Netflix Content Data Analysis

Project Overview

This project conducts an exploratory data analysis (EDA) of the "Netflix Movies and TV Shows" dataset. The goal is to uncover key trends and insights within Netflix's content library, demonstrating skills in data cleaning, analysis, and visualization.

Dataset

The dataset used for this analysis is the "Netflix Movies and TV Shows" collection, obtained from Kaggle. It includes information on content type, release year, duration, cast, director, country, and genre.

Tools & Technologies

• Python: Programming language for data manipulation and analysis.
• Pandas: For data loading, cleaning, and transformation.
• Matplotlib: For creating static, high-quality visualizations.
• Seaborn: For enhancing visualizations and statistical plotting.
• Jupyter Notebook: For interactive data analysis and presentation.

Analysis Highlights

This analysis covers several key aspects of Netflix's content:

• Content Type Distribution: Breakdown of Movies vs. TV Shows.
• Temporal Growth: Trends in content added to Netflix over the years.
• Geographical Footprint: Top content-producing countries.
• Genre Popularity: Identification of the most prevalent genres.
• Content Specifics: Average movie durations and TV show season distribution.
• Key Creators: Top directors and cast members.
• Rating Evolution: Shifts in content rating distribution over time.
• Country-Genre Specialization: Exploration of genre preferences by country.

How to View and Run

1. Clone the Repository:

   git clone [https://github.com/YourGitHubUsername/Netflix-Data-Analysis-Project.git](https://github.com/YourGitHubUsername/Netflix-Data-Analysis-Project.git)
   cd Netflix-Data-Analysis-Project

2. Download Dataset: Ensure netflix_titles.csv is in the project root (it should be included in the repo).
3. Install Dependencies:

   pip install pandas matplotlib seaborn notebook

4. Launch Jupyter Notebook:

   jupyter notebook

5. Open Netflix_Data_Analysis.ipynb: Navigate to the notebook file in your browser and open it. You can run all cells (Kernel > Restart & Run All) to regenerate the analysis and visualizations.

License

This project is open-sourced under the MIT License. See the LICENSE file for details.