Scorecard | Comprehensive Machine Learning Framework in R

Overview

This R script provides a robust framework for data preprocessing, feature engineering, and model evaluation. It is designed for machine learning applications, particularly focusing on calculating Weight of Evidence (WOE) and model training using various machine learning algorithms.

Features

• Data Preprocessing: Automated handling of missing values and data transformation.
• WOE Calculation: Function to compute Weight of Evidence for categorical variables.
• Modular Design: Structured into reusable functions for different stages of data analysis.
• Cross-Validation Support: Includes setup for cross-validation to evaluate model performance.
• Extensibility: Easily extendable for various machine learning models and custom analyses.

Dependencies

• R environment
• R packages: missForest, rpart.plot, ipred, mboost, randomForest, kernlab, e1071, nnet, neuralnet, gsubfn, proto, RSQLite, DBI, sqldf, tcltk

Installation

Ensure you have R installed on your system along with the necessary packages. Install missing packages using R's package manager.

Usage

1. Data Preparation: Load your dataset and preprocess it using the load_and_preprocess_data function.
2. WOE Calculation: Utilize calculate_woe to compute WOE values for categorical features.
3. Model Training and Evaluation: Implement machine learning models within the run_analysis function and evaluate them using cross-validation.
4. Extending the Framework: Add custom functions or additional models as per your analysis requirements.

Example

# Run the entire analysis
run_analysis("path/to/your/data.csv")

Contributing

Contributions to extend or enhance this framework are welcome. Please ensure to follow coding standards and add appropriate documentation for new features.

License

This project is open-sourced under the MIT license.