Streamlining Financial Predictions: A Web Application for Stock, Cryptocurrency, and Market Sentiment Analysis

Among the high-degree lines of study in this area, one of the most interesting ones is stock price prediction, particularly in the context of machine learning and time series forecasting models. The traditional past ones include ARIMA, and SARIMAX, while deep learning models have been recently applied for predicting the stock prices based upon historical data. However, these models do hold much promise and also come with volatility and non-linearity in financial markets. Causes for such inaccuracies in predictions include sudden market shifts, external economic events, and investor sentiment. All these factors are difficult to model well. This study will propose a web application that uses advanced time series models to try and overcome such challenges. The system is implemented with Streamlit and Flask, so it gives users a smooth interface. It has five critical modules: Nifty 50, which deals with the analysis and forecasting of stocks using ARIMA and SARIMAX models; Global Market Data, which fetches real-time data from the Yahoo Finance API for granular analysis; Cryptocurrency, which relies heavily on Ethereum data with historical analysis and price forecasting; Twitter Sentiment Analysis, which focusses on real-time analysis of market sentiment through tweets; and the prediction module, which evaluates the accuracy of the models using Root Mean Square Error to measure prediction quality. This solution will provide a straightforward yet potent platform for analyzing stocks, cryptocurrency, and sentiment in real-time. The goal is to raise precision, reliability, and overall user experience concerning financial prediction, while more valuable insights become available to the investor and the researcher, having multiple sources integrated into the model.