Title: Retail Analysis with Walmart Data

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

Project Motivation

The aim of this project is to predict sales and demand accurately for one of the leading retail stores in the US, Walmart. The analysis is carried out to builda statistical models and perform basic statistics tasks such as folllows:

a. Which stores have maximum and sales? b. Which store has maximum standard deviation i.e., the sales vary a lot?. Also, find out the coefficient of mean to standard deviation. c. Which store/s has good quarterly growth rate in Q3’2012? d. Which holidays have higher sales than the mean sales in non-holiday season for all stores together. e. Provide a monthly and semester view of sales in units and give insights.

Build prediction to forecast demand for Store 1 using Linear Regression model.

Installation

Python V-3.

Python Libraries:

• Scikit Learn.
• Pandas.
• Numpy.
• Seaborn
• Matplotlib.
• Datetime.

Data

The data file contains sales data from 2010-02-05 to 2012-11-01, in the file Walmart_Store_sales for 45 Walmart stores located in different regions. Full detail of the data is available on Kaggle.

Implementation

Two regression models were run on the data, including Linear Regression and Decision Tree Regressor. The data was splitted into X and y. A data ratio of 70:30 was used for both models and performance was reviewed using the mean squared error metric.

Result

The details and insights gotten from the results are detailed in the code.