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Every Departmental store chain like Walmart wants to predict the store sales in the nearby future so that inventory planning can be done. Along with that, sales prediction helps to increase/decrease store staff based on the rush (More sales can mean more customers are coming to the stores). Also, it is always a good idea to do sales and revenue forecasting to better understand the company's cash-flows and overall growth.
For inventory planning, you also need to know what products (or category of products aka department) will be utilised more. Under-stock some products and your sales are hit. Over-stock items like perishables and you run into losses if the product expires. That's why the sales prediction is done at a combination of store and department level (and sometimes even at product level for high-selling products).
In this problem, we have been given the sales data of 45 stores based on store, department and week. The size and type of each store has been provided. Holiday weeks have been marked. Along with these, price markdown data (almost like discount data) has been given. A few macro-indicators like CPI, Unemployment rate, Fuel price etc. are also provided.
In this project, we are going to work on Deep Learning using H2O to predict Census income.
Given a partial trajectory of a taxi, you will be asked to predict its final destination using the taxi trajectory dataset.
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.