This project demonstrates the application of unsupervised learning techniques, specifically K-means clustering, to divide customers into distinct segments. By analyzing customer data, businesses can optimize marketing efforts and product strategies to suit the unique needs of each group.

This project implements a Long Short-Term Memory (LSTM) neural network to predict Netflix stock prices using historical data. The model leverages deep learning techniques to forecast future price movements, achieving an RMSE of 0.0586 and MAPE of 5.92%. It demonstrates practical applications in financial forecasting and trading strategy development.

Predicting and diagnosing heart disease is one of the biggest challenges in the medical industry and relies on factors such as physical examination, symptoms, and signs of the patient. Heart disease is recognized as the world's deadliest disease, where the heart is unable to pump the required amount of blood to the remaining organs of the human body to perform regular functions.

This project uses Machine Learning techniques to predict heart disease based on various medical attributes, helping reduce the death rate of heart patients through early detection and diagnosis.