🪔 Diwali Sales Analysis – Exploratory Data Analysis with Python 🔎 Project Overview
The goal of this project was to perform Exploratory Data Analysis (EDA) on a Diwali Sales dataset to uncover trends that can help businesses increase sales during festive seasons.
📌 Tasks Performed
✅ Cleaned & preprocessed raw sales data
✅ Handled missing values and removed irrelevant columns
✅ Converted data types for accurate numerical analysis
✅ Created visualizations to explore customer demographics, top products, and sales performance
🛠 Tech Stack & Libraries
Python – Core programming
Pandas & NumPy – Data cleaning, wrangling, transformation
Matplotlib & Seaborn – Data visualization & storytelling through plots
📊 Key Insights 👥 Customer Demographics
💠 Women are the most active buyers and contribute more to overall revenue.
💠 Age group 26–35 years makes the highest number of purchases.
🌍 Geographical Insights
⚡ Uttar Pradesh, Maharashtra, and Karnataka are the top contributing states (orders & revenue).
These states should be prime targets for marketing campaigns.
💍 Marital Status & Occupation
✔️ Married women have the highest purchasing power.
✔️ Top contributing occupations: IT, Healthcare, Aviation.
🛒 Product Categories
Highest demand in Food, Clothing, and Electronics categories.
Insight helps businesses optimize inventory during festive seasons.
📈 Business Value
This analysis enables businesses to:
Identify high-value customer segments
Optimize inventory management
Design targeted marketing campaigns
Maximize festive season profits
📷 Visualizations Included
📊 Gender-wise sales distribution
📊 Age group vs. total revenue
📊 Top 10 states by orders & revenue
📊 Marital status vs. purchasing power
📊 Top occupations & product categories
📊 Top 10 most sold products
🌟 Key Takeaway
Married women (26–35 yrs) from Uttar Pradesh, Maharashtra, and Karnataka, working in IT, Healthcare, and Aviation, are the most likely to purchase products in Food, Clothing, and Electronics categories during Diwali.