This is an IPython Notebook for the Kaggle competition, M5 Forecasting - Accuracy. The competition is to estimate the unit sales of Walmart retail goods for the next 28 days. This repository is to provide exploratory data analysis and models for prediction by python.
This is one of the two complementary competitions that together comprise the M5 forecasting challenge. Can you estimate, as precisely as possible, the point forecasts of the unit sales of various products sold in the USA by Walmart?
In this competition, the fifth iteration, you will use hierarchical sales data from Walmart, the world’s largest company by revenue, to forecast daily sales for the next 28 days. The data, covers stores in three US States (California, Texas, and Wisconsin) and includes item level, department, product categories, and store details. In addition, it has explanatory variables such as price, promotions, day of the week, and special events. Together, this robust dataset can be used to improve forecasting accuracy.
From the competition homepage
This python notebookof is aimed for those who has no experience in retail dataa analysis and show examples of what direction for EDA and modeling they start with.
The notebook shows example for
Data Handling
- Importing Data with libararies
- Donwcasting data
- Unpivoting dates
Explorartory Data Analysis
- State, Store Level Performance
- Seasonality
Modeling
- Moving Average Modeling
- Explonential Smoothing Modeling