🪔 Diwali Sales Data Analysis

This project explores and analyzes Diwali sales data to derive meaningful business insights using Python and Pandas. The goal is to understand customer behavior, product performance, and regional performance to help businesses enhance their marketing strategies during festive seasons.

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📁 Dataset

File: Diwali_Sales_Data.csv
Source: Hypothetical e-commerce dataset collected during Diwali season.

Features:

• User_ID: Unique customer ID
• Gender: Male / Female
• Age: Age group of customers
• Occupation: Profession category
• Marital_Status: 0 = Unmarried, 1 = Married
• Product_ID: Unique ID of purchased product
• Product_Category: Product segment
• Purchase: Purchase amount (in ₹)
• State: Region of the customer

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🧪 Project Objectives

• Clean and preprocess the dataset.
• Perform exploratory data analysis (EDA).
• Identify high-performing customer segments.
• Understand purchasing behavior based on:
  • Age
  • Gender
  • State
  • Product category
• Provide business recommendations based on findings.

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🛠️ Tools and Libraries Used

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

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📊 Key Insights

• Male customers contribute significantly more to total sales.
• Most purchases are made by customers aged 26–35.
• The top purchasing states include Uttar Pradesh, Maharashtra, and Karnataka.
• Specific product categories dominate sales – helping guide inventory and promotion decisions.

(📝 See the full analysis in Analysis.ipynb)

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