Lung Cancer Risk Prediction System

Table of Contents

• Description
• Features
• Demo
• Usage
• Dependencies
• License

Description

Welcome to the Lung Cancer Risk Prediction System project! This repository contains the code and resources for a machine learning model that predicts the risk of lung cancer based on crucial factors. The project includes a Python notebook for model development and a Streamlit app for real-time risk assessment.

Features

• Utilizes the XGBoost machine learning algorithm for accurate risk predictions.
• Extracts insights from a carefully curated and preprocessed dataset obtained from Kaggle.
• Hosts a user-friendly web application powered by Streamlit, allowing users to input patient data and receive real-time risk estimates.

Demo

Check out the live demo of the Lung Cancer Risk Prediction System and see how it works in action!

Usage

1. Open the Flask app in your web browser.
2. Input the required patient data fields.
3. Click the "Predict" button to receive the lung cancer risk prediction.

Dependencies

The project relies on the following key dependencies:

• Python (>=3.6)
• Streamlit (>=1.19)
• XGBoost (>=1.5)
• Pandas (>=1.3)
• Scikit-learn (>=0.24)

License

This project is licensed under the MIT License.

For a complete list of dependencies, please refer to the requirements.txt file.