housing_price_prediction.ipynb

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    "colab": {
      "name": "housing-price-prediction.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iqbalhanif/NN-with-Keras/blob/master/housing_price_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jVL4tt_ob9eD"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DTSense/Webinar_DeepLearning_1/blob/main/1_kobar_latihan/DTSense-tensorflow2.0-tutorial-and-housing-price-prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "4cb58ef01caeeaa5907d8f9903caea33980ac7d4",
        "id": "L5WgXx1Fb9eF"
      },
      "source": [
        "# Prediksi Harga Rumah - Dataset Boston Housing Price\n",
        "\n",
        "Pada tutorial ini, kita akan memprediksi harga rumah dari dataset Boston Housing Price"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "ecc59c7ae2f4138cc0344ddd8ae36be8892e6ec4",
        "id": "Cmj-pgXwb9eH"
      },
      "source": [
        "## Predictive Model: Regression Analysis\n",
        "\n",
        "Pada Machine-Learning khususnya supervised learning masalah yang di hadapi ada dua, iaitu **Regression** dan **Classification**. Pada gambar berikut bisa di lihat perbedaan antara regresi dan klasifikasi.\n",
        "\n",
        "<img src=\"https://i.imgur.com/vvSoAzg.jpg\" alt=\"classification_regression\" width=\"400\"/>\n",
        "\n",
        "Analisis regresi adalah suatu solusi ketika diberikan input maka output yang diberikan berupa **continuous value**, sebagai contoh nilai berat badan atau nilai suhu temperatur. Sedangkan analisis klasifikasi adalah suatu solusi ketika diberikan suatu input, maka output yang di berikan berupa kelas atau kategori.\n",
        "\n",
        "Untuk dataset perumahan Boston, kita mempunyai 506 baris data, dengan masing-masing 13 fitur. Tugas kita sekarang adalah membuat model regresi yang menggunakan 13 fitur ini sebagai masukan dan mengeluarkan prediksi nilai tunggal dari \"nilai median rumah yang ditempati pemilik (dalam $ 1.000).\" sebagai output."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrrrO8ZrBeW0",
        "outputId": "7c73ed56-22f5-4e4e-9922-de2ba4e0704d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "!wget 'https://raw.githubusercontent.com/DTSense/Webinar_DeepLearning_1/main/1_kobar_latihan/datasets_106564_253992_boston.csv'"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-10-11 15:28:35--  https://raw.githubusercontent.com/DTSense/Webinar_DeepLearning_1/main/1_kobar_latihan/datasets_106564_253992_boston.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 38447 (38K) [text/plain]\n",
            "Saving to: ‘datasets_106564_253992_boston.csv.1’\n",
            "\n",
            "datasets_106564_253 100%[===================>]  37.55K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2020-10-11 15:28:36 (2.62 MB/s) - ‘datasets_106564_253992_boston.csv.1’ saved [38447/38447]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "c67f6a9eece5df23748b425da680a35294c1a2fd",
        "id": "Zu2t5CBFb9eI"
      },
      "source": [
        "# Import TensorFlow, Keras, dan Layers\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import Dense \n",
        "\n",
        "# Import module umum yang di perlukan (numpy, os, sys)\n",
        "import numpy as np\n",
        "import os\n",
        "import sys\n",
        "\n",
        "# Images, plots, display, and visualization (pandas, seaborn, matplotlib)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "# print(tf.__version__)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "ebf739c3adb0bef9fddf473c933e03254471cec7",
        "id": "frK6HIdlb9eT",
        "outputId": "40fc7c94-ec44-41fd-b031-7bbc9464babc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# check location dengan os.getcwd()\n",
        "# check isi folder dengan os.listdir()\n",
        "print(os.getcwd())\n",
        "print(os.listdir())"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n",
            "['.config', 'datasets_106564_253992_boston.csv', 'datasets_106564_253992_boston.csv.1', 'sample_data']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-SN1u0wnb9eb",
        "outputId": "17b08ce8-2ed6-4c5e-8011-b812e147bb59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Read data csv dengan pandas\n",
        "df = pd.read_csv('datasets_106564_253992_boston.csv')\n",
        "df.head()\n",
        "# Perlihatkan 5 dataset pertama"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
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              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
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              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
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            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS     NX  ...    TAX  PTRATIO       B  LSTAT  MEDV\n",
              "0  0.00632  18.0   2.31     0  0.538  ...  296.0     15.3  396.90   4.98  24.0\n",
              "1  0.02731   0.0   7.07     0  0.469  ...  242.0     17.8  396.90   9.14  21.6\n",
              "2  0.02729   0.0   7.07     0  0.469  ...  242.0     17.8  392.83   4.03  34.7\n",
              "3  0.03237   0.0   2.18     0  0.458  ...  222.0     18.7  394.63   2.94  33.4\n",
              "4  0.06905   0.0   2.18     0  0.458  ...  222.0     18.7  396.90   5.33  36.2\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwEOCofZb9em"
      },
      "source": [
        "CRIM per capita crime rate by town <br>\n",
        "ZN proportion of residential land zoned for lots over 25,000 sq.ft. <br>\n",
        "INDUS proportion of non-retail business acres per town <br>\n",
        "CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise) <br>\n",
        "NOX nitric oxides concentration (parts per 10 million) <br>\n",
        "RM average number of rooms per dwelling <br>\n",
        "AGE proportion of owner-occupied units built prior to 1940 <br>\n",
        "DIS weighted distances to five Boston employment centres <br>\n",
        "RAD index of accessibility to radial highways <br>\n",
        "TAX full-value property-tax rate per 10,000usd <br>\n",
        "PTRATIO pupil-teacher ratio by town <br>\n",
        "B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town <br>\n",
        "LSTAT % lower status of the population <br>\n",
        "MEDV Median value of owner-occupied homes (in $1000)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01qW9ZDOb9eo",
        "outputId": "779698a9-c02b-48d1-bea8-7783c21d8b31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Check the shape of dataframe\n",
        "df.shape"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(506, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMmV1IqQb9e1",
        "outputId": "ac81a811-656f-4330-8008-3363855de1c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# Check column of data frame\n",
        "df.columns"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
              "       'PTRATIO', 'B', 'LSTAT', 'MEDV'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y9-IGCW4b9e-",
        "outputId": "682c9a3c-f430-4c49-efb2-2bf9408aa55e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# Check data types in data frame\n",
        "df.dtypes"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM       float64\n",
              "ZN         float64\n",
              "INDUS      float64\n",
              "CHAS         int64\n",
              "NX         float64\n",
              "RM         float64\n",
              "AGE        float64\n",
              "DIS        float64\n",
              "RAD          int64\n",
              "TAX        float64\n",
              "PTRATIO    float64\n",
              "B          float64\n",
              "LSTAT      float64\n",
              "MEDV       float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gy82_jlEb9fG",
        "outputId": "9ad4142e-f060-4deb-970f-40bb9cd072d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# Identifying the unique number of values in the dataset\n",
        "df.nunique()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM       504\n",
              "ZN          26\n",
              "INDUS       76\n",
              "CHAS         2\n",
              "NX          81\n",
              "RM         446\n",
              "AGE        356\n",
              "DIS        412\n",
              "RAD          9\n",
              "TAX         66\n",
              "PTRATIO     46\n",
              "B          357\n",
              "LSTAT      455\n",
              "MEDV       229\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6v0tFDSb9fP"
      },
      "source": [
        "# Reset index sehingga nama column menjadi index\n",
        "nu = df.nunique().reset_index()"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z8ZzJIB8b9fV",
        "outputId": "08164e08-b7aa-4666-ab2b-ec691ea4e3fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "# Rubah column di data baru dengan nama column yang baru lalu visualisasikan dengan seaborn\n",
        "nu.columns = ['Feature', 'nunique']\n",
        "ax = sns.barplot(x = 'Feature', y='nunique', data = nu)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IoqQXzob9fa",
        "outputId": "a572916b-eb5a-4084-8b65-afae1660b378",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# Check for missing values\n",
        "df.isnull().sum()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM       0\n",
              "ZN         0\n",
              "INDUS      0\n",
              "CHAS       0\n",
              "NX         0\n",
              "RM         0\n",
              "AGE        0\n",
              "DIS        0\n",
              "RAD        0\n",
              "TAX        0\n",
              "PTRATIO    0\n",
              "B          0\n",
              "LSTAT      0\n",
              "MEDV       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXzyINV2b9fk",
        "outputId": "f4b74c8d-d4c3-422a-b183-267ef47aed43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        }
      },
      "source": [
        "# See rows with missing values\n",
        "df[df.isnull().any(axis=1)]"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [CRIM, ZN, INDUS, CHAS, NX, RM, AGE, DIS, RAD, TAX, PTRATIO, B, LSTAT, MEDV]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ylp3ZMDbb9fp",
        "outputId": "3fbdee51-43fa-4523-ac7a-5249603c755f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# Viewing the data statistics\n",
        "df.describe"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of         CRIM    ZN  INDUS  CHAS     NX  ...    TAX  PTRATIO       B  LSTAT  MEDV\n",
              "0    0.00632  18.0   2.31     0  0.538  ...  296.0     15.3  396.90   4.98  24.0\n",
              "1    0.02731   0.0   7.07     0  0.469  ...  242.0     17.8  396.90   9.14  21.6\n",
              "2    0.02729   0.0   7.07     0  0.469  ...  242.0     17.8  392.83   4.03  34.7\n",
              "3    0.03237   0.0   2.18     0  0.458  ...  222.0     18.7  394.63   2.94  33.4\n",
              "4    0.06905   0.0   2.18     0  0.458  ...  222.0     18.7  396.90   5.33  36.2\n",
              "..       ...   ...    ...   ...    ...  ...    ...      ...     ...    ...   ...\n",
              "501  0.06263   0.0  11.93     0  0.573  ...  273.0     21.0  391.99   9.67  22.4\n",
              "502  0.04527   0.0  11.93     0  0.573  ...  273.0     21.0  396.90   9.08  20.6\n",
              "503  0.06076   0.0  11.93     0  0.573  ...  273.0     21.0  396.90   5.64  23.9\n",
              "504  0.10959   0.0  11.93     0  0.573  ...  273.0     21.0  393.45   6.48  22.0\n",
              "505  0.04741   0.0  11.93     0  0.573  ...  273.0     21.0  396.90   7.88  11.9\n",
              "\n",
              "[506 rows x 14 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQrYV-oXb9fw",
        "outputId": "e91d88a4-fe45-4fe1-85da-0de371c98988",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Finding out the correlation between the features\n",
        "corr = df.corr()\n",
        "corr.shape"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIplRnZ4b9f6",
        "outputId": "5da8a075-5ffd-4bf6-ea89-00e1a56fe484",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Plotting the heatmap of correlation between features\n",
        "plt.figure(figsize=(20,20))\n",
        "sns.heatmap(corr, cbar=True, square=True, annot=True, annot_kws={'size':15}, cmap='Greens')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f94aada8da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mazq52Hnb9gD"
      },
      "source": [
        "# Spliting target variable and independent variables\n",
        "X = df.drop(['MEDV'], axis=1)\n",
        "y = df['MEDV']"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AAScFMHhb9gK"
      },
      "source": [
        "# Splitting to training and testing data, gunakan konvensi X_train, X_test, y_train, y_test\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=4)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QlacHYEhb9gT"
      },
      "source": [
        "## Preparing the data\n",
        "\n",
        "We are going to do a feature normalization . Feature normalizaion is when you subtract the mean of the feature from each feature and divide each result by the standard deviation.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yNd8eClqb9gU"
      },
      "source": [
        "# Biar lebih mudah di mengerti ganti dari X_ menjadi Feature_ dan y_ menjadi label_ *optional\n",
        "feature_train = X_train\n",
        "feature_test = X_test\n",
        "label_train = y_train\n",
        "label_test = y_test"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPOX8srnSFlI",
        "outputId": "b2a70349-c30a-4c5a-ab14-be2afa2fc11d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "feature_train.head()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>0.08664</td>\n",
              "      <td>45.0</td>\n",
              "      <td>3.44</td>\n",
              "      <td>0</td>\n",
              "      <td>0.437</td>\n",
              "      <td>7.178</td>\n",
              "      <td>26.3</td>\n",
              "      <td>6.4798</td>\n",
              "      <td>5</td>\n",
              "      <td>398.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>390.49</td>\n",
              "      <td>2.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>0.24980</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.89</td>\n",
              "      <td>0</td>\n",
              "      <td>0.624</td>\n",
              "      <td>5.857</td>\n",
              "      <td>98.2</td>\n",
              "      <td>1.6686</td>\n",
              "      <td>4</td>\n",
              "      <td>437.0</td>\n",
              "      <td>21.2</td>\n",
              "      <td>392.04</td>\n",
              "      <td>21.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>0.21409</td>\n",
              "      <td>22.0</td>\n",
              "      <td>5.86</td>\n",
              "      <td>0</td>\n",
              "      <td>0.431</td>\n",
              "      <td>6.438</td>\n",
              "      <td>8.9</td>\n",
              "      <td>7.3967</td>\n",
              "      <td>7</td>\n",
              "      <td>330.0</td>\n",
              "      <td>19.1</td>\n",
              "      <td>377.07</td>\n",
              "      <td>3.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.62976</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.949</td>\n",
              "      <td>61.8</td>\n",
              "      <td>4.7075</td>\n",
              "      <td>4</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>8.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>0.01538</td>\n",
              "      <td>90.0</td>\n",
              "      <td>3.75</td>\n",
              "      <td>0</td>\n",
              "      <td>0.394</td>\n",
              "      <td>7.454</td>\n",
              "      <td>34.2</td>\n",
              "      <td>6.3361</td>\n",
              "      <td>3</td>\n",
              "      <td>244.0</td>\n",
              "      <td>15.9</td>\n",
              "      <td>386.34</td>\n",
              "      <td>3.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        CRIM    ZN  INDUS  CHAS     NX  ...  RAD    TAX  PTRATIO       B  LSTAT\n",
              "192  0.08664  45.0   3.44     0  0.437  ...    5  398.0     15.2  390.49   2.87\n",
              "138  0.24980   0.0  21.89     0  0.624  ...    4  437.0     21.2  392.04  21.32\n",
              "251  0.21409  22.0   5.86     0  0.431  ...    7  330.0     19.1  377.07   3.59\n",
              "13   0.62976   0.0   8.14     0  0.538  ...    4  307.0     21.0  396.90   8.26\n",
              "256  0.01538  90.0   3.75     0  0.394  ...    3  244.0     15.9  386.34   3.11\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J55nu5LOb9gb",
        "outputId": "367fb198-601e-42f7-9d8c-04b543292d88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Gunakan approach Normalization untuk smoothing the data. Rumus (feature_train - train_mean) / train_std\n",
        "# Using per-feature statistics (mean, standard deviation) from the training set to do Normalization\n",
        "# Normalization digunakan pada training data, tidak langsung pada test data\n",
        "\n",
        "train_mean = np.mean(feature_train, axis=0)\n",
        "train_std = np.std(feature_train, axis=0)\n",
        "feature_train_norm = (feature_train - train_mean) / train_std\n",
        "\n",
        "feature_train_norm.head()\n",
        "\n",
        "# Note that the quantities used for normalizing the test data are computed using the\n",
        "# training data. You should never use in your workflow any quantity computed on the\n",
        "# test data, even for something as simple as data normalization."
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>-0.427448</td>\n",
              "      <td>1.484011</td>\n",
              "      <td>-1.114239</td>\n",
              "      <td>-0.256833</td>\n",
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              "      <td>-1.509687</td>\n",
              "      <td>0.365233</td>\n",
              "      <td>-1.374464</td>\n",
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              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>-0.408222</td>\n",
              "      <td>-0.479907</td>\n",
              "      <td>1.574679</td>\n",
              "      <td>-0.256833</td>\n",
              "      <td>0.569434</td>\n",
              "      <td>-0.583574</td>\n",
              "      <td>1.044346</td>\n",
              "      <td>-0.993784</td>\n",
              "      <td>-0.656796</td>\n",
              "      <td>0.140768</td>\n",
              "      <td>1.257406</td>\n",
              "      <td>0.382577</td>\n",
              "      <td>1.182332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>-0.412430</td>\n",
              "      <td>0.480231</td>\n",
              "      <td>-0.761546</td>\n",
              "      <td>-0.256833</td>\n",
              "      <td>-1.072975</td>\n",
              "      <td>0.233103</td>\n",
              "      <td>-2.137974</td>\n",
              "      <td>1.692437</td>\n",
              "      <td>-0.316173</td>\n",
              "      <td>-0.489225</td>\n",
              "      <td>0.288924</td>\n",
              "      <td>0.215063</td>\n",
              "      <td>-1.274687</td>\n",
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              "      <th>13</th>\n",
              "      <td>-0.363451</td>\n",
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              "      <td>-0.624644</td>\n",
              "      <td>1.165170</td>\n",
              "      <td>0.436961</td>\n",
              "      <td>-0.627519</td>\n",
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              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>-0.435844</td>\n",
              "      <td>3.447930</td>\n",
              "      <td>-1.069059</td>\n",
              "      <td>-0.256833</td>\n",
              "      <td>-1.387840</td>\n",
              "      <td>1.661233</td>\n",
              "      <td>-1.236376</td>\n",
              "      <td>1.195063</td>\n",
              "      <td>-0.770337</td>\n",
              "      <td>-0.995575</td>\n",
              "      <td>-1.186859</td>\n",
              "      <td>0.318794</td>\n",
              "      <td>-1.341205</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         CRIM        ZN     INDUS  ...   PTRATIO         B     LSTAT\n",
              "192 -0.427448  1.484011 -1.114239  ... -1.509687  0.365233 -1.374464\n",
              "138 -0.408222 -0.479907  1.574679  ...  1.257406  0.382577  1.182332\n",
              "251 -0.412430  0.480231 -0.761546  ...  0.288924  0.215063 -1.274687\n",
              "13  -0.363451 -0.479907 -0.429257  ...  1.165170  0.436961 -0.627519\n",
              "256 -0.435844  3.447930 -1.069059  ... -1.186859  0.318794 -1.341205\n",
              "\n",
              "[5 rows x 13 columns]"
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          "metadata": {
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          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oVJKLwtpb9gh"
      },
      "source": [
        "### Membuat Machine Learning Model\n",
        "\n",
        "Membuat NN membutuhkan konfigurasi lapisan model, kemudian menyusun model. Pertama kita menumpuk beberapa lapisan bersama-sama menggunakan `keras.Sequential`. Selanjutnya kita mengkonfigurasi fungsi **loss**, **optimizer**, dan **Metrics** untuk kita pantau. Ini ditambahkan selama langkah kompilasi model:\n",
        "\n",
        "* *Loss function* - mengukur seberapa akurat model selama pelatihan, dan untuk meminimalisir nilai loss, kita menggunakan optimizer.\n",
        "* *Optimizer* - bagaimana model diperbarui berdasarkan data yang terlihat beserta loss functionnya.\n",
        "* *Metrics* - digunakan untuk memantau langkah-langkah pelatihan dan pengujian.\n",
        "\n",
        "Mari membangun jaringan dengan 1 lapisan tersembunyi dari 20 neuron, dan menggunakan mean squared error (MSE) sebagai fungsi kerugian (yang paling umum untuk masalah regresi):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "14b77ff893198e3d49426d43edcc1bc1275b7afb",
        "id": "JcrWUOFBb9gk"
      },
      "source": [
        "# Buat Machine learning Model di dalam function build_model()\n",
        "def build_model():\n",
        "    model = keras.Sequential([\n",
        "        #####\n",
        "        Dense(64, activation=tf.nn.relu, input_shape=[13]),\n",
        "        Dense(1)\n",
        "    ])\n",
        "    # Jangan lupa untuk compile model\n",
        "    model.compile(optimizer='adam', loss='mse', metrics=['mae', 'mse'])\n",
        "    return model"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "9cef609938a182870f902be31c70a043358450f8",
        "id": "ADCrxiMbb9g2"
      },
      "source": [
        "### Train the model\n",
        "\n",
        "Training the neural network model requires the following steps:\n",
        "\n",
        "1. Feed the training data to the model—in this example, the `train_features` and `train_labels` arrays.\n",
        "2. The model learns to associate features and labels.\n",
        "3. We ask the model to make predictions about a test set—in this example, the `test_features` array. We verify that the predictions match the labels from the `test_labels` array. \n",
        "\n",
        "To start training,  call the `model.fit` method—the model is \"fit\" to the training data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "868779ae75945302557634b8fc34c8dbc79b7c7e",
        "id": "GfjLZgBEb9g4",
        "outputId": "93c88142-5a90-4685-9bf8-653c3714b2ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "# this helps makes our output less verbose but still shows progress\n",
        "class PrintDot(keras.callbacks.Callback):\n",
        "    def on_epoch_end(self, epoch, logs):\n",
        "        if epoch % 100 == 0: print('')\n",
        "        print('.', end='')\n",
        "\n",
        "# Call build_model to variable model\n",
        "####\n",
        "model = build_model()\n",
        "\n",
        "# Definisikan early stopping\n",
        "####\n",
        "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=50)\n",
        "\n",
        "# Training dengan metode .fit() dan masukkan ke variable history\n",
        "####\n",
        "history = model.fit(feature_train_norm, label_train, epochs=1000, verbose=0, validation_split=0.1, callbacks=[early_stop, PrintDot()] )\n",
        "\n",
        "hist = pd.DataFrame(history.history)\n",
        "hist['epoch'] = history.epoch\n",
        "\n",
        "# show RMSE measure to compare to Kaggle leaderboard on https://www.kaggle.com/c/boston-housing/leaderboard\n",
        "rmse_final = np.sqrt(float(hist['val_mse'].tail(1)))\n",
        "print('Final Root Mean Square Error on validation set: {}'.format(round(rmse_final, 3)))"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "....................................................................................................\n",
            "....................................................................................................\n",
            "....................................................................................................\n",
            "...........................................................................Final Root Mean Square Error on validation set: 2.184\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "8bdef28ce210b9625a7d2e18f1a55e876d9f7f1e",
        "id": "dFPL-jmTb9hQ"
      },
      "source": [
        "Sekarang lakukan ploting untuk visualisasi loss function pada training dan validation dataset. Validation Dataset disini digunakan untuk mencegah \"overfitting\" dimana kita mendapatkan akurasi yang tinggi hanya pada training tetapi tidak pada validation. ([learn more about it here](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit))"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "b253d7c2e0eb1d52c03e9140aabbd6fed5f933a3",
        "id": "feiF0fNjb9he",
        "outputId": "6b060468-96ef-440b-bcb3-fc0649f2a466",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# plot Train Error dan Val Error menggunakan matplotlib plt\n",
        "def plot_history():\n",
        "    ##\n",
        "    plt.figure()\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Mean Square Error')\n",
        "    plt.plot(hist['epoch'], hist['mse'], label='Train Error')\n",
        "    plt.plot(hist['epoch'], hist['val_mse'], label='Val Error')\n",
        "plot_history()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_uuid": "253554e724ef5841072d5eb3897f81cc974b4b03",
        "id": "x5A3CVAbb9hp"
      },
      "source": [
        "Selanjutnya lakukan komparasi bagaimana performa model pada test dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_uuid": "fa51d9cb533d0db4d343b9a6b90fcd98b3ce8174",
        "id": "JjSnB7WAb9hr",
        "outputId": "497b778a-97ed-45f8-c2a0-18115cad83e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Lakukan Normalisasi pada test data untuk evaluasi\n",
        "data_test_norm = (feature_test - train_mean) / train_std\n",
        "\n",
        "mse, _, _ = model.evaluate(data_test_norm, label_test)\n",
        "\n",
        "rmse = np.sqrt(mse)\n",
        "\n",
        "print(rmse)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4/4 [==============================] - 0s 2ms/step - loss: 9.6275 - mae: 2.2034 - mse: 9.6275\n",
            "3.1028229196653783\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWbfxES9b9h4"
      },
      "source": [
        "Jika kita lihat di kaggle leaderboard, RMSE sekitar 3.18 berarti model kita sudah cukup baik dalam memprediksi, dan berada di posisi 5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BaI9-JhMb9h5",
        "outputId": "2bb73d63-6220-454f-c339-2571d45b9559",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "source": [
        "# Lakukan percobaan dengan mengambil 2 sample dari train dataset yang sudah di normalisasikan (feature_train_norm[:2])\n",
        "percobaan = (feature_train_norm[:2])\n",
        "percobaan"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>-0.427448</td>\n",
              "      <td>1.484011</td>\n",
              "      <td>-1.114239</td>\n",
              "      <td>-0.256833</td>\n",
              "      <td>-1.021915</td>\n",
              "      <td>1.273277</td>\n",
              "      <td>-1.517903</td>\n",
              "      <td>1.262452</td>\n",
              "      <td>-0.543255</td>\n",
              "      <td>-0.088856</td>\n",
              "      <td>-1.509687</td>\n",
              "      <td>0.365233</td>\n",
              "      <td>-1.374464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>-0.408222</td>\n",
              "      <td>-0.479907</td>\n",
              "      <td>1.574679</td>\n",
              "      <td>-0.256833</td>\n",
              "      <td>0.569434</td>\n",
              "      <td>-0.583574</td>\n",
              "      <td>1.044346</td>\n",
              "      <td>-0.993784</td>\n",
              "      <td>-0.656796</td>\n",
              "      <td>0.140768</td>\n",
              "      <td>1.257406</td>\n",
              "      <td>0.382577</td>\n",
              "      <td>1.182332</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         CRIM        ZN     INDUS  ...   PTRATIO         B     LSTAT\n",
              "192 -0.427448  1.484011 -1.114239  ... -1.509687  0.365233 -1.374464\n",
              "138 -0.408222 -0.479907  1.574679  ...  1.257406  0.382577  1.182332\n",
              "\n",
              "[2 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRLjDzxzb9iL",
        "outputId": "f159fc7c-8d69-499a-e75e-960eba8fd4da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# Evaluasi nilai MEDV pada data pertama dengan index yang terlihat\n",
        "df.loc[192]"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM         0.08664\n",
              "ZN          45.00000\n",
              "INDUS        3.44000\n",
              "CHAS         0.00000\n",
              "NX           0.43700\n",
              "RM           7.17800\n",
              "AGE         26.30000\n",
              "DIS          6.47980\n",
              "RAD          5.00000\n",
              "TAX        398.00000\n",
              "PTRATIO     15.20000\n",
              "B          390.49000\n",
              "LSTAT        2.87000\n",
              "MEDV        36.40000\n",
              "Name: 192, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wPVxLecb9iV",
        "outputId": "4edd4afe-59c8-47cf-f9b5-894d97aa5055",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# Evaluasi nilai MEDV pada data kedua dengan index yang terlihat\n",
        "df.loc[138]"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM         0.2498\n",
              "ZN           0.0000\n",
              "INDUS       21.8900\n",
              "CHAS         0.0000\n",
              "NX           0.6240\n",
              "RM           5.8570\n",
              "AGE         98.2000\n",
              "DIS          1.6686\n",
              "RAD          4.0000\n",
              "TAX        437.0000\n",
              "PTRATIO     21.2000\n",
              "B          392.0400\n",
              "LSTAT       21.3200\n",
              "MEDV        13.3000\n",
              "Name: 138, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpWBsDCRb9ie",
        "outputId": "56573245-c5a8-4880-e7f9-54cd35f86e75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# Lakukan prediksi terhadap sample data yang kita pilih dan bandingkan hasilnya dengan nilai MEDV yang tertera\n",
        "model.predict(percobaan)\n",
        "# gunakan model.predict() untuk melakukan prediksi"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[33.876015],\n",
              "       [14.520835]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "id": "mfpYL1F2b9ij"
      },
      "source": [
        "## Credits (Reference)\n",
        "\n",
        "> - [Lex Fridman's MIT Deep Learning](https://github.com/lexfridman/mit-deep-learning)\n",
        "> - [Aymeric Damien](https://github.com/aymericdamien/)\n",
        "> - [GitHub Awesome Lists Topic](https://github.com/topics/awesome)\n",
        "> * [Tensorflow Tutorial and House Price Prediction](https://www.kaggle.com/arunkumarramanan/tensorflow-tutorial-and-examples)\n",
        "> - [Boston House Price Prediction](https://www.kaggle.com/shreayan98c/boston-house-price-prediction)"
      ]
    }
  ]
}