https://github.com/rohitblaze10/amazon_prime_analysis-kaggle
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. Visualizations and data-driven insights help optimize user experience and retention strategies
https://github.com/rohitblaze10/amazon_prime_analysis-kaggle
analysis eda python python-script
Last synced: about 2 months ago
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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. Visualizations and data-driven insights help optimize user experience and retention strategies
- Host: GitHub
- URL: https://github.com/rohitblaze10/amazon_prime_analysis-kaggle
- Owner: rohitblaze10
- Created: 2025-02-13T16:57:14.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-21T07:06:31.000Z (over 1 year ago)
- Last Synced: 2025-05-18T20:07:43.104Z (about 1 year ago)
- Topics: analysis, eda, python, python-script
- Language: Jupyter Notebook
- Homepage:
- Size: 649 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Amazon Prime User Analysis
## Overview
This project focuses on analyzing Amazon Prime user data to uncover insights related to user demographics, subscription behavior, and engagement trends. The dataset is processed using Python, and the analysis results are visualized for better understanding.
## Project Structure
- **Amazon_prime_analysis.xlsx**: Contains raw and processed Amazon Prime user data.
- **Cleaned_amazon_prime_users.csv**: Cleaned dataset used for further analysis.
- **Updated_amazon_prime_users.csv**: Updated dataset with refined data.
- **Amazon_Prime_Final_EDA_Output.ipynb**: Jupyter notebook containing exploratory data analysis (EDA) and key insights.
## Getting Started
### Prerequisites
Ensure you have the following installed:
- Python 3.x
- Jupyter Notebook
- Pandas
- Matplotlib / Seaborn (for visualization)
### Installation
1. Clone this repository:
```bash
git clone https://github.com/rohitblaze10/amazon-prime-analysis.git
cd amazon-prime-analysis
```
2. Install the required Python libraries:
```bash
pip install pandas matplotlib seaborn jupyter
```
## Usage
1. Open Jupyter Notebook:
```bash
jupyter notebook
```
2. Run `Amazon_Prime_Final_EDA_Output.ipynb` to process and analyze the data.
3. View results in `Amazon_prime_analysis.xlsx` or the cleaned CSV files.
## Key Findings
- **Subscription Plans:**
- The majority of users subscribe to the **Monthly** plan, followed by **Annual** and **Family** plans.
- **User Engagement:**
- Users with **auto-renewal enabled** tend to have higher engagement metrics.
- **Smart TVs** are the most used devices for accessing Prime services.
- **Popular Purchase Categories:**
- The top three purchase categories among Prime users are **Electronics, Books, and Clothing**.
- **Customer Support Interactions:**
- Users with **low feedback ratings** tend to contact customer support more frequently.
- **Demographics:**
- The dataset includes users across various age groups and locations, with balanced gender representation.
## Visualizations
Below are some key visualizations from our analysis:
1. **Subscription Plan Distribution** – Highlights the most popular plans.
2. **Device Usage for Prime Access** – Shows preferred devices for streaming.
3. **Most Purchased Categories** – Displays frequently bought product categories.
nd more...
## Future Improvements
- Improve data cleaning and preprocessing techniques.
-
- Implement predictive modeling to forecast user retention.
- Add more advanced visualizations.
## Contributing
Feel free to fork this repository and make improvements. Pull requests are welcome!
## License
[Specify a license, e.g., MIT, Apache 2.0]