Unexpected Import Detection using Django

Overview

This project, developed within the Django framework, aims to detect and report Unexpected Imports and Exports within CSV data. The objectives involve detecting discrepancies in unit prices and comparing goods against commodity descriptions.

Specifications

• Developed in Django framework to leverage its robust capabilities.
• Utilizes Python's pandas library to process uploaded CSV data efficiently.

Pandas and Django Integration

• Pandas: Provides a high-level interface for data analysis, integrated into Django's structure for efficient handling of CSV data.
• Django: Empowers the project with structured architecture and seamless handling of user interactions.

Milestones

• UnderValue/OverValue: Calculates unit prices, categorizes data as undervalued or overvalued.
• String Comparison: Compares commodity and goods descriptions to flag misclassifications.

Technical Documents

• UML Diagram: Visual representation of software system aspects.
• ER Diagram: Illustrates relationships between entities.
• DFD (Data Flow Diagram): Depicts data movement within the system.

File Structure

• UI: Includes "signin" and "register" functions with Tailwind CSS for improved user interface, integrated into Django's views and templates.

User Interaction

• Login Page: Functionality explained for user authentication.
• Registration Page: Details about user account creation, integrated into Django's views and forms.

Database and Django ORM

• Utilizes Django's ORM with SQLite3, utilizing a model named CSVData to store and manage relevant data efficiently.

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