🎯 Objective / Summary
This project analyzes Netflix content to uncover insights about movies and TV shows available on the platform. It explores patterns in genres, release years, countries, and ratings using data visualization and basic data analytics techniques.
📂 Dataset Source
The dataset used in this project is obtained from Kaggle’s Netflix Titles Dataset, which contains details about the content available on Netflix, including title, director, cast, country, date added, release year, rating, duration, and listed genres.
⚙️ Steps to Run the Project
Clone this repository:
git clone https://github.com/yourusername/netflix-data-analysis.git cd netflix-data-analysis
Install dependencies:
pip install -r requirements.txt
Open the Jupyter Notebook:
jupyter notebook "Datascienceproject (1).ipynb"
Run all cells to reproduce the analysis and visualizations.
📊 Key Results / Findings
The majority of Netflix content consists of Movies rather than TV Shows.
Most titles were released after 2010, indicating rapid expansion.
The United States contributes the largest share of titles, followed by India and the UK.
Common genres include Dramas, Comedies, and Documentaries.
Visualizations highlight global trends in Netflix’s content library over time.
🧩 Dependencies
This project uses the following Python libraries:
pandas
numpy
matplotlib
seaborn
plotly (if used for interactive visuals)
💡 Future Enhancements
Sentiment analysis on show descriptions.
Building a recommendation model using content similarity.
Creating a Streamlit dashboard for interactive exploration.