Hi, I'm Mullai

Welcome to My Data science Portfolio!

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About Me

Profile

As an aspiring data scientist, I am driven by the transformative potential of data and technology to create meaningful solutions. My professional journey is characterized by a relentless pursuit of innovation, specializing in data analysis, visualization, machine learning, Deep Learning, and Generative AI.

I am dedicated to leveraging data science as a powerful tool for solving real-world problems and driving impactful technological advancements. I approach every project with a mindset of continuous learning and strategic experimentation, seeking to consistently improve my skills and contribute to innovative solutions.

Projects

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Flipkart E-Commerce Chatbot

Developed an AI-powered customer support chatbot using LLaMA 3.3 for intent classification, ChromaDB for FAQ retrieval, and SQLite for real-time product data.

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Document Intelligence RAG System

Built an AI chatbot for querying PDF documents using a LangChain RAG pipeline, LLaMA 3.3, and ChromaDB, achieving 95% retrieval accuracy with low latency.

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Car Damage Classifier

Developed a ResNet50 CNN model to classify car damage, achieving 77.91% accuracy through fine-tuning and data augmentation for an automated damage detection system.

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Credit Risk Prediction Model

Built an interpretable Logistic Regression model for credit risk prediction, achieving a 98.4% AUC and using SMOTE-Tomek to improve the detection of high-risk applicants.

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Health Insurance Cost Predictor

Built an interpretable Logistic Regression model for credit risk prediction, achieving a 98.4% AUC and using SMOTE-Tomek to improve the detection of high-risk applicants.

Skills

Python Programming SQL Inferential Statistics Descriptive Statistics Machine Learning Deep Learning Data Cleaning Feature Engineering EDA Data Visualization Generative AI Data Storytelling Communication Skills

Tools and Technologies

Python SQL Scikit-learn TensorFlow PyTorch NLTK LangChain Hugging Face Pandas NumPy Matplotlib Seaborn Power BI Streamlit GitHub

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