CHANGELOG.md

# Changelog - Enhanced Features

<!--
Author: RSK World
Website: https://rskworld.in
Email: help@rskworld.in
Phone: +91 93305 39277
-->

## New Features Added

### 1. Model Selection Module (`model_selection.py`)
- **Model Comparison**: Compare multiple regression models using AIC, BIC, R², and other metrics
- **Stepwise Selection**: Automated forward/backward stepwise feature selection
- **Information Criteria**: Calculate and compare AIC, BIC, HQIC
- **Feature Selection Utilities**: VIF-based and correlation-based feature removal

### 2. Model Evaluation Module (`model_evaluation.py`)
- **Cross-Validation**: K-fold and time series cross-validation
- **Evaluation Metrics**: MSE, RMSE, MAE, R², MAPE
- **Prediction Visualization**: Actual vs predicted plots, residual analysis
- **Learning Curves**: Training/validation performance over sample size

### 3. Advanced Time Series Module (`advanced_time_series.py`)
- **SARIMA Models**: Seasonal ARIMA with configurable seasonal orders
- **Auto ARIMA**: Automatic ARIMA order selection based on AIC
- **Comprehensive Stationarity Tests**: ADF and KPSS tests with detailed results

### 4. Data Preprocessing Module (`data_preprocessing.py`)
- **Missing Value Handling**: Multiple imputation methods (mean, median, mode, forward fill)
- **Outlier Detection**: IQR and Z-score based outlier detection and removal
- **Data Scaling**: Standard, MinMax, and Robust scaling
- **Time Series Transformations**: Differencing, log-differencing, detrending
- **Feature Engineering**: Lag creation, rolling window features
- **Summary Statistics**: Comprehensive statistical summaries

### 5. Visualization Utilities Module (`visualization_utils.py`)
- **Correlation Matrix**: Heatmap visualization
- **Distribution Plots**: Histograms with KDE overlay
- **Time Series Plots**: Single and multiple time series visualization
- **Residual Analysis**: Comprehensive residual diagnostic plots
- **ACF/PACF Plots**: Autocorrelation function visualization
- **Forecast Comparison**: Actual vs forecast with confidence intervals
- **Model Comparison**: Bar charts for model metrics
- **Feature Importance**: Coefficient visualization
- **Learning Curves**: Training/validation performance plots

### 6. Enhanced Examples
- `examples/model_selection_example.py` - Model selection and comparison
- `examples/advanced_time_series_example.py` - SARIMA and Auto ARIMA usage

### 7. Updated Documentation
- **README.md**: Updated with all new features and usage examples
- **FEATURES.md**: Comprehensive feature documentation
- **index.html**: Updated demo page with new features
- **requirements.txt**: Added scikit-learn dependency

## Updated Files

1. **README.md** - Added new features section, updated project structure, added usage examples
2. **requirements.txt** - Added scikit-learn>=1.3.0
3. **index.html** - Updated features list and project structure

## File Count

- **Python Modules**: 15 files
- **Jupyter Notebooks**: 4 files
- **Example Scripts**: 5 files
- **Documentation**: 5 files (README, LICENSE, FEATURES, CHANGELOG, PROJECT_INFO)

## All Files Include Author Information

Every file in the project includes author information:
- Author: RSK World
- Website: https://rskworld.in
- Email: help@rskworld.in
- Phone: +91 93305 39277

## Testing

All modules include example usage in their `__main__` blocks and can be run directly:
```bash
python regression_analysis.py
python time_series_analysis.py
python model_selection.py
python model_evaluation.py
# etc.
```

## Next Steps

The project is now comprehensive and includes:
- ✅ Regression analysis
- ✅ Time series analysis (basic and advanced)
- ✅ Hypothesis testing
- ✅ Statistical diagnostics
- ✅ Econometric modeling
- ✅ Model selection
- ✅ Model evaluation
- ✅ Data preprocessing
- ✅ Visualization utilities
- ✅ Complete documentation
- ✅ Example scripts
- ✅ Jupyter notebooks

## Author

**RSK World**
- Website: https://rskworld.in
- Email: help@rskworld.in
- Phone: +91 93305 39277