Lung Cancer Prediction using Machine Learning

The primary objective of this project is to develop a reliable machine learning model for predicting lung cancer risk levels—categorized as High, Medium, or Low—based on patient data. By leveraging algorithms such as Random Forest, XGBoost, and Voting Classifier, the project aims to achieve high accuracy and robustness in predictions. This model is designed to assist healthcare professionals in early detection, enabling timely interventions and personalized treatment plans. Additionally, the project seeks to optimize machine learning techniques through data preprocessing, feature selection, and hyperparameter tuning to enhance predictive performance. Ultimately, the goal is to integrate the system into healthcare platforms, providing an accessible and efficient tool for improving patient outcomes and advancing cancer diagnostics.