A comprehensive time series forecasting of Apple stock prices using data from NASDAQ for 2018–2019. Key techniques include exploratory data analysis (EDA), ARIMA modeling (with and without exogenous variables), and Facebook Prophet for robust forecasting. The models are evaluated using RMSE to identify the most accurate predictions.

This project utilizes machine learning algorithms to predict rainfall patterns based on historical climate data. Rainfall Prediction System Using Machine Learning With Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials

Forecasting of retail sales data for a brick-and-mortar store. The focus is on exploring time series characteristics, building ARIMA and SARIMAX models, and selecting the optimal model based on AIC and RMSE metrics. The project provides insights into trends, seasonality, and prediction accuracy for business decision-making.