Metadata-Version: 2.1
Name: modelselect
Version: 0.0.1
Summary: A package helps select independent variables for traditional linear regression models
Home-page: https://github.com/shoukewei/modelselect
Author: Shouke Wei
Author-email: shouke.wei@gmail.com
License: MIT License
Keywords: python,linear regression,statsmodels,model improvement,select variables,insignificant,multicollinearity
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown

# modelselect

A package helps easily create an optimal linear regression model by removing the insignificant and multicollinearity predictor variables, which can help you reduce the interactive process and tedious work to run the model, estimate it, evaluate it, reestimate and reevaluate it, etc. 

Developed by Shouke Wei from Deepsim Academy, Deepsim Intelligence Technology Inc. (c) 2022

## Install the package
```python
pip install modelselect
```

## import the package
```python
from modelselect import LRSelector
```
then use the `LRSelector()` directly. Or 
```python
import modelselect as ms
```
then use `ms.LRSelector()`

## Document
An example: https://github.com/shoukewei/modelselect/blob/main/docs/example.ipynb


