Train new sklearn model using Iris dataset

Train a new classifier on the Iris dataset to fix the garden tutorial.

Train_Model.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b0e43fdc-4787-4b95-ae75-6f73750c0e78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: scikit-learn in /home/hayden/.local/lib/python3.10/site-packages (1.4.2)\n",
+      "Requirement already satisfied: scipy>=1.6.0 in /home/hayden/.local/lib/python3.10/site-packages (from scikit-learn) (1.13.0)\n",
+      "Requirement already satisfied: joblib>=1.2.0 in /home/hayden/.local/lib/python3.10/site-packages (from scikit-learn) (1.4.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/hayden/.local/lib/python3.10/site-packages (from scikit-learn) (3.4.0)\n",
+      "Requirement already satisfied: numpy>=1.19.5 in /home/hayden/.local/lib/python3.10/site-packages (from scikit-learn) (1.26.4)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n",
+      "1.4.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install scikit-learn\n",
+    "import sklearn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "af4e3bc3-6fcc-46d8-b7cc-d2fed9a05fc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sklearn version: 1.4.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"sklearn version: {sklearn.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ee4d871c-441c-4ee9-8af0-415047644335",
+   "metadata": {},
+   "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": 7,
+   "id": "84cd4fc6-4e68-4c79-bfeb-777bce8e62e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['model.joblib']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from joblib import dump\n",
+    "dump(classifier, 'model.joblib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "022af2af-01fd-4de5-a056-0f41337c0c1a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

model.joblib

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+size 183761