Arvindh Babu V
About Me
Hello! I am Arvindh Babu, a dedicated Artificial Intelligence student at SRM TRP Engineering College, passionate about leveraging technology to solve real-world problems. With a strong foundation in data science, machine learning, and web development, I am continuously expanding my expertise to build intelligent systems that drive innovation. My journey in AI began with a deep curiosity about how data-driven insights can shape industries. Over time, this curiosity evolved into hands-on experience, earning me an IBM Professional Certificate in Data Science and the Machine Learning Foundation in AWS Educate. These experiences have strengthened my ability to analyze data, build predictive models, and develop interactive web applications. Currently, I am working on a mini-project—an AI-powered educational chatbot—to enhance learning experiences. Additionally, I am preparing to write a research paper, aiming to contribute valuable insights to the AI community. My goal is to secure a high-paying role in data science or AI, where I can make a significant impact through innovation and automation. Beyond academics, I enjoy playing chess, a game that sharpens my strategic thinking, and curating music playlists that reflect my moods and inspirations. Whether it’s developing AI models, optimizing web applications, or exploring emerging technologies, I am always eager to learn, collaborate, and push the boundaries of what’s possible with AI.
AI-DS STUDENT
"It's not who I am underneath, but what I do that defines me."
- ♠ Birthday: 23 August 2006
- ♠ City: Tiruchirappalli, Tamil Nadu, India
- ♠ Languages Known: Tamil, English, Telugu
- ♠ Hobbies: Reading Books, Watching Movies
- ♠ Favourite Books: Hamlet, War and Peace
- ♠ Favourite Movies: Interstellar, Fight Club
- ♠ College: SRM TRP Engineering College
- ♠ Degree: Bachelor of Technology
- ♠ Branch: Artificial Intelligence and Data Science
- ♠ CGPA: 8.0
- ♠ Strengths: Leadership, Patience, Team work
- ♠ Favourite Sport/Game: Cricket, Chess
Passionate about innovation and driven by curiosity, I aim to push the boundaries of AI and Data Science to create impactful, real-world solutions. I thrive on transforming ambitious ideas into cutting-edge advancements that inspire change and unlock new possibilities. Committed to collaboration and learning from diverse perspectives, I believe in technology's power to drive progress, enhance lives, and build a sustainable future. Let’s harness AI as a force for good and shape a legacy of innovation for generations to come.
Skills
Programming Languages
Computer Science Fundamentals
Data Science & AI
Software Tools
Libraries and Frameworks
Cloud Computing & Big Data
Python Development
Web Development
Mathematics & Analytics
Soft Skills
Experience
Hands-on internships and virtual experiences in data science, web development, and cloud architecture, showcasing my ability to apply technical skills to real-world challenges and deliver impactful results.
Data Science Virtual Intern - CodSoft
February 2025 – March 2025
- Developed predictive models with 85%+ accuracy for business decision-making.
- Conducted data preprocessing, feature engineering, and statistical analysis on real-world datasets.
- Created interactive data visualizations using Matplotlib & Seaborn, providing actionable insights.
Web Development Virtual Intern - InternPe
February 2025 – March 2025
- Built responsive web applications, reducing load time by 30% through optimized front-end development.
- Developed and integrated RESTful APIs, enhancing website functionality and user experience.
- Improved website performance and user engagement through debugging and UI enhancements.
AWS APAC Solutions Architecture Virtual Experience – Forage
February 2025
- Designed a scalable hosting architecture using AWS Elastic Beanstalk, improving response times.
- Provided cost estimation strategies to optimize cloud resource utilization.
Foundation of AI Virtual Intern - Edunet Foundation
May 2025 – June 2025
- Built and trained a CNN model using TensorFlow/Keras to classify fire-based image data.
- Evaluated model performance using accuracy, confusion matrix, and classification reports.
- Documented the entire ML pipeline and presented the project to peers and mentors.
Data Analytics Virtual Intern – Edunet Foundation
May 2025 - June 2025
- Built an interactive Power BI dashboard to analyze energy consumption across electricity, gas, and water utilities.
- Cleaned and transformed raw CSV datasets using Excel to ensure accuracy and consistency.
- Designed custom visualizations, KPIs, and slicers to highlight cost trends, usage patterns, and category-wise insights.
Generative AI Virtual Intern – Prodigy Infotech
July 2025 - August 2025
- Completed 5 end-to-end projects on Generative AI using cutting-edge models and tools (GPT-2, Diffusion, pix2pix, VGG19).
- Worked extensively with Python, TensorFlow, PyTorch, HuggingFace, Matplotlib, Colab, and PIL.
- Developed a Text Generation system, Image Generation, Image Captioning System, Image-to-Image Translation, Neural Style Transfer.
Projects
"Explore my collection of projects that demonstrate my proficiency across various fields, such as robotics, IoT, machine learning, and web development, showcasing my ability to craft innovative solutions to real-world challenges."
Stock Price Prediction
A deep learning-based system that predicts future stock prices using Long Short-Term Memory (LSTM) networks. The model leverages historical stock market data to capture temporal dependencies and generate accurate forecasts. Implemented in Python, it combines data preprocessing, time series modeling, and visualization to aid in investment decision-making.
- Libraries: Pandas, NumPy, Matplotlib, Scikit-learn, TensorFlow/Keras
- Features: LSTM-based time series prediction, data normalization, trend visualization
- Impact: Provides a robust forecasting tool for investors and analysts to anticipate market trends and make data-driven decisions
Heart Disease Prediction
A robust system that predicts heart disease using diagnostic medical data through advanced classification techniques. The model employs a Random Forest classifier to identify at-risk patients and integrates SHAP for explainable AI, providing insights into feature importance. Implemented in Python, it encompasses data preprocessing, model evaluation, and visualization to support healthcare decision-making.
- Libraries: Pandas, NumPy, Matplotlib, Scikit-learn, SHAP
- Features: Random Forest classification, SHAP-based explainability, data preprocessing, performance visualization
- Impact: Empowers healthcare professionals with a reliable diagnostic tool to identify heart disease risks and make informed decisions
Movie Recommendation System
An AI-powered system that recommends movies based on user preferences using collaborative and content-based filtering techniques. Built with Python and machine learning algorithms, it analyzes user ratings, movie metadata, and behavioral patterns to deliver personalized recommendations.
- Libraries: Pandas, NumPy, Scikit-learn
- Features: Content-based and collaborative filtering, user profiling, similarity score computation
- Impact: Enhances user experience by providing accurate and scalable movie recommendations across diverse genres
Fake News Detection
A machine learning-based system that identifies and classifies fake news articles using natural language processing (NLP) techniques. The model analyzes textual data to distinguish between reliable and deceptive content by leveraging supervised learning algorithms and vectorization methods.
- Libraries: Pandas, NumPy, Scikit-learn, NLTK/TfidfVectorizer
- Features: Text preprocessing, TF-IDF vectorization, logistic regression/classification model
- Impact: Enhances information credibility by providing an automated tool to combat misinformation across digital platforms
Predictive Analytics & Machine Learning Models
Developed ML models for Titanic survival prediction, movie rating prediction, and sales forecasting, achieving an accuracy of 90%+. Built a fraud detection system using Random Forest & SMOTE, improving fraud detection rate by 20%. Implemented Iris flower classification using SVM & KNN, reaching 97% accuracy.
- Technologies Used: Python, Scikit-learn, Random Forest, SMOTE, SVM, KNN
- Projects Completed: Titanic Survival Prediction, Movie Rating Prediction, Sales Forecasting, Fraud Detection System, Iris Flower Classification
- Skills Gained: Predictive analytics, Machine learning model development, Feature engineering, Classification techniques, Accuracy optimization
Generative AI Projects
Completed 5 end-to-end projects on Generative AI using cutting-edge models and tools (GPT-2, Diffusion, pix2pix, VGG19). Developed a Text Generation system, Image Generation, Image Captioning System, Image-to-Image Translation, Neural Style Transfer.
- Technologies Used: Python, TensorFlow, PyTorch, HuggingFace, Matplotlib, Colab, and PIL.
- Projects Completed: Text Generation system, Image Generation, Image Captioning System, Image-to-Image Translation, Neural Style Transfer.
- Skills Gained: Generative Adversarial Networks (GANs), Diffusion Models (DDPMs), Transformers (GPT-2 architecture), CNN–RNN Encoder–Decoder architecture
Web Development Projects
Developed a Simple Calculator using HTML, CSS, and JavaScript, implementing basic arithmetic operations.Built a fully functional E-Commerce Website, featuring product listings, a shopping cart, and responsive design. Created a To-Do List App, enabling task management with add, delete, and update functionalities.
- Technologies Used: HTML, CSS, JavaScript
- Projects Completed: Simple Calculator, ECommerce Website, ToDo List App, Connect Four Game
- Skills Gained: Front-end development, DOM manipulation, Responsive design
Certifications
"A testament to my commitment to continuous learning and professional growth, these certifications showcase my expertise and dedication to excelling in the fields of AI, machine learning, and software development."
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