04_Abalone.ipynb

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                  "\n",
                  "Diameter Insight\n",
                  "Mean     : 0.41\n",
                  "Std      : 0.10\n",
                  "Max      : 0.65\n",
                  "Min      : 0.06\n",
                  "Skewness : -0.609\n",
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        "<a href=\"https://colab.research.google.com/github/ManullangJihan/100-Day-ML-Challenge/blob/main/04_Abalone.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Abalone\n",
        "\n",
        "## Konten\n",
        "\n",
        "1. Sex -- M, F, and I (infant)\n",
        "2. Length / continuous / mm / Longest shell measurement\n",
        "3. Diameter / continuous / mm / perpendicular to length\n",
        "4. Height / continuous / mm / with meat in shell\n",
        "5. Whole weight / continuous / grams / whole abalone\n",
        "6. Shucked weight / continuous / grams / weight of meat\n",
        "7. Viscera weight / continuous / grams / gut weight (after bleeding)\n",
        "8. Shell weight / continuous / grams / after being dried\n",
        "9. Rings / integer / -- / +1.5 gives the age in years  (Target)\n",
        "\n",
        "[Sumber Data](https://archive.ics.uci.edu/ml/datasets/Abalone)"
      ],
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      "source": [
        "!pip install shap\n",
        "!pip install dataprep\n",
        "!pip install eli5\n",
        "!pip install catboost\n",
        "!pip install optuna"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "\n",
        "import ipywidgets as widgets\n",
        "from ipywidgets import fixed"
      ],
      "metadata": {
        "id": "WFbNf0wFRkMx"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.io as pio\n",
        "\n",
        "pio.templates[pio.templates.default] = 'plotly_dark'\n",
        "plt.rcParams['figure.figsize'] = [10, 10]\n",
        "plt.rcParams.update({'font.size': 10})"
      ],
      "metadata": {
        "id": "g5eytybwWx5J"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set random default for reproducibility\n",
        "\n",
        "global_seed = 42\n",
        "\n",
        "def set_seed(seed):\n",
        "    np.random.seed(seed)\n",
        "    random.seed(seed)\n",
        "\n",
        "set_seed(global_seed)"
      ],
      "metadata": {
        "id": "_xJhgxEBGr1A"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/drive/MyDrive/abalone.data\"\n",
        "main_df = pd.read_csv(path, sep=',', header=None)\n",
        "main_df.columns = ['Sex', 'Length', 'Diameter', 'Height', 'Whole weight', 'Shucked Weight', 'Viscera Weight', 'Shell Weight', 'Rings']\n",
        "n_samples, n_features = main_df.shape\n",
        "\n",
        "print(f'Number of samples: {n_samples}')\n",
        "print(f'Number of features: {n_features}\\n')\n",
        "\n",
        "main_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "azm39a6aR_9K",
        "outputId": "532e0887-7cdd-4380-de16-7854f04e4962"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples: 4177\n",
            "Number of features: 9\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Sex  Length  Diameter  Height  Whole weight  Shucked Weight  Viscera Weight  \\\n",
              "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
              "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
              "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
              "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
              "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
              "\n",
              "   Shell Weight  Rings  \n",
              "0         0.150     15  \n",
              "1         0.070      7  \n",
              "2         0.210      9  \n",
              "3         0.155     10  \n",
              "4         0.055      7  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3948726-39ee-46bf-9f53-d72c26920400\">\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>Sex</th>\n",
              "      <th>Length</th>\n",
              "      <th>Diameter</th>\n",
              "      <th>Height</th>\n",
              "      <th>Whole weight</th>\n",
              "      <th>Shucked Weight</th>\n",
              "      <th>Viscera Weight</th>\n",
              "      <th>Shell Weight</th>\n",
              "      <th>Rings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>0.455</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.095</td>\n",
              "      <td>0.5140</td>\n",
              "      <td>0.2245</td>\n",
              "      <td>0.1010</td>\n",
              "      <td>0.150</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>0.350</td>\n",
              "      <td>0.265</td>\n",
              "      <td>0.090</td>\n",
              "      <td>0.2255</td>\n",
              "      <td>0.0995</td>\n",
              "      <td>0.0485</td>\n",
              "      <td>0.070</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>F</td>\n",
              "      <td>0.530</td>\n",
              "      <td>0.420</td>\n",
              "      <td>0.135</td>\n",
              "      <td>0.6770</td>\n",
              "      <td>0.2565</td>\n",
              "      <td>0.1415</td>\n",
              "      <td>0.210</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>M</td>\n",
              "      <td>0.440</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.5160</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.1140</td>\n",
              "      <td>0.155</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I</td>\n",
              "      <td>0.330</td>\n",
              "      <td>0.255</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.0895</td>\n",
              "      <td>0.0395</td>\n",
              "      <td>0.055</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3948726-39ee-46bf-9f53-d72c26920400')\"\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",
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              "  </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-b3948726-39ee-46bf-9f53-d72c26920400 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-b3948726-39ee-46bf-9f53-d72c26920400');\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
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove Duplicated Row if there is any\n",
        "\n",
        "duplicate_idx = main_df.duplicated()\n",
        "print(f'Number of samples before delete duplicated row: {n_samples}')\n",
        "\n",
        "main_df = main_df.loc[~duplicate_idx, :]\n",
        "n_samples = main_df.shape[0]\n",
        "print(f'Number of samples before after delete duplicated row: {n_samples}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yJy1I6n0wbge",
        "outputId": "cc187322-fb0f-499c-cc81-ff2f3ec1ad97"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples before delete duplicated row: 4177\n",
            "Number of samples before after delete duplicated row: 4177\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from plotly.subplots import make_subplots\n",
        "\n",
        "## Visualize Data Distribution\n",
        "def plot_distribution(df, feature):\n",
        "    feature_mean = df[feature].mean()\n",
        "    feature_std = df[feature].std()\n",
        "    feature_max = df[feature].max()\n",
        "    feature_min = df[feature].min()\n",
        "    feature_skew =  df[feature].skew()\n",
        "    print('')\n",
        "    print(feature + ' ' +  'Insight')\n",
        "    print(f'Mean     : {feature_mean:.2f}')\n",
        "    print(f'Std      : {feature_std:.2f}')\n",
        "    print(f'Max      : {feature_max:.2f}')\n",
        "    print(f'Min      : {feature_min:.2f}')\n",
        "    print(f'Skewness : {feature_skew:.3f}\\n')\n",
        "\n",
        "    fig = make_subplots(\n",
        "        rows=1, cols=2,\n",
        "        specs=[[{'type': 'xy'}, {'type': 'xy'}]])\n",
        "    \n",
        "    fig.add_trace(\n",
        "        go.Histogram(\n",
        "            x = df[feature],\n",
        "            name = feature,\n",
        "            ), row=1, col=1\n",
        "        )\n",
        "\n",
        "    fig.add_trace(\n",
        "        go.Violin(\n",
        "            y = df[feature],\n",
        "            name = feature,\n",
        "            box_visible = True,\n",
        "            meanline_visible = True,\n",
        "            fillcolor='lightseagreen',\n",
        "        ),\n",
        "        row=1, col=2\n",
        "    )\n",
        "\n",
        "    fig.update_layout(\n",
        "        title = f\"{feature} Distribution\",\n",
        "        width = 750,\n",
        "        height = 500,\n",
        "        showlegend = False\n",
        "    )\n",
        "\n",
        "    fig.show()\n",
        "\n",
        "num_cols = main_df.columns[main_df.dtypes != 'object']\n",
        "selected_feature = widgets.Dropdown(\n",
        "    options = num_cols,\n",
        "    value = num_cols[1],\n",
        "    description = 'Feature',\n",
        "    disabled = False)\n",
        "\n",
        "widgets.interact(\n",
        "    plot_distribution, \n",
        "    df = fixed(main_df),\n",
        "    feature = selected_feature)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705,
          "referenced_widgets": [
            "97c1635fe6824aeaaa7304a2b76f71c7",
            "80f0ea0250074680a48301d2c232c6e2",
            "124c0aba40f046f6b0d1838759c6ee90",
            "746f5387715d45f79c3ca840aebaecc6",
            "57394f43b00c42ce9ebc53cf1b5ae5d9",
            "b80f48b2aba84dc08ab3d6073ed170a8",
            "4fe5c959ffe246e48e0410661c47567d"
          ]
        },
        "id": "BdW2DD2h1Czf",
        "outputId": "9fcfd5a1-8109-46a5-bdc4-8c527c13f71e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Dropdown(description='Feature', index=1, options=('Length', 'Diameter', 'Height', 'Whole…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "97c1635fe6824aeaaa7304a2b76f71c7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.plot_distribution>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_features = main_df.columns.values.tolist()\n",
        "\n",
        "def plot_feature_interaction(df, x, y):\n",
        "    fig = px.scatter(\n",
        "        df, x=x, y=y, color=\"Rings\",\n",
        "        color_continuous_scale  = 'Viridis',\n",
        "        trendline='ols',\n",
        "        trendline_scope = 'overall',\n",
        "        trendline_color_override = 'deeppink',\n",
        "        opacity = 0.5\n",
        "    )\n",
        "\n",
        "    fig.update_layout(\n",
        "        title=f'Interaction between {x} and {y}',\n",
        "        height = 500,\n",
        "        width = 600,\n",
        "        showlegend=False)\n",
        "    \n",
        "    fig.show()\n",
        "\n",
        "feature1 = widgets.Dropdown(\n",
        "    options = all_features,\n",
        "    value = all_features[1],\n",
        "    description = 'X-axis'\n",
        ")\n",
        "\n",
        "feature2 = widgets.Dropdown(\n",
        "    options = all_features,\n",
        "    value = all_features[2],\n",
        "    description = 'Y-axis'\n",
        ")\n",
        "\n",
        "\n",
        "widgets.interact(\n",
        "    plot_feature_interaction,\n",
        "    df = fixed(main_df),\n",
        "    x = feature1,\n",
        "    y = feature2\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668,
          "referenced_widgets": [
            "c99368fdad8c4160acd2246656058001",
            "b248ddb819e64fd2a1d8631ef642ea8f",
            "101e3a210cb54443836370f543d410ff",
            "8a01c86df38941f48e8427c29560409d",
            "7ace0e79fc0242b99b1f79990c65f979",
            "97106b8fea9f4c53a066e476e9bd9bf2",
            "98c456387d6b46a582db54ffecbb383e",
            "3ad12bce04e64824976debf546057cdc",
            "dfac272b5a204e3e8f56e15a4bfc337c",
            "a30738f3101f445eb9df8ce6bb7fae1d"
          ]
        },
        "id": "Z32cVBdQo1Lp",
        "outputId": "c25064da-b974-40c0-ebb4-f235f317ff77"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Dropdown(description='X-axis', index=1, options=('Sex', 'Length', 'Diameter', 'Height', …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c99368fdad8c4160acd2246656058001"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.plot_feature_interaction>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Statistical Information\n",
        "\n",
        "means = main_df.mean(numeric_only=True)\n",
        "vars = main_df.var(numeric_only=True)\n",
        "stds = main_df.std(numeric_only=True)\n",
        "skews = main_df.skew(numeric_only=True)\n",
        "\n",
        "df_stats = {\n",
        "    'Mean': means,\n",
        "    'Var': vars,\n",
        "    'Std': stds,\n",
        "    'Skew': skews\n",
        "}\n",
        "\n",
        "def plot_stats(stats=None):\n",
        "    fig = px.bar(\n",
        "        df_stats,\n",
        "        y = stats,\n",
        "    )\n",
        "\n",
        "    fig.show()\n",
        "\n",
        "stat_list = ['Mean', 'Var', 'Std', 'Skew']\n",
        "stat_selected = widgets.Dropdown(\n",
        "    options = stat_list,\n",
        "    value = 'Mean',\n",
        "    description = \"Statistical Information\"\n",
        ")\n",
        "\n",
        "widgets.interact(\n",
        "    plot_stats,\n",
        "    stats = stat_selected,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591,
          "referenced_widgets": [
            "be6f7dc181174538b2835174d2090d4d",
            "a4df39affe24499ba2b5e1c7b0b7aa7c",
            "ca46fa9aa35a47aca8e202805ad550cd",
            "65baa4e578c742778e61aab9c5688854",
            "69c990c231614f239d1d6f1360405c2f",
            "f71dbfee67dd4aae85c46ab1a9b82ffa",
            "0e9668d1aa55461086408f646772661b"
          ]
        },
        "id": "81iYTgvBICEo",
        "outputId": "61e0f3a2-3d48-4c1d-9d25-3a825d0e17d6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Dropdown(description='Statistical Information', options=('Mean', 'Var', 'Std', 'Skew'), …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "be6f7dc181174538b2835174d2090d4d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.plot_stats>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove Outliers\n",
        "\n",
        "def clipping_outliers(df, quantile=0.9):\n",
        "    numeric_cols = df.columns[df.dtypes != 'object'].values.tolist()\n",
        "\n",
        "    for col in numeric_cols:\n",
        "        q_val = df[col].quantile(quantile)\n",
        "        idx = np.where(df[col] < q_val, True, False)\n",
        "        df[col] = df.loc[idx, col]\n",
        "    \n",
        "    return df\n",
        "\n",
        "removed_outlier_df = clipping_outliers(main_df)\n",
        "removed_outlier_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2Hlm813LLZM",
        "outputId": "afa6a117-d184-4efd-c2d3-0ec40ab49030"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4177, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove Outliers for value more than 3x Standard Deviation\n",
        "\n",
        "def remove_outliers(df, std_val=3):\n",
        "    numeric_cols = df.columns[df.dtypes != 'object'].values.tolist()\n",
        "\n",
        "    for col in numeric_cols:\n",
        "        std_val = df[col].std() * 3\n",
        "        df[col] = df.loc[(df.loc[:, col] > -std_val) & (df.loc[:, col] < std_val), col]\n",
        "    \n",
        "    return df\n",
        "\n",
        "new_df = main_df.copy()\n",
        "removed_outliers_df = remove_outliers(new_df)\n",
        "removed_outliers_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9Unz_rWP03g",
        "outputId": "a58541de-f495-402c-8143-3a5a404d16ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4177, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "main_df = main_df.loc[main_df.loc[:, 'Rings'] > 3, :]\n",
        "main_df = main_df.loc[main_df.loc[:, 'Rings'] < 24, :]\n",
        "\n",
        "main_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rn5eLn45RdR",
        "outputId": "c7337761-a5b6-4caa-dfc8-b85591447d67"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4153, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "main_df = pd.get_dummies(main_df)\n",
        "main_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vtVjFWODT0Qz",
        "outputId": "d40f83f2-714c-4e2a-f26f-20921bfd399c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Length  Diameter  Height  Whole weight  Shucked Weight  Viscera Weight  \\\n",
              "0   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
              "1   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
              "2   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
              "3   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
              "4   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
              "\n",
              "   Shell Weight  Rings  Sex_F  Sex_I  Sex_M  \n",
              "0         0.150     15      0      0      1  \n",
              "1         0.070      7      0      0      1  \n",
              "2         0.210      9      1      0      0  \n",
              "3         0.155     10      0      0      1  \n",
              "4         0.055      7      0      1      0  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>Length</th>\n",
              "      <th>Diameter</th>\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
              "      <td>0.530</td>\n",
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              "      <td>0.135</td>\n",
              "      <td>0.6770</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.440</td>\n",
              "      <td>0.365</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.5160</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.1140</td>\n",
              "      <td>0.155</td>\n",
              "      <td>10</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.330</td>\n",
              "      <td>0.255</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.0895</td>\n",
              "      <td>0.0395</td>\n",
              "      <td>0.055</td>\n",
              "      <td>7</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
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              "\n",
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              "          document.querySelector('#df-e673b594-4305-4984-92ba-1792442f6475 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-e673b594-4305-4984-92ba-1792442f6475');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = main_df.copy()\n",
        "\n",
        "new_df['Proportion_H_L'] = new_df['Height'] / new_df['Length']\n",
        "new_df['Proportion_H_WW'] = new_df['Height'] / new_df['Whole weight']\n",
        "new_df['Proportion_H_ShW'] = new_df['Height'] / new_df['Shucked Weight']\n",
        "new_df['Proportion_H_VW'] = new_df['Height'] / new_df['Viscera Weight']\n",
        "new_df['Proportion_H_Sw'] = new_df['Height'] / new_df['Shell Weight']\n",
        "\n",
        "new_df['Proportion_L_WW'] = new_df['Length'] / new_df['Whole weight']\n",
        "new_df['Proportion_L_ShW'] = new_df['Length'] / new_df['Shucked Weight']\n",
        "new_df['Proportion_L_VW'] = new_df['Length'] / new_df['Viscera Weight']\n",
        "new_df['Proportion_L_SW'] = new_df['Length'] / new_df['Shell Weight']\n",
        "\n",
        "new_df['Proportion_D_L'] = new_df['Diameter'] / new_df['Length']\n",
        "new_df['Proportion_D_H'] = new_df['Diameter'] / new_df['Height']\n",
        "new_df['Proportion_D_WW'] = new_df['Diameter'] / new_df['Whole weight']\n",
        "new_df['Proportion_D_SW'] = new_df['Diameter'] / new_df['Shell Weight']\n",
        "new_df['Proportion_D_ShW'] = new_df['Diameter'] / new_df['Shucked Weight']\n",
        "new_df['Proportion_D_VW'] = new_df['Diameter'] / new_df['Viscera Weight']\n",
        "\n",
        "new_df['Proportion_VW_SW'] = new_df['Viscera Weight'] / new_df['Shell Weight']\n",
        "new_df['Proportion_VW_WW'] = new_df['Viscera Weight'] / new_df['Whole weight']\n",
        "new_df['Proportion_VW_ShW'] = new_df['Viscera Weight'] / new_df['Shucked Weight']\n",
        "\n",
        "new_df['Proportion_ShW_WW'] = new_df['Shucked Weight'] / new_df['Whole weight']\n",
        "new_df['Proportion_ShW_SW'] = new_df['Shucked Weight'] / new_df['Shell Weight']\n",
        "\n",
        "new_df['Proportion_SW_WW'] = new_df['Shell Weight'] / new_df['Whole weight']\n",
        "\n",
        "print(new_df.shape)"
      ],
      "metadata": {
        "id": "cF_v02GFVw-R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1b4550b-158b-43ef-b086-8ef9908a5256"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4153, 32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_data(df):\n",
        "    assert isinstance(df, pd.DataFrame)\n",
        "    df.dropna()\n",
        "    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\n",
        "    return df[indices_to_keep]"
      ],
      "metadata": {
        "id": "Tz_-eLJ7dKVq"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from xgboost import XGBRegressor\n",
        "\n",
        "def visualize_model(y_test, y_pred, score):\n",
        "    fig = px.scatter(\n",
        "        x=y_test, y=y_pred,\n",
        "        trendline=\"ols\", trendline_color_override=\"deeppink\")\n",
        "    fig.update_layout(\n",
        "        title = f\"MSE Score: {score:.3f}\",\n",
        "        height = 500,\n",
        "        width = 600,\n",
        "        xaxis_title = \"Truth\",\n",
        "        yaxis_title = \"Predictions\")\n",
        "    \n",
        "    fig.show()\n",
        "\n",
        "def simple_evaluate(X, y):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(\n",
        "        X, y, test_size = 0.2, shuffle=True\n",
        "    )\n",
        "\n",
        "    params = {'n_estimators': 118, 'max_depth': 4, 'learning_rate': 0.11954781075189252, 'min_child_weight': 56}\n",
        "    reg = XGBRegressor(**params, objective=\"reg:squarederror\").fit(X_train, y_train)\n",
        "    y_pred = reg.predict(X_test)\n",
        "    y_pred = np.round(y_pred)\n",
        "    mse = mean_squared_error(y_test, y_pred)\n",
        "\n",
        "    visualize_model(y_test, y_pred, mse)"
      ],
      "metadata": {
        "id": "zg3ogxGUdprb"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from scipy.stats import yeojohnson\n",
        "\n",
        "def normalize_data(df):\n",
        "    mean = df.mean()\n",
        "    std = df.std()\n",
        "    return (df - mean) / std\n",
        "\n",
        "def normalize_skewed_data(df, cols):\n",
        "    for col in cols:\n",
        "        df[col] = yeojohnson(df[col])[0]\n",
        "    return df"
      ],
      "metadata": {
        "id": "DEw9KOD0y26e"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate raw Data\n",
        "\n",
        "X = main_df.drop('Rings', axis=1)\n",
        "y = main_df.Rings.values\n",
        "\n",
        "X = normalize_data(X)\n",
        "X = normalize_skewed_data(X, ['Height'])\n",
        "\n",
        "simple_evaluate(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "FlqS2b-WyOzQ",
        "outputId": "a12f5808-e141-4c85-abc0-2d6effbd6d59"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate Feature-Enginnering Data\n",
        "\n",
        "X = new_df.drop('Rings', axis=1)\n",
        "y = new_df.Rings.values\n",
        "\n",
        "X = normalize_data(X)\n",
        "X = normalize_skewed_data(X, ['Height'])\n",
        "\n",
        "simple_evaluate(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "_Y0Uagd_2wql",
        "outputId": "3cea588f-a87b-47b6-ac44-8ab417fa8e18"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
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              "                            \n",
              "var gd = document.getElementById('4699d52f-fabb-4475-809c-8e0139b7daed');\n",
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              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "new_df = clean_data(new_df)\n",
        "X_resampled = new_df.drop('Rings', axis=1)\n",
        "y_resampled = new_df.Rings.values\n",
        "\n",
        "X_resampled, y_resampled = SMOTE().fit_resample(X_resampled, y_resampled)"
      ],
      "metadata": {
        "id": "YsJ41Dgr3SxZ"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate Resampled Data\n",
        "\n",
        "X = normalize_data(X_resampled)\n",
        "X = normalize_skewed_data(X_resampled, ['Height'])\n",
        "\n",
        "simple_evaluate(X, y_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "EMi_UzuE3Ehp",
        "outputId": "9f82c519-8b46-4644-f838-173f2f9deee4"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate PCA transformed Data\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=20).fit(X_resampled)\n",
        "X_pca = pca.transform(X_resampled)\n",
        "\n",
        "simple_evaluate(X_pca, y_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "uxCMI2Xx32NZ",
        "outputId": "943129e0-04c0-4ef5-ae52-61465a60f6fe"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# TODO\n",
        "\n",
        "1. Transform the Target because Target skewed to the left\n",
        "2. Evaluate results\n",
        "3. Normalize Skewed Features\n",
        "4. Evaluate results with normalized skewed features"
      ],
      "metadata": {
        "id": "LNjqLDB2u6YF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from functools import partial\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "import optuna\n",
        "from optuna import trial\n",
        "\n",
        "def objective(trial):\n",
        "    params = {\n",
        "        'n_estimators': trial.suggest_int('n_estimators', 100, 1500),\n",
        "        'max_depth': trial.suggest_int('max_depth', 2, 13),\n",
        "        'learning_rate': trial.suggest_uniform('learning_rate', 0.05, 0.50),\n",
        "        'min_child_weight':  trial.suggest_int('min_child_weight', 1, 100)\n",
        "    }\n",
        "\n",
        "    model = XGBRegressor(**params, objective='reg:squarederror')\n",
        "    \n",
        "    mses = cross_val_score(\n",
        "        model, X_1, y_1, scoring='neg_mean_squared_error', cv=5\n",
        "    )\n",
        "       \n",
        "    return -1.0 * np.mean(mses)\n",
        "\n",
        "optimization_function = partial(\n",
        "    objective)\n",
        "\n",
        "study = optuna.create_study(direction='minimize')\n",
        "study.optimize(optimization_function, n_trials=15)"
      ],
      "metadata": {
        "id": "IIgF1Bos0wHC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e9f3fa5-d358-4c96-8167-451e64d3e57c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[32m[I 2022-06-08 09:52:07,768]\u001b[0m A new study created in memory with name: no-name-4b816aeb-6d8c-45c2-8930-c83349a67136\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 09:53:43,739]\u001b[0m Trial 0 finished with value: 5.765892515596842 and parameters: {'n_estimators': 972, 'max_depth': 12, 'learning_rate': 0.3732847555343503, 'min_child_weight': 73}. Best is trial 0 with value: 5.765892515596842.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 09:54:29,106]\u001b[0m Trial 1 finished with value: 5.591109682957437 and parameters: {'n_estimators': 838, 'max_depth': 6, 'learning_rate': 0.21343312423030258, 'min_child_weight': 45}. Best is trial 1 with value: 5.591109682957437.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 09:54:58,717]\u001b[0m Trial 2 finished with value: 5.034621030915826 and parameters: {'n_estimators': 1160, 'max_depth': 2, 'learning_rate': 0.18486098071290924, 'min_child_weight': 68}. Best is trial 2 with value: 5.034621030915826.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 09:56:04,144]\u001b[0m Trial 3 finished with value: 5.678328687256606 and parameters: {'n_estimators': 984, 'max_depth': 8, 'learning_rate': 0.20718668643198185, 'min_child_weight': 75}. Best is trial 2 with value: 5.034621030915826.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 09:56:24,446]\u001b[0m Trial 4 finished with value: 5.665806525868301 and parameters: {'n_estimators': 575, 'max_depth': 4, 'learning_rate': 0.4113042449942033, 'min_child_weight': 100}. Best is trial 2 with value: 5.034621030915826.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 09:58:24,756]\u001b[0m Trial 5 finished with value: 5.371482359591936 and parameters: {'n_estimators': 1303, 'max_depth': 11, 'learning_rate': 0.09740978874629122, 'min_child_weight': 41}. Best is trial 2 with value: 5.034621030915826.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 09:59:50,668]\u001b[0m Trial 6 finished with value: 5.096323936798475 and parameters: {'n_estimators': 763, 'max_depth': 13, 'learning_rate': 0.06701877293010874, 'min_child_weight': 31}. Best is trial 2 with value: 5.034621030915826.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 10:00:18,327]\u001b[0m Trial 7 finished with value: 5.647770429453309 and parameters: {'n_estimators': 456, 'max_depth': 6, 'learning_rate': 0.3945424781644646, 'min_child_weight': 14}. Best is trial 2 with value: 5.034621030915826.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 10:01:18,013]\u001b[0m Trial 8 finished with value: 5.136846789905449 and parameters: {'n_estimators': 409, 'max_depth': 11, 'learning_rate': 0.1967880430337866, 'min_child_weight': 9}. Best is trial 2 with value: 5.034621030915826.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 10:01:51,826]\u001b[0m Trial 9 finished with value: 5.5847934982155945 and parameters: {'n_estimators': 286, 'max_depth': 10, 'learning_rate': 0.36148993322113954, 'min_child_weight': 39}. Best is trial 2 with value: 5.034621030915826.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 10:02:20,480]\u001b[0m Trial 10 finished with value: 5.920453954569821 and parameters: {'n_estimators': 1475, 'max_depth': 2, 'learning_rate': 0.495568820314749, 'min_child_weight': 66}. Best is trial 2 with value: 5.034621030915826.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 10:04:43,389]\u001b[0m Trial 11 finished with value: 5.155799911342718 and parameters: {'n_estimators': 1176, 'max_depth': 13, 'learning_rate': 0.05268576411139264, 'min_child_weight': 22}. Best is trial 2 with value: 5.034621030915826.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 10:04:58,036]\u001b[0m Trial 12 finished with value: 4.749369649792153 and parameters: {'n_estimators': 701, 'max_depth': 2, 'learning_rate': 0.1314716123053462, 'min_child_weight': 60}. Best is trial 12 with value: 4.749369649792153.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 10:05:11,505]\u001b[0m Trial 13 finished with value: 4.8326578110657525 and parameters: {'n_estimators': 662, 'max_depth': 2, 'learning_rate': 0.1751400249365622, 'min_child_weight': 61}. Best is trial 12 with value: 4.749369649792153.\u001b[0m\n",
            "\u001b[32m[I 2022-06-08 10:05:16,638]\u001b[0m Trial 14 finished with value: 4.521289897816852 and parameters: {'n_estimators': 118, 'max_depth': 4, 'learning_rate': 0.11954781075189252, 'min_child_weight': 56}. Best is trial 14 with value: 4.521289897816852.\u001b[0m\n"
          ]
        }
      ]
    }
  ]
}