{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in /Users/owen/.pyenv/versions/3.10.11/lib/python3.10/site-packages (1.3.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/owen/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from scikit-learn) (3.1.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /Users/owen/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from scikit-learn) (1.3.1)\n",
      "Requirement already satisfied: scipy>=1.5.0 in /Users/owen/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from scikit-learn) (1.11.1)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /Users/owen/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from scikit-learn) (1.23.5)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49m/Users/owen/.pyenv/versions/3.10.11/bin/python3.10 -m pip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "sklearn.__version__='1.3.0'\n"
     ]
    }
   ],
   "source": [
    "%pip install scikit-learn\n",
    "import sklearn\n",
    "print(f\"{sklearn.__version__=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ljmtcrVxoxfO"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Load the Iris dataset\n",
    "iris = load_iris()\n",
    "X, y = iris.data, iris.target\n",
    "\n",
    "# Split the data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Initialize the classifier\n",
    "classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "\n",
    "# Train the classifier\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "predictions = classifier.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy\n",
    "accuracy = accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "NL58M19xo4PP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.joblib']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump\n",
    "dump(classifier, 'model.joblib')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
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}