Create ai_course_recommendations.py

blockchain_integration/pi_network/pi_network_university/ai_course_recommendations/ai_course_recommendations.py

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+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+
+# Load course data
+course_data = pd.read_csv('data/course_data.csv')
+
+# Split data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(course_data.drop('course_id', axis=1), course_data['course_id'], test_size=0.2, random_state=42)
+
+# Train random forest classifier
+rfc = RandomForestClassifier(n_estimators=100, random_state=42)
+rfc.fit(X_train, y_train)
+
+# Train neural network
+nn = Sequential()
+nn.add(Dense(64, activation='relu', input_shape=(X_train.shape[1],)))
+nn.add(Dense(32, activation='relu'))
+nn.add(Dense(1, activation='sigmoid'))
+nn.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+nn.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))
+
+# Make predictions on test set
+y_pred_rfc = rfc.predict(X_test)
+y_pred_nn = nn.predict(X_test)
+
+# Evaluate model performance
+accuracy_rfc = accuracy_score(y_test, y_pred_rfc)
+accuracy_nn = accuracy_score(y_test, y_pred_nn)
+
+print(f'Random Forest Classifier accuracy: {accuracy_rfc:.3f}')
+print(f'Neural Network accuracy: {accuracy_nn:.3f}')
+
+# Use models to make course recommendations
+def recommend_courses(user_data):
+    # Preprocess user data
+    user_data = pd.DataFrame(user_data).drop('user_id', axis=1)
+
+    # Make predictions on user data
+    predictions_rfc = rfc.predict(user_data)
+    predictions_nn = nn.predict(user_data)
+
+    # Return recommended courses
+    return course_data[course_data['course_id'].isin(predictions_rfc) | course_data['course_id'].isin(predictions_nn)]