2024-02-14 11:10:27 +00:00
|
|
|
{
|
|
|
|
|
"cells": [
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2024-02-14 11:26:41 +00:00
|
|
|
"execution_count": 1,
|
2024-02-14 11:10:27 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"import pandas as pd\n",
|
|
|
|
|
"from sklearn.datasets import load_iris\n"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2024-02-14 11:26:41 +00:00
|
|
|
"execution_count": 6,
|
2024-02-14 11:10:27 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
" sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n",
|
|
|
|
|
"0 5.1 3.5 1.4 0.2 \n",
|
|
|
|
|
"1 4.9 3.0 1.4 0.2 \n",
|
|
|
|
|
"2 4.7 3.2 1.3 0.2 \n",
|
|
|
|
|
"3 4.6 3.1 1.5 0.2 \n",
|
|
|
|
|
"4 5.0 3.6 1.4 0.2 \n",
|
|
|
|
|
"\n",
|
|
|
|
|
" species \n",
|
|
|
|
|
"0 setosa \n",
|
|
|
|
|
"1 setosa \n",
|
|
|
|
|
"2 setosa \n",
|
|
|
|
|
"3 setosa \n",
|
|
|
|
|
"4 setosa \n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"iris = load_iris()\n",
|
|
|
|
|
"df = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
|
|
|
|
|
"df['species'] = iris.target\n",
|
|
|
|
|
"df[\"species\"] = df[\"species\"].map({0:iris.target_names[0],1:iris.target_names[1],2:iris.target_names[2]})\n",
|
|
|
|
|
"print(df.head())"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2024-02-14 11:26:41 +00:00
|
|
|
"execution_count": 11,
|
2024-02-14 11:10:27 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
" sepal length (cm) sepal width (cm) petal length (cm) \\\n",
|
|
|
|
|
"count 150.000000 150.000000 150.000000 \n",
|
|
|
|
|
"mean 5.843333 3.057333 3.758000 \n",
|
|
|
|
|
"std 0.828066 0.435866 1.765298 \n",
|
|
|
|
|
"min 4.300000 2.000000 1.000000 \n",
|
|
|
|
|
"25% 5.100000 2.800000 1.600000 \n",
|
|
|
|
|
"50% 5.800000 3.000000 4.350000 \n",
|
|
|
|
|
"75% 6.400000 3.300000 5.100000 \n",
|
|
|
|
|
"max 7.900000 4.400000 6.900000 \n",
|
|
|
|
|
"\n",
|
|
|
|
|
" petal width (cm) \n",
|
|
|
|
|
"count 150.000000 \n",
|
|
|
|
|
"mean 1.199333 \n",
|
|
|
|
|
"std 0.762238 \n",
|
|
|
|
|
"min 0.100000 \n",
|
|
|
|
|
"25% 0.300000 \n",
|
|
|
|
|
"50% 1.300000 \n",
|
|
|
|
|
"75% 1.800000 \n",
|
|
|
|
|
"max 2.500000 \n"
|
|
|
|
|
]
|
2024-02-14 11:26:41 +00:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 1117.75x1000 with 20 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 500x300 with 2 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "display_data"
|
2024-02-14 11:10:27 +00:00
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"import matplotlib.pyplot as plt\n",
|
|
|
|
|
"import seaborn as sns \n",
|
|
|
|
|
"\n",
|
|
|
|
|
"#Décrire le jeu de données\n",
|
2024-02-14 11:26:41 +00:00
|
|
|
"print(df.describe())\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"#Distribution par espèce\n",
|
|
|
|
|
"sns.pairplot(df,hue='species')\n",
|
|
|
|
|
"plt.show()\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"#Corrélation entre caractéristiques\n",
|
|
|
|
|
"plt.figure(figsize=(5,3))\n",
|
|
|
|
|
"sns.heatmap(df.corr(numeric_only=True),annot=True,fmt=\".2f\")\n",
|
|
|
|
|
"plt.show()"
|
2024-02-14 11:10:27 +00:00
|
|
|
]
|
2024-02-14 11:59:19 +00:00
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 33,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"[[10 0 0]\n",
|
|
|
|
|
" [ 0 9 0]\n",
|
|
|
|
|
" [ 0 0 11]]\n",
|
|
|
|
|
" precision recall f1-score support\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" setosa 1.00 1.00 1.00 10\n",
|
|
|
|
|
" versicolor 1.00 1.00 1.00 9\n",
|
|
|
|
|
" virginica 1.00 1.00 1.00 11\n",
|
|
|
|
|
"\n",
|
|
|
|
|
" accuracy 1.00 30\n",
|
|
|
|
|
" macro avg 1.00 1.00 1.00 30\n",
|
|
|
|
|
"weighted avg 1.00 1.00 1.00 30\n",
|
|
|
|
|
"\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"from sklearn.model_selection import train_test_split\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"X = df.drop('species',axis=1)\n",
|
|
|
|
|
"y = df[\"species\"]\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"from sklearn.linear_model import LogisticRegression\n",
|
|
|
|
|
"from sklearn.metrics import confusion_matrix,classification_report\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"model = LogisticRegression(max_iter=200)\n",
|
|
|
|
|
"model.fit(X_train,y_train)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"y_pred = model.predict(X_test)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"print(confusion_matrix(y_test,y_pred))\n",
|
|
|
|
|
"print(classification_report(y_test,y_pred))"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 35,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"Précision moyenn : 0.9733333333333334\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"from sklearn.model_selection import cross_val_score\n",
|
|
|
|
|
"scores = cross_val_score(model,X,y,cv=5)\n",
|
|
|
|
|
"print(\"Précision moyenn :\",scores.mean())"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
2024-02-14 11:10:27 +00:00
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
|
|
|
|
"display_name": "Python 3",
|
|
|
|
|
"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": "3.10.0"
|
|
|
|
|
},
|
|
|
|
|
"orig_nbformat": 4
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 2
|
|
|
|
|
}
|
