Checkpoint.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOXzjG7B4PhETKJQUDSrsKC",
      "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/Swelem/obesity_risk/blob/main/Checkpoint.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preparation"
      ],
      "metadata": {
        "id": "FQ1pMTHWQPHz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download and unzip"
      ],
      "metadata": {
        "id": "ohjOCEYmQmNn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "_PY8xLGyInOJ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import lightgbm as lgb\n",
        "import os\n",
        "import glob\n",
        "#import optuna\n",
        "ran_optuna = False\n",
        "from tqdm import tqdm\n",
        "from sklearn.model_selection import train_test_split as tts\n",
        "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
        "import random\n",
        "from PIL import Image\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import GridSearchCV, train_test_split\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.preprocessing import LabelEncoder,PowerTransformer\n",
        "from imblearn.over_sampling import SMOTE\n",
        "#from optuna.samplers import TPESampler\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torchvision import datasets\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q kaggle"
      ],
      "metadata": {
        "id": "av-zrhNwN9tP"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "ujuTSnxCN_fS",
        "outputId": "729df40d-6a01-41cb-8862-9c700dece2ad"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-44d3f30f-5f97-4149-9a3a-2b2996c89538\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-44d3f30f-5f97-4149-9a3a-2b2996c89538\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"saifswelem\",\"key\":\"93c4723ccbde8c247af262277f428b4b\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle"
      ],
      "metadata": {
        "id": "eFUr_IOgOBVQ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "!mkdir /content/Dataset"
      ],
      "metadata": {
        "id": "e5Hq2gTHOB2z"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "XfxCRJXfOGL3"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "dpiIMOJ8OIGd"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.chdir('/content/Dataset')"
      ],
      "metadata": {
        "id": "ALQAR5n1OI5F"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle competitions download -c playground-series-s4e2\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jhrip1-AOKiO",
        "outputId": "62f9aebc-0dac-4b0b-c82e-5c030d8519e7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading playground-series-s4e2.zip to /content/Dataset\n",
            "\r  0% 0.00/917k [00:00<?, ?B/s]\n",
            "\r100% 917k/917k [00:00<00:00, 45.2MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip playground-series-s4e2.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2FXaz4ZOOK5",
        "outputId": "40809f74-6a30-4c83-aa4f-8eed6b8c65c0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  playground-series-s4e2.zip\n",
            "  inflating: sample_submission.csv   \n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "given_test_data = pd.read_csv('/content/Dataset/test.csv')\n",
        "\n",
        "\n",
        "given_train_data = pd.read_csv('/content/Dataset/train.csv')"
      ],
      "metadata": {
        "id": "NkiPUOKxSKFZ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Exploration"
      ],
      "metadata": {
        "id": "QdxMxfJPQwVb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "given_train_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "QhVnOf_ZSwO_",
        "outputId": "79909288-0e17-4d2c-c556-b8a25217cfdd"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  Gender        Age    Height      Weight family_history_with_overweight  \\\n",
              "0   0    Male  24.443011  1.699998   81.669950                            yes   \n",
              "1   1  Female  18.000000  1.560000   57.000000                            yes   \n",
              "2   2  Female  18.000000  1.711460   50.165754                            yes   \n",
              "3   3  Female  20.952737  1.710730  131.274851                            yes   \n",
              "4   4    Male  31.641081  1.914186   93.798055                            yes   \n",
              "\n",
              "  FAVC      FCVC       NCP        CAEC SMOKE      CH2O SCC       FAF  \\\n",
              "0  yes  2.000000  2.983297   Sometimes    no  2.763573  no  0.000000   \n",
              "1  yes  2.000000  3.000000  Frequently    no  2.000000  no  1.000000   \n",
              "2  yes  1.880534  1.411685   Sometimes    no  1.910378  no  0.866045   \n",
              "3  yes  3.000000  3.000000   Sometimes    no  1.674061  no  1.467863   \n",
              "4  yes  2.679664  1.971472   Sometimes    no  1.979848  no  1.967973   \n",
              "\n",
              "        TUE       CALC                 MTRANS           NObeyesdad  \n",
              "0  0.976473  Sometimes  Public_Transportation  Overweight_Level_II  \n",
              "1  1.000000         no             Automobile        Normal_Weight  \n",
              "2  1.673584         no  Public_Transportation  Insufficient_Weight  \n",
              "3  0.780199  Sometimes  Public_Transportation     Obesity_Type_III  \n",
              "4  0.931721  Sometimes  Public_Transportation  Overweight_Level_II  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b32e39a0-8d54-4b29-9a4e-7facfd03aa9a\" 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>id</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>family_history_with_overweight</th>\n",
              "      <th>FAVC</th>\n",
              "      <th>FCVC</th>\n",
              "      <th>NCP</th>\n",
              "      <th>CAEC</th>\n",
              "      <th>SMOKE</th>\n",
              "      <th>CH2O</th>\n",
              "      <th>SCC</th>\n",
              "      <th>FAF</th>\n",
              "      <th>TUE</th>\n",
              "      <th>CALC</th>\n",
              "      <th>MTRANS</th>\n",
              "      <th>NObeyesdad</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Male</td>\n",
              "      <td>24.443011</td>\n",
              "      <td>1.699998</td>\n",
              "      <td>81.669950</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.983297</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>2.763573</td>\n",
              "      <td>no</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.976473</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Public_Transportation</td>\n",
              "      <td>Overweight_Level_II</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Female</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.560000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>Frequently</td>\n",
              "      <td>no</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>no</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>no</td>\n",
              "      <td>Automobile</td>\n",
              "      <td>Normal_Weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Female</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.711460</td>\n",
              "      <td>50.165754</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>1.880534</td>\n",
              "      <td>1.411685</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>1.910378</td>\n",
              "      <td>no</td>\n",
              "      <td>0.866045</td>\n",
              "      <td>1.673584</td>\n",
              "      <td>no</td>\n",
              "      <td>Public_Transportation</td>\n",
              "      <td>Insufficient_Weight</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20.952737</td>\n",
              "      <td>1.710730</td>\n",
              "      <td>131.274851</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>1.674061</td>\n",
              "      <td>no</td>\n",
              "      <td>1.467863</td>\n",
              "      <td>0.780199</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Public_Transportation</td>\n",
              "      <td>Obesity_Type_III</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Male</td>\n",
              "      <td>31.641081</td>\n",
              "      <td>1.914186</td>\n",
              "      <td>93.798055</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2.679664</td>\n",
              "      <td>1.971472</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>1.979848</td>\n",
              "      <td>no</td>\n",
              "      <td>1.967973</td>\n",
              "      <td>0.931721</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Public_Transportation</td>\n",
              "      <td>Overweight_Level_II</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-b32e39a0-8d54-4b29-9a4e-7facfd03aa9a')\"\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-b32e39a0-8d54-4b29-9a4e-7facfd03aa9a 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-b32e39a0-8d54-4b29-9a4e-7facfd03aa9a');\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-97956090-120e-456c-bceb-4ba46df70fb8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-97956090-120e-456c-bceb-4ba46df70fb8')\"\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-97956090-120e-456c-bceb-4ba46df70fb8 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": "given_train_data",
              "summary": "{\n  \"name\": \"given_train_data\",\n  \"rows\": 20758,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5992,\n        \"min\": 0,\n        \"max\": 20757,\n        \"num_unique_values\": 20758,\n        \"samples\": [\n          10317,\n          4074,\n          9060\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\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      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.688071958787075,\n        \"min\": 14.0,\n        \"max\": 61.0,\n        \"num_unique_values\": 1703,\n        \"samples\": [\n          25.902283,\n          17.412629\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08731190569718149,\n        \"min\": 1.45,\n        \"max\": 1.975663,\n        \"num_unique_values\": 1833,\n        \"samples\": [\n          1.685127,\n          1.919241\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.379443076406236,\n        \"min\": 39.0,\n        \"max\": 165.057269,\n        \"num_unique_values\": 1979,\n        \"samples\": [\n          110.804337,\n          96.875502\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family_history_with_overweight\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FAVC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FCVC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5332181544582983,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 934,\n        \"samples\": [\n          2.444599,\n          2.191429\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NCP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7053745958837867,\n        \"min\": 1.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 689,\n        \"samples\": [\n          1.193589,\n          2.814518\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAEC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Frequently\",\n          \"Always\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMOKE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CH2O\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6084670184548745,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1506,\n        \"samples\": [\n          2.495851,\n          2.15157\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SCC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FAF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8383019759696896,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1360,\n        \"samples\": [\n          1.079524,\n          1.456369\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TUE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6021134769922342,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1297,\n        \"samples\": [\n          0.076654,\n          0.586163\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CALC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Sometimes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MTRANS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Automobile\",\n          \"Bike\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NObeyesdad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Overweight_Level_II\",\n          \"Normal_Weight\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "given_test_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "6Uk5qg91S-UN",
        "outputId": "091511ea-c6a6-4134-c254-ef8140d20c97"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  Gender        Age    Height      Weight  \\\n",
              "0  20758    Male  26.899886  1.848294  120.644178   \n",
              "1  20759  Female  21.000000  1.600000   66.000000   \n",
              "2  20760  Female  26.000000  1.643355  111.600553   \n",
              "3  20761    Male  20.979254  1.553127  103.669116   \n",
              "4  20762  Female  26.000000  1.627396  104.835346   \n",
              "\n",
              "  family_history_with_overweight FAVC      FCVC       NCP       CAEC SMOKE  \\\n",
              "0                            yes  yes  2.938616  3.000000  Sometimes    no   \n",
              "1                            yes  yes  2.000000  1.000000  Sometimes    no   \n",
              "2                            yes  yes  3.000000  3.000000  Sometimes    no   \n",
              "3                            yes  yes  2.000000  2.977909  Sometimes    no   \n",
              "4                            yes  yes  3.000000  3.000000  Sometimes    no   \n",
              "\n",
              "       CH2O SCC       FAF       TUE       CALC                 MTRANS  \n",
              "0  2.825629  no  0.855400  0.000000  Sometimes  Public_Transportation  \n",
              "1  3.000000  no  1.000000  0.000000  Sometimes  Public_Transportation  \n",
              "2  2.621877  no  0.000000  0.250502  Sometimes  Public_Transportation  \n",
              "3  2.786417  no  0.094851  0.000000  Sometimes  Public_Transportation  \n",
              "4  2.653531  no  0.000000  0.741069  Sometimes  Public_Transportation  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3585e4d4-c7f6-4930-ba0d-c9290aa7c4ba\" 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>id</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>family_history_with_overweight</th>\n",
              "      <th>FAVC</th>\n",
              "      <th>FCVC</th>\n",
              "      <th>NCP</th>\n",
              "      <th>CAEC</th>\n",
              "      <th>SMOKE</th>\n",
              "      <th>CH2O</th>\n",
              "      <th>SCC</th>\n",
              "      <th>FAF</th>\n",
              "      <th>TUE</th>\n",
              "      <th>CALC</th>\n",
              "      <th>MTRANS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>20758</td>\n",
              "      <td>Male</td>\n",
              "      <td>26.899886</td>\n",
              "      <td>1.848294</td>\n",
              "      <td>120.644178</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2.938616</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>2.825629</td>\n",
              "      <td>no</td>\n",
              "      <td>0.855400</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Public_Transportation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20759</td>\n",
              "      <td>Female</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>no</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Public_Transportation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20760</td>\n",
              "      <td>Female</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>1.643355</td>\n",
              "      <td>111.600553</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>2.621877</td>\n",
              "      <td>no</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250502</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Public_Transportation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20761</td>\n",
              "      <td>Male</td>\n",
              "      <td>20.979254</td>\n",
              "      <td>1.553127</td>\n",
              "      <td>103.669116</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.977909</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>2.786417</td>\n",
              "      <td>no</td>\n",
              "      <td>0.094851</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Public_Transportation</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20762</td>\n",
              "      <td>Female</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>1.627396</td>\n",
              "      <td>104.835346</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>no</td>\n",
              "      <td>2.653531</td>\n",
              "      <td>no</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.741069</td>\n",
              "      <td>Sometimes</td>\n",
              "      <td>Public_Transportation</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-3585e4d4-c7f6-4930-ba0d-c9290aa7c4ba')\"\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-3585e4d4-c7f6-4930-ba0d-c9290aa7c4ba 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-3585e4d4-c7f6-4930-ba0d-c9290aa7c4ba');\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-5a5c75be-4a2c-480d-9ee7-07682eaee6f2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5a5c75be-4a2c-480d-9ee7-07682eaee6f2')\"\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-5a5c75be-4a2c-480d-9ee7-07682eaee6f2 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": "given_test_data",
              "summary": "{\n  \"name\": \"given_test_data\",\n  \"rows\": 13840,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3995,\n        \"min\": 20758,\n        \"max\": 34597,\n        \"num_unique_values\": 13840,\n        \"samples\": [\n          33716,\n          32341,\n          24886\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\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      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.79981353918079,\n        \"min\": 14.0,\n        \"max\": 61.0,\n        \"num_unique_values\": 1539,\n        \"samples\": [\n          34.462711,\n          20.519916\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0887605851825768,\n        \"min\": 1.45,\n        \"max\": 1.98,\n        \"num_unique_values\": 1739,\n        \"samples\": [\n          1.616467,\n          1.7931\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.111818557597687,\n        \"min\": 39.0,\n        \"max\": 165.057269,\n        \"num_unique_values\": 1798,\n        \"samples\": [\n          58.443049,\n          110.008636\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"family_history_with_overweight\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FAVC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FCVC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5316064424734112,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 828,\n        \"samples\": [\n          2.923433,\n          1.813234\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NCP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7109268631672955,\n        \"min\": 1.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 649,\n        \"samples\": [\n          3.648194,\n          2.157164\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAEC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Always\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SMOKE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CH2O\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6112295139643,\n        \"min\": 1.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1366,\n        \"samples\": [\n          1.152899,\n          2.232601\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SCC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FAF\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8403611101051178,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 1260,\n        \"samples\": [\n          0.68183,\n          1.554817\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TUE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.608005301939806,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 1172,\n        \"samples\": [\n          1.281141,\n          0.66488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CALC\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"no\",\n          \"Always\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MTRANS\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Automobile\",\n          \"Motorbike\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(given_train_data.shape)\n",
        "print(given_test_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UXc-Fl73TIyo",
        "outputId": "e3841f85-5072-4548-a82d-eba8ba6b2a16"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20758, 18)\n",
            "(13840, 17)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(given_train_data[\"NObeyesdad\"].unique())\n",
        "class_counts = given_train_data[\"NObeyesdad\"].value_counts()\n",
        "class_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2MP7CMTYT7Lb",
        "outputId": "9ba2074a-35ba-4e0c-9caa-a77c121643d6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Overweight_Level_II' 'Normal_Weight' 'Insufficient_Weight'\n",
            " 'Obesity_Type_III' 'Obesity_Type_II' 'Overweight_Level_I'\n",
            " 'Obesity_Type_I']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NObeyesdad\n",
              "Obesity_Type_III       4046\n",
              "Obesity_Type_II        3248\n",
              "Normal_Weight          3082\n",
              "Obesity_Type_I         2910\n",
              "Insufficient_Weight    2523\n",
              "Overweight_Level_II    2522\n",
              "Overweight_Level_I     2427\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Visualization\n"
      ],
      "metadata": {
        "id": "plB-d7IFQ1rC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(6,4))\n",
        "plt.barh(class_counts.index, class_counts.values, color=\"blue\")\n",
        "plt.xlabel(\"Count\")\n",
        "plt.title(\"Obesity Classes\")\n",
        "plt.gca().invert_yaxis() ## Invert the y-axis to show the highest frequency class at the top\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "oCyeDEsSTbO1",
        "outputId": "c2eed5ea-6400-4ca4-d916-d7a00079fd22"
      },
      "execution_count": 22,
      "outputs": [
        {
          "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": [
        "categorical_features = given_train_data.select_dtypes(include=['object']).columns\n",
        "continuous_features = given_train_data.select_dtypes(include=['float64','int64']).columns\n",
        "## Export categorical and continuous data to separate DataFrames\n",
        "\n",
        "data_cat = given_train_data[categorical_features]\n",
        "data_cont = given_train_data[continuous_features]\n",
        "print(data_cat.columns)\n",
        "print(data_cont.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LRYUYQgsc2dh",
        "outputId": "14bbdc2e-5e00-451b-8bea-ddc4e23954eb"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Gender', 'family_history_with_overweight', 'FAVC', 'CAEC', 'SMOKE',\n",
            "       'SCC', 'CALC', 'MTRANS', 'NObeyesdad'],\n",
            "      dtype='object')\n",
            "Index(['id', 'Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_id= data_cont[\"id\"]\n",
        "data_cont.drop('id',axis=1, inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q_INwWsccx24",
        "outputId": "a35a53e3-a4c2-4478-ebc5-5a7c3ff99a69"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-24-6bfb34d38753>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data_cont.drop('id',axis=1, inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "\n",
        "## Drawing a frequency graph for each categorical variable\n",
        "fig, axes = plt.subplots(nrows=(len(data_cat.columns)+1) // 2, ncols=2,figsize=(14, len(data_cat.columns) * 2))\n",
        "fig.tight_layout(pad=5.0)\n",
        "\n",
        "## Adjustment for proper positioning of the axes\n",
        "axes = axes.ravel()\n",
        "\n",
        "for i, col in enumerate(data_cat.columns):\n",
        "    sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
        "    axes[i].set_title(f'Frequency of {col}')\n",
        "    axes[i].set_xlabel('')\n",
        "    axes[i].set_ylabel('Count')\n",
        "    axes[i].tick_params(axis='x', rotation=45)\n",
        "\n",
        "## Closing extra created axes\n",
        "for j in range(i + 1, len(axes)):\n",
        "    axes[j].axis('off')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "PNMl-qpXcVVF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "70d64a92-8f21-4756-a4ad-8031af275e66"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-25-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-25-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-25-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-25-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-25-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-25-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-25-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n",
            "<ipython-input-25-96516fd721f9>:11: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x=data_cat[col], ax=axes[i], palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1800 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing"
      ],
      "metadata": {
        "id": "HCmmw1StQ-3y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv('train.csv')\n",
        "df_test = pd.read_csv('test.csv')"
      ],
      "metadata": {
        "id": "vRwYfijmhK9V"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add BMI column\n",
        "df_train['BMI'] = df_train['Weight'] / (df_train['Height']) ** 2\n",
        "df_test['BMI'] = df_test['Weight'] / (df_test['Height']) ** 2"
      ],
      "metadata": {
        "id": "62yCf-fTiQ-j"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(df_train.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3tfeaCm2hUXi",
        "outputId": "ebbc9a53-e363-433d-d756-152d0d536b72"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                                0\n",
            "Gender                            0\n",
            "Age                               0\n",
            "Height                            0\n",
            "Weight                            0\n",
            "family_history_with_overweight    0\n",
            "FAVC                              0\n",
            "FCVC                              0\n",
            "NCP                               0\n",
            "CAEC                              0\n",
            "SMOKE                             0\n",
            "CH2O                              0\n",
            "SCC                               0\n",
            "FAF                               0\n",
            "TUE                               0\n",
            "CALC                              0\n",
            "MTRANS                            0\n",
            "NObeyesdad                        0\n",
            "BMI                               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_test.isnull().sum())"
      ],
      "metadata": {
        "id": "hAUxYOUJmi9W",
        "outputId": "85db0efe-e80e-404c-e3e8-0dde77f4bb26",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                                0\n",
            "Gender                            0\n",
            "Age                               0\n",
            "Height                            0\n",
            "Weight                            0\n",
            "family_history_with_overweight    0\n",
            "FAVC                              0\n",
            "FCVC                              0\n",
            "NCP                               0\n",
            "CAEC                              0\n",
            "SMOKE                             0\n",
            "CH2O                              0\n",
            "SCC                               0\n",
            "FAF                               0\n",
            "TUE                               0\n",
            "CALC                              0\n",
            "MTRANS                            0\n",
            "BMI                               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define function to round column\n",
        "def round_column(df: pd.DataFrame,\n",
        "                 column: str,\n",
        "                 n_decimals: int = 0) -> pd.DataFrame:\n",
        "\n",
        "    df[column] = df[column].round(n_decimals)\n",
        "    return df\n",
        "\n",
        "# Round columns 'FCVC' and 'NCP' to 1 decimal\n",
        "df_train = round_column(df_train, 'FCVC')\n",
        "df_train = round_column(df_train, 'NCP')\n",
        "df_test = round_column(df_test, 'FCVC')\n",
        "df_test = round_column(df_test, 'NCP')\n",
        "\n",
        "# print unique values of 'FCVC' and 'NCP' for train dataset\n",
        "print(f'{np.unique(df_train[\"FCVC\"]) = }')\n",
        "print(f'{np.unique(df_train[\"NCP\"]) = }')\n",
        "\n",
        "# print unique values of 'FCVC' and 'NCP' for test dataset\n",
        "print(f'{np.unique(df_test[\"FCVC\"]) = }')\n",
        "print(f'{np.unique(df_test[\"NCP\"]) = }')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4w2tvoqFiIdl",
        "outputId": "0f42ae32-dc59-4ff4-976a-be144635bd08"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "np.unique(df_train[\"FCVC\"]) = array([1., 2., 3.])\n",
            "np.unique(df_train[\"NCP\"]) = array([1., 2., 3., 4.])\n",
            "np.unique(df_test[\"FCVC\"]) = array([1., 2., 3.])\n",
            "np.unique(df_test[\"NCP\"]) = array([1., 2., 3., 4.])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(df_train.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vre8E78vGbn3",
        "outputId": "1333653e-f172-4aed-ea6d-144414d5bf46"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                                0\n",
            "Gender                            0\n",
            "Age                               0\n",
            "Height                            0\n",
            "Weight                            0\n",
            "family_history_with_overweight    0\n",
            "FAVC                              0\n",
            "FCVC                              0\n",
            "NCP                               0\n",
            "CAEC                              0\n",
            "SMOKE                             0\n",
            "CH2O                              0\n",
            "SCC                               0\n",
            "FAF                               0\n",
            "TUE                               0\n",
            "CALC                              0\n",
            "MTRANS                            0\n",
            "NObeyesdad                        0\n",
            "BMI                               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert Gender to binary\n",
        "df_train['Gender'] = df_train['Gender'].map({'Male': 1, 'Female': 0})\n",
        "df_test['Gender'] = df_test['Gender'].map({'Male': 1, 'Female': 0})\n",
        "\n",
        "# convert age to categorical\n",
        "age_mapping = {'0-18': 0, '18-30': 1, '30-40': 2, '40-50': 3, '50-60': 4, '60-70': 5, '70-80': 6}\n",
        "df_train['Age'] = df_train['Age'].map(age_mapping)\n",
        "df_test['Age'] = df_train['Age'].map(age_mapping)\n",
        "\n",
        "df_train['Age'] = pd.cut(df_train['Age'], bins=[0, 18, 30, 40, 50, 60, 70, 80], labels=['0-18', '18-30', '30-40', '40-50', '50-60', '60-70', '70-80'])\n",
        "df_test['Age'] = pd.cut(df_test['Age'], bins=[0, 18, 30, 40, 50, 60, 70, 80], labels=['0-18', '18-30', '30-40', '40-50', '50-60', '60-70', '70-80'])\n",
        "\n",
        "# convert BMI to categorical\n",
        "df_train['BMI'] = pd.to_numeric(df_train['BMI'], errors='coerce')\n",
        "df_test['BMI'] = pd.to_numeric(df_train['BMI'], errors='coerce')\n",
        "\n",
        "df_train['BMI'] = pd.cut(df_train['BMI'], bins=[0, 18.5, 25, 30, 35, 40, 60], labels=['Underweight', 'Normal', 'Overweight', 'Obesity I', 'Obesity II', 'Obesity III'])\n",
        "df_test['BMI'] = pd.cut(df_test['BMI'], bins=[0, 18.5, 25, 30, 35, 40, 60], labels=['Underweight', 'Normal', 'Overweight', 'Obesity I', 'Obesity II', 'Obesity III'])\n",
        "\n",
        "# convert family_history_with_overweight to binary (yes=1, no=0)\n",
        "df_train['family_history_with_overweight'] = df_train['family_history_with_overweight'].map({'yes': 1, 'no': 0})\n",
        "df_test['family_history_with_overweight'] = df_test['family_history_with_overweight'].map({'yes': 1, 'no': 0})\n",
        "\n",
        "# convert SMOKE to binary (yes=1, no=0)\n",
        "df_train['SMOKE'] = df_train['SMOKE'].map({'yes': 1, 'no': 0})\n",
        "df_test['SMOKE'] = df_test['SMOKE'].map({'yes': 1, 'no': 0})\n",
        "\n",
        "# convert SCC to binary (yes=1, no=0)\n",
        "df_train['SCC'] = df_train['SCC'].map({'yes': 1, 'no': 0})\n",
        "df_test['SCC'] = df_test['SCC'].map({'yes': 1, 'no': 0})\n",
        "\n",
        "# convert FAVC to binary (yes=1, no=0)\n",
        "df_train['FAVC'] = df_train['FAVC'].map({'yes': 1, 'no': 0})\n",
        "df_test['FAVC'] = df_test['FAVC'].map({'yes': 1, 'no': 0})\n",
        "\n",
        "# convert CALC to binary (always=3, frequently=2, sometimes=1, no=0)\n",
        "df_train['CALC'] = df_train['CALC'].map({'Always': 3, 'Frequently': 2, 'Sometimes': 1, 'no': 0})\n",
        "df_test['CALC'] = df_test['CALC'].map({'Always': 3, 'Frequently': 2, 'Sometimes': 1, 'no': 0})\n",
        "\n",
        "# convert CAEC to binary (no=0, Sometimes=1, Frequently=2, Always=3)\n",
        "df_train['CAEC'] = df_train['CAEC'].map({'no': 0, 'Sometimes': 1, 'Frequently': 2, 'Always': 3})\n",
        "df_test['CAEC'] = df_test['CAEC'].map({'no': 0, 'Sometimes': 1, 'Frequently': 2, 'Always': 3})\n",
        "\n",
        "# convert MTRANS to dummy variables with fusioning motobike and bike into automobile\n",
        "df_train = pd.get_dummies(df_train, columns=['MTRANS'])\n",
        "df_test = pd.get_dummies(df_test, columns=['MTRANS'])\n",
        "\n",
        "# round CH2O to 0 decimals\n",
        "df_train = round_column(df_train, 'CH2O')\n",
        "df_test = round_column(df_test, 'CH2O')\n",
        "\n",
        "# round TUE to 1 decimal\n",
        "df_train = round_column(df_train, 'TUE', n_decimals=1)\n",
        "df_test = round_column(df_test, 'TUE', n_decimals=1)\n",
        "\n",
        "# round FAF to 1 decimal\n",
        "df_train = round_column(df_train, 'FAF', n_decimals=1)\n",
        "df_test = round_column(df_test, 'FAF', n_decimals=1)\n",
        "\n",
        "# Put BMI and Age to numerical values\n",
        "df_train['BMI'] = df_train['BMI'].cat.codes\n",
        "df_train['Age'] = df_train['Age'].cat.codes\n",
        "df_test['BMI'] = df_test['BMI'].cat.codes\n",
        "df_test['Age'] = df_test['Age'].cat.codes\n",
        "\n",
        "# delete height and weight columns\n",
        "# df_train = df_train.drop(['Height', 'Weight'], axis=1)\n",
        "# df_test = df_test.drop(['Height', 'Weight'], axis=1)\n",
        "\n",
        "# Add polynomial features\n",
        "polynomial_features = PolynomialFeatures(degree=2, include_bias=False)\n",
        "X_polynomial = polynomial_features.fit_transform(df_train[['Age', 'BMI']])\n",
        "df_train.drop(['Age', 'BMI'], axis=1, inplace=True)\n",
        "df_train = pd.concat([df_train, pd.DataFrame(X_polynomial, columns=['Age', 'BMI', 'Age^2', 'Age*BMI', 'BMI^2'])], axis=1)\n",
        "X_polynomial2 = polynomial_features.fit_transform(df_train[['Weight', 'Height']])\n",
        "df_train.drop(['Weight', 'Height'], axis=1, inplace=True)\n",
        "df_train = pd.concat([df_train, pd.DataFrame(X_polynomial2, columns=['Weight', 'Height', 'Weight^2', 'Weight*Height', 'Height^2'])], axis=1)\n",
        "\n",
        "X_polynomial = polynomial_features.fit_transform(df_test[['Age', 'BMI']])\n",
        "df_test.drop(['Age', 'BMI'], axis=1, inplace=True)\n",
        "df_test = pd.concat([df_test, pd.DataFrame(X_polynomial, columns=['Age', 'BMI', 'Age^2', 'Age*BMI', 'BMI^2'])], axis=1)\n",
        "X_polynomial2 = polynomial_features.fit_transform(df_test[['Weight', 'Height']])\n",
        "df_test.drop(['Weight', 'Height'], axis=1, inplace=True)\n",
        "df_test = pd.concat([df_test, pd.DataFrame(X_polynomial2, columns=['Weight', 'Height', 'Weight^2', 'Weight*Height', 'Height^2'])], axis=1)"
      ],
      "metadata": {
        "id": "7N1RjkVFh9m4"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# convert NObeyesdad to numerical values\n",
        "df_train['NObeyesdad'] = df_train['NObeyesdad'].map({'Insufficient_Weight': 0, 'Normal_Weight': 1, 'Overweight_Level_I': 2, 'Overweight_Level_II': 3, 'Obesity_Type_I': 4, 'Obesity_Type_II': 5, 'Obesity_Type_III': 6})"
      ],
      "metadata": {
        "id": "g1lMMk6Om8kH"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# columns=['Gender','family_history_with_overweight','FAVC','SMOKE','SCC']\n",
        "# df_train=pd.get_dummies(df_train,columns=columns,drop_first=True,dtype=int)\n",
        "# df_test=pd.get_dummies(df_test,columns=columns,drop_first=True,dtype=int)\n",
        "\n",
        "\n",
        "# le=LabelEncoder()\n",
        "# a=['CAEC','CALC','MTRANS']\n",
        "# for i in a:\n",
        "#     df_train[i]=le.fit_transform(df_train[i])\n",
        "#     df_test[i]=le.fit_transform(df_test[i])\n",
        "\n",
        "# df_train['NObeyesdad']=le.fit_transform(df_train['NObeyesdad'])"
      ],
      "metadata": {
        "id": "bap8MpeImf4e"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pt = PowerTransformer(method='yeo-johnson')\n",
        "# train_features = df_train.drop('NObeyesdad', axis=1)\n",
        "# train_features_scaled = pt.fit_transform(train_features)\n",
        "\n",
        "# train_scaled = pd.DataFrame(train_features_scaled, columns=train_features.columns)\n",
        "# train_scaled['NObeyesdad'] = df_train['NObeyesdad']"
      ],
      "metadata": {
        "id": "npcyT_Stnv4S"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# def remove_outliers(data):\n",
        "#     Q1 = data.quantile(0.25)\n",
        "#     Q3 = data.quantile(0.75)\n",
        "#     IQR = Q3 - Q1\n",
        "#     data = data[~((data < (Q1 - 1.5 * IQR)) | (data > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
        "#     return data\n",
        "\n",
        "# train_scaled = remove_outliers(train_scaled)"
      ],
      "metadata": {
        "id": "YIA2oSv_oB8S"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# b=['CALC','NCP']\n",
        "# for i in b:\n",
        "#     df_train=df_train.drop(i,axis=1)"
      ],
      "metadata": {
        "id": "WU00FL8uoJE5"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = train_scaled.drop('NObeyesdad', axis=1)\n",
        "y = train_scaled['NObeyesdad']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "FgXVIK9JoNde"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "print(df_train.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9PqN9KAfGgUw",
        "outputId": "163748a1-e538-4837-96fa-17be69eed3cb"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                                0\n",
            "Gender                            0\n",
            "family_history_with_overweight    0\n",
            "FAVC                              0\n",
            "FCVC                              0\n",
            "NCP                               0\n",
            "CAEC                              0\n",
            "SMOKE                             0\n",
            "CH2O                              0\n",
            "SCC                               0\n",
            "FAF                               0\n",
            "TUE                               0\n",
            "CALC                              0\n",
            "NObeyesdad                        0\n",
            "MTRANS_Automobile                 0\n",
            "MTRANS_Bike                       0\n",
            "MTRANS_Motorbike                  0\n",
            "MTRANS_Public_Transportation      0\n",
            "MTRANS_Walking                    0\n",
            "Age                               0\n",
            "BMI                               0\n",
            "Age^2                             0\n",
            "Age*BMI                           0\n",
            "BMI^2                             0\n",
            "Weight                            0\n",
            "Height                            0\n",
            "Weight^2                          0\n",
            "Weight*Height                     0\n",
            "Height^2                          0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "9N1v7OBIm_u_",
        "outputId": "8a73a7c3-995b-4d5d-9175-e3f598a7814c"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id  Gender  family_history_with_overweight  FAVC  FCVC  NCP  CAEC  \\\n",
              "0          0       1                               1     1   2.0  3.0     1   \n",
              "1          1       0                               1     1   2.0  3.0     2   \n",
              "2          2       0                               1     1   2.0  1.0     1   \n",
              "3          3       0                               1     1   3.0  3.0     1   \n",
              "4          4       1                               1     1   3.0  2.0     1   \n",
              "...      ...     ...                             ...   ...   ...  ...   ...   \n",
              "20753  20753       1                               1     1   3.0  3.0     1   \n",
              "20754  20754       1                               0     1   3.0  4.0     2   \n",
              "20755  20755       1                               1     1   2.0  3.0     1   \n",
              "20756  20756       1                               1     1   3.0  2.0     1   \n",
              "20757  20757       1                               1     1   3.0  3.0     1   \n",
              "\n",
              "       SMOKE  CH2O  SCC  ...  Age  BMI  Age^2  Age*BMI  BMI^2      Weight  \\\n",
              "0          0   3.0    0  ... -1.0  2.0    1.0     -2.0    4.0   81.669950   \n",
              "1          0   2.0    0  ... -1.0  1.0    1.0     -1.0    1.0   57.000000   \n",
              "2          0   2.0    0  ... -1.0  0.0    1.0     -0.0    0.0   50.165754   \n",
              "3          0   2.0    0  ... -1.0  5.0    1.0     -5.0   25.0  131.274851   \n",
              "4          0   2.0    0  ... -1.0  2.0    1.0     -2.0    4.0   93.798055   \n",
              "...      ...   ...  ...  ...  ...  ...    ...      ...    ...         ...   \n",
              "20753      0   2.0    0  ... -1.0  4.0    1.0     -4.0   16.0  114.187096   \n",
              "20754      0   1.0    0  ... -1.0  0.0    1.0     -0.0    0.0   50.000000   \n",
              "20755      0   2.0    0  ... -1.0  3.0    1.0     -3.0    9.0  105.580491   \n",
              "20756      0   2.0    0  ... -1.0  2.0    1.0     -2.0    4.0   83.520113   \n",
              "20757      0   2.0    0  ... -1.0  4.0    1.0     -4.0   16.0  118.134898   \n",
              "\n",
              "         Height      Weight^2  Weight*Height  Height^2  \n",
              "0      1.699998   6669.980733     138.838752  2.889993  \n",
              "1      1.560000   3249.000000      88.920000  2.433600  \n",
              "2      1.711460   2516.602874      85.856681  2.929095  \n",
              "3      1.710730  17233.086505     224.575826  2.926597  \n",
              "4      1.914186   8798.075122     179.546924  3.664108  \n",
              "...         ...           ...            ...       ...  \n",
              "20753  1.766626  13038.692893     201.725893  3.120967  \n",
              "20754  1.710000   2500.000000      85.500000  2.924100  \n",
              "20755  1.819557  11147.240080     192.109721  3.310788  \n",
              "20756  1.700000   6975.609276     141.984192  2.890000  \n",
              "20757  1.816547  13955.854125     214.597595  3.299843  \n",
              "\n",
              "[20758 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ddd0ea54-1bd3-4438-a763-95f543ed0a67\" 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>id</th>\n",
              "      <th>Gender</th>\n",
              "      <th>family_history_with_overweight</th>\n",
              "      <th>FAVC</th>\n",
              "      <th>FCVC</th>\n",
              "      <th>NCP</th>\n",
              "      <th>CAEC</th>\n",
              "      <th>SMOKE</th>\n",
              "      <th>CH2O</th>\n",
              "      <th>SCC</th>\n",
              "      <th>...</th>\n",
              "      <th>Age</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Age^2</th>\n",
              "      <th>Age*BMI</th>\n",
              "      <th>BMI^2</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight^2</th>\n",
              "      <th>Weight*Height</th>\n",
              "      <th>Height^2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>81.669950</td>\n",
              "      <td>1.699998</td>\n",
              "      <td>6669.980733</td>\n",
              "      <td>138.838752</td>\n",
              "      <td>2.889993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>1.560000</td>\n",
              "      <td>3249.000000</td>\n",
              "      <td>88.920000</td>\n",
              "      <td>2.433600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.165754</td>\n",
              "      <td>1.711460</td>\n",
              "      <td>2516.602874</td>\n",
              "      <td>85.856681</td>\n",
              "      <td>2.929095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>131.274851</td>\n",
              "      <td>1.710730</td>\n",
              "      <td>17233.086505</td>\n",
              "      <td>224.575826</td>\n",
              "      <td>2.926597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>93.798055</td>\n",
              "      <td>1.914186</td>\n",
              "      <td>8798.075122</td>\n",
              "      <td>179.546924</td>\n",
              "      <td>3.664108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20753</th>\n",
              "      <td>20753</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>114.187096</td>\n",
              "      <td>1.766626</td>\n",
              "      <td>13038.692893</td>\n",
              "      <td>201.725893</td>\n",
              "      <td>3.120967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20754</th>\n",
              "      <td>20754</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.710000</td>\n",
              "      <td>2500.000000</td>\n",
              "      <td>85.500000</td>\n",
              "      <td>2.924100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20755</th>\n",
              "      <td>20755</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>105.580491</td>\n",
              "      <td>1.819557</td>\n",
              "      <td>11147.240080</td>\n",
              "      <td>192.109721</td>\n",
              "      <td>3.310788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20756</th>\n",
              "      <td>20756</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>83.520113</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>6975.609276</td>\n",
              "      <td>141.984192</td>\n",
              "      <td>2.890000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20757</th>\n",
              "      <td>20757</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-4.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>118.134898</td>\n",
              "      <td>1.816547</td>\n",
              "      <td>13955.854125</td>\n",
              "      <td>214.597595</td>\n",
              "      <td>3.299843</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20758 rows × 29 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ddd0ea54-1bd3-4438-a763-95f543ed0a67')\"\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-ddd0ea54-1bd3-4438-a763-95f543ed0a67 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-ddd0ea54-1bd3-4438-a763-95f543ed0a67');\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-148c0e5e-7f59-4c16-bfe4-3a03adad63be\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-148c0e5e-7f59-4c16-bfe4-3a03adad63be')\"\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-148c0e5e-7f59-4c16-bfe4-3a03adad63be button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9e9c2fd1-c4e8-466c-ac31-e2f6ddff20e0\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9e9c2fd1-c4e8-466c-ac31-e2f6ddff20e0 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply SMOTE to balance the dataset\n",
        "X_train = df_train.drop(columns=['NObeyesdad'])\n",
        "y_train = df_train['NObeyesdad']\n",
        "\n",
        "smote = SMOTE(sampling_strategy='auto', random_state=42)\n",
        "X_train, y_train = smote.fit_resample(X_train, y_train)\n",
        "\n",
        "# Create a new dataframe with the balanced dataset\n",
        "df_train = pd.concat([X_train, y_train], axis=1)"
      ],
      "metadata": {
        "id": "__8YnKwPnGsU"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_confusion_matrix(cm, classes, normalize=False, cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        title = 'Normalized confusion matrix'\n",
        "    else:\n",
        "        title = 'Confusion matrix, without normalization'\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i in range(cm.shape[0]):\n",
        "        for j in range(cm.shape[1]):\n",
        "            plt.text(j, i, format(cm[i, j], fmt),\n",
        "                     horizontalalignment=\"center\",\n",
        "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.tight_layout()\n",
        "\n",
        "# Compute confusion matrix manually\n",
        "def compute_confusion_matrix(y_true, y_pred, classes):\n",
        "    num_classes = len(classes)\n",
        "    cm = np.zeros((num_classes, num_classes), dtype=int)\n",
        "    for true_label, pred_label in zip(y_true, y_pred):\n",
        "        cm[true_label, pred_label] += 1\n",
        "    return cm\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "1oN5mC6HX3Wn"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Round 1 Models"
      ],
      "metadata": {
        "id": "1OjfDIjxW95w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score\n"
      ],
      "metadata": {
        "id": "5Iaw4d7SXA4e"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_train.drop(['NObeyesdad', 'id'], axis=1)\n",
        "y = df_train['NObeyesdad']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "models = [\n",
        "    ##('DecisionTree', DecisionTreeClassifier(random_state=42)),\n",
        "    ('RandomForest', RandomForestClassifier(random_state=42)),\n",
        "    ('GradientBoost', GradientBoostingClassifier(random_state=42)),\n",
        "    ##('LogisticRegression', LogisticRegression(max_iter=1000, random_state=42)),\n",
        "    ##('SVM', SVC(random_state=42)),\n",
        "    ##('KNN', KNeighborsClassifier()),\n",
        "    ('XGBoost', XGBClassifier(random_state=42))\n",
        "]\n",
        "best_model = None\n",
        "best_accuracy = 0\n",
        "\n",
        "for name, model in models:\n",
        "    model.fit(X_train, y_train)  # Assuming X_train and y_train are your training data\n",
        "    accuracy = model.score(X_test, y_test)  # Assuming X_test and y_test are your test data\n",
        "    print(f\"{name} Accuracy: {accuracy:.4f}\")\n",
        "    classes = model.classes_\n",
        "\n",
        "    # Assuming you have computed y_pred using your RandomForestClassifier\n",
        "    cm = compute_confusion_matrix(y_test, y_pred, classes)\n",
        "\n",
        "    # Plot confusion matrix\n",
        "    plt.figure(figsize=(10, 7))\n",
        "    plot_confusion_matrix(cm, classes=classes)\n",
        "    plt.show()\n",
        "\n",
        "    if accuracy > best_accuracy:\n",
        "        best_accuracy = accuracy\n",
        "        best_model = model\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "O_tciqQRXDy4",
        "outputId": "8e57459c-118b-47b6-b062-ad0f9dba0fb4"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForest Accuracy: 0.8879\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'y_pred' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-42-6fba19aa1ebc>\u001b[0m in \u001b[0;36m<cell line: 17>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;31m# Assuming you have computed y_pred using your RandomForestClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0mcm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_confusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0;31m# Plot confusion matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'y_pred' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "    \"\"\"\n",
        "    Generate a simple plot of the test and training learning curve.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    estimator : object type that implements the \"fit\" and \"predict\" methods\n",
        "        An object of that type which is cloned for each validation.\n",
        "\n",
        "    title : string\n",
        "        Title for the chart.\n",
        "\n",
        "    X : array-like, shape (n_samples, n_features)\n",
        "        Training vector, where n_samples is the number of samples and\n",
        "        n_features is the number of features.\n",
        "\n",
        "    y : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
        "        Target relative to X for classification or regression;\n",
        "        None for unsupervised learning.\n",
        "\n",
        "    ylim : tuple, shape (ymin, ymax), optional\n",
        "        Defines minimum and maximum yvalues plotted.\n",
        "\n",
        "    cv : int, cross-validation generator or an iterable, optional\n",
        "        Determines the cross-validation splitting strategy.\n",
        "        Possible inputs for cv are:\n",
        "          - None, to use the default 5-fold cross-validation,\n",
        "          - integer, to specify the number of folds.\n",
        "          - An object to be used as a cross-validation generator.\n",
        "          - An iterable yielding train/test splits.\n",
        "\n",
        "        For integer/None inputs, if ``y`` is binary or multiclass,\n",
        "        :class:`StratifiedKFold` used. If the estimator is not a classifier\n",
        "        or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n",
        "\n",
        "        Refer :ref:`User Guide <cross_validation>` for the various\n",
        "        cross-validators that can be used here.\n",
        "\n",
        "    n_jobs : int or None, optional (default=None)\n",
        "        Number of jobs to run in parallel.\n",
        "    \"\"\"\n",
        "    plt.figure()\n",
        "    plt.title(title)\n",
        "    if ylim is not None:\n",
        "        plt.ylim(*ylim)\n",
        "    plt.xlabel(\"Training examples\")\n",
        "    plt.ylabel(\"Score\")\n",
        "    train_sizes, train_scores, test_scores = learning_curve(\n",
        "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
        "    train_scores_mean = np.mean(train_scores, axis=1)\n",
        "    train_scores_std = np.std(train_scores, axis=1)\n",
        "    test_scores_mean = np.mean(test_scores, axis=1)\n",
        "    test_scores_std = np.std(test_scores, axis=1)\n",
        "\n",
        "    plt.grid()\n",
        "\n",
        "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
        "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
        "                     color=\"r\")\n",
        "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
        "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
        "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
        "             label=\"Training score\")\n",
        "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
        "             label=\"Cross-validation score\")\n",
        "\n",
        "    plt.legend(loc=\"best\")\n",
        "    return plt\n",
        "\n",
        "# Usage example:\n",
        "# plot learning curve\n",
        "plot_learning_curve(best_model, \"Learning Curve\", X_train, y_train, cv=5)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "SSCHPzoggz1g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_learning_curve(estimator, title, X_train, y_train, X_test, y_test, ylim=None, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "    \"\"\"\n",
        "    Generate a simple plot of the training and test learning curve.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    estimator : object type that implements the \"fit\" and \"predict\" methods\n",
        "        An object of that type which is cloned for each validation.\n",
        "\n",
        "    title : string\n",
        "        Title for the chart.\n",
        "\n",
        "    X_train : array-like, shape (n_samples, n_features)\n",
        "        Training vector, where n_samples is the number of samples and\n",
        "        n_features is the number of features.\n",
        "\n",
        "    y_train : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
        "        Target relative to X_train for classification or regression.\n",
        "\n",
        "    X_test : array-like, shape (n_samples, n_features)\n",
        "        Test vector, where n_samples is the number of samples and\n",
        "        n_features is the number of features.\n",
        "\n",
        "    y_test : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
        "        Target relative to X_test for classification or regression.\n",
        "\n",
        "    ylim : tuple, shape (ymin, ymax), optional\n",
        "        Defines minimum and maximum yvalues plotted.\n",
        "\n",
        "    train_sizes : array-like, shape (n_ticks,), dtype float or int\n",
        "        Relative or absolute numbers of training examples that will be used to\n",
        "        generate the learning curve. If the dtype is float, it is regarded as a\n",
        "        fraction of the maximum size of the training set (that is determined\n",
        "        by the selected validation method), i.e. it has to be within (0, 1].\n",
        "        Otherwise, it is interpreted as absolute sizes of the training sets.\n",
        "    \"\"\"\n",
        "    plt.figure()\n",
        "    plt.title(title)\n",
        "    if ylim is not None:\n",
        "        plt.ylim(*ylim)\n",
        "    plt.xlabel(\"Training examples\")\n",
        "    plt.ylabel(\"Score\")\n",
        "\n",
        "    train_sizes_abs = np.int32(np.ceil(train_sizes * len(X_train)))\n",
        "    train_scores = []\n",
        "    test_scores = []\n",
        "\n",
        "    for train_size in train_sizes_abs:\n",
        "        estimator.fit(X_train[:train_size], y_train[:train_size])\n",
        "        train_score = estimator.score(X_train[:train_size], y_train[:train_size])\n",
        "        test_score = estimator.score(X_test, y_test)\n",
        "        train_scores.append(train_score)\n",
        "        test_scores.append(test_score)\n",
        "\n",
        "    plt.plot(train_sizes_abs, train_scores, 'o-', color=\"r\", label=\"Training score\")\n",
        "    plt.plot(train_sizes_abs, test_scores, 'o-', color=\"g\", label=\"Test score\")\n",
        "    plt.legend(loc=\"best\")\n",
        "    return plt\n",
        "\n",
        "# Usage example:\n",
        "# plot learning curve\n",
        "plot_learning_curve(best_model, \"Learning Curve\", X_train, y_train, X_test, y_test)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "oR-HN0FDiedJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Round 2 Models"
      ],
      "metadata": {
        "id": "OdDbrhykPGGL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.metrics import accuracy_score\n"
      ],
      "metadata": {
        "id": "tgQR61RITcZs"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = [\n",
        "    ##('DecisionTree', DecisionTreeClassifier(random_state=42)),\n",
        "    ('RandomForest', RandomForestClassifier(random_state=42)),\n",
        "    ('GradientBoost', GradientBoostingClassifier(random_state=42)),\n",
        "    ##('LogisticRegression', LogisticRegression(max_iter=1000, random_state=42)),\n",
        "    ##('SVM', SVC(random_state=42)),\n",
        "    ##('KNN', KNeighborsClassifier()),\n",
        "    ('XGBoost', XGBClassifier(random_state=42))\n",
        "]\n",
        "\n",
        "# Step 9: Evaluate models using cross-validation\n",
        "best_model = None\n",
        "best_accuracy = 0\n",
        "for name, model in models:\n",
        "    scores = cross_val_score(model, X, y, cv=5, scoring='accuracy')\n",
        "    mean_accuracy = scores.mean()\n",
        "    print(f\"{name} Mean Accuracy: {mean_accuracy:.4f}\")\n",
        "    if mean_accuracy > best_accuracy:\n",
        "        best_accuracy = mean_accuracy\n",
        "        best_model = model"
      ],
      "metadata": {
        "id": "rcYcJFBtSlSU",
        "outputId": "b3bc12ff-bba2-46db-95e4-e502558910c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RandomForest Mean Accuracy: 0.8949\n",
            "GradientBoost Mean Accuracy: 0.8889\n",
            "XGBoost Mean Accuracy: 0.8991\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_learning_curve(estimator, title, X_train, y_train, X_test, y_test, ylim=None, train_sizes=np.linspace(.1, 1.0, 5)):\n",
        "    \"\"\"\n",
        "    Generate a simple plot of the training and test learning curve.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    estimator : object type that implements the \"fit\" and \"predict\" methods\n",
        "        An object of that type which is cloned for each validation.\n",
        "\n",
        "    title : string\n",
        "        Title for the chart.\n",
        "\n",
        "    X_train : array-like, shape (n_samples, n_features)\n",
        "        Training vector, where n_samples is the number of samples and\n",
        "        n_features is the number of features.\n",
        "\n",
        "    y_train : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
        "        Target relative to X_train for classification or regression.\n",
        "\n",
        "    X_test : array-like, shape (n_samples, n_features)\n",
        "        Test vector, where n_samples is the number of samples and\n",
        "        n_features is the number of features.\n",
        "\n",
        "    y_test : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
        "        Target relative to X_test for classification or regression.\n",
        "\n",
        "    ylim : tuple, shape (ymin, ymax), optional\n",
        "        Defines minimum and maximum yvalues plotted.\n",
        "\n",
        "    train_sizes : array-like, shape (n_ticks,), dtype float or int\n",
        "        Relative or absolute numbers of training examples that will be used to\n",
        "        generate the learning curve. If the dtype is float, it is regarded as a\n",
        "        fraction of the maximum size of the training set (that is determined\n",
        "        by the selected validation method), i.e. it has to be within (0, 1].\n",
        "        Otherwise, it is interpreted as absolute sizes of the training sets.\n",
        "    \"\"\"\n",
        "    plt.figure()\n",
        "    plt.title(title)\n",
        "    if ylim is not None:\n",
        "        plt.ylim(*ylim)\n",
        "    plt.xlabel(\"Training examples\")\n",
        "    plt.ylabel(\"Score\")\n",
        "\n",
        "    train_sizes_abs = np.int32(np.ceil(train_sizes * len(X_train)))\n",
        "    train_scores = []\n",
        "    test_scores = []\n",
        "\n",
        "    for train_size in train_sizes_abs:\n",
        "        estimator.fit(X_train[:train_size], y_train[:train_size])\n",
        "        train_score = estimator.score(X_train[:train_size], y_train[:train_size])\n",
        "        test_score = estimator.score(X_test, y_test)\n",
        "        train_scores.append(train_score)\n",
        "        test_scores.append(test_score)\n",
        "\n",
        "    plt.plot(train_sizes_abs, train_scores, 'o-', color=\"r\", label=\"Training score\")\n",
        "    plt.plot(train_sizes_abs, test_scores, 'o-', color=\"g\", label=\"Test score\")\n",
        "    plt.legend(loc=\"best\")\n",
        "    return plt\n",
        "\n",
        "# Usage example:\n",
        "# plot learning curve\n",
        "plot_learning_curve(best_model, \"Learning Curve\", X_train, y_train, X_test, y_test)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "u6L03SetjGxv",
        "outputId": "d0911f8e-6319-4f2a-9e72-f3c52463d2da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Round 3 Models"
      ],
      "metadata": {
        "id": "tGiOYbTAXlgJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#X = df_train.drop(['NObeyesdad', 'id'], axis=1)\n",
        "#y = df_train['NObeyesdad']\n",
        "\n",
        "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "rf = RandomForestClassifier(random_state=42, max_depth=20, n_estimators=800, criterion='gini')\n",
        "rf.fit(X_train, y_train)\n",
        "\n",
        "y_pred = rf.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Plot confusion matrix\n",
        "#ax = plt.subplot(1, 1, 1)\n",
        "#_ = utilities.plot_confusion_matrix_heatmap(ax, y_test, y_pred)\n",
        "#from sklearn.metrics import plot_confusion_matrix\n",
        "\n",
        "# Plot confusion matrix\n",
        "#plt.figure(figsize=(10, 7))\n",
        "#plot_confusion_matrix(rf, X_test, y_test, ax=plt.gca(), cmap=plt.cm.Blues, values_format='.0f')\n",
        "#plt.show()\n",
        "# Assuming you have defined rf.classes_ as the list of class labels in your RandomForestClassifier\n",
        "classes = rf.classes_\n",
        "\n",
        "# Assuming you have computed y_pred using your RandomForestClassifier\n",
        "cm = compute_confusion_matrix(y_test, y_pred, classes)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(10, 7))\n",
        "plot_confusion_matrix(cm, classes=classes)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "999UQMoVYBl1",
        "outputId": "d63e70e0-df40-43ac-ae7f-3ad9725fcd08"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.93      0.95        41\n",
            "           1       0.89      0.93      0.91        91\n",
            "           2       0.89      0.88      0.89       124\n",
            "           3       0.98      0.99      0.99       333\n",
            "           4       1.00      1.00      1.00       786\n",
            "           5       0.81      0.80      0.81       106\n",
            "           6       0.80      0.76      0.78        91\n",
            "\n",
            "    accuracy                           0.95      1572\n",
            "   macro avg       0.91      0.90      0.90      1572\n",
            "weighted avg       0.95      0.95      0.95      1572\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Xgboost\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "\n",
        "best_params = {\n",
        "    'grow_policy': 'depthwise',\n",
        "    'n_estimators': 1318,\n",
        "    'learning_rate': 0.06854096966928382,\n",
        "    'gamma': 0.6829197356513476,\n",
        "    'subsample': 0.8942042552212566,\n",
        "    'colsample_bytree': 0.32036152443164206,\n",
        "    'max_depth': 18,\n",
        "    'min_child_weight': 10,\n",
        "    'reg_lambda': 4.33935436979607e-05,\n",
        "    'reg_alpha': 0.00014155585622193723\n",
        "}\n",
        "best_params['booster'] = 'gbtree'\n",
        "best_params['objective'] = 'multi:softmax'\n",
        "best_params[\"verbosity\"] = 0\n",
        "\n",
        "xgb = XGBClassifier(**best_params)\n",
        "xgb.fit(X_train, y_train)\n",
        "\n",
        "y_pred = xgb.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Assuming you have defined rf.classes_ as the list of class labels in your RandomForestClassifier\n",
        "classes = xgb.classes_\n",
        "\n",
        "# Assuming you have computed y_pred using your RandomForestClassifier\n",
        "cm = compute_confusion_matrix(y_test, y_pred, classes)\n",
        "\n",
        "# Plot confusion matrix\n",
        "#plt.figure(figsize=(10, 7))\n",
        "#ax = plt.subplot(1, 1, 1)\n",
        "#_ = utilities.plot_confusion_matrix_heatmap(ax, y_test, y_pred, cmap='Reds')\n",
        "plt.figure(figsize=(10, 7))\n",
        "plot_confusion_matrix(cm, classes=xgb.classes_)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "1X4eVZ2SYH8t",
        "outputId": "43939d92-1f5d-40ce-fbfc-fb4267968659"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.95      0.95        41\n",
            "           1       0.91      0.92      0.92        91\n",
            "           2       0.88      0.85      0.87       124\n",
            "           3       0.98      0.98      0.98       333\n",
            "           4       0.99      1.00      1.00       786\n",
            "           5       0.81      0.82      0.81       106\n",
            "           6       0.81      0.77      0.79        91\n",
            "\n",
            "    accuracy                           0.95      1572\n",
            "   macro avg       0.91      0.90      0.90      1572\n",
            "weighted avg       0.95      0.95      0.95      1572\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "best_params = {\n",
        "    'n_estimators': 100,  # Change according to your choice\n",
        "    'learning_rate': 0.1,  # Change according to your choice\n",
        "    'subsample': 1.0,  # Change according to your choice\n",
        "    'min_samples_split': 2,  # Change according to your choice\n",
        "    'min_samples_leaf': 1,  # Change according to your choice\n",
        "    'max_depth': 3,  # Change according to your choice\n",
        "    'max_features': None,  # Change according to your choice\n",
        "}\n",
        "\n",
        "gradient_boost = GradientBoostingClassifier(**best_params)\n",
        "gradient_boost.fit(X_train, y_train)\n",
        "\n",
        "y_pred = gradient_boost.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Assuming you have defined rf.classes_ as the list of class labels in your RandomForestClassifier\n",
        "classes = gradient_boost.classes_\n",
        "\n",
        "# Assuming you have computed y_pred using your RandomForestClassifier\n",
        "cm = compute_confusion_matrix(y_test, y_pred, classes)\n",
        "\n",
        "# Plot confusion matrix\n",
        "plt.figure(figsize=(10, 7))\n",
        "plot_confusion_matrix(cm, classes=gradient_boost.classes_)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "Gs1VM5aQXqKH",
        "outputId": "ffad9e36-2146-4a96-d2b9-78edcd8c0465"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.95      0.95        41\n",
            "           1       0.89      0.91      0.90        91\n",
            "           2       0.86      0.86      0.86       124\n",
            "           3       0.98      0.98      0.98       333\n",
            "           4       1.00      1.00      1.00       786\n",
            "           5       0.83      0.77      0.80       106\n",
            "           6       0.75      0.78      0.76        91\n",
            "\n",
            "    accuracy                           0.95      1572\n",
            "   macro avg       0.89      0.89      0.89      1572\n",
            "weighted avg       0.95      0.95      0.95      1572\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_test.isnull().sum())"
      ],
      "metadata": {
        "id": "ko4kIBU5mGr3",
        "outputId": "0e9b51a2-6c2c-40f7-c42d-7a762a281647",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                                0\n",
            "Gender                            0\n",
            "family_history_with_overweight    0\n",
            "FAVC                              0\n",
            "FCVC                              0\n",
            "NCP                               0\n",
            "CAEC                              0\n",
            "SMOKE                             0\n",
            "CH2O                              0\n",
            "SCC                               0\n",
            "FAF                               0\n",
            "TUE                               0\n",
            "CALC                              0\n",
            "MTRANS_Automobile                 0\n",
            "MTRANS_Bike                       0\n",
            "MTRANS_Motorbike                  0\n",
            "MTRANS_Public_Transportation      0\n",
            "MTRANS_Walking                    0\n",
            "Age                               0\n",
            "BMI                               0\n",
            "Age^2                             0\n",
            "Age*BMI                           0\n",
            "BMI^2                             0\n",
            "Weight                            0\n",
            "Height                            0\n",
            "Weight^2                          0\n",
            "Weight*Height                     0\n",
            "Height^2                          0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_encoder_target = LabelEncoder()\n",
        "le = LabelEncoder()"
      ],
      "metadata": {
        "id": "lY5d9cA4lNrD"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_label = xgb.predict(X_test)\n",
        "\n",
        "display(len(y_test_label))\n",
        "\n",
        "df_test['NObeyesdad'] = y_test_label\n",
        "out = df_test[['id','NObeyesdad']]\n",
        "out.NObeyesdad = le.inverse_transform(out.NObeyesdad)\n",
        "display(out.head(5))\n",
        "\n",
        "out.to_csv(\"submission_non_threshold_opt.csv\",index=False)"
      ],
      "metadata": {
        "id": "Ie24ujfLs7N7",
        "outputId": "0416d3c6-b5d9-4dd3-b9cc-2a093bbccf2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        }
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "1572"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Length of values (1572) does not match length of index (13840)",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-101-503daff9e6cc>\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test_label\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdf_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'NObeyesdad'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_test_label\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'NObeyesdad'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNObeyesdad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNObeyesdad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3948\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3949\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3950\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3952\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   4141\u001b[0m         \u001b[0mensure\u001b[0m \u001b[0mhomogeneity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4142\u001b[0m         \"\"\"\n\u001b[0;32m-> 4143\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4145\u001b[0m         if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m   4868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4869\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4870\u001b[0;31m             \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire_length_match\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4871\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4872\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/common.py\u001b[0m in \u001b[0;36mrequire_length_match\u001b[0;34m(data, index)\u001b[0m\n\u001b[1;32m    574\u001b[0m     \"\"\"\n\u001b[1;32m    575\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m    577\u001b[0m             \u001b[0;34m\"Length of values \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    578\u001b[0m             \u001b[0;34mf\"({len(data)}) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Length of values (1572) does not match length of index (13840)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 12: Make predictions on the test data\n",
        "test_predictions = xgb.predict(df_test.drop(['id'], axis=1))\n",
        "\n",
        "# Step 13: Prepare the submission file\n",
        "submission_df = pd.DataFrame({'id': df_test['id'], 'NObeyesdad': label_encoder_target.inverse_transform(test_predictions)})\n",
        "submission_df.to_csv('submission.csv', index=False)"
      ],
      "metadata": {
        "id": "bT2tV_UrlMSZ",
        "outputId": "1345d31c-62e1-4239-a730-a3676055a8a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        }
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "feature_names mismatch: ['id', 'Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CAEC', 'CH2O', 'FAF', 'TUE', 'CALC', 'MTRANS', 'BMI', 'Gender_Male', 'family_history_with_overweight_yes', 'FAVC_yes', 'SMOKE_yes', 'SCC_yes'] ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CAEC', 'CH2O', 'FAF', 'TUE', 'CALC', 'MTRANS', 'BMI', 'Gender_Male', 'family_history_with_overweight_yes', 'FAVC_yes', 'SMOKE_yes', 'SCC_yes']\nexpected id in input data",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-94-a7a5dc1f2065>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Step 12: Make predictions on the test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtest_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Step 13: Prepare the submission file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msubmission_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdf_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'NObeyesdad'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlabel_encoder_target\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_predictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X, output_margin, validate_features, base_margin, iteration_range)\u001b[0m\n\u001b[1;32m   1551\u001b[0m     ) -> ArrayLike:\n\u001b[1;32m   1552\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mverbosity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbosity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1553\u001b[0;31m             class_probs = super().predict(\n\u001b[0m\u001b[1;32m   1554\u001b[0m                 \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m                 \u001b[0moutput_margin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_margin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X, output_margin, validate_features, base_margin, iteration_range)\u001b[0m\n\u001b[1;32m   1166\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_use_inplace_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1167\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1168\u001b[0;31m                     predts = self.get_booster().inplace_predict(\n\u001b[0m\u001b[1;32m   1169\u001b[0m                         \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1170\u001b[0m                         \u001b[0miteration_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0miteration_range\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36minplace_predict\u001b[0;34m(self, data, iteration_range, predict_type, missing, validate_features, base_margin, strict_shape)\u001b[0m\n\u001b[1;32m   2416\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_transform_pandas_df\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menable_categorical\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2417\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvalidate_features\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2418\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2419\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_is_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_is_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2420\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/xgboost/core.py\u001b[0m in \u001b[0;36m_validate_features\u001b[0;34m(self, feature_names)\u001b[0m\n\u001b[1;32m   2968\u001b[0m                 )\n\u001b[1;32m   2969\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2970\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_names\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2971\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2972\u001b[0m     def get_split_value_histogram(\n",
            "\u001b[0;31mValueError\u001b[0m: feature_names mismatch: ['id', 'Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CAEC', 'CH2O', 'FAF', 'TUE', 'CALC', 'MTRANS', 'BMI', 'Gender_Male', 'family_history_with_overweight_yes', 'FAVC_yes', 'SMOKE_yes', 'SCC_yes'] ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CAEC', 'CH2O', 'FAF', 'TUE', 'CALC', 'MTRANS', 'BMI', 'Gender_Male', 'family_history_with_overweight_yes', 'FAVC_yes', 'SMOKE_yes', 'SCC_yes']\nexpected id in input data"
          ]
        }
      ]
    }
  ]
}