Welcome to the Walmart Sales Analysis Project! This project dives deep into retail sales data from Walmart 🏬, exploring sales trends, seasonality, external factors, and store-wise performance. The goal is to gain actionable insights that can help decision-making in a commercial retail setting.
Retail businesses generate huge volumes of sales data every week. Analyzing this data is crucial for:
- ✨ Understanding consumer buying patterns
- ✨ Measuring the impact of holidays, temperature, and fuel prices on sales
- ✨ Identifying top-performing stores
- ✨ Forecasting future sales for better decision-making In this project, we use the Walmart Weekly Sales Dataset 🗂️ to uncover valuable insights and present them through visualizations, statistics, and reports.
- 🔹 Understand the dataset and business context
- 🔹 Clean and preprocess the data for analysis
- 🔹 Perform Exploratory Data Analysis (EDA)
- 🔹 Build interactive and insightful visualizations
- 🔹 Generate key insights and findings
- 🔹 Export results into a PowerPoint report and cleaned dataset
- Programming Language: Python 🐍
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn 📊
- Reporting: Python-PPTX for automated PowerPoint generation 🖼️
- Environment: Jupyter Notebook / VS Code
- 🏬 Store – Store ID
- 📅 Date – Week ending date
- 💰 Weekly_Sales – Revenue generated each week
- 🎉 Holiday_Flag – Indicates holiday weeks (1 = holiday, 0 = non-holiday)
- 🌡️ Temperature – Weekly average temperature
- ⛽ Fuel_Price – Fuel price in the region
- 📈 CPI – Consumer Price Index
- 👷 Unemployment – Unemployment rate
- Imported dataset using Pandas
- Verified data structure, dimensions, and column names
- Handled missing values
- Converted Date column into datetime format
- Created new features like Year, Month, Day, Week
- Exported a cleaned dataset for further analysis
- Analyzed total and average weekly sales
- Compared holiday vs non-holiday sales
- Identified top-performing stores
- Studied the effect of temperature, fuel prices, CPI, unemployment on sales
- Created multiple visualizations to make insights clearer:
- 📈 Line Plots – Sales trends over time
- 📊 Bar Charts – Store-wise sales performance
- 📉 Histogram & KDE – Distribution of weekly sales
- 🔥 Heatmaps – Correlation analysis
- 🌎 Boxplots – Holiday vs Non-Holiday comparisons
- Generated key findings, such as:
- 🎉 Holiday weeks showed a noticeable increase in sales
- 📆 December 2010 recorded the highest monthly sales
- 📉 Sales trends were influenced by economic indicators like CPI & Unemployment
- Exported all charts and insights into a professional PowerPoint presentation
- Created an executive summary with key highlights
- Saved outputs in a structured folder (outputs/)
Here are some of the visuals generated in the analysis:
- Sales Trend Over Time
- Holiday vs Non-Holiday Sales Comparison
- Top 10 Stores by Total Sales
- Correlation Heatmap of Factors (Visualizations can be added as images/screenshots in the README 📷)
💡 Key Insights:
- ✔️ Total Sales (Dataset): $6.73 Billio
- ✔️ Average Weekly Sales: $1.04 Million
- ✔️ Top Store by Sales: Store 20
- ✔️ Holiday Sales Impact: Higher than non-holidays
- ✔️ December 2010: Highest sales month recorded
- 📌 Cleaned Dataset → outputs/Walmart_cleaned.cs
- 📌 PowerPoint Report → outputs/Walmart_Sales_Report.pptx
- 📌 Python Notebook / Script → Task 1.py
This project demonstrates how data analysis & visualization can uncover patterns in sales and provide business-critical insights. By combining statistical analysis, Python programming, and visual storytelling, the project highlights the importance of data-driven decision-making in retail.
💼 Portfolio: https://linktr.ee/AbdullahUmar.DataAnalyst
📧 Email: umerabdullah048@gmail.com
🙌 Acknowledgment
A big thanks to Walmart Dataset Providers and my company for assigning me this exciting project. This project has enhanced my data analytics, visualization, and reporting skills significantly!
✨ This project is a part of my Data Analyst journey. Stay tuned for more exciting projects! 🚀