أنواع التعلم: Supervised وUnsupervised

import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification, make_blobs, make_regression
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.cluster import KMeans
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, mean_squared_error

print("=" * 55)
print("مقارنة: Supervised vs Unsupervised Learning")
print("=" * 55)

# ─────────────────────────────────────────
# 1. Supervised — Regression
# ─────────────────────────────────────────
print("\n1. SUPERVISED — Regression (التنبؤ بالأسعار)")
X_reg, y_reg = make_regression(n_samples=200, n_features=1,
                                noise=30, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(
    X_reg, y_reg, test_size=0.2, random_state=42
)
reg_model = LinearRegression()
reg_model.fit(X_train, y_train)
y_pred = reg_model.predict(X_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f"   RMSE = {rmse:.2f}  (كلما أصغر، أفضل)")

# ─────────────────────────────────────────
# 2. Supervised — Classification
# ─────────────────────────────────────────
print("\n2. SUPERVISED — Classification (تصنيف)")
X_clf, y_clf = make_classification(n_samples=300, n_features=2,
                                   n_redundant=0, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(
    X_clf, y_clf, test_size=0.2, random_state=42
)
clf_model = LogisticRegression()
clf_model.fit(X_train, y_train)
accuracy = accuracy_score(y_test, clf_model.predict(X_test))
print(f"   Accuracy = {accuracy:.1%}")

# ─────────────────────────────────────────
# 3. Unsupervised — Clustering
# ─────────────────────────────────────────
print("\n3. UNSUPERVISED — Clustering (تجميع بدون تسميات)")
# بيانات بدون labels — النموذج يكتشف المجموعات بنفسه
X_cluster, _ = make_blobs(n_samples=300, centers=4,
                           cluster_std=0.8, random_state=42)
kmeans = KMeans(n_clusters=4, random_state=42, n_init="auto")
kmeans.fit(X_cluster)
labels = kmeans.labels_

# عدد العناصر في كل مجموعة
unique, counts = np.unique(labels, return_counts=True)
print("   المجموعات المكتشفة:")
for g, c in zip(unique, counts):
    print(f"     مجموعة {g}: {c} عنصر")

print("\n" + "=" * 55)
print("ملخص:")
print("  Regression  → يتنبأ بأرقام مستمرة (مع labels)")
print("  Classification → يصنّف لفئات (مع labels)")
print("  Clustering  → يجمّع بدون labels")
print("=" * 55)