In this project, I developed a machine learning model to predict the stock market price for the next 5 days based on the last 6 months of historical data of over 200 US stocks. The model was trained on a large dataset of stock market data, including stock prices, trading volumes, and financial indicators, and used various algorithms such as random forest, support vector machines, and neural networks (LSTM) to make the predictions. I implemented the model using a full-stack approach, including data preprocessing, feature engineering, model training and testing, and deploying the model as a web application. To improve the accuracy of the model, I explored various feature engineering techniques such as time-series analysis, feature selection, and normalization, and fine-tuned the hyperparameters of the algorithms using cross-validation. I evaluated the performance of the model using various metrics such as mean squared error, mean absolute error, and R-squared. I also provided visualizations and explanations of the model's predictions to improve transparency and user understanding. Finally, I deployed the model as a web application that allows users to input stock market data and receive a prediction of the stock price for the next 5 days. This application can help investors and traders make informed decisions about buying and selling stocks. Overall, this project demonstrates my expertise in machine learning, time-series analysis, and full-stack development and shows how these skills can be applied to real-world problems in finance.