jonathanpollyn / liverprediction

This repository contains a machine learning model designed to predict liver disease based on patient data. The model has been trained and evaluated using various algorithms and has been deployed for interactive use on Streamlit.

Liver Disease Prediction Machine Learning Model

Overview

This repository contains a machine learning model designed to predict liver disease based on patient data. The model has been trained and evaluated using various algorithms and has been deployed for interactive use on Streamlit.

Project Components

• Machine Learning Model: A trained model that predicts the likelihood of liver disease based on input features.
• Streamlit Deployment: An interactive web application allowing users to input data and receive predictions.
• Medium Article: A detailed explanation of the project, including the model development process and results.

Features

• Predictive Accuracy: The model has been evaluated for accuracy, precision, recall, and F1-score using different algorithms.
• Interactive Interface: The Streamlit app provides an easy-to-use interface for making predictions.

Medium Article

For an in-depth explanation of the project, including the development process, results, and insights, please refer to the Medium article: Predicting The Presence of a Liver Disease with Machine Learning

Getting Started

Prerequisites

• Python 3.6 or higher
• Required Python libraries (listed in requirements.txt)

Installation

1. Clone this repository: git clone https://github.com/your-username/liver-disease-prediction.git
2. Navigate to the project directory: cd liver-disease-prediction
3. Install the required libraries: pip install -r requirements.txt

Running the Streamlit App

1. Start the Streamlit app https://jonathanpollynlivediseasepred.streamlit.app/

Model Details

• Algorithms Tested: Logistic Regression, K-Nearest Neighbors, Naive Bayes, Support Vector Classifier, Random Forest Classifier, and more.

• Best Performing Model: Random Forest Classifier with high accuracy and precision.

Contribution

Contributions are welcome! Please open an issue or submit a pull request if you have suggestions or improvements.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or feedback, please contact Jonathan Pollyn.