https://github.com/mednour2019/netflix-machine-learning

Machine Learning Analysis of Netflix Data
https://github.com/mednour2019/netflix-machine-learning

apprentissage ia machine-learning python tkinter

Last synced: about 1 month ago
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Machine Learning Analysis of Netflix Data

• Host: GitHub
• URL: https://github.com/mednour2019/netflix-machine-learning
• Owner: mednour2019
• Created: 2024-07-26T21:04:09.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-11-28T19:29:02.000Z (about 1 month ago)
• Last Synced: 2024-11-28T20:28:13.581Z (about 1 month ago)
• Topics: apprentissage, ia, machine-learning, python, tkinter
• Homepage: https://prtfnour.vercel.app/pdf-viewer/pdf-project-description.html?project=project22
• Size: 4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Machine Learning Analysis of Netflix Data

# The project is in master Branch

# Description

The project aims to analyze Netflix data to gain insights into different types of content, specifically focusing on distinguishing between films and TV shows. By applying machine learning techniques, the analysis will classify and provide knowledge about the nature of each entry, whether it is a film or a TV show. This will help in understanding patterns and trends within Netflix's content library.

![Screen Shot](https://prtfnour.vercel.app/images/portfolio/project22.JPG)

## Demo Video

Check out the demo video of the project [here](https://drive.google.com/file/d/1qZ4QE_hDYsGFXRwbA0jlGGG6DOADUreU/view?usp=sharing)

## Features

- 🧩 Visualize Initial Data: Provide an initial visualization of the Netflix data to understand its structure and distribution.

- 🧩 Identify Problems and Deficiencies: Detect and address any issues or deficiencies in the data, such as missing values or inconsistencies.

- 🧩 Clean and Treat Data: Perform data cleaning and preprocessing to prepare it for analysis.

- 🧩 Visualize and Analyze Data: Create visualizations to analyze the data and extract meaningful insights.

- 🧩 Transform Categorical Columns: Convert categorical columns into a suitable format for machine learning algorithms.

- 🧩 Apply Various Algorithms: Implement multiple machine learning algorithms to classify the data.

-  🧩 Choose the Best Accuracy Model: Evaluate the performance of each algorithm and select the one with the highest accuracy.

-  🧩 Visualize Results in Tkinter Desktop Application: Develop a Tkinter desktop application to visualize the entire process and results.

## Getting Started

### Prerequisites

- Python

- Machine learning

-TKINTER

## Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**.

1. Fork the Project

2. Create your Feature 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

## License

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

## Contact

Mohamed Nour KHammeri - [@My-Web-Site](https://prtfnour.vercel.app) - [email protected]

Project Link: [https://github.com/mednour2019/netflix-machine-learning](https://github.com/mednour2019/netflix-machine-learning)