This project focuses on developing a robust predictive model for cardio outcomes using advanced machine learning techniques, specifically 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗖𝗡𝗡𝘀). The goal was to leverage deep learning to analyze complex cardiovascular data and provide accurate predictions.

##𝗠𝗼𝗱𝗲𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: The CNN model was structured with multiple layers, including Dense and Dropout layers, designed to extract and learn intricate patterns from the input data. This architecture was chosen to enhance model robustness and generalize well to unseen data.

##𝗩𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝘁𝗿𝗶𝗰𝘀: The model's performance was rigorously assessed using the 𝗰𝗼𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗼𝗳 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗮𝘁𝗶𝗼𝗻 (𝗥𝟮 𝘀𝗰𝗼𝗿𝗲) 𝗶𝗻 𝗞𝗲𝗿𝗮𝘀. A high R2 score of 0.86 indicated strong predictive performance, demonstrating the model's ability to explain the variance in cardio outcomes. Additionally, the validation loss was minimized to 0.0033, validating the model's accuracy.

##𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Flask was selected as the backend framework to deploy the predictive model as a web application. This setup facilitated seamless integration with frontend interfaces, allowing users to input health data and receive real-time predictions on cardiovascular health status.

The project yielded promising results, showcasing the potential of 𝗔𝗜 𝗶𝗻 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗱𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰𝘀. By accurately predicting cardio outcomes, the model offers valuable insights for personalized medicine and early intervention strategies. The implementation of Flask ensured accessibility and user-friendliness, contributing to the practical application of machine learning in clinical settings.