I'm
M K Nitheesh
About Me
As a passionate Data Scientist, Data Analyst, and Big Data Professional, I specialize in transforming complex data into actionable insights. My expertise spans Machine Learning, Deep Learning, AI, Python, SQL, and Big Data Technologies like Hadoop and Spark. I’ve built projects like a Medical Insurance Premium Prediction model (86% accuracy), a Disease Prediction System (90% accuracy), a RAG Chatbot using LangChain and Ollama, and a Real-Time Human Detection System with YOLOv8. Additionally, I’ve developed impactful Power BI dashboards and conducted deep SQL-driven analyses. I’m driven by a passion for uncovering hidden patterns and building AI-powered solutions to solve real-world challenges.
githubPython
85%
Machine Learning
95%
Deep Learning
90%
Power BI
85%
Education
Master of Science in Physics
ITM college of Art and Science,Mayyil,Kannur,Kerala
Bachelor of Science in Physics with Computer Science and Mathematics
ITM College of Art and Science,Mayyil,Kannur,Kerala
Data Science and Big Data Course
Luminar Technolab,Kakkanad,Cochin,Kerala
Bachelor of Education
Leelavathi Shetty College of Education,Kavoor,Mangalore,Karnataka
Experience
AI/ML Intern
Training
IPSR Solution Limited
Remote
- ML + Web Integration: Implemented SVM and KNN classifiers for data classification tasks. Integrated them into a Django web application, allowing users to upload data and get real-time predictions.
- Generative AI for Image Synthesis: Experimented with generative models to create synthetic images – exploring how GANs/diffusion-based approaches can augment datasets or generate creative content.
- Power BI Analytics: Built interactive dashboards to uncover patterns in datasets, enabling non-technical stakeholders to explore model performance and data trends visually.
Generative AI Intern
Training
Zep Analytics
Remote
- LLMs & RAG Pipelines:Explored and implemented RAG architectures to enhance LLM responses with external knowledge – reducing hallucinations and improving factual accuracy.
- Data Preparation & Fine‑tuning: Curated and cleaned domain‑specific datasets; performed supervised fine‑tuning (SFT) and parameter‑efficient fine‑tuning (e.g., LoRA) to adapt base models for specialised tasks.
- Documentation & Presentation: Worked closely with mentors to document experiments, training configurations, and evaluation metrics. Presented outcomes to technical and non‑technical stakeholders.
AI - Engineer
Internship
AINQA
Saravanampatti, Coimbatore,Tamil Nadu
- Vector Databases & Embedding Models: Worked with vector databases and embedding models to efficiently store, retrieve, and process high-dimensional data, enhancing model performance.
- Data Preprocessing & Augmentation: Utilized techniques like noise removal, rephrasing, and data augmentation to refine datasets, ensuring high-quality inputs for AI models.
- Fine-Tuning Workflows: Implemented fine-tuning workflows to adapt pre-trained models to specific tasks, optimizing their performance and accuracy for targeted applications.
Data Science and Big Data
Internship
Luminar Technohub
Kakkanad,Cochin
- Advanced Machine Learning & Deep Learning: Implemented supervised and unsupervised learning models, CNNs, and transfer learning techniques for various predictive tasks.
- AI Model Deployment & Interpretability: Built Flask web applications, integrated LIME and SHAP for explainability, and optimized models for real-world use cases.
- Healthcare AI Applications: Worked on disease prediction models, leveraging medical imaging and deep learning for Alzheimer's, Brain Tumor, Diabetic Retinopathy, and more.
Data Science and Big Data Course
Training
Luminar Technolab
Kakkanad,Cochin
- Data Analysis & Visualization: Leveraged Python, SQL, Tableau, and Power BI for data preprocessing, insights extraction, and dashboard creation.
- Big Data & Cloud Technologies: Explored Hadoop, Spark, and cloud computing for efficient data handling and processing.
- Natural Language Processing (NLP): Applied NLP techniques such as text classification, sentiment analysis, and named entity recognition to derive insights from unstructured text data.
Project
Disease Prediction System (DL)
Achieved 90% accuracy with VGG16, VGG19, and DenseNet169 models to predict Alzheimer's, Brain Tumor, and Diabetic Retinopathy, integrating transfer learning and LIME for interpretability. Designed a Flask web app for medical image uploads, ensuring transparent and accurate predictions.
Blinkit Sales Analysis Using SQL (Data Analysis)
Analyzed 8,523 rows of retail sales data with SQL to uncover trends in sales performance, customer satisfaction, and inventory distribution. Devised strategies for inventory management and revenue growth, enhancing outlet performance and identifying geographic sales trends.
Hospital Emergency Room Dashboard (Power BI)
Visualized data from 9,216 patients, showcasing an average wait time of 35.3 minutes and satisfaction score of 4.99/5, streamlining hospital operations. Tracked metrics such as patients seen within 30 minutes and monitored 3,816 department referrals, using real-time data processing to optimize workflow and reduce bottlenecks.
Real-Time Human Detection System (Computer Vision)
Implemented a system with YOLOv8, OpenCV, and Flask to track human movements in live video feeds for enhanced security and surveillance. Created an interactive dashboard with multi-threading and JSON API integration, delivering real-time metrics like human count and peak detections.
LLM Chatbot with RAG (AI & NLP)
Developed a chatbot using LangChain, Ollama LLM (Gemma:2B), and RAG for context-aware responses from a custom knowledge base, integrating PDF processing, Hugging Face embeddings, and Chroma for efficient retrieval. Engineered a real-time Streamlit interface with streaming callbacks for seamless interaction.
Contact
M K NITHEESH
Aspiring Data Scientist
Sandwanam,Thilannur,P O Thazhe Chovva,Kannur,Kerala