🏬 Walmart Weekly Sales Analysis (2010–2012)

📌 Project Overview

This project performs an end-to-end sales and time-series analysis on weekly sales data of Walmart collected from Kaggle, covering the period from February 2010 to December 2012. The analysis focuses on understanding seasonality, holiday effects, and store-level performance, converting raw retail sales data into business-relevant insights using Python. Kaggle link for dataset: https://www.kaggle.com/datasets/mikhail1681/walmart-sales

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🎯 Objectives

• Analyse weekly sales trends across stores
• Identify seasonal and holiday-driven demand patterns
• Compare performance across different stores
• Understand sales volatility and consistency
• Translate sales data into actionable retail insights

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🗂 Dataset Description

• Records: 6,000+ weekly entries
• Time period: Feb 2010 – Dec 2012
• Granularity: Store-week level
• Key attributes:
  • Store
  • Weekly sales
  • Holiday flag
  • Date

📌 The dataset enables time-series and retail demand analysis.

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🛠 Tools & Technologies

• Python
  • Pandas
  • NumPy
• Visualization
  • Matplotlib
  • Seaborn
• Jupyter Notebook

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🔍 Data Understanding & Exploration

• Dataset shape and structure
• Store-wise record distribution
• Sales range and variability
• Initial trend inspection

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🔧 Data Cleaning & Preparation

Key steps performed:

• Converted date column to datetime format
• Sorted data chronologically
• Validated sales values
• Handled missing or inconsistent records
• Ensured correct data types for analysis

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🧠 Feature Engineering

• Extracted year, month, and week from date
• Classified holiday vs non-holiday weeks
• Created aggregated metrics:
  • Average weekly sales
  • Holiday vs non-holiday sales comparison
• Store-level summary statistics

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📊 Exploratory Data Analysis

🔹 Time-Series Trends

• Weekly sales movement over time
• Overall trend and fluctuations

🔹 Seasonality Analysis

• Monthly and yearly sales patterns
• Recurring seasonal peaks

🔹 Holiday Impact

• Sales comparison: holiday vs non-holiday weeks
• Magnitude of holiday-driven demand

🔹 Store Performance Analysis

• High-performing vs low-performing stores
• Consistency and volatility across stores

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📈 Key Insights

• Sales exhibit strong seasonal patterns
• Holiday weeks consistently outperform non-holiday weeks
• Certain stores show stable performance, while others are highly volatile
• Demand spikes are predictable around specific periods

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📁 Project Structure

┣ 📂 data ┃ ┗ Walmart.csv ┣ 📂 notebooks ┃ ┗ Walmart(Insights).ipynb ┃ ┗ Walmart(Visuals).ipynb ┣ 📄 README.md ┣ 📄 requirements.txt ┗ 📄 .gitignore