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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 🌸 TP-3 : Classification Iris — Introduction au ML\n",
"\n",
"**Objectif** : Classifier les iris en 3 espèces à partir de 4 features.\n",
"\n",
"**Dataset** : [Iris Flower Dataset](https://www.kaggle.com/datasets/uciml/iris)\n",
"\n",
"**Compétences** :\n",
"- Classification multi-classe\n",
"- Visualisation avec PCA\n",
"- Frontières de décision\n",
"- Comparaison d'algorithmes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.datasets import load_iris\n",
"from sklearn.model_selection import train_test_split, cross_val_score\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
"\n",
"sns.set_style('whitegrid')\n",
"print(\"✅ Bibliothèques importées !\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Chargement des données\n",
"iris = load_iris()\n",
"X = iris.data\n",
"y = iris.target\n",
"feature_names = iris.feature_names\n",
"target_names = iris.target_names\n",
"\n",
"# Création d'un DataFrame\n",
"df = pd.DataFrame(X, columns=feature_names)\n",
"df['species'] = [target_names[i] for i in y]\n",
"\n",
"print(f\"📊 Dimensions : {df.shape}\")\n",
"print(f\"\\n🌸 Espèces : {target_names}\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Pairplot pour visualiser les relations\n",
"sns.pairplot(df, hue='species', palette='viridis', height=2.5)\n",
"plt.suptitle('Pairplot du dataset Iris', y=1.02, fontsize=14)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Split et normalisation\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, test_size=0.2, random_state=42, stratify=y\n",
")\n",
"\n",
"scaler = StandardScaler()\n",
"X_train_scaled = scaler.fit_transform(X_train)\n",
"X_test_scaled = scaler.transform(X_test)\n",
"\n",
"print(f\"Train : {X_train.shape[0]} échantillons\")\n",
"print(f\"Test : {X_test.shape[0]} échantillons\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Comparaison des modèles\n",
"models = {\n",
" 'KNN': KNeighborsClassifier(n_neighbors=5),\n",
" 'SVM': SVC(kernel='rbf', random_state=42),\n",
" 'Decision Tree': DecisionTreeClassifier(random_state=42),\n",
" 'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42)\n",
"}\n",
"\n",
"results = {}\n",
"for name, model in models.items():\n",
" model.fit(X_train_scaled, y_train)\n",
" y_pred = model.predict(X_test_scaled)\n",
" accuracy = accuracy_score(y_test, y_pred)\n",
" results[name] = accuracy\n",
" print(f\"{name:15} : {accuracy:.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Visualisation avec PCA (2D)\n",
"pca = PCA(n_components=2)\n",
"X_pca = pca.fit_transform(X_scaled := StandardScaler().fit_transform(X))\n",
"\n",
"plt.figure(figsize=(10, 6))\n",
"colors = ['red', 'green', 'blue']\n",
"for i, target_name in enumerate(target_names):\n",
" plt.scatter(X_pca[y == i, 0], X_pca[y == i, 1], \n",
" c=colors[i], label=target_name, alpha=0.7, s=50)\n",
"plt.xlabel(f'PC1 ({pca.explained_variance_ratio_[0]:.2%})')\n",
"plt.ylabel(f'PC2 ({pca.explained_variance_ratio_[1]:.2%})')\n",
"plt.title('Dataset Iris - Projection PCA')\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"print(f\"Variance expliquée : {pca.explained_variance_ratio_.sum():.2%}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Matrice de confusion pour le meilleur modèle\n",
"best_model = SVC(kernel='rbf', random_state=42)\n",
"best_model.fit(X_train_scaled, y_train)\n",
"y_pred = best_model.predict(X_test_scaled)\n",
"\n",
"cm = confusion_matrix(y_test, y_pred)\n",
"\n",
"plt.figure(figsize=(8, 6))\n",
"sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
" xticklabels=target_names, yticklabels=target_names)\n",
"plt.title('Matrice de confusion - SVM')\n",
"plt.ylabel('Vrai label')\n",
"plt.xlabel('Prédiction')\n",
"plt.show()\n",
"\n",
"print(classification_report(y_test, y_pred, target_names=target_names))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.8.0"
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"nbformat": 4,
"nbformat_minor": 4
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