Language: Python
First draft is due on 4/11, you should have some visualizations and model results ready. The final draft is due on 4/17
• Perform EDA on the Grocery Sales dataset, show relevant visualizations about the dataset. Try to show some of the following characteristics of the time series. (Trends, seasonality, autocorrelation, Stationarity, linearity, outliers and extremes)
• Try to explain what impact promotions, holidays, oil prices etc. have on the sales of grocery items. If such impact can’t be determined based on the dataset provided, explain what other data fields can help with this issue.
• Use 3 methods: linear methods (ARIMA, PROPHET etc.), Boosting (XGBOOST etc.), and Neural Networks (LSTM etc.), predict the sales of grocery items, use proper metrics to evaluate the performance of the respective models and compare performances.