This repository contains code to predict the likelihood of a woman having Polycystic Ovary Syndrome (PCOS) based on various health factors.
PCOS is a common endocrine system disorder that affects women of reproductive age. This project aims to build a machine learning model that can analyze a woman's symptoms and test results to predict whether she has PCOS. The model is trained on a dataset of patient records and health indicators.
To use the model, run the application.py file. This will load the trained best_model1.pkl file and allow you to enter values to predict a PCOS likelihood.
- analysis1.ipynb - Jupyter notebook with EDA and model training
- application.py - Python file to load model and make predictions
- best_model1.pkl - Trained machine learning model
- requirements.txt - Package dependencies
- Install dependencies from requirements.txt
- Run python application.py
- Enter values when prompted
- See predicted PCOS likelihood
This project is licensed under the MIT License.