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Project description
Rudraya
This package aims to build a Machine Learning model to analyze the data using the various analytical technique. It performs Exploratory data analysis, data cleaning, and data wrangling process to select the most relevant feature that has a major contribution to the target feature.
Installation
Use the package manager pip to install Rudraya.
pip install Rudraya
Main Features
Filter Techniques:
- Missing Value
- Multicollinearity
- Correlation
- F_Regression
- Analysis Variance Test
- Forward Feature Selection
- Backword Feature Elimination
Ensemble Learning
- Average Ensemble
- Weighted Average Ensemble
- Rank Average Ensemble
- Voting Ensemble
- Stack Regression
Evaluation
- Mean Sequared Error
- Mean Absolute Error
- Mean Absolute Percentage Error
- Root Mean Square Error
- R2 score
Time domain Feature Analysis
Documentation
The documentation for the latest release is at
https://rudraya.readthedocs.io/en/latest/
https://github.com/tusharkolekar24/rudraya
Contributing
Contributions in any form are welcome, including:
- Documentation improvements
- Additional tests
- New features to existing models
- New models
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Source Distributions
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