{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Obesity Risk & Meal Planning berbasis AI\n",
        "\n",
        "Notebook awal untuk mengikuti tutorial machinelearning.co.id. Materi ini hanya untuk edukasi machine learning, bukan saran medis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "DATA_URL = \"https://autotrain.app/opendata92bd.html?file=ObesityDataSet_raw_and_data_sinthetic.csv\"\n",
        "df = pd.read_csv(DATA_URL)\n",
        "target_col = \"NObeyesdad\" if \"NObeyesdad\" in df.columns else df.columns[-1]\n",
        "X = df.drop(columns=[target_col]).copy()\n",
        "y = df[target_col]\n",
        "\n",
        "for col in X.columns:\n",
        "    if X[col].dtype == \"object\":\n",
        "        X[col] = LabelEncoder().fit_transform(X[col].astype(str))\n",
        "\n",
        "target_encoder = LabelEncoder()\n",
        "y_encoded = target_encoder.fit_transform(y.astype(str))\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42, stratify=y_encoded)\n",
        "model = RandomForestClassifier(n_estimators=200, random_state=42, class_weight=\"balanced\")\n",
        "model.fit(X_train, y_train)\n",
        "preds = model.predict(X_test)\n",
        "print(classification_report(y_test, preds, target_names=target_encoder.classes_, zero_division=0))"
      ]
    }
  ],
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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
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      "name": "python",
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  "nbformat": 4,
  "nbformat_minor": 5
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