Data analysis is crucial across industries, but handling raw data efficiently can be a daunting challenge. With this project, I created an Automated Data Analysis pipeline that simplifies data handling and transformation, making it faster.
Why Automated Data Analysis?
Manual processes are time-consuming and error-prone. To solve this, I developed a Python-based pipeline that automates these tasks while ensuring accuracy and scalability
Why Add a UI to Automated Data Analysis?
While command-line tools are powerful, they can be intimidating for non-technical users. The new interactive UI bridges the gap, enabling analysts and business users to:
Upload Excel files directly for analysis.
Generate custom plots and statistical insights without writing code.
Perform outlier detection and correlation analysis interactively.
Features Overview
File Upload for Analysis
The interface lets you upload Excel files with a single click.
Once uploaded, the app automatically Identifies numerical and
categorical columns and display summary statistics.Custom Plot Generation
Select any column and generate visualizations instantly. This is perfect for understanding trends and distributions in your data.Outlier Detection
The app supports outlier detection using methods like Z-Score. Set a threshold value, and it highlights outliers for further investigation.Correlation Heatmap
Generate a heatmap to visualize correlations between numerical features, helping identify patterns and relationships.Pair Plot Generation
The pair plot feature offers a way to explore the relationships between multiple features in a dataset through scatter plots and distributions.Behind the Scenes: How the App Works
File Handling and Data Parsing:
The uploaded Excel file is read into a pandas DataFrame for preprocessing.Dynamic Plotting
Matplotlib and Seaborn are used to create dynamic visualizations based on user input.Outlier Detection
The Z-Score method flags outliers beyond the specified threshold.Interactive Widgets
Streamlit widgets, such as dropdowns, sliders, and file upload buttons, allow users to interact with the app intuitively.
Future Enhancements
- Real-Time Data Streaming: Adding support for live data updates.
- Advanced Analytics: Incorporating machine learning models for predictions and clustering.
Conclusion
The Automated Data Analysis project demonstrates the power of combining automation with interactivity. Whether you’re a business analyst or a data enthusiast, this tool simplifies exploring and analyzing datasets.
UI Screenshots:
Top comments (0)