🪔 Diwali Sales Analysis 📊

This project presents a detailed data analysis of Diwali sales using Python and Pandas, with visualizations generated via Matplotlib and Seaborn. The goal is to derive actionable business insights by understanding customer demographics, product preferences, and regional purchasing trends during the Diwali festival season in India.

📌 Objective

To analyze the sales data of a retail store during the Diwali season and provide insights that can help improve sales, customer targeting, and marketing strategies for future festive campaigns.

📂 Project Structure

Diwali_Sales_Analysis/
│
├── Diwali_Sales_Analysis.ipynb    # Main Jupyter notebook with full analysis
├── Diwali Sales Data.csv          # Dataset used for analysis
└── README.md                      # Project documentation (this file)

🔍 Analysis Performed

• Data cleaning and preprocessing
• Handling missing values
• Exploratory Data Analysis (EDA)
• Demographic segmentation (gender, age group, marital status)
• Product category analysis
• Spending behavior by city and state
• Visual storytelling using bar plots, histograms, and count plots

📈 Key Insights

• Females in the age group 26–35 from Tier-1 cities spend the most during Diwali.
• Married customers tend to spend more than unmarried ones.
• Household and Clothing are the most purchased product categories.
• High sales concentration in metro cities like Bangalore, Delhi, and Mumbai.

These insights can help optimize:

• Targeted advertising
• Product bundling
• Inventory planning
• Regional promotions

🛠️ Tools & Libraries Used

• Python
• Pandas
• Numpy
• Matplotlib
• Seaborn
• Jupyter Notebook

📊 Sample Visualizations

Some visualizations used in the analysis:

• Gender-wise spending distribution
• Age group vs Spending behavior
• State-wise purchase patterns
• Product category preferences

🧠 Skills Applied

• Data Wrangling
• Exploratory Data Analysis (EDA)
• Data Visualization
• Consumer Behavior Analysis
• Business Intelligence

📥 Dataset Source

The dataset used is a mock dataset curated for educational purposes and practice in data analytics. [Included in this repo → Diwali Sales Data.csv]

✅ How to Use

1. Clone this repository: git clone https://github.com/prajwalnerkar/festival-sales-analysis-using-python/blob/main/README.md
2. Open Diwali_Sales_Analysis.ipynb in Jupyter Notebook or any compatible IDE.
3. Run the cells step-by-step to follow the data cleaning and analysis process.
4. Modify or extend the analysis to suit your own learning or business needs.

📃 Report Summary

A complete walkthrough of the project is available in the .ipynb notebook. It includes code comments, data insights, and visualizations with clear titles and explanations to ensure easy understanding for beginners and analysts alike.