Added detailed docstrings to all functions in src/model/models.py

[vector-one]

Pull Request Description

📋 Description

Fixes: #17

Added comprehensive Google-style docstrings to all functions in src/model/models.py to improve code documentation and make it easier for new contributors to understand function parameters, return values, and usage patterns.

🔄 Changes Made

Added

• Comprehensive Google-style docstrings to 16 functions including normalize_features(), plot_feature_importance(), plot_loss_curve(), and all model training functions
• Detailed parameter descriptions with types for all function arguments
• Return value descriptions with types for all functions
• Usage examples for each function demonstrating practical implementation

Updated

• Documentation for utility functions (normalize_features, plot_feature_importance, plot_loss_curve)
• Documentation for prediction functions (predict_duration, compare_predictions, to_submission)
• Documentation for all individual model trainers:
  • train_linear_regression
  • train_ridge_regression
  • train_lasso_regression
  • train_svr
  • train_xgb
  • train_random_forest
  • train_neural_network
• Documentation for pipeline functions (run_regression_models, hyperparameter_tuning_xgb, run_complete_pipeline)

🧪 Testing

• Tested manually:
  • Test case 1: Verified all docstrings follow Google-style format with proper Args, Returns, and Examples sections
  • Test case 2: Confirmed no syntax errors introduced - file structure and functionality remain intact
  • Test case 3: Validated that docstrings accurately describe function behavior, parameters, and return values
  • Test case 4: Checked that examples provided are realistic and helpful for understanding function usage

✅ Checklist

• My code follows the project's coding standards.
• I have performed a self-review of my own code.
• I have commented my code, particularly in hard-to-understand areas.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added tests that prove my fix is effective or that my feature works.
• I have verified that no API keys or other secrets are committed.
• I have updated .env.example with any new environment variables.

---

📝 Example Docstring Format

def normalize_features(X):
    """
    Normalize features into different ranges for model training.
    
    This function applies different normalization ranges to different feature groups:
    coordinates, distances, precipitation, time features, and binary flags.
    
    Args:
        X (pd.DataFrame): Input features containing coordinates (start_lng, start_lat, 
                         end_lng, end_lat), distances (manhattan, euclidean, gmaps_distance, 
                         gmaps_duration), precipitation, time features (weekday, hour), 
                         and flags (holiday, airport, citycenter, standalone, routing_error, 
                         short_trip).
    
    Returns:
        pd.DataFrame: Normalized features with:
            - Coordinates scaled to (-1, 1)
            - Distances scaled to (0, 10)
            - Precipitation scaled to (0, 1)
            - Time features scaled to (0, 5)
            - Flags left unchanged
    
    Examples:
        >>> X_normalized = normalize_features(train_df.drop('duration', axis=1))
        >>> print(X_normalized.shape)
        (10000, 18)
    """

---

[netlify]

❌ Deploy Preview for gopredict failed.

Name	Link
🔨 Latest commit	e7d1847
🔍 Latest deploy log	https://app.netlify.com/projects/gopredict/deploys/68e652bd923e1100086954f1

[harshitaphadtare]

@Aaryan-549 check errors!

[harshitaphadtare]

hi @Aaryan-549 is there any update? since only 4 hours remain for this to be accepted under hacktoberfest..