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Final Year Projects with Source Code
Bundle Package (5 Projects)

Final Year Projects with Source Code

Looking for a high-quality Final Year Project with complete sourcecode? CodingMSTR offers carefully developed software projects forComputer Science, IT, AI, Machine Learning, Web Development, Mobile AppDevelopment, and Full-Stack Engineering. Every project is designed tohelp students understand real-world software architecture, developmentpractices, and deployment while accelerating project completion.

Bundle Deal Price₹ 199.00

About this collection

Welcome to the CodingMSTR Final Year Projects Collection, a curated library of industry-inspired software projects created for undergraduate and postgraduate students.

Whether you're pursuing B.Tech, B.E., MCA, BCA, M.Tech, MSc Computer Science, Diploma Engineering, or other software-related programs, our projects help you build an impressive portfolio while learning modern technologies used in professional software development.

Unlike basic academic examples, these projects are built using production-oriented architectures, clean code practices, responsive UI, scalable backend services, and well-organized project structures.

What You'll Find

Our Final Year Project collection includes projects across multiple domains:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision
  • Deep Learning
  • Full Stack Web Development
  • React.js Applications
  • Next.js Projects
  • Node.js Backend Systems
  • Python Applications
  • Flutter Mobile Apps
  • FastAPI APIs
  • Real-time Dashboards
  • Authentication Systems
  • Data Visualization
  • REST APIs
  • Modern UI/UX

Featured Projects

Current projects in this collection include:

  • Python Face Detection Program
  • Online MCQ Assessment & Examination Portal
  • Deepfake Video & Audio Detection Platform
  • Live Flight Tracker Web Application
  • AI-Based Stock Price Predictor

New projects are added regularly.

Ideal For

  • Final Year Students
  • College Projects
  • University Projects
  • Academic Demonstrations
  • Portfolio Building
  • Learning Modern Software Development
  • Research Inspiration

Why Choose CodingMSTR?

  • Complete source code
  • Clean architecture
  • Modern technology stack
  • Beginner-friendly code structure
  • Real-world project design
  • Free and premium options
  • Easy to customize
  • Suitable for viva demonstrations
  • Helps build an interview-ready portfolio

Technologies Covered

Python • React • Next.js • Node.js • Flutter • FastAPI • Machine Learning • AI • Computer Vision • JavaScript • TypeScript

Internal Linking Suggestions

Link this page to: - AI Projects - Python Projects - React Projects - Next.js Projects - Flutter Projects - Machine Learning Projects - Source Code Marketplace


FAQ

What are the best Final Year Projects for Computer Science students?

Projects involving AI, Machine Learning, Full-Stack Web Development, Computer Vision, Cloud Computing, and Mobile App Development are among the most valuable because they demonstrate practical software engineering skills.

Are complete source codes included?

Yes. Every project includes complete source code. Some projects are free, while premium projects include additional documentation and advanced implementations.

Which technologies are used?

Projects use modern technologies such as React, Next.js, Node.js, Python, Flutter, FastAPI, JavaScript, TypeScript, and Machine Learning frameworks.

Can these projects be customized?

Yes. All projects are designed to be extendable so students can modify features and implement additional functionality.

Are these projects suitable for academic submissions?

Yes. They are suitable for demonstrations, learning, portfolio building, and academic final-year project work.

Python Face Detection Program

PythonMachine LearningFinal Year Project

Python Face Detection Program

This project is fully included in the collection bundle!

Buy the bundle above to download this project source code along with all other projects.

Project Description

import cv2

# Load the pre-trained face detection model from OpenCV
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Start the video capture from the webcam
cap = cv2.VideoCapture(0)

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()

    # Convert the frame to grayscale (face detection works better in grayscale)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the frame
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # Draw rectangles around the faces
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)

    # Display the resulting frame
    cv2.imshow('Face Detection', frame)

    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the capture and close any open windows
cap.release()
cv2.destroyAllWindows()