feat(features): integrate time-domain feature extraction into data pipeline

- Implement FeatureExtractor class in time_domain_features.py for calculating statistical features from dataset columns.
- Create build_features.py script to automate feature extraction from processed data and save results in a structured format.
- Adjust build_features.py to read processed data, utilize FeatureExtractor, and save feature matrix.

This update supports enhanced analysis capabilities within the thesis-project structure, allowing for more sophisticated data processing and model training stages.

Closes #1

code/src/features/build_features.py

@@ -0,0 +1,21 @@
+# src/features/build_features.py
+import pandas as pd
+from time_domain_features import FeatureExtractor
+import numpy as np
+
+def build_features(input_file, output_file):
+    data = pd.read_csv(input_file)
+    # Assuming the relevant data is in the first column
+    extractor = FeatureExtractor(data.iloc[:, 0].values)
+    features = extractor.features
+
+    # Save features to a file
+    np.savez(output_file, **features)
+
+if __name__ == "__main__":
+    import sys
+    input_path = sys.argv[1]  # 'data/processed/'
+    output_path = sys.argv[2]  # 'data/features/feature_matrix.npz'
+    
+    # Assuming only one file for simplicity; adapt as needed
+    build_features(f"{input_path}processed_data.csv", output_path)