Python EDA Diwali Sales Analysis

Overview

This project is an Exploratory Data Analysis (EDA) of Diwali sales data using Python. The objective is to uncover insights and patterns in sales data during the Diwali festival season. The analysis involves data cleaning, visualization, and deriving meaningful conclusions that can aid in understanding customer behavior, product performance, and sales trends.

Table of Contents

• Introduction
• Data
• Tools and Libraries
• Analysis
• Results and Insights
• Conclusion

Introduction

Diwali, the festival of lights, is one of the most celebrated festivals in India. Retailers and e-commerce platforms experience a significant surge in sales during this period. This project analyzes the sales data during the Diwali season to identify trends and provide actionable insights.

Data

The dataset used for this analysis includes sales records for various products during the Diwali season. Key features include:

• Customer Information: Age, Gender, Location

• Product Details: Category, Price, Discount, etc.

• Sales Information: Quantity Sold, Total Sales, Payment Method.

Snapshot of dataset

Tools and Libraries

The following Python libraries were used in this project:

• Pandas: For data manipulation and analysis
• Matplotlib: For data visualization
• Seaborn: For advanced data visualization
• NumPy: For numerical computations

Analysis

The EDA process involved the following steps:

• Data Cleaning: Handling missing values, correcting data types, and filtering relevant data.

• Data Visualization: Creating plots to visualize the distribution of sales, customer demographics, and product performance.

• Correlation Analysis: Identifying relationships between different variables in the dataset.

• Trend Analysis: Examining sales trends over time and during the Diwali season.

Results and Insights

Key findings from the analysis include:

• Customer Demographics: Identified key customer segments based on age, gender, and location that contribute significantly to sales.

• Top-Selling Products: Highlighted the most popular product categories and their contribution to total sales.

• Sales Trends: Observed the impact of discounts and promotions on sales volume.

• Payment Methods: Analyzed the preferred payment methods during the Diwali season.

Exploratory Data Analysis

1. Occupation

1. Product Category

Conclusion

This analysis provides valuable insights into customer behavior and product performance during the Diwali season. Retailers and marketers can use these findings to optimize their strategies for future sales events.

How to Run

1. Clone the repository:

git clone https://github.com/Bharathi123208/diwali-sales-analysis.git cd diwali-sales-analysis

2. Install the required dependencies:

pip install -r requirements.txt

3. Run the Jupyter Notebook:

jupyter notebook Diwali_Sales_Analysis.ipynb