Synced repo using 'sync_with_huggingface' Github Action

data/data_processing.py

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+import pandas as pd
+from sklearn.preprocessing import StandardScaler, PolynomialFeatures
+
+def load_data(file_path):
+    """Load dataset from a CSV file."""
+    return pd.read_csv(file_path)
+
+def scale_features(df):
+    """Scale numerical features using StandardScaler."""
+    numerical_cols = df.select_dtypes(include=['float64', 'int64']).columns
+    scaler = StandardScaler()
+    df[numerical_cols] = scaler.fit_transform(df[numerical_cols])
+    return df
+
+def create_polynomial_features(df, degree=2, selected_columns=None):
+    """Create polynomial features.
+
+    Args:
+        df: Input DataFrame
+        degree: Degree of polynomial features (default: 2)
+        selected_columns: List of column names to use for polynomial features. 
+                         If None, uses all numerical columns (default: None)
+    """
+    if selected_columns is not None:
+        numerical_cols = [col for col in selected_columns if col in df.columns]
+        if not numerical_cols:
+            raise ValueError("None of the selected columns found in DataFrame")
+    else:
+        numerical_cols = df.select_dtypes(include=['float64', 'int64']).columns
+    poly = PolynomialFeatures(degree=degree, include_bias=False)
+    poly_features = poly.fit_transform(df[numerical_cols])
+    poly_feature_names = poly.get_feature_names_out(numerical_cols)
+    poly_df = pd.DataFrame(poly_features, columns=poly_feature_names)
+    df = df.join(poly_df)
+    return df
+
+def process_data(file_path):
+    """Load, process, and return the dataset."""
+    df = load_data(file_path)
+    df = scale_features(df)
+    df = create_polynomial_features(df)
+    return df
+
+if __name__ == "__main__":
+    file_path = 'path_to_your_data.csv'  # Replace with your actual file path
+    processed_data = process_data(file_path)
+    processed_data.to_csv('processed_data_with_features.csv', index=False)