E-Commerce Sales Forecasting Project

Business Problem

In this project, students are provided with historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and participants must project the sales for each department in each store. To add to the challenge, selected holiday markdown events are included in the dataset. These markdowns are known to affect sales, but it is challenging to predict which departments are affected and the extent of the impact.

Data

You are provided with historical sales data for 45 Walmart stores located in different regions. Each store contains a number of departments, and you are tasked with predicting the department-wide sales for each store.

In addition, Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data.

The basic idea of analyzing the Walmart Forecasting dataset is to get a fair idea about the factors affecting the Sales of the Walmart Store.

Problem Statement

By using these data we have to Predict the walmart sales forecasting based on different parameters

Data Description

stores.csv

This file contains anonymized information about the 45 stores, indicating the type and size of store.

train.csv

This is the historical training data, which covers to 2010-02-05 to 2012-11-01. Within this file you will find the following fields:

Store - the store number Dept - the department number Date - the week Weekly_Sales - sales for the given department in the given store IsHoliday - whether the week is a special holiday week test.csv This file is identical to train.csv, except we have withheld the weekly sales. You must predict the sales for each triplet of store, department, and date in this file.

features.csv

This file contains additional data related to the store, department, and regional activity for the given dates. It contains the following fields:

Store - the store number Date - the week Temperature - average temperature in the region Fuel_Price - cost of fuel in the region MarkDown1-5 - anonymized data related to promotional markdowns that Walmart is running. MarkDown data is only available after Nov 2011, and is not available for all stores all the time. Any missing value is marked with an NA. CPI - the consumer price index Unemployment - the unemployment rate IsHoliday - whether the week is a special holiday week For convenience, the four holidays fall within the following weeks in the dataset (not all holidays are in the data):

Super Bowl: 12-Feb-10, 11-Feb-11, 10-Feb-12, 8-Feb-13 Labor Day: 10-Sep-10, 9-Sep-11, 7-Sep-12, 6-Sep-13 Thanksgiving: 26-Nov-10, 25-Nov-11, 23-Nov-12, 29-Nov-13 Christmas: 31-Dec-10, 30-Dec-11, 28-Dec-12, 27-Dec-13

Business Objective and Constraints

• The cost of a mis-classification can be very high.
• There is some latency concerns.

Repository Structure

1. Walmart_Sales_Forecasting.ipynb: The Jupyter notebook containing code for the recommendation engines
2. Data should be put in data folder

Software Require

1. Jupyter notebook
2. Python

Python libraries

• Numpy
• Pandas
• Matplotlib and seaborn
• plotly
• sklearn