Face recognition ML model | ANDROID APPLICATION TO HELP MISSING CHILDREN
WITH KOTLIN AND FIREBASE
FullStack and ML
Description: Developed an Android app using MLKit, FaceNet, and Java to recognize faces of unattended
children by comparing them against a registered official database of missing children's faces.
Technologies Used: MLKit, FaceNet, Android Java, Python (for custom ML models).
GitHub Repository: (github)Findem
Features:
Facial Recognition: Capturing and comparing faces for identification. Custom Machine Learning Models:
Developed custom ML models using Python for accurate facial recognition.
User-Friendly Interface: An intuitive UI simplifies the process for users.
Web phishing detection (Applied data science)
Description: Developed a web phishing detection application using a machine learning model trained with a
dataset from Kaggle.com. The project includes a backend implemented in Flask and a user interface created
with raw HTML, CSS, and JS. Mentors: IBM Instructors
Technologies Used:
Machine Learning: Random Forest Classifier (97% accuracy) used for web phishing detection.
Backend: Flask framework for building the server-side application.
Frontend: Raw HTML, CSS, and JS for creating the user interface.
Features:
Phishing Detection: Utilized a trained Random Forest Classifier machine learning model to accurately identify
web phishing attempts.
User Interface: Developed a user-friendly interface using HTML, CSS, and JS to interact with the phishing
detection system.
Backend: Implemented a Flask backend to handle requests and communicate with the machine learning model.
Data Collection: Collected and used a relevant dataset from Kaggle.com to train the machine learning model.
Accuracy: Achieved 97% accuracy in phishing detection, ensuring reliable results.
GitHub Repository: (github) Link
Linux TWM
Ai-enhanced Tiling window manager to read and follow user's window usage behavior in Linux systems An AI
based Tiling window manager on top of QTile that automatically adjusts according to past initial and up to usage
of the user. Developed the Model using Decision tree algorithm in google colab and integrated with QTile
window manager.
Education
Panimalar Institute of technology
Bachelor of technology in Information technology
2019 - 2023
CGPA : 8.94/10
Skills
Flutter
React JS
Tailwind CSS
Express JS
Linux
SQL & MongoDB
Spring Boot
Python, Django
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