A dataset from a retailer that has more than 45 stores in different regions (Public data from Kaggle) has been sourced. The data provided for the assessment represents two stores. Store number 20 has the highest revenue within the country and store 25 does not have a high volume of sales. The objective of the assessment is to develop different demand forecast models for these stores and compare the forecast models in terms of accuracy, trend, and seasonality alignment with the historical data provided. Students must use visual inspection, error metrics and information criteria on the test data to provide conclusions. Part A - Use Tableau to compare the two stores in terms of sales using adequate visualisation(s). - Run Holt-Winters forecasts of the next 5 months for stores 20 and 25. analyse the results of the forecasts in terms of: o Accuracy o Alignment with the historical trend o Alignment with the historical seasonality Part B page2image1352328192page2image1352328560page2image1352328912page2image1352329200 - - - Use Exploratory to generate ARIMA forecasts for stores 20 and 25. Create visualisations, interpret and describe your findings. Analyse the forecasts in terms of: o Accuracy. o Alignment with the historical trend. o Alignment with the historical seasonality. Part C Prepare a presentation: Include key findings. Highlight methodologies. Advise which methods to use for each store. Recommend improvements in terms of forecasting for the retailer. Note: All members of the group should be involved in the presentation. The allocated time for the presentation will be decided by your lecturer. could you please form a 1000 worded report on this topic with following prompts: Briefly summarise the analysis. Outline how the dataset, visualisations and forecasts could be improved. Describe how a retail organisation could use the forecast for its operation