjlZs+cUWRZ+6Z6pk8icm6Xry0JN8ARpun/m7P8ZzDodDZXMlWhwt/B5QI18MBn/j4Y/ewV5saNjAZXChyhf9wbKdMjlyJJ95+gQ9fv7sz7H/wv5JkQ2SOWNH248qXoLJHQ9GmpCG3sFetPS28N+lP5Tu1fGTPxeYMuD0rdNeZZ5qyg6VSD2HI0nMtBgkxiTyl6KG4wZFZJL9d9nBZdDs0aDF0cLVKaNJSJ9ziI6OVizCdDod7HY7z6YYDElJSYiLi0Ntba3X44IgID09HdXV1T4XuoIgoL+/38PhYpI6+Z69xsZGpKSkYOHChUHX1Z1IRPwYTqcToigiNTUVGo0G2dnZMJvNIcnbAo2HmvESBAG3bt3yKy0tKCiAVqv1m26fSSR1Ol3Q7XKfZ8D96I8vGXFKSgocDkfEnaJw5jgwEiWV73OUR/zcCeTgRNq5ZUlrwtn7x6TS8rmgJuJ3+OqHSPjzeDyZ/E2ftoeGhjAwMBBy1D8U5I5cYkyixz4LOS83vYzYabE84jTR0cZo+Vv6SC1Y1qatxf4L+/0mQWBRI7bvwd0RSX8o3ecbYHlEtbK5UhEF8keakIbYabF8f1ooJMQkwDXs8mgbk/XI90uFw+lbp9Ez2KNYrATqMzUweRNzXIKFyTPZG/NMbSZ6Bnv4XqxgYPcVk4UyR09OzLQYfu8d6zjmd6y9ZdsNt8/83R+RGI/xQP4Me7np5YhF/NzxNR5sH5i/a1odrdh/Yb/imPt8CRW2AI+07SVfX4Kap2qw5Rdbgvo8AZPzsf2yozUecti2Bfl+zNvG23j0oUc9JJexUbF4bsFzaOxs5L9bmboSjZ2Niv22ZU1lKF1SqoikASP7g+XRvWDK9sVoPocjNc/cYXJZ+UsyueS++kK14sWHfB/qaKLK8XPPZhgfHw+tVssTUAiCwJOieHuzz2RuiYmJePfdd5GRkcEXmRqNBnv37oXD4fCZ1dBkMkGr1Sqki/KogSAIMJlMXAonX8C6X6smIclY4B4NSUxMhMFg4H1aUFAAg8GgkMAyiZ98PIqLi1FRUaFIVBJoPLwd9yYvLCwsREZGht8Mlps3b/b45p98j5/RaMS+ffv4ol1tu7zNM/cyvc0V5qBdvnw5gqMVuM/YeMozzLLjOp0Ojz/+OO9Lq9WKbdu2cdvuMlD3PnOfK/I5HGguqIE55FeuXAl5H51Go0F5eTlEUVSd7fTCv/0Wb3z8K7/RPsB3wp7RYvDeIHoGR/4Q+8piKX9zKd/jIZeDvfLhK/w8eYZKlv1LnsVSnqGSSVWYxHQ0Phqbqc1EzVM1MBw3ROQbhWvmr0HNUzX8zaW77Kt0SSkaOxu5DJTtdZDD9k8xCSGrF8sex6Q81+5cU/29stioWBx/5jh6B3v9ZoL0h3v58vHYmb0TmxZsUkhJg8mcKc92WNZUpthX5q/PAs0z9z44mHcQLY6WoKR4rL0zLTOxMnWlIvHFmQ1n8NaVt3xmivTHazmvocXRAs0eDTY0bEDNUzWKhePShKV8HlVfqMa353ybX+suA2VJFdT0WTB4uz8iZTtYAo31bem232fKcwueQ89gD59nsVGxiogfS9wkz5qodp4EGg9gpC+LM4r5mDLbbG8om2cbGjZgXdo6jzLk80Xts9JdljvTMhMJMQkeqo1QbLuzZv4abF2yNeB2APn5TII60zITa9PWqo7ABnPfu9PqaPV4QcmcplOdp3D3P+4qzs/UZir2mmVqM3Hiuyew5RdbeN+wTzswmNzz6p2rCplnoLLVPC9H8zkM+J4L4dwfK2avAACFLF7+rCxdUspl0v4yfoZStj++IknSF2FZIIgJRn19PZqbm8f024GTHVEUYTQacfDgwQn1jUOWTdVgMEyY711WNlfi2p1rk/a7VwRBTB1u3LiBuXPnjnc1iFGg1dGKF/75BTQ80zApthIQSo51HEP1hWpF8p9jHcf4S76Xm15G+kPpihcSzIkfzfVFSFJPgpjIsGymocoyv0ywTz5MRKcPuP+pl4ni9BEEQRDEWFB9oVqxh46YXHjLUPt++/sjsuMv4BG56x3sRYujxatkOpKQ40dMOZjs8OTJk6r2rLEPj3tLRBJKZtHJBPvkQ6Q/8RAJbDYbBgcH+f5BgiAIgpiquEtUWxwtOJh3cFIkESI8cd++IN8akhCbgNdyXlNIPee+MRebFmxSnQE5VEjqSRAEQRAEMQkhqSdBEMFAET+CIAiCIAiCIIgpDjl+BEEQBEEQBEEQUxxy/AiCIAiCIAiCIKY45PgRBEEQBEEQBEFMccjxIwiCIAiCIAiCmOKMiePHvhWm0WiQlZWlKsX+ZELePvf0/+xYaWkpJEkK2f7GjRsnRb9FeqzNZvOYfU7BZrNh586dIY8TQRAEQRAEQUxUxsTxY98K6+npwfz58yNu32azYePGjXA6nRG3rYba2lro9XpIkgRJklBQUKDqOkmSUFpaClEU/Z5TXl6Op59+mn9nrb6+3sOpEkVxQjjVoz3WocD62Ww2K37PnFTmWOp0OsTExMBisYxHNQmCIAiCIAhi1CCpZ5hIkgSHw4Hly5d7Pc4coerqamg0mqDtNzQ0AADy8vL47woKCpCfn4/q6mpIkgSn0wmz2QyLxTLhPsI9EdBoNCgtLcVHH32kcIwbGxuRkpKi6Fuj0QiHw+HXGScIgiAIgiCIyYZqx49FTZiccfXq1YoImz+5Y7C23SMzwEiUix3XaDQQRZGXmZGRgXfffReJiYleJYZms5lf5+1YfX29wn4wi/6hoSHY7Xavx+Q23dtkNpsRHx+PAwcOICcnx6dM9OTJk7xv5GzevBl2ux2XL1/mEUe9Xq+63qIoKvrTfbzcx1MuVXU/5m28/CHvF/d55D4XKioqgrLtC51Oh8cffxzvvfceb4PVaoXBYFD0rUajgcFggNVqJcknQRAEQRAEMWVQ7fg1NDRAq9VyOeOJEycgCAKA+3JEg8EASZLQ09MDq9Wq2oGyWCzcdl9fHxwOh8IRqa+vh9VqRU9PDy9fr9fzaFpbWxueeeYZfrylpUUhi3Q4HOjr64MkSdi2bRu2b9+ucDaKi4vR2dkJSZJQV1enatFvs9mQlZWFxMRENDY2cudN7sgUFBRwm+6YTCb09fWhpKQETU1NXmWiNpsNcXFxSEpK8rheEAQYDAbk5OTg+PHjWLdunaq+BkacnpqaGrS1tXkt1+l0ori4mI+nJEmKiGVDQwPq6ur4WIuiqHqsRVFUjKXBYEB5eTnvb/lckCQJu3fvVt2uQKxbt45H/Vi0b+HChR7n6XQ6DAwMoLu7O2JlEwRBEARBEMR4EpTUk0XZ3Onu7kZMTAyXzDGn5OzZswFtOp1OOBwObN68GcD9iEtzczOXMVqtVphMJu5oqsVbVCcvLw8pKSmKqF9JSQmMRiMAYNGiRRgYGMDQ0JBf2zqdDi0tLejp6UFubi533uQOcbjY7XZotVqfEtHc3Fzk5uYiPz8/aIlnf38/Ll265PWYNwmknOeff563URAE6PV6n1FPd86ePasYy9zcXO5k2Ww2XLt2jc+FSKPT6VBYWIjq6mqv0T5GdHQ04uLixm3PKEEQBEEQBEFEGtWOX0FBAfR6PZdTuksSq6qqEB8fzyV6xcXFquw6nU60trZyuxqNBjk5OYrjX3zxRcjO1IwZMwJem52dzR0AnU6Ht99+O2LOWzh0dnb6PV5bW4vGxkZUVFQEJU8VBAH79u1DVVWVV7llZ2enX4fTXSaqVo7J9kPKpa2JiYno6OhQXfdwyc3Nhd1u9yuN1Wg00Gq1qp1ZgiAIgiAIgpjoBBXxM5lMCimn3PkrKSnhckr2YzKZVNmdP3++QsYplxYKgoAHH3wwuFbJ6O/vVzg1/vbkTTRSU1N9HhNFEcePH0dbWxt2794Ns9kcVISKRSyZ3LK4uJhf769cm80Go9GokKcGK8eUX+suzR1toqOjkZKS4reNzEFNSUkZkzoRBEEQBEEQxGgTUlZPtnhm6HQ62O12noEyGJKSkhAXF4fa2lqvxwVBQHp6Os9g6euc/v5+j08ZMBmifM+ev71dvmDJYSKd6ZFFlnxJYlNSUuBwODzaLUkSrFYrl3gyaWRjY2NI9XB3cBYtWoTjx4/7bO8jjzzCI6KiKKqO+Gk0GmRnZ/t0Ut3Hsb6+3qttFnEMNqmMWoaGhjAwMDAhor4EQRAEQRAEEQlUOX7umRbj4+Oh1Wp5QhBBEHhSFPfMm8D9hXpiYiLeffddZGRk8OyaGo0Ge/fuhcPh8Jll0mQyQavVKqSkcqdEEASYTCYuIZRn7nS/1mq1ory8PKRPKwSDvM+Ki4tRUVHh1VnZvHmzQjopbzdzqC9fvqy4xmKxwG63c4ePtb+4uFiVc+ou1TQajdi3bx93dHQ6HQ4ePAij0eiR1ZNlx8zIyOD9uW3bNg/b3sYaGJEMGwwGhbSX2XYfx87OTq+JcXQ6HXJzc706xZHAX1IdgiAIgiAIgpiMfEWSpC/GuxKEb+rr69Hc3Iy9e/eOurM6WZAkCTt27IBWq1UtJw7WtsFgCOrzGARBEAQx1ty4cQNz584d72oQBDFJoA+4T3BYZs1QZLRTkfr6eh5xjrTTB9z/nAQ5fQRBEARBEMRUghy/CY5Go0F5eTlOnjzpsYfRG+z7gnIpp78PtU822LcRR8Pps9lsGBwc5J/2IAiCIAiCIIipAkk9CYIgCIIgJiEk9SQIIhgo4kcQBEEQBEEQBDHFIcePIAiCIAiCIAhiikOOH0EQBEEQBEEQxBSHHD+CIAiCIAiCIIgpDjl+BEEQBEEQBEEQUxxy/MYRs9k8ZT6z4I4kSSgtLeXtM5vNiuNmsxlZWVmqPlExlaivr+d9snr1ajidzjErm42JKIpjVmY4RPL+EEURpaWlkCQpQrXzjdPpRGlp6ZiObSSobK5EzqEcuIZdIdtwDbuQcygHmj0aaPZoUNRQFMEafrmobK4Mu/9aHa2YZZnlYScSYx0pihqKUNlcqercYx3HJky9Q8HXePQO9kJXo4NmjwazLLPQ6mj1uJad497+29JtLH5zsddrCIIg3FHl+NXX13ss0kVR/FIu3COJyWSCJEnYvXt3xG2P9yKffXC+r68v6O/umc3mkBf6Y7nADxabzYbDhw+jra0NkiThxIkTEARhzMp3/zg9++ajvK+dTidWr149IV5EjOb9ESr19fVeHXaz2cznnSAIyM7ORnl5+ZjPQ9ewC/nv5qPV0YpWRysWv9IlGzgAACAASURBVLkYvYO9Y1b+6xdfBwDcNt6GVCbhYN7BiNhlDuWxjmMRsRdJJrsz8mUjGEdzrEiISYBtiw03XriBxNjE8a4OQRBTGFWOX0FBAfLz81FdXQ1JkuB0OmE2m2GxWKDT6Ua7jsQkpLOzE9nZ2dBoNF6Pm0wmtLS0fKnmj9PpxOzZs5GUlDTmZYuiCFEUsXnzZv47nU4Hi8WCqqoq/gKntrYWer0eBQUFY17HyUBeXh5SUlLQ2NjIf2ez2fDRRx/xCDcw8szUarX8BchYEjMtBgkxCQCAxJhExEyLGbOyr925hpWpKxEbFTtmZRK+ydRm4jPjZxFzwInwCGc8mHPYtKGJ7i+CIEJGtdRz8+bNsNvtuHz5Ml8csshBIERR5FEcb9ItX7JAX1Erp9OJjRs3qo42ysuNdJRSbluj0SjqGkjuGIxtb1EGURQVZbM+NZvNiI+Px4EDB5CTk+O1z/3Vm0XN2trakJWVFXTdJUmCw+HweoxFmby1idWpoqICxcXFQfUbs5uTk4MDBw4gPj7eoww2B+Vtl7ebRbu8lRtoDsvr7802ANjtdp/1D3U8RFHEzp07UVpaiqysLHzwwQfIyspSRD0lSYLVaoXJZPKIMOr1euTn5+O9997z6hwGwr3P3KOt7veAfDzcjwUbqZXPf2/3tbxPc3JyVNv1h0ajgcFggNVq5e1477338Pjjj3u8xFi3bh1Onjw5ZpLPyuZKzLTMxNH2o5j7xlwsO7gMv7z5S8y0zAwpUlbUUATNHo3qa13DLr/RxcrmSi4B1dXoFOe6S0TlEbSihiLMtMyE2CvCcNzAz2FRG3dJpHukk0V45OXL2ySX2cntBoLJ9gzHDRB7Rcy0zPRomz/brJ4XHBf4Od4ih2wc3Ovtr8/crwvUpmDH2r1d8rLdI6C9g70KKaJ7vY+2H/Vr25vcVT6WaseLXXO0/She+fAVr/aPdRzjv3eXXLJ2nb51GrMssxTXskj7sY5jvO7uczyY8fDXXl/yXzYffc0jgiAIIAjHTxAEGAwG5OTk4Pjx41i3bl1QBVVUVKCzsxOSJKGpqUmxcGISNEmS0NfXB4fDwRdtWq3W74I5EDabDdOnT4ckSZAkCdu2beORy3Cx2Ww4efIkenp6uH25M+yrXWoQRRFWq5XbNhgMCumYKIowGo1cNihJEo/SmEwm9PX1oaSkBE1NTR7H3W03NTXBaDQqFs4HDhzArl27cPLkSbS1teGjjz4K6DAzJ4A5ncx5ky/KdTodWlpa0NbWhhkzZiiuLygo4NK+uro6Xm81MlFmt6mpCSUlJVxi6i6nLC4u5vOwrq4OVquV92lDQwMvt6enhztCDH9z2N9cYHvViouLFU4pmwvhjkdVVRUMBgPy8/Pxj//4jzh06BAGBgbQ3d0NAOju7sbAwIDP6Oq6detw/Phx5OTkwGAwBCU/ra2thcFg4G2urq7mUS9JkrBjxw5+D7iPx6lTp7iz19fXx8dADTabDZWVlXz+WywWbN++nY+Htz6NFAsXLuRRPxbt8/Y8TEpKQlxc3JjJ4Xdm78S5onP49pxv47bxNnY9sQvr09ZDKpOwZv6aoGwVNRShd7AXt423A17LFurMOWOLavnC+VjHMbx15S3ceOEGpDIJmxZsQlFDEV+cvv3J23gt5zVIZRJuG28DuC8bPZh3ELeNt6FP0MOab4VUJkEqk7Aze6fq9rzy4Su4ducapDIJu57YheoL1XANu+AadqGooQibFmyCVCbhxgs38NaVt1Q5QCx6Y823Qp+g5/JW2xYbEmISVNnucfVg1ZFVOJR3CDdeuIGewR6cvnWaHz/afhTpD6VDKpNgzbeirKmMOxP++oz1m1QmYX3aer/tCGasGS83vczbJZVJQUWhvv/P30dCTAK/1r1+r198HWc2nOF91uJoUfSZ2Cuid7CX98lbV95SJWfemb2Tl7friV28fBZ9a3W0YsfpHThXdA5SmYSap2qwoWGDwrbYK2LLL7bgN3/zG5wrOofzvecVDu2WX2zBobxDkMokZGmz8HLTy/xatePhr+67ntjl9bhr2IX9F/bjN3/zG6/ziCAIghFUcpfc3Fzk5uYiPz8/aIleSUkJjEYjgJFF+owZM+B0OuF0OuFwOHiUgb1Rb25uhiRJSE1NBaCM/rHFnZoFqk6nw6pVq/i/Fy1ahIGBAQwNDXlEK0KJPHR0dHhd2AVqVyDOnj2riNDk5ubyxTyL4Gzbti3ocWDXyhf4LOpz6dIlft6CBQuwb98+CIKApKQkzJ49O2DkQhAEnDhxgjudzImaSJJO+TyUzwUAeP7553mfCIIAvV6veOngaw4zfM0Ftletrq5O4ZQyRzfc8SgpKcHChQsBAAaDAfHx8YrynU4n4uLiEB0d7bVPdDod8vPz+f0dLL7m9OXLl2G3231GEFetWsXnhUajQXZ2Njo7O1WVeenSJRQWFvLrmTNms9n8RjgjAXtGnDx5EtXV1V6jfey8cF9chcO1O9eQ/lB60NdtPLERvYO9OP7McVWLeSZBY84ZW1R/ZvwMmdpMAMD77e9jT84eLkF9bsFzcN1zod3ZDgB4cfGL/NzYqFisTF2Ja3euBV13X+gT9PjJX/0EALAydSVc91wYvDeIdmc7YqNi8eLiF3lbNi3YhPfb3w+7TLW2a56qQaY2EwkxCcjSZinavT5tPXdwWf8wRyMSfRbsWMs51Xkq6KhSq6MV15zX8FrOaz7PMT9p5vPEW5/IxzJTm4nYqNiI7GM91XkKxRnFvE9XzF6BxJhERdQvLioOh/IOISEmAWlCGpJikxRls7EEgLVpa9E72DsmkbfYqFheL299RhAEwfhqMCfX1taisbERjY2NWL58uWqppz+cTidaW1uRmKjc0FxSUgIASElJwdmzZ9Hd3Q2tVos7d+5AEAQkJib6XMjKYVGHAwcO8N+xxS1zVEKF7ZFiMrKSkhLs3bsXGo0mYLsC1dnhcHjI0xYsWAAAGBoagt1uh8FgCLnuKSkpfo9nZmbyvWgajQbV1dUhlzWRkO871Ol0ePvtt/kxURQ9+ryurk6VXX9zQQ2hjoea5D2BHA9RFFFRUQFg5B4PJhGP0WjEjh07uLPZ1NTEnwt2ux0pKSk+71ObzYaioiJcuXKF/05tIpfOzk4uCZYTzj0RDDqdDunp6RBFEeXl5T7PS01NVe3MhgOTmom9I/NhpmUmP3aq85TqhT27ftcTuyK2j4hJQA3HlWMTFxXH/7/V0YrVR1ZjYHiA/y6UyIgv5PsOM7WZuPj8RV4uk8PKiUTZvYO9AW0nxiZyRwGA371fMdNikBhz/+9JuH0Wzlj/5K9+gvx383nbrPnWoKPKvjjWccxjrviKdEWSa3eucRmonNIlpfz/H33oUaQJaQBGnK2mDSNKAl/OXc9gDwbvDdKePIIgJgyqI36iKOL48eNoa2vD7t27YTabI7Z3Zf78+QqJnFwyJggCBgcH8etf/xqZmZn43e9+h+7ubsTExKhaVFssFgD3s0vKJYaRiPjp9XpeZ61Wix07dvBr/bVLDXKZpjxyFh0dHdBRCITcGfC3J+/Lgs1mg9FoVPR5sNkk/c2FQIzmePibKyxRU11dHZqamlBRURFUJljmhLJ7y2g08usDlbt9+3Zs27aN95laJ5shlwN7k1qPNqmpqX4dW2DEQWWqhdGELUKZvFMujQxGhqdP0OPnz/4c+y/sj3gGTblMUx4R7B3sxYaGDdi6ZCs/NhYLfYZcpuku/5tItgfvDcJ1z4WEmISI9Fk4Y83mm1Qm4VzROWz5xZaIzJdWRyu2/GKLYq5E8gVAIOQSUPYTjkM71smVCIIgAqHK8WPSKSbxZNIteWa7UGH7YGpra/2ed/fuXSxcuBB3797F6dOng1pMabVaaDQa7nj19/cDuB/xc188BuOcyZHXSW27vMFkb76c60DH2TlarRZnz571eq18fxqT5AUj82OJNYJNWKOG1NRU1ZJYdwRBwK1bt/j+tmB45JFHuDRQHgULBbXzM1Lj4Q9BEBSSVjnsHs7NzYVer8fu3bsV+x6DLWfmzPvRDZ1OB7vd7nffHnMObTYbqqqqVJe1fPlyRTZSOe5znzn17rCEQKP1+Q/mwIf7kiZYmLwzNiqWS+aCYcnXl6DmqRps+cWWiHwbjMkQ5fvTvMHq3epoxf4L+z1sJMQk+JRgMkkdc4jUyusytZnoGexR7I0LloSYBHS7urlsNZK25fzsys8QOy2WR5wA/32mhkiMdUJMgseLBRbpYvsce1w9/FzXsIuXVdlc6ZHcRZ6V9ljHMY/j4ZL+ULpXmeratLXYf2F/ROZ872AvyprKKMMtQRATDlWOn8ViUezVEQQBJpMJxcXFYX8nTqPRYO/evXA4HF4zVAqCgH/913/F3bt3IQgClixZgqqqKtWLKZa4QqPRID4+HgsXLvRIKhIq7pkYrVYrysvL+b/9tUsebZRnsWTHCwoKYDAYkJiY6DUS6e24e+KYzZs3KzIf+rJtNBr5/rHRhvVZRkYG3n33XSQmJnpk98zLywMAngQlGOdSp9OhsLAQGRkZQX0kXafT4fHHH+fXsT2UwbbL21wIxGiPB3sJ4f6iRhRFFBcXK/bCsey9LFLuD/eIeWJiIgwGA4+6CYLAE+i4Z/WUJ4vSaDTYvn07CgsLvdr2dn/o9XpYLBY+Xu5jLZ/727dvh8ViQUyM8s17UlISMjMzYbfbvTrF4RIoqc5owPb1DN4bRM9gT8h21sxfg61LtmLZwWUR+ebZzuyd2LRgE+a+MdcjEyTb+8Yydm5o2IB1aZ7Jcl7LeQ0tjhaPzIhsD91My0z8xf/5C2xdslX1YjshJgFnNpzBW1fe4naDyW4JjDh4xRnFWHZwmSKbYyRsH20/yq+TS3YD9Zk8M6Y8i6W3sQx2rN2zbs59Yy42LdjEI2Nr5q9BljYLc9+Yi5mWmVibtpZ/my4hJgF7cvbwel+7c00RqczUZmJpwlLel9UXqvHtOd9W3V9qkM8XeZbMNfPXoOapGl62t8ycgWDtYn3C9mcGGg+WTXTuG3Px6eefYtnBZTw5kjwL6isfvsLnhK/sngRBEP74iiRJX4x3JQiCGF3Y5yjq6urG9KPxEx2z2QyHwxHUfsxgbKemptI3EQliisP22JYuKY3YXke13LhxA3Pnzh3TMgmCmLwEldWTIIjJCfvuZijS46kIiwaOltNXX18Ph8PBo9cEQRAEQRDjTdiOn/yD3N5+1H63LhTc5XXyn0h/qJ0YX8Zznk0VjEYjHA6HKnm2r8RH7Gc09naOJSwRT6j7ef3hdDrR3NysWupLEARBEAQxFpDUkyAIgiAIYhJCUk+CIIKBpJ4EQRAEQRAEQRBTHHL8CIIgCIIgCIIgpjjk+BEEQRAEQRAEQUxxyPEjCIIgCIIgCIKY4pDjRxAEQRAEQRAEMcUZU8ev5fRhVHwvA7/9tHlU7EuShNLSUkW6+mGnE015ebj1zjtwXb+Ok3o9Pj9/Pii7t955B0eTkz2uY/aOJiejKS8Pw05n0HVmNj6ZZOnxR7PebMxaSkvxx6Ehj98HO34A8MehIbSUlga8drKORyDGs12s748mJ+NocvKU69tIwvrqE7NZ8eyaCMg/nzPZP+fxZcBms2Hnzp2QJGm8q0IQBEFMECZFxO/e8BD+b/WLAR1Gi8UCrVYLvV4/JvWKnTcPT4si/vL0aUybMWNMylSLWkfHF5+fP+/heEWK0bQ9mriuX8eHzz0XkoM/2nxiNk8YB8Gd7g8+AACsaW/H+q4ufMtkiojdiTweU5GCggJIkoS6urpRsW82m+l7nBFEp9MhJiYGFotlvKtCEARBTBC+OpaFZa0oRNaKwlGxLYoiRFH0WJR8VaPB9ORkTE9JQVR8PGJ1OkQJQlC2Zz/7LGY/+2wkq8thzuNkYzTrHSUIyGloGBXbgZis4xGI8WzX4M2bmPnEE3ggOnpcyp9MPBAdjWitFjFz5iieXQQRCkajETt27IAoimP2QpQgCIKYuKiK+N0bHsJ7B8rw20+b8cbf56Piexl44+/z8fuBO/w4i8j90+FXUfG9DOz7H8vxWXcHAOD3A3f4dfLfq7H9T4dfxWtb9bj+r7/G4f0voOJ7Gaj4XgZaTh/mNiRJgtVqhclkguDm1D0QHY2s6mo8vHQpogQBT/zsZ4idNw/ASFTs4x/8AJ+fP4+mvDwPySaTWh1NTsZJvR6u69eD6lx3iZt7lOsTs5kfCzZa425bXu9PzGYcS0tDd0MDzn33u17L+Pz8ef57uYyVSQLPffe76G5owLG0NK/2/dXb3TY7R41tJqtVIwlk9oKJHv7hzh2vY+2vXWwe/GrFCnx25gw+eOwxjzlx6513FPWVR6OGnU4efWXSYPc6y9sdjCSSXddRU4OLL73k9dpA4yUvW96mQPeHGv44NIQhh8PnMX8SUPn9Jz8e7niwPrn1zjuKtsuj4/I5HMq9z+oYiqz1WyYTZj/7rOLZFSlEUeRyTY1G4xFhk8s5V69eDWcQYy23nZWVBZvNpjjudDqxevVqfk5paSkkSeJlVlRUoLi42Kuc1GazISsry+sxURRRWlqKtrY2fk4wUlRf9WKwLQS++sVsNnttt3zrATvHvV8C9Zl8PDQajWIbgxo0Gg0MBgOsVitJPgmCIAj1Us970l0ce/MHWFuyB7t/2oYU3WKc+/k/KM45vP8FPDQrFbt/2ob0RSvxm395FwDw53EP4YX/dRz/fe9pxD+S7Nf2y/tFPPhQIj69+EsAwH8t/J94eb+Ief/pP6Nw6xvY/dM27P5pmyJy2N3djYGBAeh0uqA74J7LhQvbtmHxvn1Y096O6cnJ+LdTpwDcjzyt+vhjxIZg29HUhPStW7G+qwtr2ttH6von2RswssBb39WF+Vu2BG27+4MPEK3VYn1XF9Z3dSGnoYFHMr9lMmFNezuS8vKw7MgRfg6LWg47nRi4do3/ftmRI3xPEYsMLTtyBEl5eVye527fV72HnU501NTgL0+f9ihXje3Zzz6L9V1dWPyjH/ltv+v6dXy4cSOWVFUhq7padTTpkx/+0OtY+2sXmwd/efo0Zj35JFZ9/DHWd3XhaVHkLxECcberC5+YzVjx859j1ccf425XF/quXOFtuXn4MO+z+Vu2ICkvD4/+3d8FtMv6a/6WLVj8ox/xPpfLKf2Nl+v6dTwwfTq/7lGjEdf27+dOqfz+WN/VhYeXLoWttjZgvZjjw15AMKdU7kRd/fGP+Rxe096OIYdD4Zh2nziB7DffxPquLqz6+GN8fv48Pj9/PiLjAQAXX3oJgzdv8vl28/Bh/HFoCK7r1/FJZSUfjyVVVbi4ffukl5Q6nU7U1NSgra0NkiRBkiQUFBTw46Iowmq1oqenB5IkwWAwoLy8XJXDYLPZUFlZyW1bLBZs376dO0hOpxPFxcUwGAy87Orqamg0Gi4h3b17N+rq6vhx05/msNPpxPbt22GxWCBJEnp6eiCKosJpPXDgAHbt2oWTJ0+ira0NH330kYcT5Yva2lqv9QJGnLcdO3ZAq9Xy4ydOnOAvGOvr6+FwONDX1wdJkrBt2zZFuwEgJycHqampkCQJ+fn5eO+991T1mc1mw8mTJ/l4SJIUUtROp9NhYGAA3d3dQV9LEARBTC2C2uO35vkfYlbSfABA2sIn4er7DPeG70ct/uK/PMMdMm/H1dieFhWNOY/qceezTtX1cjqdiIuLQ3SIUrIlVVWInTcPD0RHY+YTT2Dw5s2Q7LiT9J3v8IVopG0DI1GJUBajUYKAucXF/N+x8+Zh2owZGO7ri0i97vX3o+/SpYjY8kb/1avc6Qs2GjJaYx2IaTNmYPG+fYgSBEQJAh5euhR37XYAQN+lS5iekoLpCQkAgFlPPom7XV34f2Pwhj523jwkfec7/N/xixbhP1wuRdmsz1jdhhyOgBFW5pyxFxDMKWXO2bDTiSGHA7rNmwGM3B9zCgtx+8MPue25xcX8hYB7n0UCuXMtb3ffpUuYU1jI2xy/YAGmJycHFfVj7Y/UfsZI0d/fj0s+7s2zZ88qVBO5ubmqHYZLly6hsLCQv3xbuHAhUlJSuPPV2NiIlJQU5OXlBV1ndu3ChQsBAIIgwGQyobm5mTulCxYswL59+yAIApKSkjB79uygopVyW3IuX74Mu92OzX+ap3KcTiesVisMBgN3FPPy8hTtBoDdu3dzB3v58uVwOByQJClgnwFAR0eHagfWF9HR0YiLiwuqPwiCIIipSVjJXf79Tg/u/eH+AjBt4ZP8/7/xzWz8denrmBY1+vt67BFcDEYSedbPo8nJuPjSSxGzPfvZZ/Hw0qVc5hasVFQu//vgscfgCnNxwYgSBCzetw9XLZawsp36o628HF975JGgojsTmekpKXBevIi7vb0AgM/OnOHS5NHGXW75qxUrcK+/3+81kXBKh/v64Lx4kc/fo8nJOPfd7yrOcZcMd9TUhFWmO/J9h7Hz5uGJn/0MUYKAwZs3FbJZFrWc7AiCgH379qGqqspDsihJEhwOB3JycrisMDExER0dHQGsjtDZ2amQacbHx+PAgQOK41qtljtIwRLo2szMTCQlJQEYkTdWV1erjo4ZjUYAQHx8vIec0m63IyUlxedLxRkzZnhsL3Bn+fLl/P/1ej2PKAbqM51OB4vFwsfEXYKqFo1GA61WO2H/ThIEQRBjR1iO34MPJWLa18Y/YUPKBEx+MOx04uL27XjUaOQyukDyxWBhEr5VH3+Mm1araufv1jvv4PPz57lELlQpqy+YpHN9VxfmGAxofv75iDp/y44cwcNLl+JyefmkywzqDebg/WrFCr7XTOclwjAaXP3xjwHcz7ipJkPt9ORkfDXEBbycWJ2Oz0H2w2S7ruvXcWHbNoVUORRJdKjIZbPsJ5J77cYLnU6HlpYWLuUsLi5WRIKampq4rFCSJLS0tKiW0Mtlmu7SxNTU1LDqzaJkjEg6McxRlCQJbW1tMBqN3PkL9Lelv79f0X9DQ0NB1c1fnwEjjiL7vVarxY4dO4J2/phTPxH/ThIEQRBjS0iO3+8H7uCXR36EOY/qxySiNy0qGrHxs9B++YzX44IgYGBgAEMT0AlgGflc16/j6iil1WbZ/+Sw7ICfnfHeZ/LFu6221iPiFyUIuGu38yhUqHjLSBgJ20yixxyXsSAqPh73+vt9Sv6YBJI5/YEiZwz70aOKFwTyPY9qiZkzRyGTDIZorRYPREfjj0NDuLZ/v896Dzud+MRsjkiGzukJCfiz2Fi/+wW/9vDDvB8+P3/eI+I3WuMx68kncdViCTqhi5xwkrsEgiU6CTUCxJA7AhqNBtnZ2TCbzSFJApcvX46qqiqfssRFixbh+PHjfpOTpKamepVcLlq0CK2trbh8+TIA7xLLQKjtM0EQMHPmTP5vnU4Hu92OBi8RX0EQoNfrFYlT3GWp/gjUZ+6E6jwPDQ1hYGAgYGSSIAiCmPoE5fixrJr/e8cKxX6+QPz202Z+Xc/NNtS++l2P7J6BWPadv4XddtFrVs+kpCTExcWhsbExmOYEhEnNPnjsMXx25gx+tWKFR+ZAJo9j2QWZtDFKEDDHYOBZNS9u3445hff7S56xUJ6RUU3Uzl2edywtDdFarccnJ3SbNyvkcsz211euxN2uLp5V889iYjwifrHz5mFOYSGPQrF2Baq3uzyPJQaROzK+bMvbdfGll9BRU+Mzu+UD0dFYWF7OM06GG1FUMx5RgoBvmUx8TOVzIWnVKgDAsbQ0nP7OdzDvv/031d92TFm/nktjvWVpVYO8fG8ZMH21K2X9enSfOMHnUfxjj3nUm7X3g8cewxyDISKfNnkgOhqL9+zBkMPhNQNs7Lx5eFiv53Pk5uHD+EZRkcLGaI3Hw0uXYklVFS97tCTLoZKUlITMzEzY7fagXna5Z/Q0Go18Xxww8p0+g8GAxMREjwyX8syWxcXFqKioUGTP1Ov1sFgsyMjI8Jr9UqfT4eDBgzAajT6zZ7L9f0xyyWy7X5uYmAiDwRCRzxO4Z/R0ty0IAurq6mC1Wr22y2QyQavV8jpbrVaUl5erckgD9Zl7Rs9gbMux2WyIi4vjUliCIAjiy8tXJEn6ItBJ94aH8F7tDmTm/DW+8c3ssahX0LCU2XV1dfRmk5hUfGI2I2bOHIVDxRy38UwO8sehIVwsK8OcwsIpIXOcSpjNZjgcDuzduzfkfXPE1IdlJY2Uo0xMPG7cuIG5c+eOdzUIgpgkhLXHbyKh1+uh1+tRqyLVPEFMFLx9527Y6cTn588jZs6ccaoVMVFhUTty+gg1WCwWaLVacvoIgiAIAFPI8QNGsrM5HI6gP3JLEOPFA9HRSN+6VSH1jKSkkphasGQf8m/NEV8u3OWp7j9MImuz2TA4OMizlhIEQRCEKqknQRAEQRAEMbEgqSdBEMEwpSJ+BEEQBEEQBEEQhCfk+BEEQRAEQRAEQUxxyPEjCIIgCIIgCIKY4pDjRxAEQRAEQRAEMcUhx48gCIIgCIIgCGKKM6aOX8vpw6j4XgZ++2nzqNiXJAmlpaVeP+dw65130JSXh2GnU/H7T8xm3HrnnbDK9WU7HP44NISW0lJ8YjZj2OlEU15e0PV0Xb+Ok3o9/xg4g9k7mpyMk3o9XNevB10/ZqOltBR/HBoK+vrRIBLtGg8iMdajRX19vUeaeMI/E7nP6uvrUV9fP97VIAiCIAhiHJgUEb97w0P4v9UvBnQYQ/lYbcr69fj3K1cmjPMyFkQJAnIaGrDq448Rq9ONd3UixlRt13hSUFAASZJQV1c3KvZFUURpaSkkSRoV++PBaPdZOOTm5sJqtdK3TgmCIAjiS8hXx7KwrBWFyFpROCq2RVGEKIp+F1vTk5PxVbePHk9PSMA9lwt9V67g4aVLQy7fm+1weCA6GtFaLWLmzMFXNRpMT07G9JSUoGzEzpuHp0dpgcecLCJ8IjHWBKEGQRCwb98+vPrqGjTvZwAACyRJREFUq9DpdBAEYbyrRBAEQRDEGKEq4ndveAjvHSjDbz9txht/n4+K72Xgjb/Px+8H7vDjLCL3T4dfRcX3MrDvfyzHZ90dAIDfD9zh18l/r8b2Px1+Fa9t1eP6v/4ah/e/gIrvZaDiexloOX2Y25AkCVarFSaTyedCZvazzyKruhoPREcrfv9AdDTmFBbi5uHDHlG/z8+f51LQo8nJOJqc7FWC58t2uHzLZMLsZ5/FA9HRyKqu5o7pH4eG8PEPfoDPz5/n0kZ3qeknZrPfOgdC3mZ32aT8mLuMVI3dW++8o6jf5+fPAwBvj7wd7r+TyzmDKZ/JKVlZrC7y691th9Jvn5jNIcl+fY11JHA6nVi9ejWXH7pH2OTSxNWrV8MZRN3dbbvLCJn82t2+zWZDVlYWcnJycODAAcTHx3uUz87xJpu02WzYuHEjrl+/zstXGzmUJAk7d+6EKIr8Wnm59fX1XvtI/jt5n2VlZcFms6nur40bNyrON5vNin4TRdGnbV/9GQw6nQ7p6elobGwM6jqCIAiCICY3qqWe96S7OPbmD7C2ZA92/7QNKbrFOPfzf1Ccc3j/C3hoVip2/7QN6YtW4jf/8i4A4M/jHsIL/+s4/vve04h/JNmv7Zf3i3jwoUR8evGXAID/Wvg/8fJ+EfP+039G4dY3sPunbdj90zZF5LC7uxsDAwPQhSjvi503D//hcuFub6/HsY6aGgzevIn1XV1YduQIblqtEd3LFyr3XC5c2LYNi/ftw5r2dkxPTsa/nTrFj3/LZML6ri7M37IlaNuu69fxwPTpWN/VhfVdXXjUaMS1/fu5Yzz72WexvqsLi3/0o5DqfvGllxAzZw7vU7a3LXbePF4+467djoeXLkXUnxz67hMnkP3mm1jf1YVVH3+Mz8+fVzhzofLHoSFcLi/HHIOB275ptUbE9nhTW1sLg8EASZIgSRKqq6uh+VN0WhRFWK1W9PT0QJIkGAwGlJeXq3agysvLue2enh6FjFCSJOzYsQNarZaXfeLECQiCAJ1Oh5aWFjQ1NaGkpAR9fX2K406nE9u3b4fFYuG2RVFUOEgdHR3YsGED9u3bh56eHtjtdly+fFlVnwwODsJoNGLfvn2QJAl6vR61tbUAgEWLFuHWrVvo7u7m53d2diI7OxsajQY2mw3Tp0/nbdq2bRuqq6sjIle12WyorKxEW1sbJEmCxWLB9u3buXPX0NDgtT+DZfny5Whubp5SEluCIAiCIPwT1B6/Nc//ELOS5gMA0hY+CVffZ7g3fD9K9hf/5RnukHk7rsb2tKhozHlUjzufdaqul9PpRFxcHKJDjLhFCQISn34a9qNHPY4l5eXh0b/7OwAjDuK0GTMw3NcXUjmRZklVFWLnzcMD0dGY+cQTGLx5MyJ2Y+fNQ9J3vsP/Hb9oEf7D5cL/i9Aicf6WLZj97LO8LNanUYKAOQYDPjtzBsCIM/bvV64gZf16fu3c4mLuBEYJAh5euhR37faw63S3txfTYmORtGoVty2vi1q+ZTIhp6GB13Gi4GuRf/bsWUWkPDc3FwMDAwqnxxfd3d2IiYlBXl4egBEZocFgwNmzZwEAly9fht1ux+bNm4Oub2NjI1JSUrBw4UJu22QyebTDYrFwyaJer4c9iLnArgVGHCGHwwFJkqDT6fD444/j0qVLAEaeLw6HA7m5uQBGImar/jRPgBFHcWBgAEMR2Cd86dIlFBYW8notXLgQKSkpiqifKIpBR/ncEQQhYnUmCIIgCGJyEFZyl3+/04N7f7i/cEhb+CT//298Mxt/Xfo6pkVFVv7ojWAWe76IX7QIA+3tEyKaN94wWSSTPP5qxQrc6+8ftfLu9ffzfpePA4vATk9I4Od+fv48r9fR5GR01NREpA7DTid+e/AgjqWlcdsXX3opIrbHG6PRCABcTimPyDkcDuTk5HDpYGJiIjo6OvyZ4zidTlRVVXG7Go0GxcXF/LjdbkdKSkrIL2S0Wi2PTHpj/vz5iii/yWRCQUFBSGUBI/VljpA8Imaz2aDVarlz7C63zMjIQH+E7o/Ozk4UFxdz2/Hx8Thw4AA/XlBQAL1ej8TERK/SWrWwtoTrQBIEQRAEMXkIy/F78KFETPva6Dt2gUiJQCKM2HnzEJeWppBLflm5+uMfAwDWtLdjfVcX/vL0aUybMWPUyps2YwaPkE1PSMCfxcbCdf06+i5dwoMLFvC9k67r13Fh2zYsO3KEy1BDkbL6Iikvj7eZ/XzLZIqY/fFCo9FwKWJbWxuMRqMiq2NTUxOXDkqShJaWFtWyablMk/2Y/tRn4d6XLALHiMQLHn/InVSdTscjn2fPnsXy5cv5eRaLBQB4u9va2jAjgvdHXV2doj+ZFJVhMpkU0tpQnD/m8FFyF4IgCIL48hCS4/f7gTv45ZEfYc6j+jGJ6E2LikZs/Cy0X/Yuu4uUbGnWk0/i9ocfRvTTDixBxWRLWR+t1eKB6Gj8cWgI1/bvH7WIn622Fn8WG8ujeky22n/1KgZv3sTXV65UnP+1hx/mTuLn588HHfFjstDPz59XRPRi583D3a4udH/wQTjNCTm5SyBYlCmYRCLeEAQBM2fOBDDiEGZnZ8NsNocU+dHpdLDb7Wjwkd010HFWH/f9dMCIfLK1tZXv2XM6nbBarTAYDH6jgHLU9pnT6YTZbOZ7+Fi90tPTefSTSU4ZLBrJ9kwGE/Hr7+9XJJKpqKjgx5YvX46qqipVYxwdHR2ycx2uPJ4gCIIgiMlHUI4fy6r5v3esUOznC8RvP23m1/XcbEPtq9/1yO4ZiGXf+VvYbRe9ZvVMSkpCXFxc2Fnq4hcsAAD0XbkSlh05SUlJyMzMVMjIRht5dsqOmhpcfOklRZZKJpf84LHH8NmZM/jVihWKzJ0p69ej+8QJHE1OxrG0NMQ/9hiP+MlloBdfegkdNTVBZ/dk1xxNTsaQw4HFe/YoMqJ+feVKdP/JWZDvlYudNw8P6/X41YoVOJqcjJuHD+MbRUX8uL92PRAdjfStW3HVYuHXZpSX82ujBAHZb76Jm1arQko6UZK7MCftypUrYWXdTExMhMFg4BGkgoICGAwGLh2UZ8eUSxqLi4tRUVGhyK4pCALq6upgtVr5tXIpqbfj7lkodTodCgsLkZGRoTiu0+lw8OBBGI1Gr/WOBEziymy7y0TXrVuHV199FampqQpnc926dTh+/DiXYi5cuJBH/NT0mclk4mV3dnZi9+7d3LZer4fFYuH9Ie8Td4lpfHw8tFptSPLWs2fPKhxdgiAIgiCmPl+RJOmLQCfdGx7Ce7U7kJnz1/jGN7PHol5BI4oizGYz6urqwpIvfX7+PG4ePuzhjISD2WyGw+HA3r17v/QLrVvvvIPBmzenhIRyrBFFEUajEQcPHgw5gy1xP9topB3JyYLNZsOrr76KqqoqknoSxCTnxo0bmDt37nhXgyCISUJYe/wmEnq9XpGSPVQeXro0Yt/kY9/jIqePCAcWtSOnjwgX9pmMLVu2kNNHEARBEF8ypozjB4xkL3Q4HIrEFeOJXq/3+G4aQQSLIAg4ceJEUElXiC8X7h+7d/9hCWAaGxu/tJFOgiAIgviyo0rqSRAEQRAEQUwsSOpJEEQwTKmIH0EQBEEQBEEQBOEJOX4EQRAEQRAEQRBTHHL8CIIgCIIgCIIgpjjk+BEEQRAEQRAEQUxxyPEjCIIgCIIgCIKY4pDjRxAEQRAEQRAEMcUhx48gCIIgCIIgCGKKQ44fQRAEQRAEQRDEFIccP4IgCIIgCIIgiCkOOX4EQRAEQRAEQRBTHHL8CIIgCIIgCIIgpjj/P4Ed9A5Cj/yAAAAAAElFTkSuQmCC)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgYVtujdlX57",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2036475-acce-42b0-c6ae-185c5a8f74fb"
      },
      "source": [
        "#Ketik kodingannya disini, silahkan memilih berapa k feature yang mau digunakan\n",
        "selector = SelectKBest(f_classif, k=7)\n",
        "selector.fit(X_scaled, Y)\n",
        "X_new_a = selector.fit_transform(X_scaled, Y)\n",
        "print(\"nilai statistik hitung tiap feature : \", selector.scores_)\n",
        "print('\\n', \"nilai pvalue : \", selector.pvalues_)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nilai statistik hitung tiap feature :  [139.88066056 327.57607032 607.05627169   3.44940318 533.23015937\n",
            "  17.36126483 240.43782283 702.69800751 307.34168538  25.06319532\n",
            " 208.2249501   28.89657946 931.21961423  25.14183356]\n",
            "\n",
            " nilai pvalue :  [5.67588919e-278 0.00000000e+000 0.00000000e+000 1.54594261e-004\n",
            " 0.00000000e+000 8.80419429e-032 0.00000000e+000 0.00000000e+000\n",
            " 0.00000000e+000 1.36713016e-047 0.00000000e+000 1.72827268e-055\n",
            " 0.00000000e+000 9.41651367e-048]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L31rgArQtizI"
      },
      "source": [
        "## PCA\n",
        "Sebelum menentukan ingin mereduksi menjadi berapa komponen, kita bikin dulu visualisasi cumulative explained ratio sebagai gambaran berapa persen varians data asli yang tetap bisa dijelaskan meski sudah direduksi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "erPqC_qtxe_W"
      },
      "source": [
        "![pca plot.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZEAAACWCAYAAAAfU7VbAAAgAElEQVR4nO2df2wUR5r3v/fuvnBt5B/r2Szs2GYwbxhgpWCw+DVxTjEGdu/QYZtAgjXEG8wSJJI4a0sk7FgBWWbXExJLnsXZRWK5OBviueFHAMPKtxvAEG28YwxLMJEwDDns8Y+55UL79Q+Ffp27U94/nKp0j2e6e3o8eGyej2QJprurq556qp6qp6rr+buurq6vZ86cCYIgCIKIlP810RkgCIIgJi9kRAiCIAjDkBEhCIIgDENGhCAIgjAMGRGCIAjCMGRECIIgCMOQESEIgiAMQ0aEIAiCMAwZEYIgCMIwZEQIgiAIw5ARIQiCIAxDRoQgCIIwDBkRgiAIwjBxa0T6hvuw9N2laAu0TXRWDNE33AfrQSvyPsjD0MjQQ3tvcWMxhP0ChP0CqluqQ95T3VINYb+AU7dPPbR8MYobizHLNWvS1CurR2G/ELN8T2R9xILxkFlboA2zXLNQ3Fgcgxx+S3Fjcdh2MhFMRl3QZUSKG4tx6vapMR37qdunFAUeGhlC3gd5D73jNIoRBapuqQ5ZvlO3T8VFuY8UHIG0W8KmBZsmNB9ThbTENPh2+vD5S58jPSk94udZm5hMnUK0RCuzqcpU1YXv6rkpaXoS0hLTRv897dt/d9zvwI++/yN03O/AhvkbcEu8hcGRQWQkZcQuxxPMmsw1qG+vxy3xFpabl/PfT946iTWZa5A0PQnAtw0pHqnIqUBFTsWEvPtIwZEJeW88M5H1Ea8sNy/H38r+NtHZeOhMRl1QnYmwmca77e/i6SNP4/HfPg5vnxeP//ZxPoJfmbYSHfc7MDQyhLZAG0qyStA73Ivhr4YVaQRPbU/dPoXC44XI+yAPs1yz8M7VdzDLNWvMaJ5Na4X9guJa8Mg/lPuLTQ2D381+P3HrBPZ9so/fo2fqvMC0ABlJGTjfeZ7/1jfchw6xA2sy14x5b6g05dP94HJVt1QrnmkLtGHpu0vRN9wH4NvRTKhntdDjZpDnPXhaHZzv4LLJ6zrYnSavR+tBKy9PJITLm5rMtPQslN5EOkMNp2fFjcWY6ZoJb58X9tP2MXJRqw92LZT8g2f+kehvuHzL64TVlVwGxY3F/J6+4T4+og7VNrXQ0mE1l+yp26d4/YS7J1y5Qr37xK0TuuXF+pwLXRd4ueUyVytXNLoQfD1SeccaVSOyYf4GfP7S57Cl2fD5S5/DXeiGLc2Ge2X3uLVMS0xD0vQkXPmPK+gd6sXC7y/kz7cF2vD6hdfxcfHHkHZLOPhPB/F84/O8Uv90908oXVaKtZlrceDKAZx97iyGvhrCLfEWgNFKOXDlAP76s7/iXtk9AMA7V9/RVbC2QBuSpidB2i1B2i3h1WWv4hfNv8DQyBAqciq4y2fPU3v4PXpGyUnTk7Amcw3Od57nldgWaEPStCQsMC0AAJ7+nqf2hEzjF82/wNZFW/l7m59v5jMYLd7/7H28mfcmpN1SxDLRcjO0Bdpw8vZJfP7S5zxvG+Zv4NffufoOLj5/EdJuCZ+/9DkuBy7zDq1vuA+/avkVr2tpt6QYUbGR5cfFH+suq5zqlmq8d+O9sHlTQ0vPokFNz44UHMG9snuwpdngLnSPkYtafaQlpmGFeQVO3jrJf+sb7kPS9CQ+A375jy8jLTGN60LfcJ9u43fq9imFPLcu2orixmIMjQxhuXk5Dv7TQRy4cgBtgTacun0KlwOXcfH5i9wL0Tvci93Nuw21TS0d1nLJnrh1Ah33OyDtluAudOO9G+/xPkWtXMEyM+L29fZ5sfPfduKvP/srPi7+GK19rbzDVytXNLowNDKE4sZi3mewtF/+48sR5T1WRLSw3nG/gxsN9n9g1MVTd6UO6UnpSJyWyO8/33keJVklXOlXz1mN9MR0LnRbmg2r56wGAGxdtBXmRLPifUnTk/BBwQf8ncGdtxrLzcvxytJX+P/XZK7B0FdDfIYUDWsy16BnqId3QsGuLD3oLUcwryx9hcuTyYTVw3jQO9QbdiHUucrJOxHWycnfPTQypJihjRd9w31478Z72J+3n78/ErT0LBpiqWely0rRIXbwDvJ853k8M/8ZpCWm8RnBm3lvAhjVhdJlpbr16uStkwp5vrDoBYVh3TB/A15d9iqqW6pRd6VujOxDtU29ehitDtvSbPjNP/4GwKj8k6YncRmplast0IYOsYPLzAjJ05N5uZlXgr07Vm3zQtcF9A734oVFL/C038x7U6EbE4nqmkhxYzGf7j3+28f572xUwlhuXo5ftfwKy83LR5Vq2rdGhrmM5JQuKzWU2YXfX6i7kxoaGULh8UJ4+7z8t/Faq5G7tNIS09AhdkRUpt/8429QeLwQM12jse3dhW7do+q2QBvyj+VjcGSQ/zZei+hsBGo/bQcw2lhPP3uaG8dTt0/xaww220pLTMMHBR8g/1g+9n2yDxlJGYqRazT0Dffh66+/Hpe0xptY61nStCS0Bdqwes5qtAXa+Mi1b7gPN+/fVLRLYLTO9OS5b7hvTF0mT09W/P+Vpa+g8Hgh0hLTdOln33AfhkaGNAdTsdJhveWKhh99/0fc45A0PQnNzzfza7Fsm+mJygF6PKFqRI4UHMEzC55B3ZU6nH72NF7+48tY+P2FCjfFwu8vRFpiGq5uuwrgG0X6aohbyD1P7Qm5UGRkh0LwTEgNNtW7V3YPSdNHG+JLf3wp4neGQj4rSk9KV7iy9D7PlI8pHgDNhto33IfnG5/Hq8te5TKtbqke15nIhvkbIO2WAIwOIgqPF+L0s6dxS7yFnf+2U2Hwgn3w8sXQ6pZqrPpg1bgYkrTENCT//fh1BOPJw9Czk7dOchnK9Sw9KR1/ffavhuWrNXh5+Y8vY7l5OdoCbahuqdZc8NXTNh+GDocrVyy3lce6XGydmcmXGex4QJc7S64c8jUPLZ5Z8Az3q0YLc2k8s+AZ/hsTLPMZ9g71hsz30MgQ91PLYTMbI5XBXFr7/rwvYldWqDzKYQrCFDNUvoHRRnHgygFD79VDcF0nTkvkHdap26dUFyUj0RMt0hLTsNC0MGQdMrRkpgZ7Dhht+JEstrL8hdMztrNRvrYRCWsy12D4q2GcvHUSLyx6gesKm6X8ovkXEafJjNPu5t1h3SFsHaR0WSnezHsT9e31Ydsx00N529QiFjqsVa60xDS+AQgwVtdaqJXLqC4wF9nvb/wewKi+1l2pw9ZFW+Nidq65xZdZUnlDC/X/ULDRwNNHnua/MTeHHoZGhhTPykcYG+ZvwMlbJ/l0/u3Vb2PoxreNt3RZKfKP5UPYLwAAfr7s5xjqUnYsryx9Bec7z3O30qYFm3RvQWUurZv3b/JdWSzPwe6NE7dO8LT7hvuw6oNV6Bnq4df3PLWHl0uep+TpyXjjqTfw3o33AIw2gq2LtvLpekZSBjYu2KjYoRac9r5P9vHZYLA76ukjTyN5ejLOPHcGy83LUd1SrXA9srpii7kr01by+rCl2fCTuT/h9wanzdJlSh6c9uO/fTwil9eRgiN8hwuD6YOazLRIS0zD/rz9PO+bFmxSuCC0ZKZHz97MexOrPljF79FbH8BoB5I4LREnb59UuEyTpifh9LOnUXi8kKcrT1sLdo/cHcbclxe6LsB+2g53oRtpiWlIS0zDyrSVyD+Wz+tUrW1qlSsaHY6mXKHqOtzml0jRapsMo7pw8fmLWPXBKt6GNi3YFDdbgf+uq6vr65kzZ2rfSRAEgdGOvuB4AX77j79VfCtFPJrE7bEnBEEQRPxDRoQgCIIwDLmzCIIgCMPQTIQgCIIwDBkRgiAIwjBkRAiCIAjDkBEhCIIgDENGhCAIgjAMGZEoEUURP/3pT+Hz+RS/lZaWQhTFCcwZQRBE7JmyRsTpdEIQBP6Xn58/plP3er38+ooVKxSGQJ6G1+tFKCRJQmVlJdatWwer1cp/N5lMyMnJQWVlJSRJGv/CEQRBxAlT1ogAwN69eyFJEiRJgt1uR0lJCTckHo8HZWVlaG9vhyRJuHTpEv785z/zTl8URQQCARw7dgyXLl0KmX5jYyMAoKCgYMy1oqIimM1mfg9BEMRURNWISJKEiooKeL1e5Ofnhx3RezwexahfPnKXj/bVRvWxZu3atQAAn88HURThdrvhcrn4DEIQBPzsZz+DIAj8PgBYsmQJAoHAmDKLooimpiaUlpbyZ4LZuHEjmpqayK1FEMSURXMmMjw8jLKyMtTU1ECSJNhsNhw6dIhf93g8cLvd6O3t5aN+m200MI4oiujo6OC/Nzc3w+l0TninygyE3AUVzKVLl5CTkwOTyaR4Rp5GcnIyMjLCByDKyMhAcnLymGcJgiCmCrrcWfIRe25uLgKBACRJ4iN6h8PBO1s5JpMJ27Zt4/+3Wq1ISUnRZUR8Ph9WrFihmMUIggCn06m3bAoOHToEi8WCxYsXa97LjF92djYEQUBOTs4Yl5bf74fZbA47CwFGZzdmsxl+v99QngmCIOIdQ2sifr8fDx48gCiK+Prrr0MaEIZ8gTs9PR23b9/W9Q6r1YrLly/zWQz7czgcuvNZVVXF3x0IBPDWW2+pdvqM4FlGdnY2Ojo6FMavs7NTVx4yMzN130sQBDHZMGRELBYLEhISYDKZ8L3vfS/sfR6PB16vl7u6ent7MX/+fF3vGI+ZiHxhva6ujhsQk8mEe/fuhXUzXbp0CYcPH0ZqaioEQUBWVhaOHz+uuD8zM1NXHjo7O3XfSxAEMdmIyIiIogin04mcnBwIggCTyYSFCxeirq4u7FZWZnCAUZfSw5yJqKVdWFioWJ+RJAn/8i//gv7+fgQCATQ3Nyveu3fvXoVLy2KxcLdeOCRJQiAQgMViiTrPBEEQ8YguI5KXl8fdUXa7HUVFRfyaw+GA2Wzmo3b5Dqy1a9fC7/fzazNmzNA9E4k1DocDNpsN6enpEAQBubm5+Id/+Afcv38fg4ODYxbdc3Nz4fV6udGxWq3w+/24fv162Hf09PSETIsgCGKqoBpPRJIkvP7667Db7XzHFfEtHo8HLS0tYddanE4nMjMzFUaXIAhiKjGlPzaMNewjw1AfFHo8HgQCgZAfIhIEQUwVyIhEgSAIqKysRFNT05izs1paWlBZWalrNxhBEMRkhcLjEgRBEIahmQhBEARhGDIiBEEQhGHIiBAEQRCGISNCEARBGIaMCEEQBGGYSWNEJElCaWnphMUjmUowWYY7j8zpdIaM9DjVkcfFCRU3J5ZMNv2WH6zq8XiiTkuehs/nQ0VFBUUFnSRMGiNiBK/Xi9LSUlLGINjHkf39/RGdRxZtRxfP9eHz+dDQ0MAjXZ45c0b1dOrxxuVywWw285Mh2AGk8s5VFEXk5+dH3WmPBw6Hg58pN95YrVYkJibC5XKNe9rE+DOljQgRms7OTn6IZigcDgcuX778SJ35JYoi5syZoxpkLFZ4vV54vV7s2LGD/2a1WuFyuVBbW8tnhIcOHYLNZnskjtEpKytDIBCYNDOzR5m4Do/LRl6CICA1NRWHDx9WXA+XNhvF5eXlKY50l+c92KXzsEbIoijy0Tw76j743fJyB1+PNt/sZOFQyI/fD65np9PJ64AdyKnXlaGnPphLQ65Lcl0JloncBceek7tY5M8GyyyUDqsFDtPS79LSUrS3t3PZsbx5PB4cOHAA+fn5yM/Px9mzZ8fkXZKksIHdbDYbCgsL8eGHH4Y0NFoEt4/guoqlnsnfHewaDU67qqpqzPOCIMBut8PtdsflzJX4lrgNjytJEiorK2G32yFJEvr7+7F9+3Z+XS1tdox8c3Mztm/fzt02chfF+fPnecPo7+8HEPoMrFjg9/vhdDrR1NQ05t2iKKKkpISXOzgWitF8sw6DGYKSkpIxDZzJrb29HSkpKYrnHQ4HrwP5Mfl6RsV66gMASkpK0NnZCUmSUF9fr+hAGhsbUV9fz+PSsE5V/mxmZmbYZ81mM8+z/L3M8JSUlCgMHOtwvV6vQr+bm5tRVlam6BQPHz6MPXv2oKmpCe3t7fjLX/7Crzc0NGDfvn2wWCz46KOP0NraqghwpnXS88aNG3H69Gnk5eXBbrfrdrGJooiDBw9y91xwXcVKz4DRQUN1dTV/t8vlwq5du3iZmetOHmYhFFarFYODg+jp6dH1XmJiiNvwuOyI9XAHGEaTNgCsX7+el4mFwNUbgVA+4g032lIjJSUFNTU1MJlMY9597tw5WCyWsOU2mm+TyYQzZ85wQ8A65HhyW23fvh1lZWUARqNJDg4O4sGDBwCAbdu2cR0zmUyw2WyK2cPevXt5Jxn8LADFMf5ymG+/vr5eYeCKior4LEHeebPZwbVr13gaixYt4vWZkZGBOXPm8HcVFhZy+drt9jEuRFEUkZyczGPuBMNi36xduxZr166NQJrAwMCAIp9yYqVnAHDt2jVs2bKFP7948WJYLBb4fD74fD50dHTomlElJCQgOTn5oW5wICInbsPj6iGatIMjJ5aUlOh+lnU88r9oO2NmmDs7O1Vjt0eT73hHvk5jtVrx/vvvc90Kds2EcoGEo6ioSBE7JtKFaa2gYsuXL+drKYIgoK6uTnfoBDU3GjBa7qqqKpw7d07hAdDCZDKhpqYGtbW1IV14sdSzzs5OPtMN54rWgyAIMJvNmjIiJpa4DY+rRTRpi6KIXbt2oby8nBuB+vp63e+OdiYSCtag1ULpRpvvyYrP50NZWZnCjRbpriBm+Ht7e+F2uyMyJPJOTG1NyQhqBopFEq2vr0dzczOqqqoiWlOURwe12+0oKSnhhiTWesZmuvK/SGMSUWTQyUHchsc1mUzo6uri/lCXyzVmNKOVdnAaofIGjHZStbW1uvIFjO9MhL07NzcXwKgr5vTp06qdhVa+2ag9knj0emAjQ3mY4EjQqg81fvCDHyhmJZHMROQkJCTo7pSYG8ftdvPO9/r16/D7/RG7lsJhMpnGuN4Y586dAzAaIdRms2Hv3r2GF5qDyzweehaO3Nxcxa4yOSaTCQMDA/yax+MJW5cPHjzA4ODgQ91qTURO3IbHtVqt2LJlC7KysvgIXT761JN2cBpsSm8ymWC323m5du3ahS1btujK13gwMDDA85SVlQWXy8VHaVarFUeOHEFZWdmYnTGxzjfbhZSVlYXjx48jPT19jBtkx44dCtdSJCP6cPWh57knn3ySP+d2u1FeXq7rncE7gVJTU2E2m3Vvky0qKoLdbueuMLbJZLw6toyMDCQnJ3ODwfB6vSgpKVGsN+7YsQN+v1/X9xPB7r/gfEejZ/JdXVVVVdx1xXTBZrPB5XLx+gpuew6Hg6fd2dkZdpbj8/mQnJw8IduuCf1QeNyHjCiKKC8vxxtvvBE3C9rExOL1ernbikbdo1DfM3mgjw0JYoKx2Wxjts4/6gR/wU/ELxNiRII/cgr+G29fPhFbgj/GG88NB48KkXyhPdXbj8/n49+nEfEPhcclCIIgDEPuLIIgCMIwZEQIgiAIw5ARIQiCIAxDRoQgCIIwDBkRgiAIwjBkRAiCIAjDkBEhCIIgDENGhCAIgjAMGRGCIAjCMGRECIIgCMOQESEIgiAMQ0aEIAiCMAwZEYIgCMIwZEQIgiAIw5ARIQiCIAxDRoQgCIIwDBkRgiAIwjBkRAiCIAjDkBEhCIIgDENGhCAIgjAMGRGCIAjCMGRECIIgCMOQESEIgiAMQ0aEIAiCMEzURsTpdEIQBAiCAI/HMx550oUoisjPz4cgCFixYgV8Pl9M3iNJEkpLS+H1emOS/lTkUZKZz+fDihUrIAgC8vPzIYpixGl4PB4IghBTefl8Pvz0pz81lL9YwNpvaWkpJEma6OzEDbHSBY/Ho9o/O51Ow++M2og4HA5IkoS9e/eGvB4r5TWZTDhz5gx6e3sxf/58Q2k4nU5Nw+dyuWA2m2Gz2Qy941HkUZKZ1WrF5cuX0d7ejpSUlInODhHHTOTgau3atXC73WHfvXHjRlRXVxsajJM7SwWv1wuv14sdO3ZMdFYmDSSzyCkqKoIkSY+E0WWwQWBdXR0EQZjo7MQNsdIFk8mEmpoaHDx4MOSA3mq1oqKiAnV1dRHPDFWNiCRJqKiogNfr5a4jvVN2Nl3NysrC8ePHkZ6eHpHrSRRFbrWZuyDSqa/c1SZ/L5syVlVVoaSkhN/jdDoVZXe73XA4HDCZTIp02dRQnj6z8NHITF529qy83KFGMiwf7N8HDhxAfn4+8vPzcfbs2THl8nq9PN1gF6Q8LUA5i9STtlGZ6clbsEzk79WCyS3Us+yd8vorLS3lMvd6vXzGasRtq5ZvLZeslszUyiUvmyAIyMrKwsDAgK48M92V62zwb2rlYnpz584dfo+87cplGaoe1dLWUx/h2o8emWnJpbS0FO3t7bxPCtZ/edry9zqdTqSmpuLw4cPIy8sbk3ctXVArk16sVisWLlyIc+fOhby+ePFiAMD169cjSldzJjI8PIyysjLU1NRwC3no0CHNhNlIo729Hc8++yx6e3shSRIuX74Mq9WqK3N+vx9OpxNNTU3o7+8HADQ2Nup61uPxIBAIoL+/H5Ikoby8HLt27YIoitza7927F/X19byDdjgc/Pmenh4MDg6GzWtJSQkyMzMhSRLq6+vhdrt5pRqVGTCqLCUlJbDb7TxfkYzWGhoasG/fPlgsFnz00UdobW1FR0cHRFGEKIo4ePAg2tvbedpFRUW60tVKG4hMZs3NzXA6nfxZr9eLsrKysHlrbGzkddXb28tnPHpg7jVJktDf349AIMAbr81mQ319Pc8L06+33nqLy7yqqgqdnZ2QJAnt7e1oaGjQPe1Xy7cel6yanqmVy+fzobq6msszEncbqz95Gf1+P2w2Gx8caNXH7du38fzzz6Ompga9vb3w+/28c2Ltr76+PmKZAcr6aG5uhtvtVhg3tfajJjM9HD58GHv27EFTUxPa29vxl7/8hcvp/PnzvHMP7q8cDgf6+/uxfft2NDc3j9FxNV2QJAmVlZW8TCxtl8ulO9+M3NxctLS0hDRAgiAgJycHly5diihNXe4sl8vFFSs3NxeBQOChLIalpKSgpqYGJpOJF7Czs1PzOVEU4Xa7YbfbufIUFBTAYrHobvyiKCI5ORkJCQkhr+/du5crQHZ2NgYHB/HgwQN+3ajMzp07B4vFgoKCAl35DKawsJC/V15+xsDAAK5duxaTtCORmdVqRUpKCkRR5DOY8vLysAZo27ZtvAMzmUyw2Wzw+/2aeRZFEYFAgLvXBEGA3W5XNKSioiLYbDY0NDSgqamJjyYZ27dvR1lZGQAgIyMDc+bM0T2zNJpvRjg90yrXhx9+iC1btugesMkxmUyw2+28M5EkCTdu3MDGjRsjKhdrA5GWWytteX3I9QhQbz96dEGLRYsW8T4pWBfWr1/P5R1Jf6XF9evX4ff7sXbtWp52aWmpYgCnF5PJNKavkmOxWCLu3w2tifj9/rCZiDV6C5iSkjLGpRIJkTR0venpkVlnZyfMZnNM/MTML1pbWxvVbqJwRCqzgYEBiKKIBw8ewO/3w2KxhL032NVVVVWl6x2iKKKtrY27UwVBQF5e3pj7duzYgfPnz2PdunW6Ol69ZTWaby30lsso2dnZvJPq6ekBMGpAGVrlmj9/vkKODodD96w3GpmptZ/xkNny5cu5HARBQF1dHV+/kO/UEwQBJSUlEaWthsViCTs4iwTWJ4Zr91pGJhSGjMh4FcgIejtY1kExWEelF7UOzQh6ZZaZmTmu7w2G7SaSJAl2ux0lJSXjZkgilRkz9AkJCarP+nw+lJWVKdwA4XYDhmL+/PncnRrKxcHcBS+++KLqDhY5esoabb610CpXNGRkZCA5ORk+nw/Xrl3DokWLeLqxLFe0aWu1n1jJTBRF7Nq1C+Xl5TzdcO46IwQPQkVR1L3GFZxPAGEH2FrehFBEZEREUYTT6UROTo5uoZtMJgwMDIR1IzHrrbVQ5PP5UFtbi9zcXF3vtNlsCv8xm+ayxSNgVOHCTWWNWORQhJMZG20FL+xlZ2fj9OnTqh0ZM4Zerzeq0U6ojpDN9FijiERRI5HZoUOHkJycjIyMDD71l6+RBPODH/yAK77X6x0zOmWLmsGLkqwzVFuTYn7rNWvWwOFwqOajsbERfr9ft5tIK99G0SqXXLeN1CWrk5s3b6Kzs5O7UhixKle0aau1Hz26EC2sTbH+So4gCDCbzRGvOTBdYwvizP1rt9sj9rZoGQm/3x+xJ0SXEWG7CdLT02G32/m0VL5jQL7TSb5QZTKZ4HA4eBqRfBg4MDCArKwsvrvE5XLxqSPrhNPT03H8+HFkZWUp0nY4HDCbzUhNTYUgCHC73aisrFQIh/lN2T3yDp0pXLidDEZlpoXVasWRI0dQVlY2ZicG+zdzR7ndbrzzzju68xTsJmCL/0wR5fJYt24dXnzxxYi+fdCSWVVVFX93IBBQLF4XFRXBbrcrXA1Mj6xWK5588kmuC2z9RA+CIOCtt95CIBAIufPL4/GgtraWr4MsXrwYFotFMUM7fPiwQo/q6+u5zNguIfkuROYm1Mq3lg5HU67gunQ4HBHPFNeuXcsNrLyziqY+5DuYSkpKuE6wthdN2ux5tfajJrNoYOtIrN3v2rULW7ZsGXPfjh07FO2QvVtNF0wmE99UIQgCUlNTYTabI9oUw7h06VLYSYAkSWhpadE1UJfzd11dXV/PnDkz5EVJkvD666/Dbrc/9D3soiiivLwcb7zxhqHFwfGAbSeUdxpaTKTM4oFwMvN4POjs7FTsgJsMeL1euN1uhcEjiMmIz+fDL3/5S9TW1obsz4zqOn1sqILNZotoey5BMiOIeIS5NHfu3BnSgLAt4cE7E/VARkSDsrIyBAKBR+IcqPGCZBbfyD9eDP4b7x17kwX5x4vBf7E8my9agneEBf8xd9m5c+dUvSMffvghKioqDHl9VN1ZBEEQBKEGzUQIgiAIw5ARIQiCIAxDRoQgCIIwDBkRgiAIwjBkRAiCIAjDPPXCYscAAA10SURBVJLhcaONMBZJSFQWg0B+tErX0aNoLijAiCjiM6cTl0tL8T8P4UDLL1pbH9q7jPJFaytOzJ6NE7Nno8lmw9CdOxOdpSnJiCiiuaAAXUePYujOHTTZbPiitZVfZ/F8HsXtvkRkPNLhcY2iNyRqNB/wPIowo/r0sWPY1N2NdV4vkubNm+hsxTWfOZ3oOnp03NM1mUzIyclBZWUlxUAnVCF3Voxgp4OGiukww2LBjNmz8V1BQOLcuUgwm/GdCToVOZ4Y6e/HtJQUMhwPge8KAmbMno0ZFgump6YiyWrF9KAvmYuKimA2m3UHgiMeTeI2PK5W2FI9yL9CDfXe+/fvhy2X/JA0I1+ssihuoYLjPLZyJVbU1eE7CQmYs3kznpCdJ9V19Ci6jh7FZ04nd+swN8P/PHiAy6WlCrcDu5f9+9Y776C5oADNBQXo+cMfcGL2bH6dcdfj4WkHj2K7jh7l15jLLfjdLG+RupvkZQr17Igo4isDx1uzZ5sLCnj6cred2jUtmbHyyuUSLLNw5RoRRS6zJpttzLtZ+uFkoqYLLD+3Dx7E1dde49fldS1POzjf30lIwIq6Ojy2ciWmm0x46ve/D2m8N27ciKamJnJrEWGJ2/C4esKWquHz+TBjxgx+tn95efmYIPS//OUvQ5YrOLSoy+XioXX1onZaphZXX3sNiXPnYlN3N5a+/TbuNjToXse429CAxVVVmDF7NgJ/+hPyzp7F4K1b3Bj0NDbi/927h03d3fjxhQu429DAO64vWltx1+3G+k8/xabubsy123G9slLx7o+fe47nLSM/H/4TJ3Tlq+voUTwIBLDh1i1s6u7Gj8rKcHXXLoyIIu8QP37uOfR/+inOLlkS0viFY0QU0bJtG+ba7djU3Y1N3d3cSP/Pgwe4XlnJr224dQsAcPPXv9Yts9sHD2L47t2QMlMrFwB82d2Nz5xOrP7DH7D+00/xZXc3+m/cAAAM3bmDz6qr8eMLF7CpuxvLamsVzwLhdWHO5s3Y1N2N+Tt3Yunbb/NyswHJiCji9sGDPO1N3d2Ys3mzLnnKkccVIYhQxHV4XK2wpWpYrVasX7+e/19PCFuv1wtRFHHt2jWFG4odD663IUmShEAgYDiw1fydO3mDT83Oxn8NDeG/dco7Iz8fyd+MKOdu2YLvBrnJMgoK8KOf/xwAMCMtDTMsFvR/Ey73bxcv4gmHg7s1frhmDf5raAhf9vWFzNusVavwIBDQNHAjooi7bjfmbtnC3XYZ69djxuzZGLpzh3eITx87htQlS7gRe0Lnib//cf48ZsyejQxZfTP6b9zAl93d+OGaNQBGR+ALX31VYSSMyGxEFDXLBQDTUlKwtKYG000mTDeZ8NjKlfjym3gw/deuYe6WLXwGkLpokeLZYHlHqgtfDQzwujUKi4Ex3pE+ialD3IfHjTRsKUMet4DFe1ALymMymfgieWdnJ4+Nws7vP3z4sO53RxpFcaL4TkICEsxmAKPuqgeBAD5+7jnu/ji7ZAmGggznrFWr+L/lbjktpqWkjPG5jxfDd++qriux9afxhBmCaMo1fPeuwhV1asEC9IzT+sN0kwlLa2pw0+Ua45qMlMzMzHGJFU5MTb5r5KGHFR5XHrb0d7/7HSwWi+4YHS6XCwDQ398PQRD4WfrhCI74VV9fbyjoCwDNcK/xAjMccsPw9LFjeGzlypD3RsNXAwOjndg3o+7/liR82d0dVZqMxLlzMXz3btjrX3Z3478liRuZaNZeGDO+qd9oy7X07bcNuZn0kDRvHtZ9s6bYdfQoWrZtQ86770Zs9Do7O2MetpmYvMR1eNxIwpaGgoV5ZDulws1EWLhJVq7c3FzU1tYa9gPH2gXARsFftLbi6muvGU6HuXqS5s3DdxISMPOpp/CZ02l4xBoO5saRr+0wF1TqokVRp5+anY2eM2cUGw4YzFX0H+fPAxg1hncbGjDXbjc0g+g5e5bLLNpyzVq1Cjddrqi+hUmcOxf3PvlE08jPMDioidY1S0x94jY8rlbYUq3Qohs3bsTp06e5O2rx4sVjvulgeUpNTUVOTg4vl81mg8vl4iE6g3dvqYVEZeTm5sLtdo/rrhbmz2cuirsNDciqrIwojZ7GRpxasIAvXMtHpnM2b8Zcu50vbIfaTWSUJxwOJJjN/N133W4srqwcl63NSfPm4an338eV8vIx+Z5uMiHn3Xdx1+3mLqMEszmi0b9cZnfdboXMoinXYytXYlltLT5avTrkjjg9sHUgeZ0CY3dmXSkv52szkdDT04PBwcEJiy5KxD9xGx53ssNkJzdOxOTji9ZW3G1owNL9+x/Jb3mcTicyMzNJh4mw0MeGMUIQBJSWlqKhoYG2RxKTEo/Hg0AgEPJbJ4JgkBGJIVarFRUVFWO+TyGIeEcURbS0tKCyspKO7CFUofC4BEEQhGFoJkIQBEEYhowIQRAEYRgyIgRBEIRhyIgQBEEQhiEjQhAEQRiGwuMaYLKGx9WChUmN9sA+NT5zOqdU2NvxkBk7Cj/UsS3jxdCdO/jkhRdiUqdGYDFe4kX3gW9j5rCjf1j4YDlOp9NwvzFVofC4Bpiq4XHZgX0/vnAB01TKFY5466geBtHKjJhcbNy4EdXV1fQBsQxDp/gS2kQSHhfAI3OkxhMOh+44IY8KczZvjtlJvvHKdJMJeXEWdpeFRkicO1cRPliO/ANivQHypjpxGx7X4/GMcQOF+k0rjckWHhcIH8412vC4X7S2jgkb+8kLL+h2LQXni6XLfv9o9Wr87eJFfoCj3G2l5vb5orVV87fgdwe7GdRgclMLIRscgpjJSU94XCMyC74WysWnFh5Xq1zysp2YPRsfrV6t+/h7I/UhfzebkQ5//jm/Jzgkcbg8a6Wtpz7UwiFryewJhwNzNm9WhA8OZvHixQC+beOPOnEbHjc7OxtdXV3o6ekBMGrQWlpadB9DP1nD46qFetWDVqjXaOg5cwY5776LTd3dWP/pp/iitRVftLbyUeWPL1zArFWreGTCdV4vP4pdze3D7pF3ol/6/Tz+d3CI2/Wffoq7brfuNYSbv/41EsxmHh73QSDAO57HVq7E0rff5n7wnrNnAUBx4KJaeFyjMgO+HY2v//RTJIVpE2qhktXKFRx6NxJ3m1Z9aJULAIZ8Pvy5uBhLa2rGhAVmkSyXvv12xDIDlPXx9LFjuOt2K4ybWvtRk5leBEFATk4OLl26FNFzU5W4DY9rtVrx5JNP4to34T3ZkdRr167V/fxkDI+rFupVD1qhXqPh8ZIS3okEh3qNhukmE+ba7fjbxYsARkeL//fGDVg2bQIAfNnXh2lJSVwmwferMSKKeBAIwLpjB4BRl8XcLVsUMTjmbN6Mx1auROe//it6m5qw8NVXFUY7XHhcPUQrs3DhcbXK5T9xQhF6NxK06kNvuZbV1irirugtt1ba8vpImjcP01JSMNLfD0C9/ejRBb1YLJaHFiY83jG0JsLC48baH8hichQUFODatWtYt24dTDrjIbCj2OVhbdUMUHB4XBYfRY7dbtf17mjC42qFep1IvmhtxcfPPaf4LdxoMlJSs7PR29Q0Grv8mw5hRloagNHG/+9HjuDfjxxRPDN/507NdEf6+yFevYqzS5Yofs8IcjNad+zgI1g9HS8bmWsRK5npLZdR1OoD0C5XktWqkGMk62DRyEyt/YynzEwmEx+UPurrInEdHtdqtWJwcBA+nw83btzA1q1bdT87WcPjaoV6nSiG7tzBlfJyRfjcUP5so8xIS8P/TkrC0J07+NLvx/cWLRozGzAa0yPJasXqP/whbEAm5i6b9+KLuPO732GGxaJpIPRECoy1zLTKFQ1q9RHLckWbtlb7GS+ZBfcXjzJxHR7XZDJh3bp1eOONNzBr1qyIo6tNxvC4aqFeGdGEx2XxxlnHORRBGf/+scd44/uitRW3Dx5UXJ+emoqvBgYMfQPCwvMO3LyJ4bt38cM1a/i1pHnz8GV3N1+viATWGfpU1vFYuua8PDzhcKiGCJaHx9WDlsyMolUuedjcEVHE1V27Ioorr1YfQOzKFW3aau1Hjy7oxe/38/7lUSduw+MysrOz8Z//+Z9YtmyZ4vepGh5XLdRrtOFxH1u5Eo+tXImzS5bg1IIFMP/kJ4oFXbbjRb7Liu3ISZo3D4/ZbDyU692GBvyf4mJF+tNNJjzhcODj554bs+NILW3GD9esQc832z7lI8XgELehdiqF4zsJCVi6fz8eBAKKZ9liatfRo7jpcvF1kNRFizBj9my0bNvG86YWHjcambHdU2eXLMHfLl7ER6tX6/4QU6tc8rC5F/75n/GEw4EZs2drpisnXH3o0YVwyHdHXX3tNdw+eFCxSyqatNnzau1HTWZ6YZt8cnNzI3puqhL34XGZG6q2tlb3ekg8QOFxpwaPenhcYixerxdut5u+E/mGuD47i7mhIllQjxcoPC5BTD0m2ykUD4O4NCLsbKvU1FSYzeZJO5Kn8LgEMbX48MMPUVFREfH67FSGwuMSBEEQhonLmQhBEAQxOSAjQhAEQRiGjAhBEARhGDIiBEEQhGHIiBAEQRCGISNCEARBGIaMCEEQBGEYMiIEQRCEYf4/HivvhWQGb0cAAAAASUVORK5CYII=)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tET0fiIZ0bAz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "d65add9f-dcd6-4bcd-ab51-a6288a01ab34"
      },
      "source": [
        "#Ketik kodingan disini\n",
        "pca = PCA()\n",
        "pca_data = pca.fit_transform(X_scaled)\n",
        "\n",
        "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
        "plt.xlabel(\"number of components\")\n",
        "plt.ylabel(\"cumulative explained varience\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'cumulative explained varience')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tUyDKoiJ4k-R"
      },
      "source": [
        "Berapa nilai n_components agar cumulative explained variance minimal 75%? Sekarang tentukan berapa n_components yang menurutmu cukup bagus lalu transform datamu dengan kodingan seperti ini\n",
        "\n",
        "![pca 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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEDRgbMk3vSh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "720b3a36-870c-4dca-e130-285685018fad"
      },
      "source": [
        "# Ketik kodingannya disini\n",
        "n = 5\n",
        "pca = PCA(n_components=n)\n",
        "X_new_pca = pca.fit_transform(X_scaled)\n",
        "total_var = pca.explained_variance_ratio_.sum() * 100\n",
        "print(total_var, '% total varience is exxplained by',n, 'principal components')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "56.54671010217841 % total varience is exxplained by 5 principal components\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UbKdrvu0tmZl"
      },
      "source": [
        "## LDA\n",
        "\n",
        "![lda 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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYmwIkkktld8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "e7eefe1a-f1b5-4115-dba1-9e705de4a31b"
      },
      "source": [
        "#Ketik kodingannya disini\n",
        "lda = LinearDiscriminantAnalysis()\n",
        "lda_data = lda.fit_transform(X_scaled, Y)\n",
        "\n",
        "plt.plot(np.cumsum(lda.explained_variance_ratio_))\n",
        "plt.xlabel(\"number of components\")\n",
        "plt.ylabel(\"cumulative explained varience\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'cumulative explained varience')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kHT4FPPut6CS"
      },
      "source": [
        "Berapa nilai n_components agar cumulative explained value minimal 75%? Sekarang tentukan berapa n_components yang menurutmu cukup bagus lalu transform datamu dengan kodingan seperti ini\n",
        "\n",
        "![lda 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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8LpOebrruC77",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0823e9ee-6978-4451-ec72-3aa42e93804d"
      },
      "source": [
        "#Ketik kodingannya disini\n",
        "l = 5\n",
        "lda = LinearDiscriminantAnalysis(n_components=1)\n",
        "X_new_lda = lda.fit_transform(X_scaled, Y)\n",
        "total_var = lda.explained_variance_ratio_.sum() * 100\n",
        "print(total_var, '% of total varience is explained by',1, 'features')"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "57.804331485385894 % of total varience is explained by 1 features\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXMmGOvA0Y0e"
      },
      "source": [
        "#Model KNN\n",
        "Buat model knn dari data asli dan data yang sudah dilakukan dimensional reduction\n",
        "\n",
        "Evaluasi berdasarkan akurasi dan juga lama training hingga membuat prediksi. Berikut ada contoh menghitung waktu dengan library 'time'\n",
        "\n",
        "![knn 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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6nH2J6B12T_4"
      },
      "source": [
        "Kodingan diatas hanya contoh, tidak untuk diketik ulang. Nah silahkan memulai proses training data dengan knn"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxSGoayLkiL9"
      },
      "source": [
        "#Training dan Testing data asli dan data dengan feature selection\n",
        "# Raw\n",
        "x_train_raw, x_test_raw, y_train_raw, y_test_raw = train_test_split(X, Y, test_size=0.3, random_state=42)\n",
        "\n",
        "#Feature Selection\n",
        "x_train_fs, x_test_fs, y_train_fs, y_test_fs = train_test_split(X_new_a, Y, test_size=0.3, random_state=42)\n",
        "\n",
        "#PCA\n",
        "x_train_pca, x_test_pca, y_train_pca, y_test_pca = train_test_split(X_new_pca, Y, test_size=0.3, random_state=42)\n",
        "\n",
        "#LDA\n",
        "x_train_lda, x_test_lda, y_train_lda, y_test_lda = train_test_split(X_new_lda, Y, test_size=0.3, random_state=42)\n"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHw5eYajkqed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "14b30125-8eda-46db-b1d9-8c2b955551a9"
      },
      "source": [
        "#Train Model and Predict menggunakan data asli\n",
        "acc = []\n",
        "# Will take some time\n",
        "from sklearn import metrics\n",
        "for i in range(1,100):\n",
        "  neigh = KNeighborsClassifier(n_neighbors=i).fit(x_train_raw, y_train_raw)\n",
        "  yhat = neigh.predict(x_test_raw)\n",
        "  acc.append(metrics.accuracy_score(y_test_raw, yhat))\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(range(1,100),acc,color = 'blue', linestyle='dashed',\n",
        "         marker='o',markerfacecolor='red', markersize=5)\n",
        "plt.title('accuracy vs. K Value')\n",
        "plt.xlabel('K')\n",
        "plt.ylabel('Accuracy')\n",
        "print('Maximum accuracy:-', max(acc),\"at K = \",acc.index(max(acc)))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Maximum accuracy:- 0.32900677200902934 at K =  92\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "etOLavSGkzaG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d10cf2a3-7ea0-4766-9839-33663ffbefef"
      },
      "source": [
        "n = 10\n",
        "KNN = KNeighborsClassifier(n_neighbors=n)\n",
        "\n",
        "#Train Model and Predict menggunakan data asli\n",
        "model_raw = KNN.fit(x_train_raw, y_train_raw)\n",
        "pred_raw = model_raw.predict(x_test_raw)\n",
        "print(\"Akurasi model data asli : \", metrics.accuracy_score(y_test_raw, pred_raw))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi model data asli :  0.27539503386004516\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RmJKi_O3M1J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e046e671-8b67-431e-9fd8-e313412951ca"
      },
      "source": [
        "#Train Model and Predict menggunakan data dengan feature selection\n",
        "model_fs = KNN.fit(x_train_fs, y_train_fs)\n",
        "pred_fs = model_fs.predict(x_test_fs)\n",
        "print(\"Akurasi model data feature selection : \", metrics.accuracy_score(y_test_fs, pred_fs))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi model data feature selection :  0.4040632054176072\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NSqi4Bp3Zrs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69630c8d-2f83-4784-e1b2-adf2e0c4ac55"
      },
      "source": [
        "#Train Model and Predict menggunakan data dengan PCA\n",
        "model_pca = KNN.fit(x_train_pca, y_train_pca)\n",
        "pred_pca = model_pca.predict(x_test_pca)\n",
        "print(\"Akurasi model data pca : \", metrics.accuracy_score(y_test_pca, pred_pca))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi model data pca :  0.34452595936794583\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RLoceA9Olqzq",
        "outputId": "a02fbb9d-f585-485c-8abe-aa56400fe168"
      },
      "source": [
        "#Train Model and Predict menggunakan data dengan LDA\n",
        "model_lda = KNN.fit(x_train_lda, y_train_lda)\n",
        "pred_lda = model_lda.predict(x_test_lda)\n",
        "print(\"Akurasi model data lda : \", metrics.accuracy_score(y_test_lda, pred_lda))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Akurasi model data lda :  0.27172686230248305\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sK7u1bocmQas"
      },
      "source": [
        "Apa yang dapat kamu simpulkan dari akurasi dan lama waktu training dari data sebelum dan sesudah dimensionality reduction?"
      ]
    }
  ]
}