Metadata-Version: 2.1
Name: backwards_regression
Version: 0.1.0
Summary: Backwards Regression Python Library - Automated feature selection in linear and logistic regression models.
Home-page: https://github.com/knowusuboaky/backwards_regression
Author: Kwadwo Daddy Nyame Owusu - Boakye
Author-email: kwadwo.owusuboakye@outlook.com
License: MIT
Keywords: statistics modeling regression feature elimination engineering
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: pandas
Requires-Dist: statsmodels

# Backwards Regression Python Library - Automated feature selection in linear and logistic regression models.

The Backwards Regression Python Library is an open-source toolkit for automated feature selection in regression models. It supports both linear and logistic regression, dynamically selecting the appropriate method based on the target variable.

## Installation
```bash
pip install backwards_regression
```

## Load Package
```bash
## Load Package
from backwards_regression import fit_logistic
from backwards_regression import fit_linear
```

## Usage
```bash
## (Linear) With interactions included - set to True and Without Interactions included - set to False
result, dropped_vars = fit_linear(X, y, threshold_in=0.01, threshold_out=0.05, include_interactions=True, verbose=True)

## Print Selected features
print("Final included features:", result)

## Print Eliminated features
print("Dropped variables:", dropped_vars)
```
```bash
## (Logistic) With interactions included - set to True and Without Interactions included - set to False
result, dropped_vars = fit_logistic(X, y, threshold_in=0.01, threshold_out=0.05, include_interactions=True, verbose=True)

## Print Selected features
print("Final included features:", result)

## Print Eliminated features
print("Dropped variables:", dropped_vars)
```

## Key Features
- Automated backward regression for linear and logistic regression models.
- Inclusion and exclusion of features based on user-defined significance thresholds.
- Optional inclusion of interaction terms for enhanced model complexity.

This library is suitable for data scientists, researchers, and practitioners working with regression problems who seek a streamlined approach to feature selection. The library intelligently adapts to the nature of the target variable, supporting both linear and logistic regression models.

## Documentation & Examples
For documentation and usage examples, visit the GitHub repository: https://github.com/knowusuboaky/backwardsreg

Author: Kwadwo Daddy Nyame Owusu - Boakye\
Email: kwadwo.owusuboakye@outlook.com\
License: MIT

