Logistic Regression Analysis Tool

Interactive statistical modeling application for binary classification

Data Science Project

About the Logistic Regression Tool

This interactive application allows users to perform logistic regression analysis on their own datasets. Built with Python's Streamlit framework, it provides a user-friendly interface for statistical modeling and binary classification.

Upload and analyze your own datasets
Visualize model performance metrics
Generate classification reports and ROC curves
Calculate odds ratios for feature importance
Make predictions with custom input values

View on HuggingFace

Interactive Demo

Try the Logistic Regression Tool

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

How to Use

Upload your dataset using the file uploader
Select predictor variables (features)
Choose your binary target variable (must have exactly 2 unique values)
Adjust test/train split percentage if needed
Explore the model results and visualizations

Technical Implementation

Under the Hood

Python Backend

Built with Python's statistical and machine learning libraries including statsmodels, scikit-learn, pandas, and numpy for robust data analysis.

Streamlit Framework

Uses Streamlit to create an interactive web interface that makes statistical modeling accessible without requiring coding knowledge.

Visualizations

Incorporates Plotly for interactive visualizations including ROC curves, probability distributions, and model diagnostics.