Solves a kaggle problem with Top 3% in leaderboard regarding cirrhosis outcome prediction

ML Algorithms Approaches to solve Cirrhosis-Prediction kaggle problem
Explore the docs »

Report Bug · Request Improvements

• Devise a multi class classification algorithm to predict the the outcomes of patients with cirrhosis

• Submissions are evaluated using the multi-class logarithmic loss. Each id in the test set had a single true class label, Status. For each id, we must submit a set of predicted probabilities for each of the three possible outcomes, e.g., Status_C, Status_CL, and Status_D.

• Python
• Jupyter Notebook

To get a local copy up and running follow these simple steps.

• Install python 3.10

1. Clone the repository

   git clone https://github.com/Abhinav1004/Cirrhosis-Prediction.git

2. Create Virtual Environment in Working directory

   cd Cirrhosis-Prediction
   virtualenv venv 
   source venv/bin/activate

3. Install the required libraries

   pip install -r requirements.txt

4. Install the required libraries

   pip install -r requirements.txt

5. Download the data from competition website link and paste the data in data folder present in Working Directory

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Abhinav Kumar Jha - [email protected]

Project Link: https://github.com/Abhinav1004/Cirrhosis-Prediction