This project demonstrates how to build a Streamlit web application for analyzing product defects using data visualization techniques in Matplotlib and Seaborn. The dataset, sourced from Kaggle, contains product defect reports with repair costs, defect severity levels, and inspection methods. The goal is to generate insights through interactive visualizations and streamline data exploration.
- Introduction
- Data Visualization with Matplotlib and Seaborn
- Building an Interactive Dashboard with Streamlit
- Running the Streamlit App
- Conclusion
Understanding and effectively communicating sentiment analysis results requires clear and insightful visualizations. This project showcases how to use Matplotlib and Seaborn to create meaningful charts and how to build an interactive web application using Streamlit.
We cover several visualization techniques, including:
- Bar Charts: Used to compare sentiment distributions across different categories.
- Line Charts: Illustrating sentiment trends over time.
Each chart includes customization techniques such as adding labels, adjusting legends, and using color palettes to enhance readability.
The project extends visualization capabilities by integrating Streamlit to build an interactive dashboard. Users can:
- Select different datasets for visualization.
- Apply filters to refine insights.
- View dynamic updates based on user input.
To run the interactive dashboard, follow these steps:
- Install dependencies:
pip install -r requirements.txt
- Run the Streamlit application:
streamlit run app.py
- Open the provided URL in your web browser to explore the dashboard.
This project demonstrates how combining Matplotlib, Seaborn, and Streamlit can enhance the presentation and interpretation of analysis results. The interactive visualizations provide a powerful way to explore data and derive meaningful insights.
For more details, refer to the full blog post medium.com.