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| title: Tesla Production & Deliveries Dashboard | |
| emoji: 🚗 | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.47.2 | |
| app_file: app.py | |
| pinned: false | |
| Tesla Production & Deliveries Dashboard | |
| CS 5130 – Final Project (Gradio + pandas) | |
| 📌 Project Overview | |
| This project is an interactive Business Intelligence Dashboard built using Gradio and pandas. | |
| It helps non-technical users explore and understand business data. | |
| The dashboard allows users to: | |
| Upload datasets (CSV or Excel) | |
| View basic statistics | |
| Apply interactive filters | |
| Create different types of visualizations | |
| Generate automated insights | |
| Export data and charts | |
| Two sample Tesla datasets (1K rows and 50K rows) are included for testing and demonstration. | |
| ⭐ Key Features | |
| 1. Data Upload & Validation | |
| Upload CSV or Excel files | |
| Built-in Tesla sample datasets | |
| Automatic detection of: | |
| Numeric columns | |
| Categorical columns | |
| Date columns | |
| Dataset preview | |
| Clear error handling and messages | |
| 2. Summary Statistics | |
| Numeric summary (mean, median, std, min, max, quartiles) | |
| Categorical summary (unique values, mode, frequency) | |
| Missing value report | |
| Correlation heatmap for numeric columns | |
| 3. Interactive Filtering | |
| Numeric range filters | |
| Categorical multi-select filters | |
| Date range filters | |
| Filtered data preview | |
| Export filtered result to CSV | |
| 4. Visualizations | |
| Supports at least 4 required chart types: | |
| Time series plot | |
| Histogram | |
| Box plot | |
| Category bar chart | |
| Scatter plot | |
| Correlation heatmap | |
| Additional features: | |
| User selects columns | |
| Supports aggregation (sum, mean, count, median) | |
| Download charts as PNG | |
| 5. Automated Insights | |
| Top and bottom performing models | |
| Region ranking by estimated deliveries | |
| Production vs. delivery comparison | |
| Overall trend summary | |
| 📁 Project Structure | |
| project/ | |
| │── app.py # Main Gradio application | |
| │── data_processor.py # Data loading, cleaning, filtering | |
| │── visualizations.py # Chart creation functions | |
| │── insights.py # Insight generation functions | |
| │── utils.py # Helper utilities | |
| │── prepare_tesla_data.py # Synthetic dataset generator | |
| │── requirements.txt # Dependencies | |
| │── README.md # Documentation | |
| └── data/ | |
| ├── tesla_deliveries_1k.csv | |
| └── tesla_deliveries_50k.csv | |
| ▶ How to Run | |
| 1. Install dependencies | |
| pip install -r requirements.txt | |
| 2. Run the Gradio app | |
| python app.py | |
| 3. Open the browser link | |
| Gradio will show a local URL such as: | |
| http://127.0.0.1:7860 | |
| 🧰 Technologies Used | |
| Python | |
| pandas | |
| NumPy | |
| Matplotlib / Seaborn | |
| Gradio | |
| 🤖 Use of AI Tools | |
| AI tools (ChatGPT / Claude / GitHub Copilot) were used for: | |
| Code suggestions | |
| Debugging | |
| Improving documentation | |
| Refining design ideas | |
| All AI-generated code was reviewed, tested, and modified by me to ensure it works for this project. | |
| 📌 Notes | |
| Sample Tesla datasets are synthetic and created for class demonstration. | |
| Dashboard is for educational use only. |