{"id":19571865,"url":"https://github.com/shruti23-ui/diwali_sales_analysis","last_synced_at":"2026-05-15T23:02:05.438Z","repository":{"id":261278902,"uuid":"883813869","full_name":"shruti23-ui/Diwali_Sales_Analysis","owner":"shruti23-ui","description":"Diwali Sales Analysis: A data analysis project exploring Diwali sales trends, focusing on demographic insights like age and gender-based purchasing behavior. 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The project is beginner-friendly and leverages Python for data cleaning, analysis, and visualization.\n\n## Project Overview\n\nDiwali, 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.\n\n## Technologies Used\n\n- **Python**: For data processing and analysis\n- **Pandas**: Data manipulation\n- **Matplotlib \u0026 Seaborn**: Data visualization\n- **Jupyter Notebook**: Interactive coding environment\n\n## Project Learnings\n\n1. **Data Cleaning and Manipulation**: Processed raw sales data for better accuracy in analysis.\n2. **Exploratory Data Analysis (EDA)**: Used pandas, matplotlib, and seaborn libraries to perform detailed EDA.\n3. **Customer Segmentation**: Identified key customer demographics such as age group, gender, and occupation to understand purchasing patterns.\n4. **Sales Optimization**: Discovered top-selling product categories and regions, which can guide inventory planning and marketing efforts.\n\n## Data Insights\n\nThrough this analysis, we explored:\n\n- **Gender-based Buying Patterns**: Understanding the difference in purchase patterns between male and female customers.\n- **Age Group Analysis**: Identifying the age groups that are more likely to make purchases during Diwali.\n- **Occupation Influence**: Analyzing which occupations contribute the most to sales.\n- **Product Popularity**: Recognizing top-performing products to better manage stock and meet demand.\n\n## Requirements\n\nThe project requires Python 3 and the following libraries:\n- `numpy`\n- `pandas`\n- `matplotlib`\n- `seaborn`\n\nInstall the required libraries using:\n```bash\npip install numpy pandas matplotlib seaborn\n```\n## Getting Started\n\n1. Clone this repository:\n```bash\ngit clone https://github.com/shruti23-ui/Diwali_Sales_Analysis.git\n```\n2. Navigate to the project directory:\n```bash\ncd Diwali_Sales_Analysis\n```\n3. Open the Jupyter Notebook:\n```bash\njupyter notebook Diwali_Sales_Analysis.ipynb\n```\n\n## Results and Observations\n\n- **Gender:** The analysis shows a higher purchasing power among female buyers compared to male buyers.\n- **Age:** Insights suggest certain age groups have higher purchase frequencies.\n\n## Contribution\n\nFeel free to contribute by opening issues or submitting pull requests. \n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n---\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshruti23-ui%2Fdiwali_sales_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshruti23-ui%2Fdiwali_sales_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshruti23-ui%2Fdiwali_sales_analysis/lists"}