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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)
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