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This project demonstrates a machine learning solution for predicting diabetes based on user-provided health data. The application uses Streamlit for an interactive web interface and advanced interpretability tools like SHAP and permutation importance to explain model predictions.
A machine learning service that predicts a user's gender based on transaction data, using behavioral patterns and purchase history to deliver accurate, data-driven insights
This repository contains the dataset and a python file. The python file contains basic EDA, Label encoding, and Modelling using Logistic Regression. ROC analysis is also included.