Netflix Data Files

This repository contains a comprehensive dataset and Python-based analysis for Netflix series and movies. The project focuses on exploring, analyzing, and generating insights from Netflix's content library.

Table of Contents

• Overview
• Features
• Technologies Used
• Installation
• Usage
• Dataset Description
• Recommendations
• Project Structure
• Contributing
• License

Overview

The Netflix Data Files repository provides a structured approach to understanding Netflix's vast content library. The dataset contains key metadata for each title, such as:

• Show ID
• Type (Movie/Series)
• Title
• Director
• Cast
• Country
• Date Added
• Release Year
• Rating
• Duration
• Description

Using Python, the data is processed and analyzed to uncover patterns, trends, and insights. Additionally, machine learning algorithms are used to generate personalized recommendations and analytics.

Features

1. Data Exploration and Visualization

   • Analysis of content types, genres, and release patterns.
   • Visualizations using libraries like Matplotlib and Seaborn.

2. Machine Learning for Recommendations

   • Personalized title recommendations based on user preferences.

3. Comprehensive Dataset

   • Includes a well-structured CSV file containing Netflix titles and metadata.

4. Interactive Jupyter Notebooks

   • All code and results are presented in an easy-to-follow Jupyter Notebook.

Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn
• Jupyter Notebook
• CSV Data Files

Installation

1. Clone the repository:

   git clone https://github.com/paarthbhatt/netflix-data-files.git

2. Navigate to the project directory:

   cd netflix-data-files

3. Set up a virtual environment (optional):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

4. Install the required dependencies:

   pip install -r requirements.txt

5. Launch Jupyter Notebook:

   jupyter notebook

6. Open the provided notebook file and start exploring the analysis.

Usage

1. Open the netflix_analysis.ipynb file in Jupyter Notebook.
2. Run the cells sequentially to:
   • Load and preprocess the dataset.
   • Explore various analytics visualizations.
   • Generate personalized recommendations.
3. View results and insights directly within the notebook.

Dataset Description

The dataset includes the following key columns:

• show_id: Unique identifier for each title.
• type: Indicates if the content is a "Movie" or "TV Show".
• title: The title of the show or movie.
• director: Director(s) of the content.
• cast: Main cast members.
• country: Country of origin.
• date_added: When the content was added to Netflix.
• release_year: Year the content was released.
• rating: Content rating (e.g., PG, R).
• duration: Length of the movie or show (seasons).
• description: Short description of the content.

Recommendations

The project includes a recommendation system that suggests Netflix titles based on user preferences and viewing history. Recommendations are generated using collaborative filtering and similarity algorithms.

Project Structure

netflix-data-files/
├── data/
│   └── netflix_titles.csv
├── notebooks/
│   └── netflix_analysis.ipynb
├── requirements.txt
├── README.md
└── LICENSE

Contributing

Contributions are welcome! Follow these steps:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Description of changes"

4. Push to the branch:

   git push origin feature-name

5. Submit a pull request.

License

This project is licensed under the MIT License.

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Dive into the world of Netflix data analytics and uncover fascinating insights with this project. Happy coding!