This repository is the official implementation of our Stroke Prediction project.
In this project, we used two different models, Logistic Regression and XGBoost, to predict the occurrence of stroke based on various health parameters. The XGBoost model achieved higher accuracy.
The data used for training the models was obtained from two different datasets available on Kaggle.
- The first one is The Stroke Prediction
- And the second one is The Synthetic Stroke Prediction
This project requires Python 3.6 or later. The dependencies for the project are listed in the requirements.txt file.
You can install the requirements via pip using the following command:
pip install -r requirements.txt
If you are using Conda, you can create a new environment for this project and install the requirements using the following commands:
conda create -n stroke_prediction python=3.8
conda activate stroke_prediction
pip install -r requirements.txt
This will create a new Conda environment called "stroke_prediction" and install the necessary packages within that environment.
The entire project is contained within a single Jupyter notebook: main.ipynb. This notebook includes all the steps from data loading, preprocessing, model training, evaluation, and results visualization.
We have a live demo of the model hosted on Hugging Face using Gradio. You can interact with the model and make predictions in real-time.
You can download our pre-trained models here:
- Our Logistic model trained on the Kaggle datasets.
- Our XGBoost model trained on the Kaggle datasets.
Our XGBoost model achieved higher accuracy in predicting stroke occurrence in Kaggle competetion:
| Model name | Accuracy |
|---|---|
| Logistic Regression | (0.88975) |
| XGBoost | (0.89516) |