Amazon_video_user_analysis

📊 Amazon Prime User Data Analysis

Welcome to the Amazon Prime User Data Analysis project! This repository contains an in-depth analysis of Amazon Prime user data. Below, you'll find all the necessary information to understand, explore, and expand this project.

📂 Project Overview

• Goal: Analyze Amazon Prime user data to uncover insights on usage patterns, popular content, user demographics, and engagement levels.
• Data Source: The dataset includes anonymized Amazon Prime user interaction data, covering content views, user demographics, and engagement metrics.
• Methods Used: Data preprocessing, visualization, and statistical analysis using Python and libraries like Pandas, NumPy, and Matplotlib/Seaborn for plotting insights.

🛠️ Key Features

• Exploratory Data Analysis (EDA):
  • Detailed data cleaning, handling missing values, and data transformation.
  • Uncover trends, popular genres, user engagement metrics, and more.
• Visualizations:
  • Interactive and static visualizations of user demographics, usage patterns, and popular content.
  • Heatmaps, bar charts, line graphs, and user segmentation visuals.
• Insights & Conclusions:
  • Key takeaways on user preferences and content popularity.
  • Recommendations for data-driven decision-making.

📌 Getting Started

To get started with the project on your local machine, follow these steps:

1. Clone this repository:

   git clone https://github.com/yourusername/Amazon-Prime-User-Data-Analysis.git

2. Install required libraries: Ensure you have Python installed, and then run:

   pip install -r requirements.txt

3. Run the Jupyter Notebook: Launch the Jupyter Notebook to explore the data and code:

   jupyter notebook "Amazon Prime User Data Analysis.ipynb"

💻 Technologies Used

• Python 🐍
• Jupyter Notebook 📒
• Pandas & NumPy for data manipulation
• Matplotlib & Seaborn for data visualization
• Scikit-learn for potential machine learning analyses

📈 Project Structure

• Data: Contains the Amazon Prime user dataset used for analysis (ensure data privacy and compliance).
• Notebooks: Jupyter Notebook(s) with all steps of the analysis.
• Images: Generated images and plots for insights.

🚀 Future Work

• Enhanced Analysis: Further drill down into specific user segments for targeted insights.
• Machine Learning Models: Predict user preferences based on past behavior.
• Dashboard Integration: Build an interactive dashboard with Tableau or Power BI for a real-time view.

🤝 Contributions

Have suggestions to improve this project? Feel free to fork the repository, make changes, and submit a pull request!