Diwali Sales Analysis

This project performs an in-depth analysis of Diwali sales data, focusing on identifying key customer demographics, sales trends, and product categories to optimize marketing strategies and improve customer satisfaction. The project is beginner-friendly and leverages Python for data cleaning, analysis, and visualization.

Project Overview

Diwali, the festival of lights, is a peak time for sales across various industries in India. By analyzing sales data from this period, businesses can gain valuable insights into customer behavior, preferences, and high-demand products. This project explores the data to provide actionable insights for potential business growth and inventory management.

Technologies Used

• Python: For data processing and analysis
• Pandas: Data manipulation
• Matplotlib & Seaborn: Data visualization
• Jupyter Notebook: Interactive coding environment

Project Learnings

1. Data Cleaning and Manipulation: Processed raw sales data for better accuracy in analysis.
2. Exploratory Data Analysis (EDA): Used pandas, matplotlib, and seaborn libraries to perform detailed EDA.
3. Customer Segmentation: Identified key customer demographics such as age group, gender, and occupation to understand purchasing patterns.
4. Sales Optimization: Discovered top-selling product categories and regions, which can guide inventory planning and marketing efforts.

Data Insights

Through this analysis, we explored:

• Gender-based Buying Patterns: Understanding the difference in purchase patterns between male and female customers.
• Age Group Analysis: Identifying the age groups that are more likely to make purchases during Diwali.
• Occupation Influence: Analyzing which occupations contribute the most to sales.
• Product Popularity: Recognizing top-performing products to better manage stock and meet demand.

Requirements

The project requires Python 3 and the following libraries:

• numpy
• pandas
• matplotlib
• seaborn

Install the required libraries using:

pip install numpy pandas matplotlib seaborn

Getting Started

1. Clone this repository:

git clone https://github.com/shruti23-ui/Diwali_Sales_Analysis.git

2. Navigate to the project directory:

cd Diwali_Sales_Analysis

3. Open the Jupyter Notebook:

jupyter notebook Diwali_Sales_Analysis.ipynb

Results and Observations

• Gender: The analysis shows a higher purchasing power among female buyers compared to male buyers.
• Age: Insights suggest certain age groups have higher purchase frequencies.

Contribution

Feel free to contribute by opening issues or submitting pull requests.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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