Machine Learning Information Technology

MEHUL PARMAR

380-***-**** Somerset, NJ *****

*****.******@**.***.*** ***************@*****.*** GitHub LinkedIn CAREER OBJECTIVE:

To be a knowledge-oriented professional working in a highly competitive and healthy environment to explore my skills and knowledge in the advancement of a reputed organization. To carry out the task assigned to me with the best of my efforts, working dedicatedly towards the success of the organization. To work consistently and remain committed to performing the task assigned to me enthusiastically. EDUCATIONAL QUALIFICATION:

Long Island University, Brooklyn, NY Expected: May-2025 MS Artificial Intelligence

(Up to Sem 3 GPA: 3.89 / 4.0)

Gujarat Technological University, Gujarat, India June-2022 Bachelor's of Engineering, Information Technology

GPA: 3.55

TECHNICAL SKILLS:

Programming Languages: Python, Machine Learning, Data Science, HTML, CSS, JavaScipt

Frameworks: Bootstrap, Django, NodeJS

Databases: MYSQL, SQL, SQLite

OS: Windows, Linux

Version Control: GitHub, Git

Learning Deep Learning, AI & ML in Bioinformatics, and Image Vision Computing.

Soft Skills: Communication skills, Problem-solving, Time management, Leadership, Team player, Interpersonal skills.

PROFESSIONAL EXPERIENCE:

Python Developer August 2021-May 2023

BrainyBeam Technologies Pvt. Ltd. (Ahmedabad, GJ, India)

Writing efficient, reusable, testable, and scalable code

Understanding, analyzing, and implementing – Business needs, feature modification requests, and erosion software components

Developing – Backend components to enhance performance and receptiveness, server-side logic, and platform, statistical learning models, and highly responsive web applications

Designing and implementing – High availability and low latency applications, data protection, and security features

Performance tuning and automation of the application

Working with Python libraries like Pandas, NumPy, etc.

Creating predictive models for AI and ML-based features

Implement security and data protection solutions

Coordinate with internal teams to understand user requirements and provide technical solutions PROJECTS:

Medical Image Analysis Code Spring2024-LIU

I spearheaded a project focused on medical image analysis, leveraging deep learning techniques and aiming to enhance clinical diagnosis, treatment planning, and disease management.

Developing robust models utilizing Convolutional Neural Networks (CNN), Random Forest and Support Vector Machines (SVM).

I achieved impressive accuracies of 86%, 85% and 81%, respectively.

By employing Python and Jupyter Notebook, along with essential libraries such as NumPy, PyTorch and OpenCV, I meticulously crafted classification reports, confusion matrices, ROC curves and AUC scores for comprehensive model evaluation.

With a primary focus on brain images derived from CT scans and X-rays, the project aimed to significantly improve diagnostic precision and reduce interpretation time, thereby elevating the standard of patient care within clinical environments.

Autism Spectrum Disorder Detection Code Spring2024-LIU

Conducted research focusing on leveraging machine learning techniques for Autism Spectrum Disorder(ASD) detection, aiming to enhance screening efficiency and diagnostic accuracy.

Utilizing Python and Jupyter Notebook, I explored various classification algorithms including Decision Trees, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (K-Neighbors), Logistic Regression and Mutli-layer Perceptron.

I meticulously evaluated model performance using classification reports, confusion matrices, ROC curves, AUC scores, and F-beta scores.

By addressing the shortcomings of existing ASD screening tools, particularly in alignment with DSM-5 criteria, the project aimed to contribute toward more reliable and effective diagnostic methodologies. Android Malware Detection Model Code Fall2023- LIU

I have developed an Android Malware detection model using Python and Machine Learning principles.

The system analyzed mobile application characteristics to differentiate between benign and malicious software.

Using various machine learning algorithms enhanced the detection system's accuracy and effectiveness.

Evaluation metrics like Confusion Matrix, ROC Curve, and Precision-Recall Curve provided valuable system performance insights.

This project significantly contributed to mobile security, mitigating security risks and safeguarding user data and privacy.

Cyberbullying Detection Model Code Fall2023- LIU

Developed a Python-based machine learning model using Gradient Boosting, SVM, Random Forest, and Naive Bayes algorithms to detect cyberbullying incidents on social media platforms.

Achieved high accuracy and efficiency through extensive testing, including accuracy, precision, and F1-score evaluation, as well as confusion matrix analysis. Contributed to creating a safer online environment by proactively identifying and addressing cyberbullying behaviors. Financial Monitoring System April-2022 - GTU

I have developed a user-friendly Financial Data Monitoring and Evaluation module using Python, SQLite, HTML, and CSS. Designed to streamline daily expense management for both non-salaried and salaried individuals, the system eliminates manual paperwork and ensures systematic record management.

Leveraged basic machine learning libraries to enhance functionality and usability. Contributed to creating an accessible and efficient tool for end-users to track and manage their expenses seamlessly. ACHIEVEMENTS AND CERTIFICATION:

Runner up in Smart India Hackathon-2020.

Certification in HTML5 by Michigan University through Coursera.

Certification in Cybersecurity and the Internet of Things Course by Kennesaw State University through Coursera.

Certification in Python by Ganpat University.

Certification HTML, SQL, and PHP Course in Solo Learn.

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