{"id":25487876,"url":"https://github.com/rohitblaze10/amazon_prime_analysis-kaggle","last_synced_at":"2026-04-28T12:02:12.281Z","repository":{"id":277392210,"uuid":"932280413","full_name":"rohitblaze10/Amazon_Prime_Analysis-Kaggle","owner":"rohitblaze10","description":"Exploratory data analysis on Amazon Prime users to uncover trends in subscriptions, engagement, and customer behavior. Key insights include age group distribution, renewal patterns, device usage, and customer feedback. 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The dataset is processed using Python, and the analysis results are visualized for better understanding.\n\n## Project Structure\n- **Amazon_prime_analysis.xlsx**: Contains raw and processed Amazon Prime user data.\n- **Cleaned_amazon_prime_users.csv**: Cleaned dataset used for further analysis.\n- **Updated_amazon_prime_users.csv**: Updated dataset with refined data.\n- **Amazon_Prime_Final_EDA_Output.ipynb**: Jupyter notebook containing exploratory data analysis (EDA) and key insights.\n\n## Getting Started\n### Prerequisites\nEnsure you have the following installed:\n- Python 3.x\n- Jupyter Notebook\n- Pandas\n- Matplotlib / Seaborn (for visualization)\n\n### Installation\n1. Clone this repository:\n   ```bash\n   git clone https://github.com/rohitblaze10/amazon-prime-analysis.git\n   cd amazon-prime-analysis\n   ```\n2. Install the required Python libraries:\n   ```bash\n   pip install pandas matplotlib seaborn jupyter\n   ```\n\n## Usage\n1. Open Jupyter Notebook:\n   ```bash\n   jupyter notebook\n   ```\n2. Run `Amazon_Prime_Final_EDA_Output.ipynb` to process and analyze the data.\n3. View results in `Amazon_prime_analysis.xlsx` or the cleaned CSV files.\n\n## Key Findings\n- **Subscription Plans:**\n  - The majority of users subscribe to the **Monthly** plan, followed by **Annual** and **Family** plans.\n- **User Engagement:**\n  - Users with **auto-renewal enabled** tend to have higher engagement metrics.\n  - **Smart TVs** are the most used devices for accessing Prime services.\n- **Popular Purchase Categories:**\n  - The top three purchase categories among Prime users are **Electronics, Books, and Clothing**.\n- **Customer Support Interactions:**\n  - Users with **low feedback ratings** tend to contact customer support more frequently.\n- **Demographics:**\n  - The dataset includes users across various age groups and locations, with balanced gender representation.\n\n## Visualizations\nBelow are some key visualizations from our analysis:\n1. **Subscription Plan Distribution** – Highlights the most popular plans.\n   ![sub](https://github.com/user-attachments/assets/839ecb5c-078c-4620-9d21-1a3b58aa09b2)\n\n2. **Device Usage for Prime Access** – Shows preferred devices for streaming.\n   ![device](https://github.com/user-attachments/assets/aae5cadb-77d5-42ac-aab5-73805a432ad3)\n\n3. **Most Purchased Categories** – Displays frequently bought product categories.\n   ![NO](https://github.com/user-attachments/assets/e51328c4-fb1d-4f0b-b604-55538a767f48)   \nnd more...\n## Future Improvements\n- Improve data cleaning and preprocessing techniques.\n- \n- Implement predictive modeling to forecast user retention.\n- Add more advanced visualizations.\n\n## Contributing\nFeel free to fork this repository and make improvements. Pull requests are welcome!\n\n## License\n[Specify a license, e.g., MIT, Apache 2.0]\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frohitblaze10%2Famazon_prime_analysis-kaggle","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frohitblaze10%2Famazon_prime_analysis-kaggle","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frohitblaze10%2Famazon_prime_analysis-kaggle/lists"}