Applied Machine Learning models such as ARIMA, LSTM and FBProphet to predict stock prices. Built on Streamlit using Python. Features: The models implemented in this research are: ARIMA: Auto Regressive Integrated Moving Average The most basic model that has been implemented for a long time for time series forecasting. Different variants are available to account for Seasonality and Trends in data. Works well with Univariate data. LSTM: Long Short Term Memory Advanced Deep Learning model that is primarily being used for text translation by Google, FB and many other organizations. Works well with complex input and multivariate analysis. FBProphet: Facebook's in-house time series forecasting model. Developed and tuned with accounting for recent developments in statistical modelling techniques. Was primarily developed for "producing reliable forecasts for planning and goal setting".