Linear Regression Analysis Tool

Interactive statistical modeling application for continuous outcome prediction

Data Science Project

About the Linear Regression Tool

This comprehensive application provides an interactive interface for performing linear regression analysis with diagnostic checks and visualizations. Built with Python's statsmodels and Streamlit, it offers professional-grade statistical modeling capabilities.

Handle missing data with multiple imputation methods
Apply various data transformations (log, sqrt, Box-Cox)
Check model assumptions with statistical tests
Detect multicollinearity with VIF analysis
Generate interactive visualizations with Plotly

View on HuggingFace

Features

Advanced Regression Capabilities

Model Diagnostics

Comprehensive checks including normality of residuals, homoscedasticity, and multicollinearity detection with VIF scores.

Data Transformations

Multiple transformation options including log, square root, inverse, and Box-Cox to handle non-linear relationships.

Performance Metrics

Detailed evaluation with R², adjusted R², mean squared error, and visual assessment of actual vs predicted values.

Interactive Demo

Try the Linear Regression Tool

The application is embedded below. Upload your dataset (CSV, Excel, or text file) and explore the linear regression analysis capabilities.

How to Use

Upload your dataset using the file uploader
Handle any missing values using the imputation options
Select predictor variables (features) and target variable
Apply transformations if needed to improve model fit
Adjust test/train split percentage
Explore model results and diagnostic checks

Technical Implementation

Under the Hood

Statsmodels

Professional-grade statistical modeling with detailed output including p-values, confidence intervals, and diagnostic tests.

Plotly Visualizations

Interactive plots including QQ plots, residual analysis, and actual vs predicted comparisons.

Data Preprocessing

Comprehensive data cleaning including missing value imputation, standardization, and transformation options.