This project explores and visualizes the Netflix dataset to gain insights into the platform’s content library. It focuses on understanding content distribution, popular genres, top-producing countries, ratings, and release year trends.
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Data Cleaning
- Handled missing values in columns like
director,cast,country,date_added, andrating. - Converted
date_addedto datetime format. - Extracted and standardized duration (minutes vs. seasons).
- Handled missing values in columns like
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Exploratory Data Analysis (EDA)
- Distribution of Movies vs. TV Shows.
- Top 10 most common genres.
- Top 10 countries producing Netflix content.
- Distribution of age ratings (e.g., TV-MA, PG-13).
- Content distribution across release years.
- Top 10 directors on Netflix.
- Monthly addition trends of Netflix content.
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Visualizations
- Count plots, bar charts, histograms, and distribution plots using Matplotlib and Seaborn.
- Python
- Pandas – data manipulation
- NumPy – numerical operations
- Matplotlib & Seaborn – data visualization
- Netflix has a larger share of Movies compared to TV Shows.
- The most common genres include Dramas, Comedies, and Documentaries.
- The USA and India are among the top contributors of content.
- TV-MA is the most frequent age rating.
- The number of Netflix titles increased significantly in the last decade.
- Clone this repository:
git clone https://github.com/your-username/netflix-content-analysis.git cd netflix-content-analysis
Install dependencies: pip install -r requirements.txt Run the notebook: jupyter notebook "Netflix Content Analysis and Visualization.ipynb" 📌 Future Improvements Perform genre-wise trends across years. Create interactive dashboards with Plotly or Streamlit. Build a recommendation system based on genres and ratings. 📂 Project Structure Netflix Content Analysis/
│── Netflix Content Analysis and Visualization.ipynb # Main notebook │── netflix_titles.csv # Dataset │── requirements.txt # Dependencies │── README.md # Documentation