Netflix Data Analysis and Visualization

Exploring trends and insights in Netflix's library of TV shows and movies
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Table of Contents

1. About The Project
2. Built With
3. Getting Started
4. Usage
5. Roadmap
6. Contributing
7. License
8. Contact
9. Acknowledgments

About The Project

The Netflix Data Analysis project aims to explore and visualize trends within Netflix's catalog of TV shows and movies. By using this dataset, we answer key questions about content distribution, genre popularity, and trends in Netflix's growth over time.

Key Features:

• Data Cleaning: Handle missing data and duplicates.
• Exploratory Data Analysis (EDA): Trends analysis (genres, content type, year distribution).
• Visualizations: Bar charts, histograms, and time-series analysis using Matplotlib and Seaborn.
• Optional: An interactive dashboard using Streamlit for real-time exploration of the data.

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Built With

• ![Python][python-url]
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Getting Started

To get a local copy up and running, follow these simple steps.

Prerequisites

You will need Python installed along with the required libraries.

Installation

1. Clone the repository

   git clone https://github.com/your-username/netflix-analysis.git

2. Install the required dependencies

   pip install -r requirements.txt

3. Launch Jupyter Notebook or Streamlit (for interactive dashboard)

   jupyter notebook

   OR

   streamlit run app.py

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Usage

The project offers insights into Netflix's catalog by answering questions such as:

• What is the most common genre?
• How has Netflix's content evolved over time?
• What are the longest and shortest movies available?

Run the provided Jupyter notebook to explore and visualize the data. You can also explore the interactive dashboard (if implemented) to visualize insights in real-time.

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Roadmap

• Add more data analysis features
• Build a content-based recommendation system
• Improve visualizations for interactive dashboards
• Provide support for additional datasets

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Contributing

Contributions are welcome! Please feel free to fork this project and submit a pull request.

1. Fork the project
2. Create your branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a pull request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Acknowledgments

• Pandas Documentation
• Matplotlib Documentation
• Seaborn Documentation
• Streamlit
• GitHub Emoji Cheat Sheet

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