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https://github.com/shruti23-ui/diwali_sales_analysis

Diwali Sales Analysis: A data analysis project exploring Diwali sales trends, focusing on demographic insights like age and gender-based purchasing behavior. Uses Python for data cleaning, visualization, and insights extraction.
https://github.com/shruti23-ui/diwali_sales_analysis

data-analysis-python dataanalysis-projects matplotlib-pyplot python3 seaborn-plots

Last synced: 24 days ago
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Host: GitHub
URL: https://github.com/shruti23-ui/diwali_sales_analysis
Owner: shruti23-ui
Created: 2024-11-05T16:07:14.000Z (3 months ago)
Default Branch: main
Last Pushed: 2024-11-05T16:57:52.000Z (3 months ago)
Last Synced: 2024-11-05T17:48:34.938Z (3 months ago)
Topics: data-analysis-python, dataanalysis-projects, matplotlib-pyplot, python3, seaborn-plots
Language: Jupyter Notebook
Homepage: https://colab.research.google.com/drive/12h0e-PW6b8VDrIAc8cn-FNJennC9kk-1?usp=sharing
Size: 839 KB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

# 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:
```bash
pip install numpy pandas matplotlib seaborn
```
## Getting Started

1. Clone this repository:
```bash
git clone https://github.com/shruti23-ui/Diwali_Sales_Analysis.git
```
2. Navigate to the project directory:
```bash
cd Diwali_Sales_Analysis
```
3. Open the Jupyter Notebook:
```bash
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](LICENSE) file for details.

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