Walmart Sales Analysis Project.

The project aims to explore the Walmart Sales data to understand top performing branches and products, sales trend of of different products, customer behaviour. The aims is to study how sales strategies can be improved and optimized. 

The dataset was obtained from the Kaggle Walmart Sales Forecasting Competition. 

The dataset contains sales transactions from a three different branches of Walmart, respectively located in Mandalay, Yangon and Naypyitaw.

• Product Analysis

Conduct analysis on the data to understand the different product lines, the products lines performing best and the product lines that need to be improved.

• Sales Analysis

This analysis aims to answer the question of the sales trends of product. The result of this can help use measure the effectiveness of each sales strategy the business applies and what modificatoins are needed to gain more sales.

1. Data Wrangling: This is the first step where inspection of data is done to make sure NULL values and missing values are detected and data replacement methods are used to replace, missing or NULL values.

• Build a database

• Create table and insert the data.

• Select columns with null values in them. There are no null values in our database as in creating the tables, we set NOT NULL for each field, hence null values are filtered out. 

2. Feature Engineering: This will help use generate some new columns from existing ones.

• Add a new column named time_of_day to give insight of sales in the Morning, Afternoon and Evening. This will help answer the question on which part of the day most sales are made.

• Add a new column named day_name that contains the extracted days of the week on which the given transaction took place (Mon, Tue, Wed, Thur, Fri). This will help answer the question on which week of the day each branch is busiest.

• Add a new column named month_name that contains the extracted months of the year on which the given transaction took place (Jan, Feb, Mar). Help determine which month of the year has the most sales and profit.

3. Exploratory Data Analysis (EDA): Exploratory data analysis is done to answer the listed questions and aims of this project.

• Generic Question

1. How many unique cities does the data have?

2. In which city is each branch?

• Product

1. How many unique product lines does the data have?

2. What is the most common payment method?

3. What is the most selling product line?

4. What is the total revenue by month?

5. What month had the largest COGS?

6. What product line had the largest revenue?

7. What is the city with the largest revenue?

8. What product line had the largest VAT?

9. Fetch each product line and add a column to those product line showing "Good", "Bad". 

10. Which branch sold more products than average product sold?

11. What is the most common product line by gender?

12. What is the average rating of each product line?

• Sales

1. Number of sales made in each time of the day per weekday

2. Which of the customer types brings the most revenue?

3. Which city has the largest tax percent/ VAT (Value Added Tax)?

4. Which customer type pays the most in VAT?