Exploratory Data Analysis of Disney+ Movies & TV Shows dataset using Python.
📦 Dataset: Disney+ Dataset on Kaggle
Source: https://www.kaggle.com/datasets/shivamb/disney-movies-and-tv-shows
Includes metadata: title, type, rating, genres, and release dates
- Understand Disney+ content by type, genre, rating, and release year
- Analyze trends over time, and explore top genres
- Build an interactive dashboard for storytelling and insights
- Add advanced features like:
- Mood-based recommendations
- Animated timeline 📊
- Data download 📂
- YouTube trailer links 🎥
| Phase | Description | Dates |
|---|---|---|
| Phase 1 | Setup, Data Cleaning, GitHub Init | March 16–17 |
| Phase 2 | Exploratory Data Analysis | March 18–22 |
| Phase 3 | Dashboard Development & Interactivity | March 23–25 |
| Phase 4 | Final Touches & Deployment | March 27 |
Exploratory Data Analysis + Interactive Dashboard on Disney+ Movies & TV Shows using Python and Streamlit.
![Dashboard Screenshot]
📊 Dashboard Features
Feature Description 🎛️ Sidebar filters Filter by year, type, genre
📊 Visual Tabs Neatly separates insights into sections
🧠 Insights beside charts Clear takeaways throughout
📈 Animated Timeline (Plotly) See how Disney+ grew by year
🤖 Mood-Based Recommender Get genre picks based on how you feel
🎬 “Surprise Me” Button Random movie picker with trailer link
📂 Download Button Export filtered dataset
🎨 Netflix-style Theme Custom dark theme for a polished feel
Want to try the app without setting anything up locally?
🔗 Check out the Streamlit Dashboard
No installation needed — it's all in the cloud via Streamlit!
🔧 Tools & Libraries Used Python 3
Pandas, Matplotlib, Seaborn
Plotly, Altair (for animations)
Streamlit (for the dashboard)
GitHub (project hosting and README)
📥 How to Run Locally
To run the Disney+ Dashboard on your own machine:
- Clone the repository:
git clone https://github.com/cam-spec/DisneyPlus-EDA-Analysis.git
- Navigate to the project directory:
cd DisneyPlus-EDA-Analysis
- Install the required packages:
pip install -r requirements.txt
4.Run the Streamlit application:
streamlit run app.py
📂 Note: Make sure the dataset file (cleaned_disney_plus_titles.csv) is in the same directory as app.py. If not, update the file path in the script to match your setup. Since the original path points to a local machine, you’ll need to adjust it for your own directory.
Explore the step-by-step EDA process and feature experimentation using Jupyter Notebooks:
- DisneyPlus Phase 2: EDA + Feature Engineering: Final visualizations, modeling ideas, and insights before deployment to Streamlit.
- GitHubProject.ipynb: Supporting project analysis and visualization testing.
📂 Note: Make sure the dataset file (cleaned_disney_plus_titles.csv) is in the same directory as the notebook or app script. If you're running the code locally and encounter a file path error, it's likely because the original path points to my local computer setup. To fix this, simply download the dataset and update the path in your code to match your own directory structure.
🧪 Example Output from EDA Notebook – Content Rating Distribution
- Integrate a recommendation model based on user preferences
- Use a content-based filtering approach
- Add more metadata (runtime, countries, languages)
🧑💻 Author
Camron Njock
🎓 BSc Data Science @ National College of Ireland