01 Synopsis
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MCA Project Report
MCA Data Sanitization and Restoring Using Python and ML Report
Complete MCA final-year project report with documentation, diagrams and viva-ready structure. Instant PDF & Word download — plagiarism-free and faculty-aligned.
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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Abstract
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Table of Content
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01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
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04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
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05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
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07
Chapter 7 — Results and Discussion
- All screenshots
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08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
Project's Overview
DataSecure Pro is a final year project built with Python Flask and machine learning for students who want a practical data privacy and sanitization web application. This final year project allows users to upload CSV or XLSX datasets, preview data, run sanitization modes such as masking, anonymization, cleaning, encoding, and sensitive-column detection, then download sanitized outputs. Users can also request controlled restoration of sanitized files, while admins approve, reject, or run restoration workflows using stored mappings where possible. The admin side of this final year project includes users, categories, sensitive data types, sanitize/restore rules, datasets, sanitized files, restoration tickets, ML models, value mappings, reports, and audit logs. With ML-assisted column detection, KPI exports, and governance workflows, DataSecure Pro is suitable for a final year major project in data privacy, Flask, ML, and cybersecurity.
Login Credentials
Administrator
- Username:
admin - Password:
ChangeMeAdmin!2026or the value ofSEED_ADMIN_PASSWORD - Email:
[email protected]
Demo Users
All demo users use password: user123 unless changed in seed logic.
priyanair— owns bundledseed_sample.csvdataset rowjohnc
- Admin dashboard for this final year project with users, datasets, sanitized/restored artefacts, mappings, pending restoration tickets, and recent audit events
- User search, dossier view, edit, active toggle, and purge account workflow
- Per-user file list
- Category CRUD with delete blocking when datasets reference the category
- Sensitive data type CRUD with delete blocking when rules reference the type
- Sanitize and restore rule management with JSON config and active flag
- Global dataset list/search and dataset dossier with lineage
- Dataset status, category, and workspace field editing
- Deep dataset delete with cleanup
- Original dataset preview
- Operator-triggered sanitization
- Sanitized file browsing, search, detail, preview, compare, and deep delete
- Restoration ticket queue with approve, reject, and run restoration actions
- ML model registry and Train/refresh workflow
- Value mapping list with dataset filters, pagination, delete, and bulk old mapping delete
- Sanitization and restoration KPI reports
- CSV and PDF report downloads
- Audit log filtering by action and user ID
- Preview and compare tools for original, sanitized, and restored rows
- User login and portal access in this final year project
- Dashboard with datasets and recent activity overview
- Upload CSV/XLSX tabular files within configured size limits
- Optional category assignment and workspace notes
- My datasets list with metadata, preview, edit, delete, and original download
- Sanitization mode selection for a dataset
- ML column detection preview
- Sanitization engine using ML detection and rules
- Sanitized output listing and detail
- Sanitized file compare/preview flows
- Sanitized file delete or download
- Restoration request creation against sanitized files
- Column-scope restoration request handling
- Restoration status tracking
- Restored artefact downloads when completed and allowed
- User-visible KPI-style reports
- Account history and profile update
- Public landing page included in this final year project
- SQLite default database with
DATABASE_URLoverride - CSV and XLSX upload support
- ReportLab-based PDF reporting
- SensitiveColumnDetector trained on curated column labels
- RandomForest model using TF-IDF word/character n-grams and lexical features
- Heuristic support for email and phone-like cell patterns
APP_DATA_ROOTsupport for writable shared-hosting storage- Seed script with demo users, categories, rules, and sample CSV
- Upload folders for original, sanitized, and restored files
- ML model storage under
ml_models/ - Optional scripts for building sensitive column labels and importing CSV headers
- Suitable for final year project demo, viva, source code review, and report preparation
- Open the final year project folder:
cd "Dual Data Sanitization and Restoration Using Machine Learning" - Create a virtual environment:
python -m venv .venv - Activate the environment:
- Windows:
.venv\Scripts\activate - macOS/Linux:
source .venv/bin/activate
- Windows:
- Install dependencies:
pip install -r requirements.txt - Optional: set environment variables such as:
SECRET_KEYDATABASE_URLAPP_DATA_ROOTSEED_ADMIN_PASSWORD
- Seed the database and demo files:
python seed.py - Run the final year Flask project:
python run.py - Open:
http://127.0.0.1:5000 - First-time flow: login as admin or seeded user, upload a dataset, preview ML detection, run sanitization, and test restoration workflow
README Included
Setup Support Available
Administrator
- Username:
admin - Password:
ChangeMeAdmin!2026or the value ofSEED_ADMIN_PASSWORD - Email:
[email protected]
Demo Users
All demo users use password: user123 unless changed in seed logic.
priyanair— owns bundledseed_sample.csvdataset rowjohnc