BSc IT Project Guide: Normalization Tool
1. Project Title
Normalization Tool to standardize and normalize data for analysis
2. Objective
To develop a web-based or desktop application that standardizes and normalizes datasets to prepare them for data analysis or machine learning.
3. Introduction
Data normalization is a key step in data preprocessing that ensures consistent scaling and formatting across datasets. This project will build a tool that can handle various normalization techniques such as Min-Max scaling, Z-score standardization, and decimal scaling.
4. Tools and Technologies
- Frontend: HTML, CSS, JavaScript (React or Vue.js)
- Backend: Python (Flask or Django)
- Libraries: NumPy, pandas, scikit-learn
- Database: SQLite or PostgreSQL (optional)
- Deployment: Localhost or cloud-based platform
5. Functional Requirements
- Upload CSV or Excel datasets
- Choose normalization method (Min-Max, Z-score, etc.)
- Preview data before and after normalization
- Download normalized dataset
- View basic statistics of each feature
6. Non-Functional Requirements
- User-friendly interface
- Fast data processing
- Secure file handling
- Cross-platform compatibility
7. System Architecture
Client (UI) → Backend Server (Normalization Engine) →
Optional Database
Frontend sends uploaded data to backend → Backend processes it → Sends back
normalized output
8. Project Modules
- User Interface Module
- File Upload and Validation Module
- Normalization Engine (core logic)
- Data Visualization Module (basic plots)
- Export/Download Module
9. Future Scope
- Integrate more preprocessing tools like encoding and
scaling
- Provide data visualization dashboards
- API support for batch processing
10. Conclusion
This tool aims to assist data scientists and analysts in cleaning and preparing their data efficiently. It reduces the effort in normalization and helps ensure high-quality input for further analysis.