Walmart Weekly Sales Analysis

📌 Project Overview

This project explores Walmart weekly sales data to understand how economic and seasonal factors influence revenue. The analysis focuses on uncovering patterns, trends, and potential business insights that could support strategic decision-making.

🎯 Business Questions

• How do seasonal patterns affect weekly sales?
• Do economic factors such as fuel price, CPI, and unemployment influence revenue?
• Are holiday weeks associated with higher sales performance?
• Can simple machine learning models help predict weekly sales?

📊 Dataset Overview

• Source: Walmart Weekly Sales Dataset (Kaggle)
• Records: 6,436 weekly sales observations
• Features:
  • Store
  • Date
  • Weekly_Sales
  • Holiday_Flag
  • Temperature
  • Fuel_Price
  • CPI
  • Unemployment

🧪 Methodology

1. Data Cleaning and Preprocessing
2. Exploratory Data Analysis (EDA)
3. Feature Relationship Analysis
4. Linear Regression Modeling
5. Business Insight Interpretation

🔍 Key Insights

• Weekly sales exhibit strong seasonal peaks toward the end of the year, indicating increased consumer spending during holiday seasons.
• Holiday weeks generally show higher sales compared to non-holiday weeks.
• Economic indicators such as CPI and unemployment show weak correlations with weekly sales, suggesting limited direct impact at the store level.

📊 Key Findings

• Sales peak during Q4 holiday seasons (Thanksgiving & Christmas).
• Holiday weeks show significantly higher sales compared to non-holiday weeks.
• Economic indicators (CPI, fuel price, unemployment) show weak correlation with weekly sales.
• Simple regression models can capture seasonality patterns but limited predictive power.

💡 Business Recommendations

• Increase inventory and marketing campaigns during Q4 holiday seasons to capture peak demand.
• Optimize promotional strategies during holiday weeks to maximize revenue uplift.
• Consider store-level localized factors for more accurate sales forecasting models.

🛠 Tech Stack

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Scikit-learn
• Jupyter Notebook

▶ How to Run

pip install -r requirements.txt
jupyter notebook

📌 Key Features

- Exploratory Data Analysis (EDA)
- Time series sales trend analysis
- Holiday vs non-holiday sales comparison
- Business insights & recommendations

📁 Project Structure

walmart-sales-analysis/
│
├── data/            # Raw dataset
├── notebook/         # Jupyter notebook analysis
├── requirements.txt  # Python dependencies
└── README.md          # Project documentation

👤 Author

Azizah Adilah

LinkedIn: https://linkedin.com/in/azizah-adilah

GitHub: https://github.com/azizahadilaa