Project Description:

The Diwali Sales Analysis project focuses on exploring and analyzing customer purchasing behavior during the festive season using Python. The main objective is to derive meaningful business insights that can help increase sales, improve marketing strategies, and target potential customers more effectively.

Objectives:

To analyze sales data and identify top-performing product categories and regions.

To understand customer demographics such as age, gender, marital status, and occupation.

To find correlations between customer profiles and their spending patterns.

To visualize sales trends and insights for better decision-making.

Tools & Technologies Used:

Programming Language: Python

Libraries: Pandas, NumPy, Matplotlib, Seaborn

Environment: Jupyter Notebook / VS Code

Key Steps in the Project:

Data Collection & Cleaning: Import the dataset and handle missing or duplicate values.

Exploratory Data Analysis (EDA): Perform data exploration to understand patterns and distributions.

Visualization: Create insightful charts (bar plots, histograms, pie charts, heatmaps) using Matplotlib and Seaborn.

Customer Segmentation: Analyze customer demographics to identify the most valuable segments.

Insights Generation: Summarize key findings, such as which gender, age group, or region spends the most during Diwali.

Outcome:

Identified that married women aged 26–35 years from Tier 1 cities are the top buyers during the Diwali season.

Helped understand which product categories generate the highest revenue.

Provided data-driven insights for targeted marketing campaigns.