Create advanced_data_analytics.py

blockchain_integration/pi_network/pi_network_university/advanced_data_analytics.py

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+import pandas as pd
+import numpy as np
+from sklearn.decomposition import PCA
+from sklearn.manifold import TSNE
+
+# Load course metrics data
+course_metrics = pd.read_csv('data/course_metrics.csv')
+
+# Define advanced data analytics function
+def analyze_course_metrics(course_metrics):
+    # Extract features from course metrics
+    features = np.array([course_metrics['feature1'], course_metrics['feature2'], ...])
+
+    # Apply PCA to reduce dimensionality
+    pca = PCA(n_components=3)
+    features_pca = pca.fit_transform(features)
+
+    # Apply t-SNE to reduce dimensionality further
+    tsne = TSNE(n_components=2)
+    features_tsne = tsne.fit_transform(features_pca)
+
+    # Return analyzed course metrics
+    return features_tsne