Analyze predictive demand forecasting methods to understand how machine learning can improve sales planning and revenue generation in e-commerce businesses.
Evaluate historical sales trends, customer purchase patterns, and seasonal fluctuations affecting product demand in online retail markets.
Examine the effectiveness of machine learning models in forecasting short-term and long-term e-commerce demand accurately.
Assess the limitations of conventional forecasting methods compared with machine learning-driven predictive systems in digital commerce environments.
Investigate the influence of external factors such as promotions, festivals, pricing changes, and market trends on online demand.
Study demand forecasting in the context of e-commerce businesses.
Identify factors influencing online demand such as pricing, promotions, and customer behavior.
Understand machine learning techniques used for forecasting.
Collect or simulate e-commerce sales datasets.
Perform exploratory data analysis to identify trends and seasonality.
Apply machine learning models like regression, random forest, or ARIMA for forecasting.
Evaluate model performance using statistical metrics.
Analyze how demand forecasting impacts pricing and promotional strategies.
Use visualization tools like Power BI or Tableau for insights.
Develop a predictive model to forecast future demand.
Compare results with traditional forecasting methods.
Identify key drivers of demand variability.
Suggest improvements in inventory and pricing strategies.
Provide recommendations for e-commerce businesses to implement ML-based forecasting systems.
