This 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 primary goal of this project is to analyze Walmart’s sales data to identify and understand the various factors influencing sales across its different branches.
The dataset was obtained from the Kaggle Walmart Sales Forecasting Competition. This dataset contains sales transactions from a three different branches of Walmart, respectively located in Mandalay, Yangon and Naypyitaw. The data contains 17 columns and 1000 rows:
Product Analysis: Analyze the data to evaluate the performance of different product lines, identify the top-performing categories, and determine which product lines require improvement.
Sales Analysis: Examine sales trends across various products to assess the impact of different sales strategies. The findings will help measure the effectiveness of current approaches and highlight areas where adjustments can boost sales.
Customer Analysis: Investigate customer segments to uncover purchasing patterns and assess the profitability of each group, providing insights into customer behavior and segment value.
- Data Cleaning: The initial step involves examining the data to identify any NULL or missing values, followed by applying appropriate techniques to handle and replace these gaps in the dataset.
- 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.
- Exploratory Data Analysis (EDA): Exploratory Data Analysis is performed to find useful insights on the above dataset and answer the questions related to products, customers and sales.