Retail Analysis with Walmart Data

1. Project Motivation
2. Installation
3. Data
4. Implementation
5. Results

1. Project Motivation

In this project we focused retail analysis with Walmart data and answer the following questions:

1. Which stores have maximum and sales?
2. Which store has maximum standard deviation i.e., the sales vary a lot?. Also, find out the coefficient of mean to standard deviation.
3. Which store(s)s has good quarterly growth rate in Quarter-3, 2012?
4. Find out holidays which have higher sales than the mean sales in non-holiday season for all stores together.
5. Provide a monthly and semester view of sales in units and give insights.
6. Build prediction to forecast demand.

2. Requirements

• Python versions 3.*.
• Python Libraries:
  • sklearn.
  • Pandas.
  • numpy.
  • seaborn
  • matplotlib.
  • datetime.

3. Data

There are sales data available for 45 stores of Walmart in Kaggle. This is the data that covers sales from 2010-02-05 to 2012-11-01.

4. Implementation

In this project, we used RandomForestRegressor and LinearRegression to predict of sales. The data have been split into training and testing with a ratio of 80:20.

5. Result

The details of the results show in the code.