Tensorflow2_prjct.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPfSb3426CVYPI6jDK+g7r7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/valeriaciampa/project.data/blob/main/Tensorflow2_prjct.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n"
      ],
      "metadata": {
        "id": "kg_VoDcSPmWc"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(tf.version)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7xuFsZs8PtAl",
        "outputId": "e776704f-cda1-4d89-cea7-856ec49480c9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<module 'tensorflow._api.v2.version' from '/usr/local/lib/python3.10/dist-packages/tensorflow/_api/v2/version/__init__.py'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow_decision_forests\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7oS0PWMKhTHJ",
        "outputId": "cd8d7d46-a1aa-4156-d1bb-4cc7d84fe857"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow_decision_forests\n",
            "  Downloading tensorflow_decision_forests-1.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.0 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (1.26.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (2.1.4)\n",
            "Requirement already satisfied: tensorflow==2.17.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (2.17.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (1.16.0)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (1.4.0)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (0.44.0)\n",
            "Collecting wurlitzer (from tensorflow_decision_forests)\n",
            "  Downloading wurlitzer-3.1.1-py3-none-any.whl.metadata (2.5 kB)\n",
            "Requirement already satisfied: tf-keras~=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (2.17.0)\n",
            "Collecting ydf (from tensorflow_decision_forests)\n",
            "  Downloading ydf-0.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.2 kB)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (0.2.0)\n",
            "Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (3.11.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (0.4.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (24.1)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (3.20.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (2.32.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (71.0.4)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (1.16.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (1.64.1)\n",
            "Requirement already satisfied: tensorboard<2.18,>=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (2.17.0)\n",
            "Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (3.4.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (0.37.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->tensorflow_decision_forests) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->tensorflow_decision_forests) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->tensorflow_decision_forests) (2024.1)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow==2.17.0->tensorflow_decision_forests) (13.8.1)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow==2.17.0->tensorflow_decision_forests) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow==2.17.0->tensorflow_decision_forests) (0.12.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow==2.17.0->tensorflow_decision_forests) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow==2.17.0->tensorflow_decision_forests) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow==2.17.0->tensorflow_decision_forests) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow==2.17.0->tensorflow_decision_forests) (2024.8.30)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow==2.17.0->tensorflow_decision_forests) (3.7)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow==2.17.0->tensorflow_decision_forests) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow==2.17.0->tensorflow_decision_forests) (3.0.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow==2.17.0->tensorflow_decision_forests) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow==2.17.0->tensorflow_decision_forests) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow==2.17.0->tensorflow_decision_forests) (2.18.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow==2.17.0->tensorflow_decision_forests) (0.1.2)\n",
            "Downloading tensorflow_decision_forests-1.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.5/15.5 MB\u001b[0m \u001b[31m51.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading wurlitzer-3.1.1-py3-none-any.whl (8.6 kB)\n",
            "Downloading ydf-0.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.3 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.3/9.3 MB\u001b[0m \u001b[31m60.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: ydf, wurlitzer, tensorflow_decision_forests\n",
            "Successfully installed tensorflow_decision_forests-1.10.0 wurlitzer-3.1.1 ydf-0.8.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n"
      ],
      "metadata": {
        "id": "xxFfayInhl11"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix, classification_report, accuracy_score\n"
      ],
      "metadata": {
        "id": "kbZWsAgwoOd_"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = sns.load_dataset (\"penguins\")"
      ],
      "metadata": {
        "id": "AnmgilMZo7rH"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "FnbdxOSzpIqC",
        "outputId": "25ad5c4f-0fc9-40ed-dedf-d7878a8eba20"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
              "0  Adelie  Torgersen            39.1           18.7              181.0   \n",
              "1  Adelie  Torgersen            39.5           17.4              186.0   \n",
              "2  Adelie  Torgersen            40.3           18.0              195.0   \n",
              "3  Adelie  Torgersen             NaN            NaN                NaN   \n",
              "4  Adelie  Torgersen            36.7           19.3              193.0   \n",
              "\n",
              "   body_mass_g     sex  \n",
              "0       3750.0    Male  \n",
              "1       3800.0  Female  \n",
              "2       3250.0  Female  \n",
              "3          NaN     NaN  \n",
              "4       3450.0  Female  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0e6b5be1-ac3e-4a30-a079-611ee24f0ba8\" 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>species</th>\n",
              "      <th>island</th>\n",
              "      <th>bill_length_mm</th>\n",
              "      <th>bill_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "      <th>sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.1</td>\n",
              "      <td>18.7</td>\n",
              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3800.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>40.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>195.0</td>\n",
              "      <td>3250.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adelie</td>\n",
              "      <td>Torgersen</td>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e6b5be1-ac3e-4a30-a079-611ee24f0ba8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0e6b5be1-ac3e-4a30-a079-611ee24f0ba8 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-0e6b5be1-ac3e-4a30-a079-611ee24f0ba8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6e52c6cc-d1fb-42b3-b1a3-3b016ced54c1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6e52c6cc-d1fb-42b3-b1a3-3b016ced54c1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6e52c6cc-d1fb-42b3-b1a3-3b016ced54c1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 344,\n  \"fields\": [\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Adelie\",\n          \"Chinstrap\",\n          \"Gentoo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Torgersen\",\n          \"Biscoe\",\n          \"Dream\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.4595837139265315,\n        \"min\": 32.1,\n        \"max\": 59.6,\n        \"num_unique_values\": 164,\n        \"samples\": [\n          48.2,\n          49.8,\n          45.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9747931568167816,\n        \"min\": 13.1,\n        \"max\": 21.5,\n        \"num_unique_values\": 80,\n        \"samples\": [\n          16.9,\n          18.7,\n          18.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.061713679356888,\n        \"min\": 172.0,\n        \"max\": 231.0,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          201.0,\n          180.0,\n          212.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 801.9545356980956,\n        \"min\": 2700.0,\n        \"max\": 6300.0,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          4350.0,\n          4150.0,\n          3525.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "id": "fpe9OZP2sJXp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "b27e8195-3201-4851-e87e-ec4882ea24a8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    species  island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
              "339  Gentoo  Biscoe             NaN            NaN                NaN   \n",
              "340  Gentoo  Biscoe            46.8           14.3              215.0   \n",
              "341  Gentoo  Biscoe            50.4           15.7              222.0   \n",
              "342  Gentoo  Biscoe            45.2           14.8              212.0   \n",
              "343  Gentoo  Biscoe            49.9           16.1              213.0   \n",
              "\n",
              "     body_mass_g     sex  \n",
              "339          NaN     NaN  \n",
              "340       4850.0  Female  \n",
              "341       5750.0    Male  \n",
              "342       5200.0  Female  \n",
              "343       5400.0    Male  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dcd922be-0dad-49a7-93a8-8128d5881fb0\" 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>species</th>\n",
              "      <th>island</th>\n",
              "      <th>bill_length_mm</th>\n",
              "      <th>bill_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "      <th>sex</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>339</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>46.8</td>\n",
              "      <td>14.3</td>\n",
              "      <td>215.0</td>\n",
              "      <td>4850.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>50.4</td>\n",
              "      <td>15.7</td>\n",
              "      <td>222.0</td>\n",
              "      <td>5750.0</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>342</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>45.2</td>\n",
              "      <td>14.8</td>\n",
              "      <td>212.0</td>\n",
              "      <td>5200.0</td>\n",
              "      <td>Female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>343</th>\n",
              "      <td>Gentoo</td>\n",
              "      <td>Biscoe</td>\n",
              "      <td>49.9</td>\n",
              "      <td>16.1</td>\n",
              "      <td>213.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>Male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dcd922be-0dad-49a7-93a8-8128d5881fb0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dcd922be-0dad-49a7-93a8-8128d5881fb0 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-dcd922be-0dad-49a7-93a8-8128d5881fb0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ebf68561-9683-403a-81ee-8264d172e218\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ebf68561-9683-403a-81ee-8264d172e218')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ebf68561-9683-403a-81ee-8264d172e218 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Gentoo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"island\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Biscoe\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4918199506919954,\n        \"min\": 45.2,\n        \"max\": 50.4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          50.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8220908303425682,\n        \"min\": 14.3,\n        \"max\": 16.1,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          15.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.509249752822894,\n        \"min\": 212.0,\n        \"max\": 222.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          222.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 376.3863263545405,\n        \"min\": 4850.0,\n        \"max\": 5750.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5750.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "9khPmh2Id95Q"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "LAjJMo-6eJSa",
        "outputId": "22a0d21e-4c04-4182-a7f6-4b0cfe032f34"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       bill_length_mm  bill_depth_mm  flipper_length_mm  body_mass_g\n",
              "count      333.000000     333.000000         333.000000   333.000000\n",
              "mean        43.992793      17.164865         200.966967  4207.057057\n",
              "std          5.468668       1.969235          14.015765   805.215802\n",
              "min         32.100000      13.100000         172.000000  2700.000000\n",
              "25%         39.500000      15.600000         190.000000  3550.000000\n",
              "50%         44.500000      17.300000         197.000000  4050.000000\n",
              "75%         48.600000      18.700000         213.000000  4775.000000\n",
              "max         59.600000      21.500000         231.000000  6300.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e96311d0-4c20-4121-8023-dcaf52561a3e\" 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>bill_length_mm</th>\n",
              "      <th>bill_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>333.000000</td>\n",
              "      <td>333.000000</td>\n",
              "      <td>333.000000</td>\n",
              "      <td>333.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>43.992793</td>\n",
              "      <td>17.164865</td>\n",
              "      <td>200.966967</td>\n",
              "      <td>4207.057057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.468668</td>\n",
              "      <td>1.969235</td>\n",
              "      <td>14.015765</td>\n",
              "      <td>805.215802</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>32.100000</td>\n",
              "      <td>13.100000</td>\n",
              "      <td>172.000000</td>\n",
              "      <td>2700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>39.500000</td>\n",
              "      <td>15.600000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>3550.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>44.500000</td>\n",
              "      <td>17.300000</td>\n",
              "      <td>197.000000</td>\n",
              "      <td>4050.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>48.600000</td>\n",
              "      <td>18.700000</td>\n",
              "      <td>213.000000</td>\n",
              "      <td>4775.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>59.600000</td>\n",
              "      <td>21.500000</td>\n",
              "      <td>231.000000</td>\n",
              "      <td>6300.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e96311d0-4c20-4121-8023-dcaf52561a3e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e96311d0-4c20-4121-8023-dcaf52561a3e 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-e96311d0-4c20-4121-8023-dcaf52561a3e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b7386672-54fb-43c8-a512-197b36b015a6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b7386672-54fb-43c8-a512-197b36b015a6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b7386672-54fb-43c8-a512-197b36b015a6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"bill_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105.09816384915693,\n        \"min\": 5.468668342647559,\n        \"max\": 333.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          43.99279279279279,\n          44.5,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"bill_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112.56529060018214,\n        \"min\": 1.9692354633199007,\n        \"max\": 333.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17.164864864864864,\n          17.3,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87.83322997856419,\n        \"min\": 14.015765288287879,\n        \"max\": 333.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          200.96696696696696,\n          197.0,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1999.914028923884,\n        \"min\": 333.0,\n        \"max\": 6300.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4207.057057057057,\n          4050.0,\n          333.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G5i9uPIgegZM",
        "outputId": "96924eb3-e52e-44d6-f6de-408faaf1882d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 333 entries, 0 to 343\n",
            "Data columns (total 7 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   species            333 non-null    object \n",
            " 1   island             333 non-null    object \n",
            " 2   bill_length_mm     333 non-null    float64\n",
            " 3   bill_depth_mm      333 non-null    float64\n",
            " 4   flipper_length_mm  333 non-null    float64\n",
            " 5   body_mass_g        333 non-null    float64\n",
            " 6   sex                333 non-null    object \n",
            "dtypes: float64(4), object(3)\n",
            "memory usage: 20.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_cols = data.select_dtypes(include=['object', 'category']).columns"
      ],
      "metadata": {
        "id": "cFSwpEdffBcC"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for col in categorical_cols:\n",
        "  plt.figure(figsize =(6,4))\n",
        "  sns.countplot(data=data, x=col)\n",
        "  plt.title(f'Distribution of {col}')\n",
        "  plt.xticks(rotation=45)\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "J-amiGJ-feOZ",
        "outputId": "b550af2d-8515-433c-e5ba-012b8bfc482b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_features = data.select_dtypes(include=['float64','int']).columns.difference(['species'])"
      ],
      "metadata": {
        "id": "RPotawSzgo15"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for feature in numeric_features:\n",
        "  plt.figure(figsize=(6,4))\n",
        "  sns.boxplot(x='species', y=feature, data=data)\n",
        "  plt.title(f'Distribution of {feature} by Species')\n",
        "  plt.xlabel('Species')\n",
        "  plt.ylabel(feature)\n",
        "  plt.xticks(rotation=45)\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xGzxFuPKhxJ3",
        "outputId": "5173e048-005b-42a3-9d3e-f93b58a36b8e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ricodificare le variabili categoriali da etichette in numeri per analizzare i dati\n",
        "\n",
        "species_encoder =LabelEncoder()\n",
        "island_encoder = LabelEncoder()\n",
        "sex_encoder = LabelEncoder()\n",
        "\n",
        "data['species'] = species_encoder.fit_transform(data['species'])\n",
        "data['island'] = island_encoder.fit_transform(data['island'])\n",
        "data['sex'] = sex_encoder.fit_transform(data['sex'])\n",
        "\n",
        "species_mapping = dict(zip(species_encoder.classes_, range(len(species_encoder.classes_))))\n",
        "island_mapping = dict(zip(island_encoder.classes_, range(len(island_encoder.classes_))))\n",
        "sex_mapping = dict(zip(sex_encoder.classes_, range(len(sex_encoder.classes_))))"
      ],
      "metadata": {
        "id": "XY2YwSqRj4Pb"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-learn\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b9UnKqaYqUBV",
        "outputId": "2252a4fd-8fd5-4329-c62e-e4b1fdb6dd73"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.5.2)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow_decision_forests\n",
        "import tensorflow_decision_forests as tfdk\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_data, test_data = train_test_split(data, test_size=0.3, random_state = 42)\n",
        "\n",
        "# Convert pandas DataFrames to TensorFlow Datasets.\n",
        "trans_ds = tfdk.keras.pd_dataframe_to_tf_dataset(train_data, label='species') # Fixed typo: 'pd_datframe_to_tf_dataset' to 'pd_dataframe_to_tf_dataset'\n",
        "test_ds = tfdk.keras.pd_dataframe_to_tf_dataset(test_data, label='species')  # Fixed typo: 'pd_datframe_to_tf_dataset' to 'pd_dataframe_to_tf_dataset'\n",
        "\n",
        "# Create and train the model.\n",
        "model = tfdk.keras.RandomForestModel()  # Fixed typo: 'moedel' to 'model'\n",
        "model.fit(trans_ds, verbose=2) # using trans_ds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pci_eG-4q9sb",
        "outputId": "502a8b5d-e3b3-440e-9c21-120442ad44dd"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow_decision_forests in /usr/local/lib/python3.10/dist-packages (1.10.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (1.26.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (2.1.4)\n",
            "Requirement already satisfied: tensorflow==2.17.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (2.17.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (1.16.0)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (1.4.0)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (0.44.0)\n",
            "Requirement already satisfied: wurlitzer in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (3.1.1)\n",
            "Requirement already satisfied: tf-keras~=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (2.17.0)\n",
            "Requirement already satisfied: ydf in /usr/local/lib/python3.10/dist-packages (from tensorflow_decision_forests) (0.8.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=24.3.25 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (0.2.0)\n",
            "Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (3.11.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes<0.5.0,>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (0.4.1)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (24.1)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (3.20.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (2.32.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (71.0.4)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (1.16.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (1.64.1)\n",
            "Requirement already satisfied: tensorboard<2.18,>=2.17 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (2.17.0)\n",
            "Requirement already satisfied: keras>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (3.4.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.17.0->tensorflow_decision_forests) (0.37.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->tensorflow_decision_forests) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->tensorflow_decision_forests) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->tensorflow_decision_forests) (2024.1)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow==2.17.0->tensorflow_decision_forests) (13.8.1)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow==2.17.0->tensorflow_decision_forests) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.2.0->tensorflow==2.17.0->tensorflow_decision_forests) (0.12.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow==2.17.0->tensorflow_decision_forests) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow==2.17.0->tensorflow_decision_forests) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow==2.17.0->tensorflow_decision_forests) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow==2.17.0->tensorflow_decision_forests) (2024.8.30)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow==2.17.0->tensorflow_decision_forests) (3.7)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow==2.17.0->tensorflow_decision_forests) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.18,>=2.17->tensorflow==2.17.0->tensorflow_decision_forests) (3.0.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.18,>=2.17->tensorflow==2.17.0->tensorflow_decision_forests) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow==2.17.0->tensorflow_decision_forests) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.2.0->tensorflow==2.17.0->tensorflow_decision_forests) (2.18.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.2.0->tensorflow==2.17.0->tensorflow_decision_forests) (0.1.2)\n",
            "Use /tmp/tmpcyb8f1d5 as temporary training directory\n",
            "Reading training dataset...\n",
            "Training tensor examples:\n",
            "Features: {'island': <tf.Tensor 'data:0' shape=(None,) dtype=int64>, 'bill_length_mm': <tf.Tensor 'data_1:0' shape=(None,) dtype=float64>, 'bill_depth_mm': <tf.Tensor 'data_2:0' shape=(None,) dtype=float64>, 'flipper_length_mm': <tf.Tensor 'data_3:0' shape=(None,) dtype=float64>, 'body_mass_g': <tf.Tensor 'data_4:0' shape=(None,) dtype=float64>, 'sex': <tf.Tensor 'data_5:0' shape=(None,) dtype=int64>}\n",
            "Label: Tensor(\"data_6:0\", shape=(None,), dtype=int64)\n",
            "Weights: None\n",
            "Normalized tensor features:\n",
            " {'island': SemanticTensor(semantic=<Semantic.NUMERICAL: 1>, tensor=<tf.Tensor 'Cast:0' shape=(None,) dtype=float32>), 'bill_length_mm': SemanticTensor(semantic=<Semantic.NUMERICAL: 1>, tensor=<tf.Tensor 'Cast_1:0' shape=(None,) dtype=float32>), 'bill_depth_mm': SemanticTensor(semantic=<Semantic.NUMERICAL: 1>, tensor=<tf.Tensor 'Cast_2:0' shape=(None,) dtype=float32>), 'flipper_length_mm': SemanticTensor(semantic=<Semantic.NUMERICAL: 1>, tensor=<tf.Tensor 'Cast_3:0' shape=(None,) dtype=float32>), 'body_mass_g': SemanticTensor(semantic=<Semantic.NUMERICAL: 1>, tensor=<tf.Tensor 'Cast_4:0' shape=(None,) dtype=float32>), 'sex': SemanticTensor(semantic=<Semantic.NUMERICAL: 1>, tensor=<tf.Tensor 'Cast_5:0' shape=(None,) dtype=float32>)}\n",
            "Training dataset read in 0:00:12.790249. Found 233 examples.\n",
            "Training model...\n",
            "Standard output detected as not visible to the user e.g. running in a notebook. Creating a training log redirection. If training gets stuck, try calling tfdf.keras.set_training_logs_redirection(False).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
            "I0000 00:00:1727625256.353840     281 kernel.cc:774] Start Yggdrasil model training\n",
            "I0000 00:00:1727625256.353900     281 kernel.cc:775] Collect training examples\n",
            "I0000 00:00:1727625256.353918     281 kernel.cc:787] Dataspec guide:\n",
            "column_guides {\n",
            "  column_name_pattern: \"^__LABEL$\"\n",
            "  type: CATEGORICAL\n",
            "  categorial {\n",
            "    min_vocab_frequency: 0\n",
            "    max_vocab_count: -1\n",
            "  }\n",
            "}\n",
            "default_column_guide {\n",
            "  categorial {\n",
            "    max_vocab_count: 2000\n",
            "  }\n",
            "  discretized_numerical {\n",
            "    maximum_num_bins: 255\n",
            "  }\n",
            "}\n",
            "ignore_columns_without_guides: false\n",
            "detect_numerical_as_discretized_numerical: false\n",
            "\n",
            "I0000 00:00:1727625256.354532     281 kernel.cc:394] Number of batches: 1\n",
            "I0000 00:00:1727625256.354554     281 kernel.cc:395] Number of examples: 233\n",
            "I0000 00:00:1727625256.354606     281 kernel.cc:794] Training dataset:\n",
            "Number of records: 233\n",
            "Number of columns: 7\n",
            "\n",
            "Number of columns by type:\n",
            "\tNUMERICAL: 6 (85.7143%)\n",
            "\tCATEGORICAL: 1 (14.2857%)\n",
            "\n",
            "Columns:\n",
            "\n",
            "NUMERICAL: 6 (85.7143%)\n",
            "\t1: \"bill_depth_mm\" NUMERICAL mean:17.1047 min:13.2 max:21.5 sd:1.93129\n",
            "\t2: \"bill_length_mm\" NUMERICAL mean:44.1725 min:32.1 max:59.6 sd:5.33338\n",
            "\t3: \"body_mass_g\" NUMERICAL mean:4199.25 min:2700 max:6300 sd:815.634\n",
            "\t4: \"flipper_length_mm\" NUMERICAL mean:201.021 min:172 max:231 sd:13.8389\n",
            "\t5: \"island\" NUMERICAL mean:0.630901 min:0 max:2 sd:0.694112\n",
            "\t6: \"sex\" NUMERICAL mean:0.497854 min:0 max:1 sd:0.499995\n",
            "\n",
            "CATEGORICAL: 1 (14.2857%)\n",
            "\t0: \"__LABEL\" CATEGORICAL integerized vocab-size:4 no-ood-item\n",
            "\n",
            "Terminology:\n",
            "\tnas: Number of non-available (i.e. missing) values.\n",
            "\tood: Out of dictionary.\n",
            "\tmanually-defined: Attribute whose type is manually defined by the user, i.e., the type was not automatically inferred.\n",
            "\ttokenized: The attribute value is obtained through tokenization.\n",
            "\thas-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.\n",
            "\tvocab-size: Number of unique values.\n",
            "\n",
            "I0000 00:00:1727625256.354636     281 kernel.cc:810] Configure learner\n",
            "I0000 00:00:1727625256.354969     281 kernel.cc:824] Training config:\n",
            "learner: \"RANDOM_FOREST\"\n",
            "features: \"^bill_depth_mm$\"\n",
            "features: \"^bill_length_mm$\"\n",
            "features: \"^body_mass_g$\"\n",
            "features: \"^flipper_length_mm$\"\n",
            "features: \"^island$\"\n",
            "features: \"^sex$\"\n",
            "label: \"^__LABEL$\"\n",
            "task: CLASSIFICATION\n",
            "random_seed: 123456\n",
            "metadata {\n",
            "  framework: \"TF Keras\"\n",
            "}\n",
            "pure_serving_model: false\n",
            "[yggdrasil_decision_forests.model.random_forest.proto.random_forest_config] {\n",
            "  num_trees: 300\n",
            "  decision_tree {\n",
            "    max_depth: 16\n",
            "    min_examples: 5\n",
            "    in_split_min_examples_check: true\n",
            "    keep_non_leaf_label_distribution: true\n",
            "    num_candidate_attributes: 0\n",
            "    missing_value_policy: GLOBAL_IMPUTATION\n",
            "    allow_na_conditions: false\n",
            "    categorical_set_greedy_forward {\n",
            "      sampling: 0.1\n",
            "      max_num_items: -1\n",
            "      min_item_frequency: 1\n",
            "    }\n",
            "    growing_strategy_local {\n",
            "    }\n",
            "    categorical {\n",
            "      cart {\n",
            "      }\n",
            "    }\n",
            "    axis_aligned_split {\n",
            "    }\n",
            "    internal {\n",
            "      sorting_strategy: PRESORTED\n",
            "    }\n",
            "    uplift {\n",
            "      min_examples_in_treatment: 5\n",
            "      split_score: KULLBACK_LEIBLER\n",
            "    }\n",
            "  }\n",
            "  winner_take_all_inference: true\n",
            "  compute_oob_performances: true\n",
            "  compute_oob_variable_importances: false\n",
            "  num_oob_variable_importances_permutations: 1\n",
            "  bootstrap_training_dataset: true\n",
            "  bootstrap_size_ratio: 1\n",
            "  adapt_bootstrap_size_ratio_for_maximum_training_duration: false\n",
            "  sampling_with_replacement: true\n",
            "}\n",
            "\n",
            "I0000 00:00:1727625256.355683     281 kernel.cc:827] Deployment config:\n",
            "cache_path: \"/tmp/tmpcyb8f1d5/working_cache\"\n",
            "num_threads: 2\n",
            "try_resume_training: true\n",
            "\n",
            "I0000 00:00:1727625256.364775    3039 kernel.cc:888] Train model\n",
            "I0000 00:00:1727625256.365008    3039 random_forest.cc:428] Training random forest on 233 example(s) and 6 feature(s).\n",
            "I0000 00:00:1727625256.377883    3043 random_forest.cc:812] Training of tree  1/300 (tree index:0) done accuracy:0.964706 logloss:1.27213\n",
            "I0000 00:00:1727625256.386044    3043 random_forest.cc:812] Training of tree  11/300 (tree index:10) done accuracy:0.944206 logloss:0.386654\n",
            "I0000 00:00:1727625256.392121    3043 random_forest.cc:812] Training of tree  21/300 (tree index:20) done accuracy:0.939914 logloss:0.0958232\n",
            "I0000 00:00:1727625256.394897    3043 random_forest.cc:812] Training of tree  31/300 (tree index:30) done accuracy:0.95279 logloss:0.0911608\n",
            "I0000 00:00:1727625256.397005    3042 random_forest.cc:812] Training of tree  41/300 (tree index:41) done accuracy:0.95279 logloss:0.0947784\n",
            "I0000 00:00:1727625256.404756    3042 random_forest.cc:812] Training of tree  51/300 (tree index:50) done accuracy:0.944206 logloss:0.0958407\n",
            "I0000 00:00:1727625256.406608    3042 random_forest.cc:812] Training of tree  61/300 (tree index:60) done accuracy:0.961373 logloss:0.0899765\n",
            "I0000 00:00:1727625256.411679    3042 random_forest.cc:812] Training of tree  71/300 (tree index:70) done accuracy:0.965665 logloss:0.0874259\n",
            "I0000 00:00:1727625256.418240    3043 random_forest.cc:812] Training of tree  81/300 (tree index:80) done accuracy:0.965665 logloss:0.0873092\n",
            "I0000 00:00:1727625256.421804    3043 random_forest.cc:812] Training of tree  91/300 (tree index:91) done accuracy:0.965665 logloss:0.0868646\n",
            "I0000 00:00:1727625256.424467    3042 random_forest.cc:812] Training of tree  101/300 (tree index:100) done accuracy:0.965665 logloss:0.0866668\n",
            "I0000 00:00:1727625256.428589    3042 random_forest.cc:812] Training of tree  111/300 (tree index:110) done accuracy:0.969957 logloss:0.0863809\n",
            "I0000 00:00:1727625256.435700    3043 random_forest.cc:812] Training of tree  121/300 (tree index:121) done accuracy:0.965665 logloss:0.0883236\n",
            "I0000 00:00:1727625256.439092    3042 random_forest.cc:812] Training of tree  131/300 (tree index:130) done accuracy:0.969957 logloss:0.0870079\n",
            "I0000 00:00:1727625256.441413    3042 random_forest.cc:812] Training of tree  141/300 (tree index:140) done accuracy:0.974249 logloss:0.0889537\n",
            "I0000 00:00:1727625256.443106    3043 random_forest.cc:812] Training of tree  151/300 (tree index:150) done accuracy:0.969957 logloss:0.0895446\n",
            "I0000 00:00:1727625256.447560    3043 random_forest.cc:812] Training of tree  161/300 (tree index:161) done accuracy:0.969957 logloss:0.0911717\n",
            "I0000 00:00:1727625256.455821    3043 random_forest.cc:812] Training of tree  171/300 (tree index:170) done accuracy:0.969957 logloss:0.0909415\n",
            "I0000 00:00:1727625256.463250    3042 random_forest.cc:812] Training of tree  181/300 (tree index:180) done accuracy:0.974249 logloss:0.0909185\n",
            "I0000 00:00:1727625256.464739    3042 random_forest.cc:812] Training of tree  192/300 (tree index:191) done accuracy:0.974249 logloss:0.0911223\n",
            "I0000 00:00:1727625256.466184    3043 random_forest.cc:812] Training of tree  202/300 (tree index:201) done accuracy:0.974249 logloss:0.0913756\n",
            "I0000 00:00:1727625256.468962    3042 random_forest.cc:812] Training of tree  212/300 (tree index:212) done accuracy:0.974249 logloss:0.0907149\n",
            "I0000 00:00:1727625256.470331    3042 random_forest.cc:812] Training of tree  222/300 (tree index:220) done accuracy:0.978541 logloss:0.0911144\n",
            "I0000 00:00:1727625256.473020    3042 random_forest.cc:812] Training of tree  232/300 (tree index:232) done accuracy:0.974249 logloss:0.0908326\n",
            "I0000 00:00:1727625256.475974    3042 random_forest.cc:812] Training of tree  242/300 (tree index:241) done accuracy:0.974249 logloss:0.0908164\n",
            "I0000 00:00:1727625256.477379    3042 random_forest.cc:812] Training of tree  252/300 (tree index:251) done accuracy:0.974249 logloss:0.09051\n",
            "I0000 00:00:1727625256.481180    3043 random_forest.cc:812] Training of tree  262/300 (tree index:262) done accuracy:0.969957 logloss:0.0907238\n",
            "I0000 00:00:1727625256.485656    3043 random_forest.cc:812] Training of tree  272/300 (tree index:272) done accuracy:0.974249 logloss:0.0905404\n",
            "I0000 00:00:1727625256.499552    3043 random_forest.cc:812] Training of tree  282/300 (tree index:282) done accuracy:0.974249 logloss:0.0904278\n",
            "I0000 00:00:1727625256.501743    3043 random_forest.cc:812] Training of tree  292/300 (tree index:291) done accuracy:0.969957 logloss:0.0907468\n",
            "I0000 00:00:1727625256.503847    3043 random_forest.cc:812] Training of tree  300/300 (tree index:299) done accuracy:0.969957 logloss:0.0910416\n",
            "I0000 00:00:1727625256.513355    3039 random_forest.cc:892] Final OOB metrics: accuracy:0.969957 logloss:0.0910416\n",
            "I0000 00:00:1727625256.514678    3039 kernel.cc:920] Export model in log directory: /tmp/tmpcyb8f1d5 with prefix bf4ca2939bc14939\n",
            "I0000 00:00:1727625256.527710    3039 kernel.cc:938] Save model in resources\n",
            "I0000 00:00:1727625256.531838     281 abstract_model.cc:833] Model self evaluation:\n",
            "Number of predictions (without weights): 233\n",
            "Number of predictions (with weights): 233\n",
            "Task: CLASSIFICATION\n",
            "Label: __LABEL\n",
            "\n",
            "Accuracy: 0.969957  CI95[W][0.944313 0.985817]\n",
            "LogLoss: : 0.0910416\n",
            "ErrorRate: : 0.0300429\n",
            "\n",
            "Default Accuracy: : 0.420601\n",
            "Default LogLoss: : 1.0624\n",
            "Default ErrorRate: : 0.579399\n",
            "\n",
            "Confusion Table:\n",
            "truth\\prediction\n",
            "    1   2   3\n",
            "1  94   4   0\n",
            "2   1  49   0\n",
            "3   0   2  83\n",
            "Total: 233\n",
            "\n",
            "\n",
            "2024-09-29 15:54:16.574478: I tensorflow_decision_forests/tensorflow/ops/inference/kernel.cc:1234] Loading model from path /tmp/tmpcyb8f1d5/model/ with prefix bf4ca2939bc14939\n",
            "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
            "I0000 00:00:1727625256.607430     281 decision_forest.cc:761] Model loaded with 300 root(s), 3944 node(s), and 6 input feature(s).\n",
            "I0000 00:00:1727625256.621856     281 abstract_model.cc:1323] Engine \"RandomForestGeneric\" built\n",
            "2024-09-29 15:54:16.621916: I tensorflow_decision_forests/tensorflow/ops/inference/kernel.cc:1062] Use fast generic engine\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model trained in 0:00:00.303269\n",
            "Compiling model...\n",
            "Model compiled.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf_keras.src.callbacks.History at 0x7843cd923e50>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inspector = model.make_inspector()\n",
        "\n",
        "importance_types = inspector.variable_importances().keys()\n",
        "\n",
        "for importance_type in importance_types:\n",
        "  print(\"\\n\" + \"=\" * 30)\n",
        "  print(f\"Importance type: {importance_type}\")\n",
        "  print(\"-\" * 30)\n",
        "  importances = inspector.variable_importances() [importance_type]\n",
        "  sorted_importances = sorted(importances, key=lambda x: x[1], reverse=True)\n",
        "  for feature, importance in sorted_importances:\n",
        "    feature_name = feature.name if isinstance(feature, tfdk.py_tree.dataspec.SimpleColumnSpec) else feature\n",
        "    print(f\"{feature_name:20}: {importance: .4f}\")\n",
        "\n",
        "print(\"\\n\" + \"=\"*30)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6RcjjIurGTs",
        "outputId": "11917743-7d95-4360-dc0d-0360870f8d95"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "==============================\n",
            "Importance type: NUM_AS_ROOT\n",
            "------------------------------\n",
            "bill_depth_mm       :  143.0000\n",
            "flipper_length_mm   :  115.0000\n",
            "bill_length_mm      :  41.0000\n",
            "body_mass_g         :  1.0000\n",
            "\n",
            "==============================\n",
            "Importance type: NUM_NODES\n",
            "------------------------------\n",
            "bill_length_mm      :  630.0000\n",
            "bill_depth_mm       :  348.0000\n",
            "body_mass_g         :  302.0000\n",
            "flipper_length_mm   :  267.0000\n",
            "island              :  245.0000\n",
            "sex                 :  30.0000\n",
            "\n",
            "==============================\n",
            "Importance type: INV_MEAN_MIN_DEPTH\n",
            "------------------------------\n",
            "bill_depth_mm       :  0.4227\n",
            "bill_length_mm      :  0.4119\n",
            "flipper_length_mm   :  0.3735\n",
            "island              :  0.3060\n",
            "body_mass_g         :  0.2759\n",
            "sex                 :  0.2452\n",
            "\n",
            "==============================\n",
            "Importance type: SUM_SCORE\n",
            "------------------------------\n",
            "bill_length_mm      :  23859.0603\n",
            "bill_depth_mm       :  21419.3378\n",
            "flipper_length_mm   :  16734.7522\n",
            "island              :  6425.7241\n",
            "body_mass_g         :  2557.7010\n",
            "sex                 :  303.6369\n",
            "\n",
            "==============================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_predictions = model.predict(test_ds)\n",
        "predicted_labels = np.argmax(test_predictions, axis=1)\n",
        "\n",
        "test_labels = test_data ['species'].values\n",
        "\n",
        "accuracy = accuracy_score(test_labels, predicted_labels)\n",
        "print(f\"Accuracy: {accuracy:.2f}\")\n",
        "\n",
        "conf_matrix = confusion_matrix(test_labels, predicted_labels)\n",
        "print(\"\\nConfusion Matrix:\")\n",
        "print(conf_matrix)\n",
        "\n",
        "species_names = species_encoder.classes_\n",
        "report = classification_report(test_labels, predicted_labels, target_names=species_names)\n",
        "print(\"n\\Classification Report:\")\n",
        "print(report)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qeZE9teRjCGL",
        "outputId": "9a0275e9-41bf-4f9f-ef5d-c7bc51a10dfb"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 93ms/step\n",
            "Accuracy: 0.99\n",
            "\n",
            "Confusion Matrix:\n",
            "[[48  0  0]\n",
            " [ 1 17  0]\n",
            " [ 0  0 34]]\n",
            "n\\Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "      Adelie       0.98      1.00      0.99        48\n",
            "   Chinstrap       1.00      0.94      0.97        18\n",
            "      Gentoo       1.00      1.00      1.00        34\n",
            "\n",
            "    accuracy                           0.99       100\n",
            "   macro avg       0.99      0.98      0.99       100\n",
            "weighted avg       0.99      0.99      0.99       100\n",
            "\n"
          ]
        }
      ]
    }
  ]
}