Welcome to the Netflix Content Analysis project by Digital PrepHub.
This project explores Netflix's content library using Python to uncover trends, analyze content distribution, and generate actionable insights through data visualization.
The goal of this project is to perform Exploratory Data Analysis (EDA) on Netflix's dataset and answer business questions related to movies, TV shows, genres, ratings, release years, and content availability.
This project demonstrates a real-world data analytics workflow, from data cleaning to visualization and business insights.
- Clean and prepare Netflix dataset
- Perform Exploratory Data Analysis (EDA)
- Analyze Movies vs TV Shows
- Identify Top Genres
- Study Content Ratings
- Analyze Release Year Trends
- Explore Country-wise Content Distribution
- Generate Business Insights
- Python
- Jupyter Notebook
- Pandas
- NumPy
- Matplotlib
- Plotly (Optional)
- Data Cleaning
- Data Transformation
- Exploratory Data Analysis (EDA)
- Data Visualization
- Business Insight Generation
- Python Programming
- Feature Engineering
- How many Movies and TV Shows are available?
- Which countries produce the most Netflix content?
- What are the most popular genres?
- How has Netflix's content grown over time?
- What are the most common content ratings?
- Which years had the highest number of releases?
- Movie vs TV Show distribution
- Duration analysis of Movies and TV Shows
📂 Netflix Content Analysis
│── Dataset (Sample)
│── Jupyter Notebook
│── Visualizations
│── Screenshots
│── Documentation
│── README.md
Add screenshots of:
- Data Cleaning Process
- Charts & Graphs
- Final Dashboard (if created)
- Notebook Output
- Growth trends in Netflix's content library
- Genre popularity analysis
- Country-wise content contribution
- Rating distribution
- Movie vs TV Show comparison
- Content release trends over the years
- Aspiring Data Analysts
- Python Beginners
- Students
- Career Switchers
- Portfolio Builders
The complete project available through Digital PrepHub includes:
- ✅ Complete Python Notebook (.ipynb)
- ✅ Raw Dataset
- ✅ Cleaned Dataset
- ✅ EDA Documentation
- ✅ Business Insights Report
- ✅ Charts & Visualizations
- ✅ Resume Project Description
- ✅ LinkedIn Project Description
- ✅ Interview Questions
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