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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
}
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