KOMPUTASI BIG DATA_ACT_PERT_8.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "source": [
        "# KUMAL_KBD_ACT_PERT 8"
      ],
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      }
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "\n",
        "# Praktikum VIII\n",
        "\n",
        "Selamat datang pada sesi ke-8 praktikum unggulan Universitas Gunadarma. Melanjutkan kegiatan praktikum sebelumnya, pada minggu ini Anda akan melanjutkan tahapan pengolahan data yaitu data training dan model generation. \n",
        "\n",
        "### Dataset\n",
        "Dataset yang Anda gunakan yaitu dataset Beton (concrete) yang berisi informasi mengenai komposisi beton, pengaruh waktu dan hasil kekuatan beton yang berisi 1030 row data dengan 9 fitur. \n",
        "\n",
        "### Studi Kasus\n",
        "Dengan menggunakan dataset tersebut Anda akan diminta menyelesaikan permasalahan klasifikasi, dimana model akan diminta untuk menentukan kekuatan beton berdasarkan kombinasi fitur yang dimiliki. \n",
        "\n",
        "### Algoritma\n",
        "Algoritma yang digunakan adalah K-Nearest Neighbour."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqLnL107rxxQ"
      },
      "source": [
        "# Import Dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2xsTWbTfrxxR"
      },
      "outputs": [],
      "source": [
        "# Import library\n",
        "import pandas as pd\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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          "height": 206
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              "   cement   slag    ash  water  superplastic  coarseagg  fineagg  age  \\\n",
              "0   141.3  212.0    NaN  203.5           NaN      971.8    748.5   28   \n",
              "1   168.9   42.2  124.3  158.3          10.8     1080.8    796.2   14   \n",
              "2   250.0    NaN   95.7  187.4           5.5      956.9    861.2   28   \n",
              "3   266.0  114.0    NaN  228.0           NaN      932.0    670.0   28   \n",
              "4   154.8  183.4    NaN  193.3           9.1     1047.4    696.7   28   \n",
              "\n",
              "   strength  \n",
              "0     29.89  \n",
              "1     23.51  \n",
              "2     29.22  \n",
              "3     45.85  \n",
              "4     18.29  "
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              "      <th>cement</th>\n",
              "      <th>slag</th>\n",
              "      <th>ash</th>\n",
              "      <th>water</th>\n",
              "      <th>superplastic</th>\n",
              "      <th>coarseagg</th>\n",
              "      <th>fineagg</th>\n",
              "      <th>age</th>\n",
              "      <th>strength</th>\n",
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              "      <th>0</th>\n",
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              "      <td>212.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>971.8</td>\n",
              "      <td>748.5</td>\n",
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              "      <td>29.89</td>\n",
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              "      <th>1</th>\n",
              "      <td>168.9</td>\n",
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              "      <td>124.3</td>\n",
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              "      <td>10.8</td>\n",
              "      <td>1080.8</td>\n",
              "      <td>796.2</td>\n",
              "      <td>14</td>\n",
              "      <td>23.51</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>250.0</td>\n",
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              "      <td>95.7</td>\n",
              "      <td>187.4</td>\n",
              "      <td>5.5</td>\n",
              "      <td>956.9</td>\n",
              "      <td>861.2</td>\n",
              "      <td>28</td>\n",
              "      <td>29.22</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>266.0</td>\n",
              "      <td>114.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>228.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>932.0</td>\n",
              "      <td>670.0</td>\n",
              "      <td>28</td>\n",
              "      <td>45.85</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>154.8</td>\n",
              "      <td>183.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>193.3</td>\n",
              "      <td>9.1</td>\n",
              "      <td>1047.4</td>\n",
              "      <td>696.7</td>\n",
              "      <td>28</td>\n",
              "      <td>18.29</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "#### import dataset \n",
        "data = pd.read_csv('https://gitlab.com/andreass.bayu/file-directory/-/raw/main/new_concreate.csv')\n",
        "\n",
        "# Lihat 5 data awal\n",
        "data.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cwXz_tBlxHhd"
      },
      "source": [
        "# Review Dataset"
      ]
    },
    {
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      "outputs": [
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          "data": {
            "text/plain": [
              "            cement        slag         ash        water  superplastic  \\\n",
              "count  1030.000000  559.000000  464.000000  1030.000000    651.000000   \n",
              "mean    281.167864  136.158676  120.288793   181.567282      9.816897   \n",
              "std     104.506364   72.351823   33.675470    21.354219      4.580328   \n",
              "min     102.000000   11.000000   24.500000   121.800000      1.700000   \n",
              "25%     192.375000   95.000000   97.850000   164.900000      6.950000   \n",
              "50%     272.900000  135.700000  121.400000   185.000000      9.400000   \n",
              "75%     350.000000  189.000000  141.000000   192.000000     11.600000   \n",
              "max     540.000000  359.400000  200.100000   247.000000     32.200000   \n",
              "\n",
              "         coarseagg      fineagg          age     strength  \n",
              "count  1030.000000  1030.000000  1030.000000  1030.000000  \n",
              "mean    972.918932   773.580485    45.662136    35.817961  \n",
              "std      77.753954    80.175980    63.169912    16.705742  \n",
              "min     801.000000   594.000000     1.000000     2.330000  \n",
              "25%     932.000000   730.950000     7.000000    23.710000  \n",
              "50%     968.000000   779.500000    28.000000    34.445000  \n",
              "75%    1029.400000   824.000000    56.000000    46.135000  \n",
              "max    1145.000000   992.600000   365.000000    82.600000  "
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              "      <th>cement</th>\n",
              "      <th>slag</th>\n",
              "      <th>ash</th>\n",
              "      <th>water</th>\n",
              "      <th>superplastic</th>\n",
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              "      <th>strength</th>\n",
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              "      <th>count</th>\n",
              "      <td>1030.000000</td>\n",
              "      <td>559.000000</td>\n",
              "      <td>464.000000</td>\n",
              "      <td>1030.000000</td>\n",
              "      <td>651.000000</td>\n",
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              "      <td>1030.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>281.167864</td>\n",
              "      <td>136.158676</td>\n",
              "      <td>120.288793</td>\n",
              "      <td>181.567282</td>\n",
              "      <td>9.816897</td>\n",
              "      <td>972.918932</td>\n",
              "      <td>773.580485</td>\n",
              "      <td>45.662136</td>\n",
              "      <td>35.817961</td>\n",
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              "      <th>std</th>\n",
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              "      <td>21.354219</td>\n",
              "      <td>4.580328</td>\n",
              "      <td>77.753954</td>\n",
              "      <td>80.175980</td>\n",
              "      <td>63.169912</td>\n",
              "      <td>16.705742</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>102.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>24.500000</td>\n",
              "      <td>121.800000</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>801.000000</td>\n",
              "      <td>594.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.330000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>192.375000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>97.850000</td>\n",
              "      <td>164.900000</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>932.000000</td>\n",
              "      <td>730.950000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>23.710000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>272.900000</td>\n",
              "      <td>135.700000</td>\n",
              "      <td>121.400000</td>\n",
              "      <td>185.000000</td>\n",
              "      <td>9.400000</td>\n",
              "      <td>968.000000</td>\n",
              "      <td>779.500000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>34.445000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>350.000000</td>\n",
              "      <td>189.000000</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>192.000000</td>\n",
              "      <td>11.600000</td>\n",
              "      <td>1029.400000</td>\n",
              "      <td>824.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>46.135000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>540.000000</td>\n",
              "      <td>359.400000</td>\n",
              "      <td>200.100000</td>\n",
              "      <td>247.000000</td>\n",
              "      <td>32.200000</td>\n",
              "      <td>1145.000000</td>\n",
              "      <td>992.600000</td>\n",
              "      <td>365.000000</td>\n",
              "      <td>82.600000</td>\n",
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              "    }\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-e9201d30-2bc9-4fa8-ba37-2577b1c79b0f 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-e9201d30-2bc9-4fa8-ba37-2577b1c79b0f');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Lihat deskripsi dari tiap kolom\n",
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-08-02T19:51:57.440926Z",
          "iopub.status.busy": "2021-08-02T19:51:57.440544Z",
          "iopub.status.idle": "2021-08-02T19:51:57.447612Z",
          "shell.execute_reply": "2021-08-02T19:51:57.446412Z",
          "shell.execute_reply.started": "2021-08-02T19:51:57.440898Z"
        },
        "id": "Gr28BmmQxYuE",
        "outputId": "14af6270-27f3-4ab3-bf56-1458ed590c36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cement          float64\n",
              "slag            float64\n",
              "ash             float64\n",
              "water           float64\n",
              "superplastic    float64\n",
              "coarseagg       float64\n",
              "fineagg         float64\n",
              "age               int64\n",
              "strength        float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Lihat tipe data dari tiap kolom\n",
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-08-02T19:51:57.449285Z",
          "iopub.status.busy": "2021-08-02T19:51:57.448989Z",
          "iopub.status.idle": "2021-08-02T19:51:57.459741Z",
          "shell.execute_reply": "2021-08-02T19:51:57.458818Z",
          "shell.execute_reply.started": "2021-08-02T19:51:57.449259Z"
        },
        "id": "1ETYk0wwxbiV",
        "outputId": "f7758928-86a0-46de-82bf-fdd3749420bd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1030, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# mlihat jumlah atribut dan data / dimensi data\n",
        "data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-08-02T19:51:57.461308Z",
          "iopub.status.busy": "2021-08-02T19:51:57.461003Z",
          "iopub.status.idle": "2021-08-02T19:51:57.589875Z",
          "shell.execute_reply": "2021-08-02T19:51:57.588794Z",
          "shell.execute_reply.started": "2021-08-02T19:51:57.46128Z"
        },
        "id": "Qoo0mIOzxnsl",
        "outputId": "647e06c1-3d98-436a-d546-1a4311b12f23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Attribute name: cement\n",
            "-------------------\n",
            "362.6    20\n",
            "425.0    20\n",
            "251.4    15\n",
            "310.0    14\n",
            "446.0    14\n",
            "         ..\n",
            "288.4     1\n",
            "317.9     1\n",
            "264.5     1\n",
            "485.0     1\n",
            "276.4     1\n",
            "Name: cement, Length: 278, dtype: int64\n",
            "-------------------\n",
            "Attribute name: slag\n",
            "-------------------\n",
            "189.0    30\n",
            "106.3    20\n",
            "24.0     14\n",
            "20.0     12\n",
            "145.0    11\n",
            "         ..\n",
            "161.6     1\n",
            "133.7     1\n",
            "139.0     1\n",
            "162.0     1\n",
            "115.0     1\n",
            "Name: slag, Length: 184, dtype: int64\n",
            "-------------------\n",
            "Attribute name: ash\n",
            "-------------------\n",
            "118.3    20\n",
            "141.0    16\n",
            "24.5     15\n",
            "79.0     14\n",
            "94.0     13\n",
            "         ..\n",
            "99.6      1\n",
            "109.0     1\n",
            "132.6     1\n",
            "60.0      1\n",
            "90.3      1\n",
            "Name: ash, Length: 155, dtype: int64\n",
            "-------------------\n",
            "Attribute name: water\n",
            "-------------------\n",
            "192.0    118\n",
            "228.0     54\n",
            "185.7     46\n",
            "203.5     36\n",
            "186.0     28\n",
            "        ... \n",
            "190.5      1\n",
            "201.3      1\n",
            "167.9      1\n",
            "167.8      1\n",
            "179.6      1\n",
            "Name: water, Length: 195, dtype: int64\n",
            "-------------------\n",
            "Attribute name: superplastic\n",
            "-------------------\n",
            "11.6    37\n",
            "8.0     27\n",
            "7.0     19\n",
            "6.0     17\n",
            "9.9     16\n",
            "        ..\n",
            "6.3      1\n",
            "2.2      1\n",
            "18.3     1\n",
            "4.0      1\n",
            "7.2      1\n",
            "Name: superplastic, Length: 110, dtype: int64\n",
            "-------------------\n",
            "Attribute name: coarseagg\n",
            "-------------------\n",
            "932.0     57\n",
            "852.1     45\n",
            "944.7     30\n",
            "968.0     29\n",
            "1125.0    24\n",
            "          ..\n",
            "990.0      1\n",
            "924.1      1\n",
            "892.0      1\n",
            "924.0      1\n",
            "979.5      1\n",
            "Name: coarseagg, Length: 284, dtype: int64\n",
            "-------------------\n",
            "Attribute name: fineagg\n",
            "-------------------\n",
            "755.8    30\n",
            "594.0    30\n",
            "670.0    23\n",
            "613.0    22\n",
            "801.0    16\n",
            "         ..\n",
            "760.1     1\n",
            "764.0     1\n",
            "794.0     1\n",
            "805.3     1\n",
            "768.3     1\n",
            "Name: fineagg, Length: 302, dtype: int64\n",
            "-------------------\n",
            "Attribute name: age\n",
            "-------------------\n",
            "28     425\n",
            "3      134\n",
            "7      126\n",
            "56      91\n",
            "14      62\n",
            "90      54\n",
            "100     52\n",
            "180     26\n",
            "91      22\n",
            "365     14\n",
            "270     13\n",
            "360      6\n",
            "120      3\n",
            "1        2\n",
            "Name: age, dtype: int64\n",
            "-------------------\n",
            "Attribute name: strength\n",
            "-------------------\n",
            "33.40    6\n",
            "35.30    4\n",
            "71.30    4\n",
            "23.52    4\n",
            "79.30    4\n",
            "        ..\n",
            "17.34    1\n",
            "48.70    1\n",
            "31.64    1\n",
            "35.85    1\n",
            "52.61    1\n",
            "Name: strength, Length: 845, dtype: int64\n",
            "-------------------\n"
          ]
        }
      ],
      "source": [
        "# hitung dan melihat jumlah data per label kelas\n",
        "for col in data.columns:\n",
        "    print('Attribute name:',col)\n",
        "    print('-------------------')\n",
        "    print(data[col].value_counts())\n",
        "    print('-------------------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-08-02T19:51:57.591277Z",
          "iopub.status.busy": "2021-08-02T19:51:57.591Z",
          "iopub.status.idle": "2021-08-02T19:51:58.664357Z",
          "shell.execute_reply": "2021-08-02T19:51:58.663562Z",
          "shell.execute_reply.started": "2021-08-02T19:51:57.59125Z"
        },
        "id": "1u13oyo3Il6D",
        "outputId": "03580a98-4843-4b46-e9ca-326a98bdc0ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc7cfcc0c90>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#import library seaborn untuk visualisasi\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# Plot figure untuk menentukan distribusi kelas\n",
        "plt.figure(figsize=(8,5))\n",
        "\n",
        "# menghitung baris setiap kelas\n",
        "sns.countplot(x=\"strength\", data=data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "luyhkwx2O0f4"
      },
      "source": [
        "#### *Dataset memiliki distribusi nilai untuk kelas strength yang beragam.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JFU-t0ovzcmM"
      },
      "source": [
        "# Dataset preparation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-08-02T19:51:58.666882Z",
          "iopub.status.busy": "2021-08-02T19:51:58.666267Z",
          "iopub.status.idle": "2021-08-02T19:51:58.723304Z",
          "shell.execute_reply": "2021-08-02T19:51:58.722606Z",
          "shell.execute_reply.started": "2021-08-02T19:51:58.666835Z"
        },
        "id": "zGMvzHbtzim7"
      },
      "outputs": [],
      "source": [
        "# Buat salinan dataframe\n",
        "df = data.copy(deep = True)\n",
        "\n",
        "# mengubah/convert nilai \"?\" nilai ke bentuk Na / NaN untuk diproses lebih lanjut\n",
        "for col in data.columns:\n",
        "  df[[col]] = data[[col]].replace('?',np.NaN)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-08-02T19:51:58.7255Z",
          "iopub.status.busy": "2021-08-02T19:51:58.724873Z",
          "iopub.status.idle": "2021-08-02T19:51:58.786331Z",
          "shell.execute_reply": "2021-08-02T19:51:58.785238Z",
          "shell.execute_reply.started": "2021-08-02T19:51:58.725456Z"
        },
        "id": "RhZ9EGZr1ZtZ",
        "outputId": "9091ff2d-f7d0-4b8f-f077-1db446542dde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cement            0\n",
              "slag            471\n",
              "ash             566\n",
              "water             0\n",
              "superplastic    379\n",
              "coarseagg         0\n",
              "fineagg           0\n",
              "age               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "\n",
        "# seleksi kolom fitur/feature columns dari dataset\n",
        "null_data = df.iloc[:,:-1]\n",
        "\n",
        "# temukan nilai null untuk semua atribut dan jumlahkan total nilai null\n",
        "null_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-08-02T19:51:58.788237Z",
          "iopub.status.busy": "2021-08-02T19:51:58.787813Z",
          "iopub.status.idle": "2021-08-02T19:51:58.912695Z",
          "shell.execute_reply": "2021-08-02T19:51:58.911541Z",
          "shell.execute_reply.started": "2021-08-02T19:51:58.788196Z"
        },
        "id": "v9_9h1ys1rTL",
        "outputId": "c9cff076-4b97-4e07-c1b2-60e9483e58ac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cement          0\n",
              "slag            0\n",
              "ash             0\n",
              "water           0\n",
              "superplastic    0\n",
              "coarseagg       0\n",
              "fineagg         0\n",
              "age             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "\n",
        "# jatuhkan/drop semua baris yang memiliki nilai null\n",
        "df = df.dropna()\n",
        "\n",
        "# pilih kolom fitur/feature columns dari dataset\n",
        "null_data = df.iloc[:,:-1]\n",
        "\n",
        "# cek ulang nilai null\n",
        "null_data.isnull().sum()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sza89HfmrxxW"
      },
      "source": [
        "**StandardScaler** *adalah class dari sklearn untuk melakukan normalisasi data agar data yang digunakan tidak memiliki penyimpangan yang besar.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0ZDtpu3VrxxW"
      },
      "outputs": [],
      "source": [
        "# Import library standard scaler \n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Buat dataframe dengan tipe data int64\n",
        "colname= []\n",
        "for col in df.columns:\n",
        "  if df[col].dtype == \"int64\":\n",
        "      colname.append(col)\n",
        "\n",
        "# Buat salinan dataset untuk keperluan persiapan data / data preparation\n",
        "df_copy = df.copy(deep = True)\n",
        "df_fe = df.copy()\n",
        "\n",
        "\n",
        "# Buat kerangka data untuk fitur kategoris / categorical features\n",
        "df_fe.drop('strength',axis='columns', inplace=True)\n",
        "df_fe.drop(colname,axis='columns', inplace=True)\n",
        "\n",
        "\n",
        "# buat dataframe untuk kelas target / target class\n",
        "df_cl = df.copy()\n",
        "df_cl.drop(df_copy.iloc[:,:-1],axis='columns', inplace=True)\n",
        "\n",
        "\n",
        "\n",
        "# membuat objek scaler / scaler object\n",
        "std_scaler = StandardScaler()\n",
        "std_scaler\n",
        "\n",
        "\n",
        "# Normalisasikan atribut numerik dan tetapkan ke dalam dataframe baru\n",
        "df_norm = pd.DataFrame(std_scaler.fit_transform(df_copy[colname]), columns=colname)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YjQQ3LksrxxX"
      },
      "source": [
        "Mengapa Menskalakan data untuk KNN?\n",
        "Ketika Anda menggunakan algoritma berbasis jarak seperti KNN, proses penyesuaian skala data sangatlah penting karena akan mempengaruhi nilai dari proses perhitungan jarak dilakukan dengan skala yang sama.\n",
        "\n",
        "Sebagai ilustrasi perhitungan jarak menggunakan dua fitur yang besaran/rentangnya sangat bervariasi dengan menggunakan perhitungan jarak eucledian.\n",
        "Jarak Euclidean = [(100000–80000)^2 + (30–25)^2]^(1/2)\n",
        "\n",
        "fitur dengan jangkauan yang lebih besar akan mendominasi atau mengecilkan fitur yang lebih kecil sepenuhnya dan ini akan berdampak pada kinerja semua model berbasis jarak karena akan memberikan bobot yang lebih tinggi pada variabel yang memiliki nilai yang lebih tinggi.¶"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kQqeWENvrxxX"
      },
      "outputs": [],
      "source": [
        "# import library Ordinal Encoder dari package library sklearn.preprocessing \n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "ord_enc = OrdinalEncoder()\n",
        "\n",
        "# enconde fitur kategoris/categorical features menjadi fitur numerik/numerical features   \n",
        "for col in df_fe.columns[:]:\n",
        "  if df_fe[col].dtype == \"object\":\n",
        "    df_fe[col] = ord_enc.fit_transform(df_fe[[col]])\n",
        "    "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9qjPY68rxxX"
      },
      "source": [
        "#### Karena dataset ini belum memiliki label kelas, mari kita tentukan kolom strength sebagai kolom 'target' yang ingin di prediksi. Berdasarkan hasil analisa statistik awal, didapatkan nilai rata-rata dari kolom strength adalah 35, sehingga mari kita tentukan bahwa threshold(batas ambang) dari sebuah campuran semen yang di nilai kuat adalah yang memiliki nilai strength > 35.\n",
        "\n",
        "Sehingga, selanjutnya kita akan melakukan proses encoding untuk kolom fitru strength, dimana nilai yang > 35 akan diubah menjadi nilai 1 (yang berarti kuat) dan nilai <35 diubah menjadi nilai 0 (yang berarti kategori kurang kuat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MeHbQww3rxxY"
      },
      "outputs": [],
      "source": [
        "# Melakukan proses encoding untuk mengubah fitur strength menjadi nilai biner. \n",
        "df_cl[\"strength\"] = np.where(df_cl[\"strength\"]<35, 0, 1)    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L_nHWEDMrxxY",
        "outputId": "932d13f5-fa42-433f-ead2-23fba31dd2d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(225, 2)\n",
            "(225, 8)\n",
            "(225, 2)\n"
          ]
        }
      ],
      "source": [
        "# Masukkan kolom id ke datasets yang berbeda\n",
        "df_norm.insert(0, 'id', range(0, 0 + len(df_norm)))\n",
        "df_fe.insert(0, 'id', range(0, 0 + len(df_fe)))\n",
        "df_cl.insert(0, 'id', range(0, 0 + len(df_cl)))\n",
        "\n",
        "# Lihat shapes datasets yang telah di proses \n",
        "print(df_norm.shape)\n",
        "print(df_fe.shape)\n",
        "print(df_cl.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "q_cd0788rxxY",
        "outputId": "7d7bcd4a-1f1b-4aae-a38d-ea1dbe249b2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  cement   slag    ash  water  superplastic  coarseagg  fineagg  \\\n",
              "0 -0.720253   168.9   42.2  124.3  158.3          10.8     1080.8    796.2   \n",
              "1 -0.129574   155.0  184.0  143.0  194.0           9.0      880.0    699.0   \n",
              "2 -0.129574   151.8  178.1  138.7  167.5          18.3      944.0    694.6   \n",
              "3 -0.129574   167.0  187.0  195.0  185.0           7.0      898.0    636.0   \n",
              "4  2.908201   213.8   98.1   24.5  181.7           6.7     1066.0    785.5   \n",
              "\n",
              "   strength  \n",
              "0         0  \n",
              "1         0  \n",
              "2         1  \n",
              "3         0  \n",
              "4         1  "
            ],
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Gabungkan semua datasets\n",
        "df_feature = pd.merge(df_norm,df_fe, on=[\"id\"])\n",
        "df_final = pd.merge(df_feature,df_cl, on=[\"id\"])\n",
        "\n",
        "# Drop kolom id dari gabungan dataset\n",
        "df_final.drop('id',axis='columns', inplace=True)\n",
        "\n",
        "# Lihat 5 data awal dari gabungan dataset\n",
        "df_final.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5WnXD0r-lRp"
      },
      "source": [
        "# Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-08-02T19:51:59.498489Z",
          "iopub.status.busy": "2021-08-02T19:51:59.498043Z",
          "iopub.status.idle": "2021-08-02T19:52:01.842571Z",
          "shell.execute_reply": "2021-08-02T19:52:01.841482Z",
          "shell.execute_reply.started": "2021-08-02T19:51:59.498435Z"
        },
        "id": "gMj_fHYx-rex",
        "outputId": "cb6ea801-4c87-4e98-e701-88bec95ef2c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "p = df_final.hist(figsize = (20,20))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lwIyyxhFrxxa"
      },
      "source": [
        "**Scatter matrix plot adalah** plot yang digunakan untuk membuat sekumpulan scatter plot dari beberapa pasang variabel. Hal ini sangat bermanfaat terutama ketika ingin menganalisis bagaimana bentuk hubungan antar variabel. Plot ini sangat bermanfaat untuk digunakan untuk data yang ukurannya tidak terlalu besar. Untuk menggunakan scatter matrix kita harus memanggil fungsi *scatter_matrix* dari *pandas.plotting*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-08-02T19:52:01.844486Z",
          "iopub.status.busy": "2021-08-02T19:52:01.84408Z",
          "iopub.status.idle": "2021-08-02T19:52:52.875142Z",
          "shell.execute_reply": "2021-08-02T19:52:52.872924Z",
          "shell.execute_reply.started": "2021-08-02T19:52:01.844429Z"
        },
        "id": "09AdO4UK_tPx",
        "outputId": "474e2c6f-faf0-4032-be5a-fe7a4695f024",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1800 with 81 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from pandas.plotting import scatter_matrix\n",
        "\n",
        "p=scatter_matrix(df_final,figsize=(25, 25))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-08-02T19:52:52.876884Z",
          "iopub.status.busy": "2021-08-02T19:52:52.876585Z",
          "iopub.status.idle": "2021-08-02T19:52:54.5258Z",
          "shell.execute_reply": "2021-08-02T19:52:54.524791Z",
          "shell.execute_reply.started": "2021-08-02T19:52:52.876855Z"
        },
        "id": "jXTWC_4jA5To",
        "outputId": "0237f373-4b5a-4f7f-c94d-51683cc4ea4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Buat visualisasi korelasi data dengan heatmap\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        " # plot heatmap\n",
        "plt.figure(figsize=(12,10))\n",
        "p=sns.heatmap(df_final.corr(), annot=True,cmap ='RdYlGn')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMKB9YChrxxa"
      },
      "source": [
        "#### Koefisien Korelasi Pearson: \n",
        "\n",
        "Koefisien pearson mambantu kita mengetahui keterhubungan antara dua fitur atau variable. Nilai Koefisien Korelasi Pearson dapat berkisar antara -1 sampai +1. 1 berarti sangat berkorelasi dan 0 berarti tidak ada korelasi.\n",
        "\n",
        "Heatmap diatas merupakan hasil visualisasi representasi informasi dua dimensi dengan bantuan gradasi warna untuk mempermudah interpretasi hasil. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wt28KAJZCb3A"
      },
      "source": [
        "# Proses Modelling dengan KNN "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "shj4zJVgrxxb",
        "outputId": "eb432947-ccdf-4f3a-a29f-d83371f28cb6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  cement   slag    ash  water  superplastic  coarseagg  fineagg  \\\n",
              "0 -0.720253   168.9   42.2  124.3  158.3          10.8     1080.8    796.2   \n",
              "1 -0.129574   155.0  184.0  143.0  194.0           9.0      880.0    699.0   \n",
              "2 -0.129574   151.8  178.1  138.7  167.5          18.3      944.0    694.6   \n",
              "3 -0.129574   167.0  187.0  195.0  185.0           7.0      898.0    636.0   \n",
              "4  2.908201   213.8   98.1   24.5  181.7           6.7     1066.0    785.5   \n",
              "\n",
              "   strength  \n",
              "0         0  \n",
              "1         0  \n",
              "2         1  \n",
              "3         0  \n",
              "4         1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-93117aba-6c48-4250-9926-cb9653f8b753\">\n",
              "    <div 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>age</th>\n",
              "      <th>cement</th>\n",
              "      <th>slag</th>\n",
              "      <th>ash</th>\n",
              "      <th>water</th>\n",
              "      <th>superplastic</th>\n",
              "      <th>coarseagg</th>\n",
              "      <th>fineagg</th>\n",
              "      <th>strength</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.720253</td>\n",
              "      <td>168.9</td>\n",
              "      <td>42.2</td>\n",
              "      <td>124.3</td>\n",
              "      <td>158.3</td>\n",
              "      <td>10.8</td>\n",
              "      <td>1080.8</td>\n",
              "      <td>796.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.129574</td>\n",
              "      <td>155.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>880.0</td>\n",
              "      <td>699.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.129574</td>\n",
              "      <td>151.8</td>\n",
              "      <td>178.1</td>\n",
              "      <td>138.7</td>\n",
              "      <td>167.5</td>\n",
              "      <td>18.3</td>\n",
              "      <td>944.0</td>\n",
              "      <td>694.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.129574</td>\n",
              "      <td>167.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>898.0</td>\n",
              "      <td>636.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.908201</td>\n",
              "      <td>213.8</td>\n",
              "      <td>98.1</td>\n",
              "      <td>24.5</td>\n",
              "      <td>181.7</td>\n",
              "      <td>6.7</td>\n",
              "      <td>1066.0</td>\n",
              "      <td>785.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93117aba-6c48-4250-9926-cb9653f8b753')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-93117aba-6c48-4250-9926-cb9653f8b753 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-93117aba-6c48-4250-9926-cb9653f8b753');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "## menampilkan 5 data awal dari dataset yang akan digunakan\n",
        "df_final.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xjPTROMPrxxb",
        "outputId": "e32ba5af-f52c-44ec-912d-831847525e54"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  cement   slag    ash  water  superplastic  coarseagg  fineagg\n",
              "0 -0.720253   168.9   42.2  124.3  158.3          10.8     1080.8    796.2\n",
              "1 -0.129574   155.0  184.0  143.0  194.0           9.0      880.0    699.0\n",
              "2 -0.129574   151.8  178.1  138.7  167.5          18.3      944.0    694.6\n",
              "3 -0.129574   167.0  187.0  195.0  185.0           7.0      898.0    636.0\n",
              "4  2.908201   213.8   98.1   24.5  181.7           6.7     1066.0    785.5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-304a3fd5-99cf-4bd1-a501-8a227829b0d1\">\n",
              "    <div 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>age</th>\n",
              "      <th>cement</th>\n",
              "      <th>slag</th>\n",
              "      <th>ash</th>\n",
              "      <th>water</th>\n",
              "      <th>superplastic</th>\n",
              "      <th>coarseagg</th>\n",
              "      <th>fineagg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.720253</td>\n",
              "      <td>168.9</td>\n",
              "      <td>42.2</td>\n",
              "      <td>124.3</td>\n",
              "      <td>158.3</td>\n",
              "      <td>10.8</td>\n",
              "      <td>1080.8</td>\n",
              "      <td>796.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.129574</td>\n",
              "      <td>155.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>880.0</td>\n",
              "      <td>699.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.129574</td>\n",
              "      <td>151.8</td>\n",
              "      <td>178.1</td>\n",
              "      <td>138.7</td>\n",
              "      <td>167.5</td>\n",
              "      <td>18.3</td>\n",
              "      <td>944.0</td>\n",
              "      <td>694.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.129574</td>\n",
              "      <td>167.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>898.0</td>\n",
              "      <td>636.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.908201</td>\n",
              "      <td>213.8</td>\n",
              "      <td>98.1</td>\n",
              "      <td>24.5</td>\n",
              "      <td>181.7</td>\n",
              "      <td>6.7</td>\n",
              "      <td>1066.0</td>\n",
              "      <td>785.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-304a3fd5-99cf-4bd1-a501-8a227829b0d1')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-304a3fd5-99cf-4bd1-a501-8a227829b0d1 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-304a3fd5-99cf-4bd1-a501-8a227829b0d1');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "## memisahkan data fitur dengan label yang akan di pelajari\n",
        "train_data = df_final.drop(\"strength\",axis = 1)\n",
        "train_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJi10j8Srxxb",
        "outputId": "a03afbfa-ee34-4393-8b17-82ca5f625221"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    1\n",
              "3    0\n",
              "4    1\n",
              "Name: strength, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "## memisahkan data label dengan label yang akan di pelajari\n",
        "df_final.strength = df_final.strength.astype(np.int64)\n",
        "label_data = df_final.strength\n",
        "label_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cPYFO180rxxb"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(train_data,label_data,test_size=0.4,random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDc2vojUrxxb"
      },
      "source": [
        "# Proses Training \n",
        "\n",
        "##### Train Test Split \n",
        "Train dan test split dimana kita akan memisahkan data yang akan digunakan oleh algoritma dalam proses training dan testing. Proses ini akan membantu kita melakukan evaluasi terhadap model machine learning yang dibangun.\n",
        "\n",
        "![1_-8_kogvwmL1H6ooN1A1tsQ.png](attachment:1_-8_kogvwmL1H6ooN1A1tsQ.png)\n",
        "\n",
        "##### Cross Validation\n",
        "Ketika model dipecah menjadi pelatihan dan pengujian, ada kemungkinan bahwa jenis titik data tertentu dapat sepenuhnya menjadi bagian pelatihan (training) atau pengujian (testing). Hal ini akan menyebabkan model berkinerja buruk. Oleh karena itu masalah over-fitting dan underfitting dapat dihindari dengan baik dengan teknik validasi silang (cross validation)\n",
        "\n",
        "![1_4G__SV580CxFj78o9yUXuQ.png](attachment:1_4G__SV580CxFj78o9yUXuQ.png)\n",
        "\n",
        "##### Stratify \n",
        "Parameter stratify membuat pembagian sehingga proporsi nilai dalam sampel yang dihasilkan akan sama dengan proporsi nilai yang diberikan untuk stratifikasi parameter.\n",
        "\n",
        "Misalnya, jika variabel y adalah variabel kategori biner dengan nilai 0 dan 1 dan ada 25% dari nol dan 75% dari satu, stratify=y akan memastikan bahwa pembagian acak Anda memiliki 25% dari 0 dan 75% dari 1.\n",
        "\n",
        "For Reference : https://towardsdatascience.com/train-test-split-and-cross-validation-in-python-80b61beca4b6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-08-02T19:52:54.628001Z",
          "iopub.status.busy": "2021-08-02T19:52:54.627748Z",
          "iopub.status.idle": "2021-08-02T19:54:21.340998Z",
          "shell.execute_reply": "2021-08-02T19:54:21.33994Z",
          "shell.execute_reply.started": "2021-08-02T19:52:54.627976Z"
        },
        "id": "lun0cHXJC96A",
        "outputId": "8ba88170-63fe-4cf7-bd75-436d69af3cf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nilai k KNN yang optimal adalah 1\n",
            "Skor optimalnya adalah 0.70\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# import library model KNN dengan alias/as 'KNeighborsClassifier'\n",
        "from sklearn.neighbors import KNeighborsClassifier \n",
        "import sklearn.metrics as metrics\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# buat variabel kosong untuk menyimpan metrik KNN/KNN metrics\n",
        "scores=[]\n",
        "# Kita coba nilai k yang berbeda untuk KNN (dari k=1 sampai k=26)\n",
        "lrange=list(range(1,25))\n",
        "# loop proses KNN\n",
        "for k in lrange:\n",
        "  # masukkan nilai k dan ukuran 'jarak'\n",
        "  knn=KNeighborsClassifier(n_neighbors=k)\n",
        "  # masukan data train/ data latih untuk melatih KNN\n",
        "  knn.fit(X_train,y_train.ravel())\n",
        "  # lihat prediksi KNN dengan memasukkan data uji/data test\n",
        "  y_pred=knn.predict(X_test)\n",
        "  # tambahkan performance metric akurasi\n",
        "  scores.append(metrics.accuracy_score(y_test,y_pred))\n",
        "plt.figure(2,figsize=(15,5))\n",
        "\n",
        "\n",
        "optimal_k = lrange[scores.index(max(scores))]\n",
        "print(\"Nilai k KNN yang optimal adalah %d\" % optimal_k)\n",
        "print(\"Skor optimalnya adalah %.2f\" % max(scores))\n",
        "\n",
        "\n",
        "# plot hasilnya\n",
        "plt.plot(lrange, scores,ls='dashed')\n",
        "plt.xlabel('Nilai dari k untuk KNN')\n",
        "plt.ylabel('Accuracy Score')\n",
        "plt.title('Accuracy Scores untuk nilai k dari k-Nearest-Neighbors')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qtyFnbnwrxxc",
        "outputId": "6e102a23-c71f-443e-e04a-cdfb83c2a373"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "knn = KNeighborsClassifier(13)\n",
        "\n",
        "knn.fit(X_train,y_train)\n",
        "knn.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-CPaQPsrxxc"
      },
      "source": [
        "# Evaluasi Hasil Matriks\n",
        "\n",
        "##### Presisi \n",
        "Presisi adalah rasio pengamatan positif yang diprediksi dengan benar dengan total pengamatan positif yang diprediksi. Pertanyaan yang dijawab metrik ini adalah dari semua penumpang yang berlabel selamat, berapa banyak yang benar-benar selamat? Presisi tinggi berhubungan dengan tingkat positif palsu yang rendah. \n",
        "\n",
        "#### Presisi = TP/TP+FP\n",
        "\n",
        "##### Recall (Sensitivity) \n",
        "Recall adalah rasio pengamatan positif yang diprediksi dengan benar dengan semua pengamatan di kelas yang sebenarnya - ya. Contoh: Dari semua penumpang yang benar-benar selamat, berapa banyak yang kami beri label? Recall yang lebih besar dari 0,5 adalah baik.\n",
        "\n",
        "#### Recall = TP/TP+FN\n",
        "\n",
        "##### Skor F1 \n",
        "Skor F1 adalah rata-rata tertimbang dari Precision dan Recall. Oleh karena itu, skor ini memperhitungkan positif palsu dan negatif palsu. Secara intuitif tidak semudah memahami akurasi, tetapi F1 biasanya lebih berguna daripada akurasi, terutama jika Anda memiliki distribusi kelas yang tidak merata. Akurasi bekerja paling baik jika positif palsu dan negatif palsu memiliki biaya yang sama. Jika biaya positif palsu dan negatif palsu sangat berbeda, lebih baik melihat Precision dan Recall.\n",
        "\n",
        "#### Skor F1 = 2(Presisi Recall) / (Recall + Precision)\n",
        "\n",
        "For Reference: http://joshlawman.com/metrics-classification-report-breakdown-precision-recall-f1/ : https://blog.exsilio.com/all/accuracy-precision-recall-f1-score-interpretation-of-performance-measures/\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "4_Si6SEarxxc",
        "outputId": "f3e24371-3961-459f-a62b-43fa27821ebd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "y_pred = knn.predict(X_test)\n",
        "from sklearn import metrics\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "p = sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9xWULRWurxxd",
        "outputId": "2aedba60-24d2-429f-e2b2-c2da36450530"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      0.47      0.54        45\n",
            "           1       0.58      0.73      0.65        45\n",
            "\n",
            "    accuracy                           0.60        90\n",
            "   macro avg       0.61      0.60      0.59        90\n",
            "weighted avg       0.61      0.60      0.59        90\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#import classification_report\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# =============================SOAL====================================="
      ],
      "metadata": {
        "id": "A19-RLGv7x3h"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WaX69cZErxxd"
      },
      "source": [
        "# Instruksi Tugas untuk jurusan teknik mesin, teknik industri, dan agro teknologi pada fakultas FTI, dan fakultas FTSP\n",
        "\n",
        "Buatlah laporan akhir yang menjelaskan langkah-langkah serta penjelasan tahapan yang Anda lakukan pada praktikum ini, yang didalamnya menjawab beberapa pertanyaan sebagai berikut\n",
        "1. Rubahlah fitur cement menjadi fitur target class yang ingin Anda prediksi, dengan nilai fitur > 281 merupakan kelas 1 dan < 281 menjadi nilai 0. Berapa akurasi model dan nilai k untuk skenario ini?\n",
        "2. Pada hasil diagram heatmap, terlihat banyak konfigurasi warna dan nilai yang ditampilkan. Jelaskan arti kolom berwarna merah pada hasil diagram heatmap yang dihasilkan! Pasangan variabel apa saja yang bernilai merah?\n",
        "3. Apa yang terjadi jika, nilai test set diganti menjadi 0.3 kemudian 0.2 dan 0,1 ? \n",
        "4. Buatlah tabel yang berisi hasil pengujian Anda dan sertakan berapa nilai k dan nilai akurasi untuk masing-masing skenario uji.\n",
        "5. Apakah terjadi perbedaan nilai akurasi model? Jika Ya, jelaskan pendapat Anda mengapa hal tersebut bisa terjadi.\n",
        "6. Berapa nilai presisi dan recall dari masing-masing model yang Anda coba serta jelaskan arti dari nilai tersebut."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RrYsNCJvrxxd"
      },
      "source": [
        "# =============================JAWAB===================================="
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#1. Rubahlah fitur cement menjadi fitur target class yang ingin Anda prediksi, dengan nilai fitur > 281 merupakan kelas 1 dan < 281 menjadi nilai 0. Berapa akurasi model dan nilai k untuk skenario ini?"
      ],
      "metadata": {
        "id": "cg86uFPOs4_z"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "ORUGoR4Rrxxd",
        "outputId": "b87f1081-5c03-418f-a662-48adcfc930bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 225 entries, 0 to 224\n",
            "Data columns (total 9 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   age           225 non-null    float64\n",
            " 1   cement        225 non-null    float64\n",
            " 2   slag          225 non-null    float64\n",
            " 3   ash           225 non-null    float64\n",
            " 4   water         225 non-null    float64\n",
            " 5   superplastic  225 non-null    float64\n",
            " 6   coarseagg     225 non-null    float64\n",
            " 7   fineagg       225 non-null    float64\n",
            " 8   strength      225 non-null    int64  \n",
            "dtypes: float64(8), int64(1)\n",
            "memory usage: 17.6 KB\n",
            "None\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  cement   slag    ash  water  superplastic  coarseagg  fineagg  \\\n",
              "0 -0.720253   168.9   42.2  124.3  158.3          10.8     1080.8    796.2   \n",
              "1 -0.129574   155.0  184.0  143.0  194.0           9.0      880.0    699.0   \n",
              "2 -0.129574   151.8  178.1  138.7  167.5          18.3      944.0    694.6   \n",
              "3 -0.129574   167.0  187.0  195.0  185.0           7.0      898.0    636.0   \n",
              "4  2.908201   213.8   98.1   24.5  181.7           6.7     1066.0    785.5   \n",
              "\n",
              "   strength  \n",
              "0         0  \n",
              "1         0  \n",
              "2         1  \n",
              "3         0  \n",
              "4         1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2f867c2b-5825-427e-bdf9-9655f4befc0b\">\n",
              "    <div 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",
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              "        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>age</th>\n",
              "      <th>cement</th>\n",
              "      <th>slag</th>\n",
              "      <th>ash</th>\n",
              "      <th>water</th>\n",
              "      <th>superplastic</th>\n",
              "      <th>coarseagg</th>\n",
              "      <th>fineagg</th>\n",
              "      <th>strength</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.720253</td>\n",
              "      <td>168.9</td>\n",
              "      <td>42.2</td>\n",
              "      <td>124.3</td>\n",
              "      <td>158.3</td>\n",
              "      <td>10.8</td>\n",
              "      <td>1080.8</td>\n",
              "      <td>796.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.129574</td>\n",
              "      <td>155.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>880.0</td>\n",
              "      <td>699.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.129574</td>\n",
              "      <td>151.8</td>\n",
              "      <td>178.1</td>\n",
              "      <td>138.7</td>\n",
              "      <td>167.5</td>\n",
              "      <td>18.3</td>\n",
              "      <td>944.0</td>\n",
              "      <td>694.6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.129574</td>\n",
              "      <td>167.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>898.0</td>\n",
              "      <td>636.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.908201</td>\n",
              "      <td>213.8</td>\n",
              "      <td>98.1</td>\n",
              "      <td>24.5</td>\n",
              "      <td>181.7</td>\n",
              "      <td>6.7</td>\n",
              "      <td>1066.0</td>\n",
              "      <td>785.5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f867c2b-5825-427e-bdf9-9655f4befc0b')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-2f867c2b-5825-427e-bdf9-9655f4befc0b 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-2f867c2b-5825-427e-bdf9-9655f4befc0b');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "print(df_final.info())\n",
        "df_final.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QHJPYhBVrxxd",
        "outputId": "5ed4ced9-3152-456b-bde3-ba9b9b625122"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of           age  cement   slag    ash  water  superplastic  coarseagg  fineagg  \\\n",
              "0   -0.720253       0   42.2  124.3  158.3          10.8     1080.8    796.2   \n",
              "1   -0.129574       0  184.0  143.0  194.0           9.0      880.0    699.0   \n",
              "2   -0.129574       0  178.1  138.7  167.5          18.3      944.0    694.6   \n",
              "3   -0.129574       0  187.0  195.0  185.0           7.0      898.0    636.0   \n",
              "4    2.908201       0   98.1   24.5  181.7           6.7     1066.0    785.5   \n",
              "..        ...     ...    ...    ...    ...           ...        ...      ...   \n",
              "220 -0.129574       1   19.0  141.0  154.0          10.9      942.0    801.0   \n",
              "221 -0.129574       0  145.0  113.0  178.5           8.0      867.2    824.0   \n",
              "222 -0.129574       0  133.0  103.0  200.0           7.0      916.0    753.0   \n",
              "223 -0.129574       0  115.0   89.0  202.0           9.0      860.0    829.0   \n",
              "224 -0.129574       0  116.0   90.3  179.6           8.9      870.1    768.3   \n",
              "\n",
              "     strength  \n",
              "0           0  \n",
              "1           0  \n",
              "2           1  \n",
              "3           0  \n",
              "4           1  \n",
              "..        ...  \n",
              "220         1  \n",
              "221         0  \n",
              "222         1  \n",
              "223         0  \n",
              "224         1  \n",
              "\n",
              "[225 rows x 9 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df_final['cement'] = df_final ['cement'].apply(lambda x: 1 if x >= 281 else 0)\n",
        "df_final.head"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2. Pada hasil diagram heatmap, terlihat banyak konfigurasi warna dan nilai yang ditampilkan. Jelaskan arti kolom berwarna merah pada hasil diagram heatmap yang dihasilkan! Pasangan variabel apa saja yang bernilai merah?*teks yang dimiringkan*"
      ],
      "metadata": {
        "id": "7x5l5X4EtQYj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "y-rjt-01rxxe",
        "outputId": "25e5dcff-3b65-4535-977d-f5758fe925b0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Buat visualisasi korelasi data dengan heatmap\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        " # plot heatmap\n",
        "plt.figure(figsize=(10,8))\n",
        "p=sns.heatmap(df_final.corr(), annot=True,cmap ='RdYlGn')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ud1Voo_Srxxe"
      },
      "source": [
        "# Penjelasan\n",
        "Pasangan variabel yang bernilai merah:\n",
        "* slag & cement\n",
        "* coarseagg & water\n",
        "* fineagg & water\n",
        "\n",
        "Pasangan variabel yang bernilai merah berarti mereka memiliki tingkat kolerasi negatif yang relatif tinggi. Hal ini berarti ketika salah satu nilai dari variabel bertambah, maka nilai variabel yang satunya akan berkurang. Sebagai contoh, variabel slag (terak) dan cement (semen) memiliki tingkat korelasi negatif yang paling tinggi diantara pasangan variabel lainnya. Hal ini berarti, ketika nilai dari variabel semen meningkat, maka pada kebanyakan observasi, nilai dari variabel terak menurun. Hal ini juga berlaku sebaliknya."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3. Apa yang terjadi jika, nilai test set diganti menjadi 0.3 kemudian 0.2 dan 0,1 ? "
      ],
      "metadata": {
        "id": "REmFnOZvty_2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##test set diganti menjadi 0.3\n",
        "x_train,x_test,y_train,y_test = train_test_split(train_data,label_data,test_**size=0.3**,random_state=42)"
      ],
      "metadata": {
        "id": "zir174Miue58"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "iJyQqFTqrxxe",
        "outputId": "5eeafbc0-28dc-4387-9a25-924e47613a2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test set diganti menjadi 0.3\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWIAAAEWCAYAAABc752tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAZaElEQVR4nO3de7yVZZ338c93b1RARAWEEmXUEXSMZ9CG8THKQ9oBFMScyjxNqUk2KaU1pmUeeqZneqamsrNbyXw8IJqipo1ijXgoT2BqAh7wkCIoSCKIIKff/HHfW5fEXnstuBfXvdf+vl+v++Va9+n6beT12xe/+7quWxGBmZml05I6ADOz7s6J2MwsMSdiM7PEnIjNzBJzIjYzS8yJ2MwsMSdi22SSekn6taTXJF27Cfc5VtK0ImNLRdL+kp5IHYd1DfI44u5D0jHAGcCewDLgYeBbEXHPJt73eOA0YFRErNnkQEtOUgBDI2Ju6lisObhH3E1IOgP4AfB/gUHAEOCnwPgCbv83wJPdIQnXQlKP1DFYFxMR3pp8A7YFXgc+UeWcrcgS9fx8+wGwVX7sIGAe8GVgIbAAOCE/dgGwClidt3EScD5wRcW9dwEC6JF//wzwDFmv/Fng2Ir991RcNwp4EHgt/++oimPTgf8D/D6/zzRgQAc/W3v8Z1bEfwRwKPAk8BfgaxXn7wvcCyzJz/0xsGV+7K78Z1me/7xHVdz/q8BLwOXt+/Jr/jZv47359x2BRcBBqf9ueCvH5h5x9/A+oCcwtco5Xwf2A/YGRpAlo3Mqjr+LLKEPJku2P5G0fUScR9bLnhIRfSJiUrVAJG0N/BAYExHbkCXbhzdwXj/glvzc/sD3gFsk9a847RjgBGAgsCXwlSpNv4vsz2AwcC5wMXAc8A/A/sA3JO2an7sWOB0YQPZndwjwLwARcUB+zoj8551Scf9+ZP86mFDZcEQ8TZakr5DUG7gUuCwipleJ17oRJ+LuoT/wSlQvHRwLfDMiFkbEIrKe7vEVx1fnx1dHxG/IeoN7bGQ864DhknpFxIKImLWBcw4DnoqIyyNiTURMBh4HxlWcc2lEPBkRK4BryH6JdGQ1WT18NXA1WZK9MCKW5e3PJvsFRETMjIj78nafAy4CDqzhZzovIt7M43mHiLgYmAvcD7yb7BefGeBE3F0sBgZ0UrvcEfhzxfc/5/veusd6ifwNoE+9gUTEcrJ/zp8CLJB0i6Q9a4inPabBFd9fqiOexRGxNv/cnihfrji+ov16ScMk3SzpJUlLyXr8A6rcG2BRRKzs5JyLgeHAjyLizU7OtW7Eibh7uBd4k6wu2pH5ZP+sbjck37cxlgO9K76/q/JgRNwWER8m6xk+TpagOounPaYXNzKmevyMLK6hEdEX+BqgTq6pOvxIUh+yuvsk4Py89GIGOBF3CxHxGlld9CeSjpDUW9IWksZI+o/8tMnAOZJ2kDQgP/+KjWzyYeAASUMkbQuc3X5A0iBJ4/Na8ZtkJY51G7jHb4Bhko6R1EPSUcBewM0bGVM9tgGWAq/nvfXPr3f8ZWC3Ou95ITAjIj5LVvv++SZHaU3DibibiIj/JBtDfA7ZE/sXgFOBG/JT/g2YATwK/Al4KN+3MW3dDkzJ7zWTdybPljyO+WQjCQ7krxMdEbEYGEs2UmMx2YiHsRHxysbEVKevkD0IXEbWW5+y3vHzgcskLZH0yc5uJmk8MJq3f84zgPdKOrawiK1L84QOM7PE3CM2M0vMidjMrGCSvijpMUmzJH2ps/OdiM3MCiRpOHAy2aSoEcBYSbtXu8aJ2MysWH8H3B8Rb+Rj7+8Ejqx2QWkXJ+k15Gg/RbS/cs09/5w6BCuhcUPGdDbOu1P15JyVL1z9Od45lb0tItryz48B38qn468gW9NkRrX7lTYRm5mVVZ502zo4NkfS/yNbiGo52bj6tRs6t51LE2ZmgNRS89aZiJgUEf+QLxL1Ktkqfx1yj9jMDGgpcBlpSQMjYqGkIWT14f2qne9EbGYGNfV063BdXiNeDXwhIpZUO9mJ2MwMkDb5ed9bImL/es53IjYzA1I+MnMiNjOj8NJEXZyIzcxwIjYzS67IURP1ciI2M8M9YjOz5JyIzcwSU6evJWwcJ2IzM9wjNjNLrqXFD+vMzBJzj9jMLCmXJszMEnMiNjNLTC5NmJml5R6xmVliLS2tydp2IjYzw6UJM7PkXJowM0vMidjMLDGXJszMEpOnOJuZpVXky0Pr5URsZkba0kS6ls3MSkRqqXnr/F46XdIsSY9JmiypZ7XznYjNzACk2reqt9FgYCIwMiKGA63Ap6pd49KEmRkU3S3tAfSStBroDczffE2bmXVVLS01b5ImSJpRsU1ov01EvAh8F3geWAC8FhHTqjXtHrGZGdTVLY2INqBtQ8ckbQ+MB3YFlgDXSjouIq4ooGkzs+YVUs1bJz4EPBsRiyJiNXA9MKraBU7EZmYAqmOr7nlgP0m9lQ1OPgSYU+0ClybMzABaipnQERH3S/oV8BCwBvgjHZQx2jkRm5lBp8PS6hER5wHn1Xq+E7GZGUCrpzibmaXltSbMzBJLl4ediM3MgMIe1m0MJ2IzM3CP2MwstWj1GzrMzNJyj9jMLDGPmjAzS8wP68zMEnNpwswsMZcmzMwS8xRnM7PE3CM2M0ssYY3YC8OX3BdOHM2M2/+Dmb/9DqeeNCZ1OFYS69au43unfIdJ51Rd5tbqEC2qeSuaE3GJ7TVsJ044+mD2H3cO+370q4w5ZB92+5tBqcOyErh76p0MGuK/C4WSat8K5kRcYnsOHcyDf5zLipWrWLt2HXffN4cjxuybOixLbMmiJcy5fzb7jtkvdSjNpbhXJdWtYTViSXuSvcl0cL7rReCmiKj67iZ726wnXuD8fz2Kftv1YcXKVYz+4N489OizqcOyxG782VTGnnw4K1esTB1Kc0m41kRDWpb0VeBqst8dD+SbgMmSzqpy3QRJMyTNWPP63EaE1qU8MXc+//mzm/j1lWdz0+Vn8cjsP7N23brUYVlCs++bRZ/t+rDTsJ1Th9J8mrBHfBLwnvxV0m+R9D1gFvDtDV0UEW3kL9nrNeToaFBsXcplU6Zz2ZTpAFxw5lG8uOAvaQOypJ6b9Qyz732Mxx+YzZpVa1j5xkqu+vblHHPW8alD6/qacIrzOmBH4M/r7X93fsxqtEP/vixavJSdd+zP+NH/yIFHnJs6JEvo0JPGcehJ4wCY+8hT3HntHU7CRWnCRPwl4HeSngJeyPcNAXYHTm1Qm01p8kWn02/7PqxevZYvfeNSXlv6RuqQzJpSNNtaExFxq6RhwL6882HdgxGxthFtNqsPffyC1CFYSe0+Yii7jxiaOozmUdDDOkl7AFMqdu0GnBsRP+jomoaNmoiIdcB9jbq/mVmhCipNRMQTwN4AklrJOqFTq13jKc5mZtCoWRWHAE9HxPrPyzZD02ZmXU0dM+sqh9rm24QO7vopYHJnTbtHbGYGdZUmKofadkTSlsDhwNmd3c+J2MwMiOLXkBgDPBQRL3d2ohOxmRlAj8IT8dHUUJYA14jNzDIFrr4maWvgw8D1tTTtHrGZGRQ6sy4ilgP9az3fidjMDPwWZzOz1Brx5o1aORGbmUFTLvpjZta1tDoRm5ml1YB30dXKidjMDFyaMDNLzonYzCytBkxxrpkTsZkZ+GGdmVlyLk2YmSXmRGxmlpinOJuZpeUpzmZmqZVx1ISkZUC0f83/G/nniIi+DY7NzGzzKeOoiYjYZnMGYmaWUkvC12TU1LSkD0g6If88QNKujQ3LzGzzKvAFHXXrtEYs6TxgJLAHcCmwJXAF8P7iwzEzSyNhibimh3UfA/YBHgKIiPmSXLYws6aiMj6sq7AqIkJSwFsvxTMzayplrxFfI+kiYDtJJwO/BS5ubFhmZpuXWmrfitZpjzgivivpw8BSYBhwbkTcXnwoZmbplL1GDPAnoBfZOOI/NS4cM7M0ipxYJ2k74BJgOFnePDEi7u2w7Rpu+FngAeBI4OPAfZJOLCZcM7NyKHj42oXArRGxJzACmFPt5Fp6xP8K7BMRi7Ng1R/4A/CLmsIxM+sCiipNSNoWOAD4DEBErAJWVbumlkS8GFhW8X1Zvs/MrGm0FDfFeVdgEXCppBHATOCLEbG8owuqrTVxRv5xLnC/pBvJah3jgUeLitjMrAzq6RFLmgBMqNjVFhFt+ecewHuB0yLifkkXAmcB3+joftV6xO2TNp7Ot3Y31h6umVnXUE8izpNuWweH5wHzIuL+/PuvyBJxh6ot+nNB7WGZmXVtRdWII+IlSS9I2iMingAOAWZXu6aWtSZ2AM4E3gP0rGjs4E2M18ysNApeF/404EpJWwLPACdUO7mWh3VXAlOAscApwKfJCtFmZk2jyAkdEfEw2WJpNallsl7/iJgErI6IOyPiRMC9YTNrKi2tqnkrWi094tX5fxdIOgyYD/QrPBIzs4TKPsX53/IByl8GfgT0BU5vaFRmZptZqRNxRNycf3wN+GBjwzEzS6OUiVjSj3j75aF/JSImNiQiM7MECh41UZdqPeIZmy0KM7PEWlrTtV1tQsdlmzMQM7OUSlmaMDPrTsr+zjozs6bnHrGZWWKlTMSpR02seN5rDtlfGzbqv1OHYCU07g+bfo9SJmI8asLMupEeDXg7c81td3TAoybMrDtpUYcFgIardRnMrwJ74WUwzaxJpZzQUUtn/EqyN5DuClwAPAc82MCYzMw2u5Y6tka03Rkvg2lmTa9FUfNWNC+DaWZGedeaaOdlMM2s6fUocyL2Mphm1h2o5KMmLmUDEzvyWrGZWVMoe2ni5orPPYGPkdWJzcyaRsL5HDWVJq6r/C5pMnBPwyIyM0ug1BM6NmAoMLDoQMzMUiryYZ2k54BlwFpgTUSMrNp2DTdcxjtrxC+RzbQzM2saDagRfzAiXqnlxFpKE9tsejxmZuWWsjTRaX1a0u9q2Wdm1pW1qPatBgFMkzRT0oTOTq62HnFPoDcwQNL2QHvzfYHBNYViZtZF1DNqIk+ulQm2LSLaKr5/ICJelDQQuF3S4xFxV0f3q1aa+BzwJWBHYCZvJ+KlwI/riNnMrPTqKU3kSbetyvEX8/8ulDQV2BeoPxFHxIXAhZJOi4gf1RyhmVkXVNTC8JK2BloiYln++SPAN6tdU0vT6yRtV9HI9pL+ZdNCNTMrlwKXwRwE3CPpEeAB4JaIuLXaBbWMIz45In7S/iUiXpV0MvDTGq41M+sSiho1ERHPACPquaaWRNwqSRERAJJagS03Ij4zs9Iq+1oTtwJTJF2Uf/9cvs/MrGmUeq0Jsll0E4DP599vBy5uWERmZgmUukccEeuAn+cbkvYnWyD+C40Nzcxs82ltKfmiP5L2AY4GPgk8C1zfyKDMzDa3UpYmJA0jS75HA68AUwBFhN/SYWZNp6zLYD4O3A2MjYi5AJL8rjoza0opa8TVeuNHAguAOyRdLOkQ3p7mbGbWVApe9Kcu1aY43wDckE/RG0+27sRAST8DpkbEtOLDMTNLY4syL4MZEcsj4qqIGAfsBPwRLwxvZk2mlD3iDYmIV8lWHOpw1SEzs66o1OOIzcy6g1YnYjOztNwjNjNLrKzjiM3Muo0t3CM2M0vLpQkzs8RcmjAzS8yjJszMEnNpwswssaLe4rxRbadr2sysPFpdIzYzS6uUC8ObmXUnrhGbmSVWdCKW1ArMAF6MiLHVznUiNjOjITXiLwJzgL6dnZiyLGJmVho9WmrfOiNpJ+Aw4JJa2nYiNjOjvoXhJU2QNKNim7De7X4AnAmsq6VtlybMzKhvZl1EdPiCDEljgYURMVPSQbXcz4nYzIxC15p4P3C4pEOBnkBfSVdExHEdXeBEXGILFizizDO/z+LFS5Dgk58czac/fXjqsKwEPnPU3/OJcXsSwJNPL+asb01n1aq1qcPq0oqq00bE2cDZAHmP+CvVknCRbVsDtLa2ctZZJ/Kb3/yUKVO+y1VX3cLcuc+nDssSGzRga47/xHCOPPE6xh53DS0tLRz2od1Th9XldZmXh9rmNXBgPwYO7AdAnz692W23nXn55cXsvvuQxJFZaj1aW+i5VQ/WrF1Hr549WPjK8tQhdXlbtBQ/xTkipgPTOzvPibiLmDfvZebMeZoRI/ZIHYol9vIry5k0+RGmTz2ON99cwz0PzOP3D8xLHVaXl3Jm3WYvTUg6ocqxt4aEtLVN2Zxhldry5SuYOPHf+drXTqZPn96pw7HE+m6zJYfsvwsHf/xKPnD45fTu1YPDPzo0dVhdXncrTVwAXLqhA+8cEvJkuqWQSmT16jVMnPjvjBt3EB/5yKjU4VgJjBq5E/PmL+XVJSsBmDb9Wfb5X+/iptueShxZ19Z0i/5IerSjQ8CgRrTZjCKCr3/9h+y2286ccMIRqcOxkpj/8uvs/Z5B9NyqByvfXMP7Rg7msccXpQ6ry1MTLvozCPgo8Op6+wX8oUFtNp2ZM2dz4413MGzYLowfPxGAM874Zw48cGTiyCylR2cv5LY7nuGGX/4Ta9YGc558hatvnJ06rC6vGVdfuxnoExEPr39A0vQGtdl0Ro58D0888evUYVgJ/XDSDH44aUbqMJpK05UmIuKkKseOaUSbZmabQn5Dh5lZWgkrE07EZmbQnA/rzMy6FPeIzcwSq2cZzKI5EZuZ4dKEmVlyLk2YmSXmRGxmllgzzqwzM+tS3CM2M0uswHfW1c2J2MwMj5owM0uu6Rb9MTPratwjNjNLzA/rzMwS8/A1M7PEikrEknoCdwFbkeXYX0XEedWucSI2M6PQ0sSbwMER8bqkLYB7JP1XRNzX0QVOxGZmFPeGjogI4PX86xb5VvXmKUdsmJmVhurZpAmSZlRsE95xL6lV0sPAQuD2iLi/WtvuEZuZUd/wtYhoA9qqHF8L7C1pO2CqpOER8VhH57tHbGYGtNax1SoilgB3AKOrnedEbGZG1iOudat+H+2Q94SR1Av4MPB4tWtcmjAzAwocN/Fu4DJJrWSd3Wsi4uZqFzgRm5kBKigRR8SjwD71XONEbGYGSOkqtU7EZmZAytUmnIjNzAAlHLvgRGxmhksTZmYl4NKEmVlSRY2a2BhOxGZmOBGbmSWXzb9Iw4nYzAxwjdjMLDGXJszMkvPwNTOzpNwjNjNLTPWsDF8wJ2IzM0B1LfleLCdiMzPAoybMzBJzacLMLDknYjOzpLwMpplZcu4Rm5kl1eL1iM3MUnMiNjNLKuXMunS/AszMSkV1bFXuIu0s6Q5JsyXNkvTFzlp2j9jMjELHEa8BvhwRD0naBpgp6faImN3RBU7EZmYUN8U5IhYAC/LPyyTNAQYDHSZiRUQhjVvjSJoQEW2p47By8d+LdCRNACZU7Grb0P8LSbsAdwHDI2Jph/dzIi4/STMiYmTqOKxc/Pei3CT1Ae4EvhUR11c71w/rzMwKJmkL4Drgys6SMDgRm5kVStlTv0nAnIj4Xi3XOBF3Da4D2ob470U5vR84HjhY0sP5dmi1C1wjNjNLzD1iM7PEnIjNzBJzIi45SaMlPSFprqSzUsdj6Un6haSFkh5LHYsVw4m4xCS1Aj8BxgB7AUdL2ittVFYCvwRGpw7CiuNEXG77AnMj4pmIWAVcDYxPHJMlFhF3AX9JHYcVx4m43AYDL1R8n5fvM7Mm4kRsZpaYE3G5vQjsXPF9p3yfmTURJ+JyexAYKmlXSVsCnwJuShyTmRXMibjEImINcCpwGzAHuCYiZqWNylKTNBm4F9hD0jxJJ6WOyTaNpzibmSXmHrGZWWJOxGZmiTkRm5kl5kRsZpaYE7GZWWJOxFaVpLX5GwYek3StpN6bcK9fSvp4/vmSagsYSTpI0qiNaOM5SQNq3b/eOa/X2db5kr5Sb4xm63Mits6siIi9I2I4sAo4pfKgpB4bc9OI+GxEzK5yykFA3YnYrCtyIrZ63A3snvdW75Z0EzBbUquk70h6UNKjkj4H2UsUJf04X0/5t8DA9htJmi5pZP55tKSHJD0i6XeSdiFL+KfnvfH9Je0g6bq8jQclvT+/tr+kaZJmSboEUGc/hKQbJM3Mr5mw3rHv5/t/J2mHfN/fSro1v+ZuSXsW8Ydp1m6jejPW/eQ93zHArfmu9wLDI+LZPJm9FhH/KGkr4PeSpgH7AHuQraU8CJgN/GK9++4AXAwckN+rX0T8RdLPgdcj4rv5eVcB34+IeyQNIZtt+HfAecA9EfFNSYcBtcwyOzFvoxfwoKTrImIxsDUwIyJOl3Rufu9TyV7SeUpEPCXpfwM/BQ7eiD9Gsw1yIrbO9JL0cP75brLXhI8CHoiIZ/P9HwH+vr3+C2wLDAUOACZHxFpgvqT/3sD99wPuar9XRHS0zu6HgL2yN5UD0FdSn7yNI/Nrb5H0ag0/00RJH8s/75zHuhhYB0zJ918BXJ+3MQq4tqLtrWpow6xmTsTWmRURsXfljjwhLa/cBZwWEbetd17VV4jXqQXYLyJWbiCWmkk6iCypvy8i3pA0HejZwemRt7tk/T8DsyK5RmxFuA34vKQtACQNk7Q1cBdwVF5DfjfwwQ1cex9wgKRd82v75fuXAdtUnDcNOK39i6T2xHgXcEy+bwywfSexbgu8mifhPcl65O1agPZe/TFkJY+lwLOSPpG3IUkjOmnDrC5OxFaES8jqvw/lL7S8iOxfW1OBp/Jj/59sxbB3iIhFwASyMsAjvF0a+DXwsfaHdcBEYGT+MHA2b4/euIAskc8iK1E830mstwI9JM0Bvk32i6DdcmDf/Gc4GPhmvv9Y4KQ8vln4dVVWMK++ZmaWmHvEZmaJORGbmSXmRGxmlpgTsZlZYk7EZmaJORGbmSXmRGxmltj/AKfDB4Heiq7MAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.neighbors import KNeighborsClassifier \n",
        "import sklearn.metrics as metrics\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "x_train,x_test,y_train,y_test = train_test_split(train_data,label_data,test_size=0.3,random_state=42)\n",
        "df_final.strength = df_final.strength.astype(np.int64)\n",
        "p = sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
        "y_pred = knn.predict(x_test)\n",
        "print(\"test set diganti menjadi 0.3\")\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "\n",
        "# buat variabel kosong untuk menyimpan metrik KNN/KNN metrics\n",
        "scores=[]\n",
        "# Kita coba nilai k yang berbeda untuk KNN (dari k=1 sampai k=26)\n",
        "lrange=list(range(1,25))\n",
        "# loop proses KNN\n",
        "for k in lrange:\n",
        "  # masukkan nilai k dan ukuran 'jarak'\n",
        "  knn=KNeighborsClassifier(n_neighbors=k)\n",
        "  # masukan data train/ data latih untuk melatih KNN\n",
        "  knn.fit(x_train,y_train.ravel())\n",
        "  # lihat prediksi KNN dengan memasukkan data uji/data test\n",
        "  y_pred=knn.predict(x_test)\n",
        "  # tambahkan performance metric akurasi\n",
        "  scores.append(metrics.accuracy_score(y_test,y_pred))\n",
        "plt.figure(2,figsize=(15,5))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##test set diganti menjadi 0.2\n",
        "x_train,x_test,y_train,y_test = train_test_split(train_data,label_data,test_**size=0.2**,random_state=42)"
      ],
      "metadata": {
        "id": "P9POp7Iru6dM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "SF6KRfU-rxxg",
        "outputId": "3188e050-3646-4854-eff2-844d67296b37"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test set diganti menjadi 0.2\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.neighbors import KNeighborsClassifier \n",
        "import sklearn.metrics as metrics\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "x_train,x_test,y_train,y_test = train_test_split(train_data,label_data,test_size=0.2,random_state=42)\n",
        "df_final.strength = df_final.strength.astype(np.int64)\n",
        "p = sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
        "y_pred = knn.predict(x_test)\n",
        "print(\"test set diganti menjadi 0.2\")\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "\n",
        "# buat variabel kosong untuk menyimpan metrik KNN/KNN metrics\n",
        "scores=[]\n",
        "# Kita coba nilai k yang berbeda untuk KNN (dari k=1 sampai k=26)\n",
        "lrange=list(range(1,25))\n",
        "# loop proses KNN\n",
        "for k in lrange:\n",
        "  # masukkan nilai k dan ukuran 'jarak'\n",
        "  knn=KNeighborsClassifier(n_neighbors=k)\n",
        "  # masukan data train/ data latih untuk melatih KNN\n",
        "  knn.fit(x_train,y_train.ravel())\n",
        "  # lihat prediksi KNN dengan memasukkan data uji/data test\n",
        "  y_pred=knn.predict(x_test)\n",
        "  # tambahkan performance metric akurasi\n",
        "  scores.append(metrics.accuracy_score(y_test,y_pred))\n",
        "plt.figure(2,figsize=(15,5))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##test set diganti menjadi 0.1\n",
        "x_train,x_test,y_train,y_test = train_test_split(train_data,label_data,test_**size=0.1**,random_state=42)"
      ],
      "metadata": {
        "id": "BMdS3bbxvBgs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "3y2n2Vo2rxxg",
        "outputId": "56d95c7b-a9e2-4aa6-f903-8427793152c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test set diganti menjadi 0.1\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 0 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAEWCAYAAABLzQ1kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAZIElEQVR4nO3dfbxVZZn/8c/3HDTwAUVQ8vkZyeGnVmjl/HwoyzQZKYsctJoSxCxx1PrlTDaiNVNNmelojWISTCoqowZpozjOj9DSBI0U1BA1lFAxnkRCQ7nmj7VObA5w9jqHvfe69znf9+u1Xu59r7Xvde0jr+vc51r3upciAjMzS09L2QGYmdmmOUGbmSXKCdrMLFFO0GZmiXKCNjNLlBO0mVminKBti0nqI+lnklZKmrIF/ZwuaXotYyuLpKMk/a7sOKy5yfOgew5JpwEXAIOBVcAc4F8i4oEt7PfTwFjgyIh4c4sDTZykAA6MiAVlx2Ldm0fQPYSkC4ArgG8CA4G9gB8Cw2vQ/d7A/J6QnIuQ1KvsGKybiAhv3XwDdgBeA0Z0cMzbyBL44ny7Anhbvu9YYBHwJWAJ8CLwuXzfpcCfgbX5OUYBlwA3VPS9DxBAr/z9Z4FnyUbxzwGnV7Q/UPG5I4FZwMr8v0dW7JsBfAP4Zd7PdGDAZr5bW/xfqYj/o8BHgPnAMuCrFccfATwIrMiPvRrYOt83M/8uq/Pve2pF/xcCLwE/aWvLP7N/fo535e93A14Bji3734a3tDePoHuG9wG9gTs6OOYi4L3AYcChZEnqaxX7306W6HcnS8I/kNQvIsaRjcpviYjtIuL6jgKRtC3wb8CJEbE9WRKes4njdgLuyo/tD1wO3CWpf8VhpwGfA3YBtga+3MGp3072M9gduBi4DvgU8G7gKOCfJO2bH/sWcD4wgOxndxzwBYCIODo/5tD8+95S0f9OZH9NjKk8cUQ8Q5a8b5C0DfBjYFJEzOggXjMn6B6iP/DH6LgEcTrw9YhYEhGvkI2MP12xf22+f21E/Jxs9HhQF+NZBwyR1CciXoyIeZs45iTg6Yj4SUS8GRGTgaeAv6k45scRMT8i1gC3kv1y2Zy1ZPX2tcDNZMn3yohYlZ//CbJfTETEIxHxUH7e3wPXAscU+E7jIuKNPJ4NRMR1wALg18CuZL8QzTrkBN0zLAUGVKmN7gYsrHi/MG/7Sx/tEvyfgO06G0hErCYrC3weeFHSXZIGF4inLabdK96/1Il4lkbEW/nrtgT6csX+NW2flzRI0p2SXpL0KtlfCAM66BvglYh4vcox1wFDgKsi4o0qx5o5QfcQDwJvkNVdN2cx2Z/nbfbK27piNbBNxfu3V+6MiHsi4kNkI8mnyBJXtXjaYvpDF2PqjH8ni+vAiOgLfBVQlc90OB1K0nZkdf3rgUvyEo5Zh5yge4CIWElWd/2BpI9K2kbSVpJOlPSd/LDJwNck7SxpQH78DV085RzgaEl7SdoB+Me2HZIGShqe16LfICuVrNtEHz8HBkk6TVIvSacCBwN3djGmztgeeBV4LR/dn91u/8vAfp3s80pgdkSMJqutX7PFUVq35wTdQ0TE98jmQH+NbAbBC8A5wE/zQ/4ZmA08BjwOPJq3deVc9wK35H09woZJtSWPYzHZzIZj2DgBEhFLgWFkM0eWks3AGBYRf+xKTJ30ZbILkKvIRve3tNt/CTBJ0gpJn6zWmaThwAms/54XAO+SdHrNIrZuyTeqmJklyiNoM7NEOUGbmdWYpAmSlkiau4l9X5IU+bWeDjlBm5nV3kSy6w4bkLQncDzwfJFOnKDNzGosImaSXQRv7/tkF7wLXfxLdlGXPnuN9NVL28ia5y8tOwRL0qBq89Sr6kzOef2Fm89iw1v6x0fE+I4+k8/m+UNE/FYqFm6yCdrMLFV5Mu4wIVfK12D5Kll5ozAnaDMzQKprxXd/YF+gbfS8B/CopCMi4qXNfcgJ2swMaKnjMt4R8TjZqosASPo9MLTajVe+SGhmRjaCLrpV70uTydbAOUjSIkmjuhKTR9BmZkDRC3dFRMTIKvv3KdKPE7SZGZBiQcEJ2syMul8k7BInaDMznKDNzJJVz1kcXZVeRGZmJfAI2swsUU7QZmaJUtXHTjaeE7SZGR5Bm5klq6UlvXSYXkRmZqXwCNrMLEkucZiZJcoJ2swsUXKJw8wsTR5Bm5klqqWltewQNuIEbWaGSxxmZslyicPMLFFO0GZmiXKJw8wsUfKt3mZmaarlQ2NrxQnazAyXOMzMkuWLhGZmqXKJw8wsUekNoJ2gzcwAaEkvQztBm5mBR9BmZqkK16DNzBKVXn52gjYzA6AlvQztBG1mBp5mZ2aWrFYnaDOzNHkEbWaWqPTysxO0mRmQ5EXCBKdmm5mVQJ3YqnUlTZC0RNLcirZvSHpM0hxJ0yXtVq0fJ2gzMyBaWwpvBUwETmjX9t2IOCQiDgPuBC6u1okTtJkZ1HQEHREzgWXt2l6teLstENX6cQ3azAw6NYtD0hhgTEXT+IgYX+Bz/wJ8BlgJvL/a8R5Bm5lBdpGw4BYR4yNiaMVWNTkDRMRFEbEncCNwTtWQtvQ7mZl1CzUscRRwI/Dxagc5QZuZQVbiKLp1qXsdWPF2OPBUtc+4Bm1mBjW91VvSZOBYYICkRcA44COSDgLWAQuBz1frxwnazAxqeqt3RIzcRPP1ne3HCdrMDJK81ds16MRc892zWPjoNcy+9zsb7fv7M09izfOT6d9v+xIis5RMmjSNYcO+yEknfYGJE6eWHU63EC0qvDWKE3RifjLlFwz/zLc3at9j15047uj/w/OLXikhKkvJ/PkLmTLlHqZM+R5Tp17FjBmzWLhwcdlhNb86XyTsCifoxPzy4adYtuK1jdq/M+4zXPTNm4iq9x5Zd/fMMy9wyCEH0adPb3r1auXww4cwffqDZYfV/Bo7za6QutWgJQ0mm0qye970B2BaRDxZr3N2V8M+9G4Wv7SMx598vuxQLAGDBu3NFVf8hOXLX6V3762ZOXM2Q4YcWP2D1rFia2w0VF0StKQLgZHAzcDDefMewGRJN0fExn/Ds+Htk736DaXXdgfUI7ym0qf31nzlnI8y7FPfLDsUS8T+++/J6NEfZ9Soi+nTpzeDB+9HS0t6yaXpJHiRsF4j6FHAX0XE2spGSZcD84BNJuj8dsnxAH32Guk/5oH99h7I3nvuzMN3/ysAu++6Ew/+/JscdfLXePmVlSVHZ2UZMeJ4Row4HoDLL/8PBg7sX3JE3UCC60HXK0GvA3Yjm4xdadd8nxU073cvsPe71s9nf+qX/8ZfD7uIpctXlRiVlW3p0hX0778jixcvYfr0X3HrrZeVHVLz60EJ+jzgPklPAy/kbXsBB1BggZCebNJVYznqfe9gQL/tWfDrq/nG5f/JpFtmlB2WJWbs2G+xYsUqevVqZdy4s+nbd7uyQ2p6kV5+RlGnaQGSWoAj2PAi4ayIeKvI513isE1Z8/ylZYdgSRq0xel1v7NuK5xznr324w1J53WbxRER64CH6tW/mVlN9aASh5lZc0lwIowTtJkZNPQOwaKcoM3MwCUOM7NUhUfQZmaJ6uUEbWaWJo+gzcwS5Rq0mVmi0svPTtBmZkBDn5RSlBO0mRm4xGFmlqxWJ2gzszR5FoeZWaJc4jAzS5QTtJlZmnyrt5lZqnyR0MwsUS5xmJklygnazCxR6eVnJ2gzM/Ct3mZm6WqmWRySVgFtjyFvizzy1xERfescm5lZ4zTTLI6I2L6RgZiZlaklwad6FwpJ0v+V9Ln89QBJ+9Y3LDOzxpKKb9X70gRJSyTNrWj7rqSnJD0m6Q5JO1brp2qCljQOuBD4x7xpa+CG6iGamTWPWiZoYCJwQru2e4EhEXEIMJ/1OXWzioygPwacDKwGiIjFgMsfZtatSCq8VRMRM4Fl7dqmR8Sb+duHgD2q9VMkQf85IoL8gqGkbQt8xsysqbS0FN8kjZE0u2Ib08nTnQH8V7WDikyzu1XStcCOks7MO76uk8GYmSVNnbhIGBHjgfFdOo90EfAmcGO1Y6sm6Ii4TNKHgFeBQcDFEXFvVwIzM0tVI6ZBS/osMAw4Lq9MdKjojSqPA33IyhyPdzk6M7NE1ftGQkknAF8BjomIPxWKqUCno4GHgVOATwAPSTpjSwI1M0tNjafZTQYeBA6StEjSKOBqsgkW90qaI+maav0UGUH/P+CdEbE0P3F/4FfAhAKfNTNrCrUscUTEyE00X9/Zfook6KXAqor3q/I2M7Nuo6WZbvWWdEH+cgHwa0lTyWrQw4HHGhCbmVnDJLhWUocj6LabUZ7JtzZT6xeOmVk5mipBR8SljQzEzKxMTZWg20jamWxqyF8BvdvaI+IDdYzLzKyhElyvv9Ct3jcCTwH7ApcCvwdm1TEmM7OGq/FiSTVRJEH3j4jrgbUR8YuIOAPw6NnMupWWVhXeGqXINLu1+X9flHQSsBjYqX4hmZk1XlPWoIF/lrQD8CXgKqAvcH5dozIza7CmTNARcWf+ciXw/vqGY2ZWjqZK0JKuYv1DYzcSEefWJSIzsxKkOIujoxH07IZFYWZWspbWsiPYWEc3qkxqZCBmZmVqqhKHmVlPUuRZg43mBG1mhkfQZmbJaqoEXfYsjjXPe60m29gBJz9YdgiWoAXTBm1xH02VoPEsDjPrQXp14qnejeJZHGZmQIuqPmS74YouN3ohcDBebtTMuqkUb1Qputzok3i5UTPrxlo6sTUypmq83KiZdXstisJbo3i5UTMz0ixxeLlRMzOgVzMmaC83amY9gZp0FseP2cQNK3kt2sysW2jWEsedFa97Ax8jq0ObmXUbCd6nUqjEcVvle0mTgQfqFpGZWQma8kaVTTgQ2KXWgZiZlakpLxJKWsWGNeiXyO4sNDPrNpqyBh0R2zciEDOzMqVY4qhaF5d0X5E2M7Nm1qLiW6N0tB50b2AbYICkfkBbWH2B3RsQm5lZwzTbLI6zgPOA3YBHWJ+gXwWurnNcZmYNlWKJo6P1oK8ErpQ0NiKuamBMZmYNl+KC/UVCWidpx7Y3kvpJ+kIdYzIza7haLjcqaYKkJZLmVrSNkDRP0jpJQ4vGVM2ZEbGi7U1ELAfOLNK5mVmzqPFyoxOBE9q1zQVOAWYWjanIjSqtkhQRASCpFdi66AnMzJpBLWdnRMRMSfu0a3sSQJ14Om2RBH03cIuka/P3Z+VtZmbdRmdK0JLGAGMqmsZHxPgah1QoQV+YB3J2/v5e4LpaB2JmVqbOjKDzZFzzhNxekTsJ1wHX5BuSjiJbuP+L9Q3NzKxxWluaaJpdJUnvBEYCnwSeA26vZ1BmZo2W4Cy7Du8kHESWlEcCfwRuARQRfqqKmXU7tbxRJV+W+ViyO7EXAeOAZWTVh52BuyTNiYgPd9RPRyPop4D7gWERsSA/qZ9FaGbdUo1ncYzczK47OtNPR6P6U4AXgf8v6TpJx7H+dm8zs26lqRZLioifAj+VtC0wnGxdjl0k/TtwR0RMb1CMZmZ1t1WCa3FUrYtHxOqIuCki/gbYA/gNXrDfzLqZphpBb0p+m3dD5v+ZmTVSUz5RxcysJ2h1gjYzS5NH0GZmiWqqBfvNzHqSrTyCNjNLk0scZmaJconDzCxRnsVhZpYolzjMzBKV4lO9naDNzIBW16DNzNKU4ADaCdrMDFyDNjNLlhO0mVmiXIM2M0uUZ3GYmSXKJQ4zs0T5TkIzs0R5LQ7rtEmTpjFlyj1EBCNGfJjPfnZ42SFZCb517pF8YOgeLF35Oh8ZOw2A804/jA++Z0/WrYNlK1/nK1c+wJJla0qOtHklWIJOMibLzZ+/kClT7mHKlO8xdepVzJgxi4ULF5cdlpXg9vue4YxL/nuDth/dPo9h5/6Mk8/7Gf8z6wXOOfXQkqLrHlJ8aKwTdMKeeeYFDjnkIPr06U2vXq0cfvgQpk9/sOywrASz5r3Mitfe2KDttTVr//J6m969SO8P9OayVUsU3hrFCTphgwbtzSOPzGP58ldZs+Z1Zs6czUsv/bHssCwhF3zqndx//Sc4+Zj9uPLG35QdTlPzCBqQ9LkO9o2RNFvS7PHjb2lkWEnaf/89GT3644wadTGjR1/C4MH70dLi36m23uU3/IajRv0n037xLJ8+aXDZ4TQ1J+jMpZvbERHjI2JoRAwdM+bURsaUrBEjjuf226/gxhu/zQ47bMc+++xWdkiWoKkznuPDR+5ddhhNraUTW6PUZRaHpMc2twsYWI9zdldLl66gf/8dWbx4CdOn/4pbb72s7JAsEXvvuj0LX1wFwAffsyfPLlpZckTNTT1oHvRA4MPA8nbtAn5Vp3N2S2PHfosVK1bRq1cr48adTd++25UdkpXg+18+mvcMGUi/vr15YMInuHLyHI559x7st3tf1kWweMlq/umHD5UdZlPrSXcS3glsFxFz2u+QNKNO5+yWbrrpX8sOwRJw/mUzN2qbcu+CEiLpvlK8ulOXBB0RozrYd1o9zmlmtiXkOwnNzNKUYIXDCdrMDNK8SJhi2cXMrOHUia1qX9IESUskza1o20nSvZKezv/br1o/TtBmZmTLjRbdCpgInNCu7R+A+yLiQOC+/H2HnKDNzMhKHEW3aiJiJrCsXfNwYFL+ehLw0Wr9OEGbmdG5EkflshT5NqbAKQZGxIv565cocNOeLxKamdG5WRwRMR4Y39VzRUSowLw+J2gzMxpyJ+HLknaNiBcl7QosqRpT3UMyM2sCtZzFsRnTgL/LX/8dMLXaBzyCNjOjts8klDQZOBYYIGkRMA74NnCrpFHAQuCT1fpxgjYzo7Y3qkTEyM3sOq4z/ThBm5mRZr3XCdrMjDRv9XaCNjPDiyWZmSWrJy3Yb2bWVJygzcwSlWB+doI2MwM/UcXMLFkeQZuZJcrT7MzMEtVadgCb4ARtZoZH0GZmCUsvQztBm5kBcoI2M0uTlN5ySU7QZmaASxxmZolSgguOOkGbmeESh5lZwlziMDNLkmdxmJklygnazCxRUno3eztBm5kBrkGbmSXKJQ4zs2R5mp2ZWZI8gjYzS5QSXG/UCdrMDFCCS/Y7QZuZAZ7FYWaWKJc4zMyS5QRtZpYkLzdqZpYsj6DNzJLU4vWgzcxS5QRtZpakFO8kTO9XhplZKdSJrUpP0t9LmitpnqTzuhqRE7SZGdk86KJblX6GAGcCRwCHAsMkHdCVmJygzczIbvUuulXxDuDXEfGniHgT+AVwSpdiioiufM4aSNKYiBhfdhyWFv+7KI+kMcCYiqbxbf8vJL0DmAq8D1gD3AfMjoixnT6PE3T6JM2OiKFlx2Fp8b+LdEkaBXwBWA3MA96IiE7Xol3iMDOrsYi4PiLeHRFHA8uB+V3px9PszMxqTNIuEbFE0l5k9ef3dqUfJ+jm4DqjbYr/XaTrNkn9gbXAFyNiRVc6cQ3azCxRrkGbmSXKCdrMLFFO0ImTdIKk30laIOkfyo7HyidpgqQlkuaWHYvVlxN0wiS1Aj8ATgQOBkZKOrjcqCwBE4ETyg7C6s8JOm1HAAsi4tmI+DNwMzC85JisZBExE1hWdhxWf07QadsdeKHi/aK8zcx6ACdoM7NEOUGn7Q/AnhXv98jbzKwHcIJO2yzgQEn7Stoa+FtgWskxmVmDOEEnLF9L9hzgHuBJ4NaImFduVFY2SZOBB4GDJC3KV06zbsi3epuZJcojaDOzRDlBm5klygnazCxRTtBmZolygjYzS5QTtHVI0luS5kiaK2mKpG22oK+Jkj6Rv/5RRws/STpW0pFdOMfvJQ0o2t7umNc6ea5LJH25szGaFeUEbdWsiYjDImII8Gfg85U7JXXpsWkRMToinujgkGOBTidos+7ECdo6437ggHx0e7+kacATklolfVfSLEmPSToLQJmr8/Ws/xvYpa0jSTMkDc1fnyDpUUm/lXSfpH3IfhGcn4/ej5K0s6Tb8nPMkvTX+Wf7S5ouaZ6kHwGq9iUk/VTSI/lnxrTb9/28/T5JO+dt+0u6O//M/ZIG1+KHaVaNHxprheQj5ROBu/OmdwFDIuK5PMmtjIjDJb0N+KWk6cA7gYPI1rIeCDwBTGjX787AdcDReV87RcQySdcAr0XEZflxNwHfj4gH8icl3wO8AxgHPBARX5d0ElDkrroz8nP0AWZJui0ilgLbArMj4nxJF+d9n0P2cNbPR8TTkt4D/BD4QBd+jGad4gRt1fSRNCd/fT9wPVnp4eGIeC5vPx44pK2+DOwAHAgcDUyOiLeAxZL+ZxP9vxeY2dZXRGxuneMPAgdLfxkg95W0XX6OU/LP3iVpeYHvdK6kj+Wv98xjXQqsA27J228Abs/PcSQwpeLcbytwDrMt5gRt1ayJiMMqG/JEtbqyCRgbEfe0O+4jNYyjBXhvRLy+iVgKk3QsWbJ/X0T8SdIMoPdmDo/8vCva/wzMGsE1aKuFe4CzJW0FIGmQpG2BmcCpeY16V+D9m/jsQ8DRkvbNP7tT3r4K2L7iuOnA2LY3ktoS5kzgtLztRKBflVh3AJbnyXkw2Qi+TQvQ9lfAaWSlk1eB5ySNyM8hSYdWOYdZTThBWy38iKy+/Gj+INNryf46uwN4Ot/3H2QrsG0gIl4BxpCVE37L+hLDz4CPtV0kBM4FhuYXIZ9g/WySS8kS/DyyUsfzVWK9G+gl6Ung22S/INqsBo7Iv8MHgK/n7acDo/L45uHHjlmDeDU7M7NEeQRtZpYoJ2gzs0Q5QZuZJcoJ2swsUU7QZmaJcoI2M0uUE7SZWaL+F0fS2/Zo0cYAAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.neighbors import KNeighborsClassifier \n",
        "import sklearn.metrics as metrics\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "x_train,x_test,y_train,y_test = train_test_split(train_data,label_data,test_size=0.1,random_state=42)\n",
        "df_final.strength = df_final.strength.astype(np.int64)\n",
        "p = sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"YlGnBu\" ,fmt='g')\n",
        "y_pred = knn.predict(x_test)\n",
        "print(\"test set diganti menjadi 0.1\")\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "\n",
        "# buat variabel kosong untuk menyimpan metrik KNN/KNN metrics\n",
        "scores=[]\n",
        "# Kita coba nilai k yang berbeda untuk KNN (dari k=1 sampai k=26)\n",
        "lrange=list(range(1,25))\n",
        "# loop proses KNN\n",
        "for k in lrange:\n",
        "  # masukkan nilai k dan ukuran 'jarak'\n",
        "  knn=KNeighborsClassifier(n_neighbors=k)\n",
        "  # masukan data train/ data latih untuk melatih KNN\n",
        "  knn.fit(x_train,y_train.ravel())\n",
        "  # lihat prediksi KNN dengan memasukkan data uji/data test\n",
        "  y_pred=knn.predict(x_test)\n",
        "  # tambahkan performance metric akurasi\n",
        "  scores.append(metrics.accuracy_score(y_test,y_pred))\n",
        "plt.figure(2,figsize=(15,5))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4. Buatlah tabel yang berisi hasil pengujian Anda dan sertakan berapa nilai k dan nilai akurasi untuk masing-masing skenario uji."
      ],
      "metadata": {
        "id": "5aO2XL5d7Ai5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.1 Buatlah tabel yang berisi hasil pengujian Anda dan sertakan berapa nilai k dan nilai akurasi untuk masing-masing skenario uji. ***UNTUK 0.3***"
      ],
      "metadata": {
        "id": "QK9nysxs0P1_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimal_k = lrange[scores.index(max(scores))]\n",
        "print(\"Nilai k KNN yang optimal adalah %d\" % optimal_k)\n",
        "print(\"Skor optimalnya adalah %.2f\" % max(scores))\n",
        "\n",
        "# plot hasilnya\n",
        "plt.plot(lrange, scores,ls='dashed')\n",
        "plt.xlabel('Nilai dari k untuk KNN')\n",
        "plt.ylabel('Accuracy Score')\n",
        "plt.title('Accuracy Scores untuk nilai k dari k-Nearest-Neighbors')\n",
        "plt.show()\n",
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "rRDeAbfXyKtM",
        "outputId": "98b562c4-aa8a-4672-fa3f-e6e1a77a6bdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nilai k KNN yang optimal adalah 1\n",
            "Skor optimalnya adalah 0.68\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.62      0.58      0.60        36\n",
            "           1       0.56      0.59      0.58        32\n",
            "\n",
            "    accuracy                           0.59        68\n",
            "   macro avg       0.59      0.59      0.59        68\n",
            "weighted avg       0.59      0.59      0.59        68\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.2 Buatlah tabel yang berisi hasil pengujian Anda dan sertakan berapa nilai k dan nilai akurasi untuk masing-masing skenario uji **UNTUK 0.2**"
      ],
      "metadata": {
        "id": "vfQMZnQ50CsY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimal_k = lrange[scores.index(max(scores))]\n",
        "print(\"Nilai k KNN yang optimal adalah %d\" % optimal_k)\n",
        "print(\"Skor optimalnya adalah %.2f\" % max(scores))\n",
        "\n",
        "# plot hasilnya\n",
        "plt.plot(lrange, scores,ls='dashed')\n",
        "plt.xlabel('Nilai dari k untuk KNN')\n",
        "plt.ylabel('Accuracy Score')\n",
        "plt.title('Accuracy Scores untuk nilai k dari k-Nearest-Neighbors')\n",
        "plt.show()\n",
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "2c81b1DryObn",
        "outputId": "76310b5b-c282-4ac4-ac8f-53a0066272a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nilai k KNN yang optimal adalah 13\n",
            "Skor optimalnya adalah 0.71\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.61      0.61      0.61        23\n",
            "           1       0.59      0.59      0.59        22\n",
            "\n",
            "    accuracy                           0.60        45\n",
            "   macro avg       0.60      0.60      0.60        45\n",
            "weighted avg       0.60      0.60      0.60        45\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.3 Buatlah tabel yang berisi hasil pengujian Anda dan sertakan berapa nilai k dan nilai akurasi untuk masing-masing skenario uji. **UNTUK 0.1**"
      ],
      "metadata": {
        "id": "ziNGW1x80It_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "optimal_k = lrange[scores.index(max(scores))]\n",
        "print(\"Nilai k KNN yang optimal adalah %d\" % optimal_k)\n",
        "print(\"Skor optimalnya adalah %.2f\" % max(scores))\n",
        "\n",
        "# plot hasilnya\n",
        "plt.plot(lrange, scores,ls='dashed')\n",
        "plt.xlabel('Nilai dari k untuk KNN')\n",
        "plt.ylabel('Accuracy Score')\n",
        "plt.title('Accuracy Scores untuk nilai k dari k-Nearest-Neighbors')\n",
        "plt.show()\n",
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "8R5tnzb2vvZP",
        "outputId": "239ea226-e2fc-42bf-9637-8cae196e206d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nilai k KNN yang optimal adalah 1\n",
            "Skor optimalnya adalah 0.78\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.69      0.75        13\n",
            "           1       0.67      0.80      0.73        10\n",
            "\n",
            "    accuracy                           0.74        23\n",
            "   macro avg       0.74      0.75      0.74        23\n",
            "weighted avg       0.75      0.74      0.74        23\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#5. Apakah terjadi perbedaan nilai akurasi model? Jika Ya, jelaskan pendapat Anda mengapa hal tersebut bisa terjadi.\n",
        "\n",
        "###JAWAB\n",
        "\n",
        "###ya terjadi perbedaan dalam nilai akurasi model di karenakan di ubah nya test set menjadi 0.3 0.2 0.1 perubahan itu mengakibatkan perubahan akurasi model di karenakan ukuran test size nya di ubah dan mengahilkan grafik yang berbeda "
      ],
      "metadata": {
        "id": "lMiUV4cY048E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#6. Berapa nilai presisi dan recall dari masing-masing model yang Anda coba serta jelaskan arti dari nilai tersebut."
      ],
      "metadata": {
        "id": "kFfd-j_O10hH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "set test 0.3"
      ],
      "metadata": {
        "id": "m6i482Ng5ynU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8KMtU-O5Cvp",
        "outputId": "19205a92-20fa-448c-f477-76e432bd7fb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.62      0.58      0.60        36\n",
            "           1       0.56      0.59      0.58        32\n",
            "\n",
            "    accuracy                           0.59        68\n",
            "   macro avg       0.59      0.59      0.59        68\n",
            "weighted avg       0.59      0.59      0.59        68\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "set test 0.2"
      ],
      "metadata": {
        "id": "WQi4Cb5T557F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_XCLjidK5n4D",
        "outputId": "94e6f952-bbe2-4e7e-d0f1-16541b3544a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.61      0.61      0.61        23\n",
            "           1       0.59      0.59      0.59        22\n",
            "\n",
            "    accuracy                           0.60        45\n",
            "   macro avg       0.60      0.60      0.60        45\n",
            "weighted avg       0.60      0.60      0.60        45\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "set test 0.1"
      ],
      "metadata": {
        "id": "LlSB0h1H58Fl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eyhP1b4W5wpt",
        "outputId": "602ae0aa-210b-443e-8808-b88c0976bbf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.69      0.75        13\n",
            "           1       0.67      0.80      0.73        10\n",
            "\n",
            "    accuracy                           0.74        23\n",
            "   macro avg       0.74      0.75      0.74        23\n",
            "weighted avg       0.75      0.74      0.74        23\n",
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "CTi9qYLhwBlO",
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        "QMKB9YChrxxa",
        "WaX69cZErxxd",
        "RrYsNCJvrxxd",
        "ud1Voo_Srxxe"
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  "nbformat": 4,
  "nbformat_minor": 0
}