Forecasting

Fueling Future Growth: This project applies time series analysis techniques using Python to forecast monthly tractor sales. By leveraging historical sales data and developing an ARIMA model, we predicted future sales trends with precision.

Key performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate the model's accuracy, empowering stakeholders with insights to make informed decisions for inventory management and business growth.

Optimizing Transportation through Predictive Analytics: In this project, we forecast Amtrak's ridership patterns using ARIMA modeling. By analyzing monthly ridership data and identifying trends, seasonality, and cycles, we provided Amtrak with actionable insights to manage passenger flow and optimize resources.

The accuracy of the model was evaluated with MAE and RMSE, ensuring reliable forecasts to help Amtrak meet future demand and make data-driven operational decisions.

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