Netflix hosts thousands of movies and TV shows from around the world. This project explores Netflix's content library using Python, Pandas, and Matplotlib to uncover interesting trends and patterns hidden within the dataset.
By transforming raw data into meaningful visualizations, the project provides insights into content distribution, ratings, release trends, movie durations, and country-wise contributions.
- 🎬 Compare Movies and TV Shows available on Netflix
- 📊 Analyze content ratings and audience classifications
- ⏱️ Explore movie duration patterns
- 📅 Study release trends over the years
- 🌍 Identify top contributing countries
- 📈 Visualize Netflix's content growth over time
| Technology | Purpose |
|---|---|
| 🐍 Python | Programming Language |
| 🐼 Pandas | Data Cleaning & Analysis |
| 📊 Matplotlib | Data Visualization |
| 💻 Jupyter Notebook / VS Code | Development Environment |
The project uses the Netflix Titles Dataset (netflix_titles.csv) containing information about Netflix content.
| Column | Description |
|---|---|
| type | Movie or TV Show |
| title | Content Title |
| director | Director Name |
| cast | Cast Members |
| country | Country of Origin |
| release_year | Release Year |
| rating | Audience Rating |
| duration | Runtime or Seasons |
📌 Dataset Source: Kaggle Netflix Dataset
A bar chart comparing the number of Movies and TV Shows available on Netflix.
A pie chart showing the proportion of content ratings such as TV-MA, TV-14, PG, and more.
A histogram illustrating the distribution of movie durations across the Netflix catalog.
A scatter plot showing content release trends over different years.
A bar chart highlighting the countries contributing the most content to Netflix.
A line chart comparing yearly release trends for Movies and TV Shows.
The following visualization files are automatically generated and saved:
Movies_vs_TVShows.png
Content_Rating_Pie.png
Movie_Duration_Histogram.png
Release_Year_Scatter.png
Top10_Countries.png
Movies_vs_TVShows_Trend.png
git clone https://github.com/Kavyaa13/netflix-data-visualization.git
cd netflix-data-visualizationpip install pandas matplotlibpython main.pyOr open the notebook:
jupyter notebookAll visualizations will be generated and saved automatically in the project directory.
📌 Netflix hosts significantly more Movies than TV Shows.
📌 TV-MA and TV-14 are among the most common content ratings.
📌 Most movies have a duration between 90 and 120 minutes.
📌 Netflix content production increased rapidly after 2015.
📌 A small number of countries contribute a large portion of Netflix's catalog.
- 🚀 Interactive dashboards using Plotly
- 🌐 Streamlit Web Application
- 🎭 Genre-based Analysis
- ⭐ Director & Cast Popularity Analysis
- 🌍 Country-wise Trend Comparison
- 🤖 Recommendation System Integration
Through this project, I gained practical experience in:
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Data Visualization
- Working with Real-World Datasets
- Extracting Insights from Data
Kavya
📊 Data Visualization & Analytics Project
🐍 Built with Python, Pandas & Matplotlib