diff --git a/RBFNN/README.md b/RBFNN/README.md
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+# RBF Neural Network (RBFNN)
+
+A simple and efficient implementation of Radial Basis Function Neural Network using NumPy and scikit-learn.
+
+## 📁 Structure
+- `rbfnn/`: RBFNN model implementation
+- `examples/`: Regression and classification demos
+- `requirements.txt`: Install dependencies
+- `README.md`: Project overview
+
+## 🚀 Usage
+
+### Install dependencies
+```bash
+pip install -r requirements.txt
+```
+### Run regression example
+```bash
+python examples/regression_example.py
+```
+### Run classification example
+```bash
+python examples/classification_example.py
+```
\ No newline at end of file
diff --git a/RBFNN/radial_basis_function_network.py b/RBFNN/radial_basis_function_network.py
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+import numpy as np
+from sklearn.cluster import KMeans
+from numpy.linalg import pinv
+
+
+class RBFNN:
+    def __init__(self, num_centers=10, gamma=1.0):
+        self.num_centers = num_centers
+        self.gamma = gamma
+        self.centers = None
+        self.weights = None
+
+    def _rbf(self, x, center):
+        return np.exp(-self.gamma * np.linalg.norm(x - center) ** 2)
+
+    def _compute_activations(self, X):
+        G = np.zeros((X.shape[0], self.num_centers))
+        for i, x in enumerate(X):
+            for j, c in enumerate(self.centers):
+                G[i, j] = self._rbf(x, c)
+        return G
+
+    def train(self, X, y):
+        kmeans = KMeans(n_clusters=self.num_centers, random_state=0).fit(X)
+        self.centers = kmeans.cluster_centers_
+        G = self._compute_activations(X)
+        self.weights = pinv(G).dot(y)
+
+    def predict(self, X):
+        G = self._compute_activations(X)
+        return G.dot(self.weights)
diff --git a/RBFNN/requirements.txt b/RBFNN/requirements.txt
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index 000000000000..e69de29bb2d1
diff --git a/RBFNN/tests/classification_example.py b/RBFNN/tests/classification_example.py
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+++ b/RBFNN/tests/classification_example.py
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+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.metrics import accuracy_score
+from rbfnn.model import RBFNN
+import numpy as np
+
+data = load_iris()
+X = data.data
+y = data.target.reshape(-1, 1)
+
+encoder = OneHotEncoder(sparse_output=False)
+y_encoded = encoder.fit_transform(y)
+
+scaler = StandardScaler()
+X_scaled = scaler.fit_transform(X)
+
+X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_encoded, test_size=0.3)
+
+model = RBFNN(num_centers=10, gamma=1.0)
+model.train(X_train, y_train)
+
+y_pred_probs = model.predict(X_test)
+y_pred = np.argmax(y_pred_probs, axis=1)
+y_true = np.argmax(y_test, axis=1)
+
+print("Classification Accuracy:", accuracy_score(y_true, y_pred))
diff --git a/RBFNN/tests/regression_example.py b/RBFNN/tests/regression_example.py
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+++ b/RBFNN/tests/regression_example.py
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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from rbfnn.model import RBFNN
+
+# Generate sine wave data
+X = np.linspace(0, 2 * np.pi, 100).reshape(-1, 1)
+y = np.sin(X).ravel()
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# Train model
+model = RBFNN(num_centers=10, gamma=1.0)
+model.train(X_train, y_train)
+
+# Predict
+y_pred = model.predict(X_test)
+
+# Plot
+plt.scatter(X_test, y_test, label="True")
+plt.scatter(X_test, y_pred, label="Predicted", color="red", marker="x")
+plt.title("RBFNN Regression - Sine Function")
+plt.legend()
+plt.show()