PARAMESHWARAN H

[email protected] • Tirupattur, Tamil Nadu, India
LinkedIn: linkedin.com/in/parameshwaran-h-26065430 • GitHub: github.com/Paramesh137

PROFESSIONAL SUMMARY

Aspiring Software Developer and AI/ML Engineer with hands-on experience building five production-style applications spanning Machine Learning, Computer Vision, and Full-Stack Web Development. Proficient in Python, React.js, Flask, SQL, and Scikit-learn, with practical exposure to ML model training and evaluation, REST API development, backend logic, and responsive UI design. Built and deployed projects including a car price prediction system (ML regression, R² 0.85) and a real-time facial recognition attendance system, which won 3rd Prize at a college-level hackathon. Seeking a Software Development Engineer (SDE) or AI/ML Engineer internship to apply strong programming fundamentals, problem-solving skills, and end-to-end product-building experience to real-world engineering challenges.

WORK EXPERIENCE

AI/ML Intern
06/2026 - Present
InternPe , Remote
• Engineered an end-to-end machine learning pipeline for car price prediction using the Quikr used-cars dataset, covering data cleaning, feature engineering, model training, and evaluation with Scikit-learn.
• Built and trained a regression model achieving an R² score of ~0.85, validating prediction accuracy against real-world pricing data.
• Developed a Flask REST API to serve model predictions and integrated it with a React.js front-end for real-time price estimation.
• Designed and deployed the application as a complete, production-style full-stack ML product, demonstrating skills across data science, backend development, and UI integration.
App / Python Developer Intern
12/2025 - 01/2026
SKMT Electronics, NSIC Technical Services Centre , Tirupattur,Tamilnadu
• Developed smart home automation controls using Python, integrating hardware/software logic for automated device control.
• Built Python automation scripts to streamline repetitive tasks and improve workflow efficiency.
• Gained hands-on exposure to applied AI and automation system design in a real-world electronics environment.
Web Development Intern
03/2024 - 03/2024
NSIC Technical Services Centre , Chennai
• Designed and built responsive web pages using HTML, CSS, and JavaScript, focusing on cross-device compatibility and clean UI structure.
• Applied front-end development fundamentals in a short-term, hands-on technical training environment.

EDUCATION

B.E., Computer Science and Engineering
09/2023 - 05/2027
PSV College of Engineering and Technology , Krishnagiri, Tamil Nadu GPA: 8.01 / 10
HSC (Class 12)
06/2022 - 05/2023
Government Higher Secondary School, Mallapalli , Mallapalli, Tirupattur GPA: 75%
SSLC (Class 10)
06/2020 - 05/2021
Government Higher Secondary School, Mallapalli , Mallapalli, Tirupattur GPA: 80%

SKILLS

PROJECTS

Car Price Prediction System
Technologies: Python, Scikit-learn, Flask, React
• Engineered a production-ready machine learning pipeline using Linear Regression (R² ≈ 0.85) to predict used car prices from the Quikr dataset, covering data cleaning, feature engineering, model training, and performance evaluation.
• Built and deployed the solution in two forms: a self-contained HTML/JavaScript application for lightweight use, and a full-stack version with a Flask REST API backend and React.js front-end featuring a custom automotive instrument-cluster-inspired UI design.
• Applied data preprocessing and exploratory data analysis (EDA) techniques to handle missing values, outliers, and categorical encoding, improving model reliability on real-world pricing data.
AI Campus Management System
Technologies: Flask, SQLite, OpenCV (LBPH)
• Developed a facial recognition-based attendance and campus management platform using OpenCV's LBPH algorithm, enabling real-time webcam capture and automated identity verification for attendance tracking.
• Designed a relational database schema in SQLite and built role-based dashboards (admin/faculty/student) with secure access control and dynamic data visualization using Chart.js.
• Built backend REST endpoints with Flask to handle real-time image processing, attendance logging, and analytics data delivery to the front-end.
• Won 3rd Prize at the Pre-Incubation Center Hackathon, competing against inter-department teams, for delivering a functional AI-based face-detection solution under time constraints.
AssetFlow — Enterprise Asset Management ERP
Technologies: React, Vite
• Built a full enterprise-grade ERP front-end for asset management, featuring modular dashboards, role-based views, and real-time asset tracking workflows across multiple user roles.
• Architected a scalable, reusable component structure using React.js and Vite, mirroring production-grade ERP UX patterns for maintainability and performance.
• Implemented state management and dynamic UI rendering to handle real-time updates across asset tracking, inventory status, and role-specific dashboard views.
• Optimized build performance and development workflow using Vite's fast bundling and hot-module-replacement (HMR) capabilities.
Diabetes Prediction System
Technologies: Python, Scikit-learn, Pandas
• Built a binary classification model to predict diabetes risk from patient health metrics (glucose levels, BMI, blood pressure, age, etc.), supporting early diagnosis and preventive healthcare decisions.
• Applied data preprocessing techniques including handling missing values, outlier detection, and feature scaling to improve model input quality.
• Performed feature engineering and exploratory data analysis (EDA) to identify key predictive health indicators and improve model performance.
• Evaluated model performance using metrics such as accuracy, precision, recall, and confusion matrix analysis to validate prediction reliability.
IPL Match Winner Predictor
Technologies: Python, Scikit-learn, Pandas
• Built a classification model to predict IPL match outcomes using historical team performance, venue statistics, toss decisions, and head-to-head match data.
• Performed feature engineering to derive predictive variables from raw match data, including team win rates, home/away advantage, and historical matchup trends.
• Applied data preprocessing and exploratory data analysis (EDA) to clean and structure historical IPL datasets for model training.
• Trained and evaluated the model using Scikit-learn classification algorithms, assessing performance through accuracy and other relevant metrics.

CERTIFICATIONS

Oracle Cloud Infrastructure 2025 Certified Foundations Associate
04/2025
Oracle AI Foundations Associate
Data Analytics Job Simulation — Deloitte (Forage)
05/2025
Full-Stack MERN Participation — Nexila Technologies
Cybersecurity & Networking Workshop Certifications
09/2024
Nativeva, Tata Forage

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