Machine Learning Projects

Machine Learning Operations is a set of practices that automates and streamlines the entire lifecycle of machine learning models, from development to production, deployment, and monitoring. By integrating DevOps principles, it reduces technical debt, improves model quality, and enables faster, more reliable, and scalable deployments. Click any project to see details,workflows,code snippets.
The Ocean’s Memory: Regression prediction on Ocean Temperature Using over 60 Years of CalCOFI Environmental Data

The Ocean’s Memory: Regression prediction on Ocean Temperature Using over 60 Years of CalCOFI Environmental Data

Built a leakage-safe regression pipeline on nearly one million CalCOFI oceanographic records, using selected environmental and spatial features to predict Pacific Ocean temperature across over 60 years of observations.

predictive regression analysis mode selection
Open Code Demo
Animal Classification with Regularized Models and Compact Ensembles

Animal Classification with Regularized Models and Compact Ensembles

Created a mini-classifier to separate zoo animals into biological groups using features such as hair, feathers, milk, eggs, fins, legs, and aquatic behavior. Regularized models and compact ensemble methods were compared to build a simple, controlled classification pipeline.

Animal Classification Logistic Regression
Open Code
Forest Cover Type Classification with XGBoost

Forest Cover Type Classification with XGBoost

End-to-end multiclass (seven) classification pipeline using the UCI Forest CoverType dataset with 580K+ instances and 54 features to classify forest cover classes from cartographic and environmental features.

xgboost pipeline preprocessing
Open Code
Parametric and Non-Parametric statistical tests

Parametric and Non-Parametric statistical tests

Built a comprehensive statistical testing workflow covering both parametric and non-parametric methods. The project demonstrates how to choose and apply appropriate tests such as t-test, ANOVA, Mann-Whitney U, Kruskal-Wallis, Wilcoxon, Chi-square, Pearson, and Spearman using Python and SciPy.

parametric non-parametric
Open Code Demo
Ensemble Learning for Penguin Species Multiclass Classification Using Morphological and Ecological Features

Ensemble Learning for Penguin Species Multiclass Classification Using Morphological and Ecological Features

Built a multiclass ML pipeline to classify penguin species using body measurements and ecological features. Compared baseline and ensemble models with cross-validation and tuning to select the best model.

model selection ensemble learning algorithms
Open Code Demo
End-to-End Census Income Classification with EDA and Hyperparameter Tuning

End-to-End Census Income Classification with EDA and Hyperparameter Tuning

Built an end-to-end machine learning pipeline on the Adult Census Income dataset to predict whether an individual earns above or below $50K per year. The project covers data cleaning, EDA, preprocessing, model comparison, hyperparameter tuning, and final evaluation with Gradient Boosting as the best

pandas NumPy scikit-learn
Open Code
Cloud-Native ML Deployment Architecture (Docker, Registry, GKE)

Cloud-Native ML Deployment Architecture (Docker, Registry, GKE)

Building and deploying a machine learning model to predict diabetes risk using patient health data, packaging the model in a Docker container, exposing it through a FastAPI service, and deploying it to Kubernetes with an automated CI/CD pipeline.

container registry kubernetes CI/CD
Open Code Demo
Engineering a Production-Grade LLM Tutor for Structured Mathematics Reasoning

Engineering a Production-Grade LLM Tutor for Structured Mathematics Reasoning

Engineering a production-grade LLM-powered IGCSE mathematics tutoring platform by integrating a React frontend, a Django backend, and PostgreSQL persistence, orchestrating controlled model inference with structured prompt governance, containerizing the system with Docker, and implementing monitoring

LLM Architecture Prompt Engineering MLOPs
Open Code
Price predictive regression analysis, operationalizing Tabular ML: CI/CD, Docker, Kubernetes, Observability

Price predictive regression analysis, operationalizing Tabular ML: CI/CD, Docker, Kubernetes, Observability

Building a regression pipeline to predict housing prices using the California housing dataset, applying preprocessing and feature engineering with scikit-learn, tracking experiments with MLflow, serving predictions through a FastAPI API, containerizing the service with Docker, monitoring it using Pr

EDA GitHub Actions MLFlow
Open Code
Building a RAG Question Answering System Using LLM models and Vector Databases

Building a RAG Question Answering System Using LLM models and Vector Databases

Developing a retrieval-augmented question answering system over 7,500+ pages of Cambridge IGCSE Mathematics past papers by ingesting and chunking exam PDFs, generating embeddings and indexing them in a vector database, retrieving relevant context through semantic search, and generating step-by-step

RAG LangChain ChromaDB
Open
Web Scraping and Analysis of Job Market Data in Germany

Web Scraping and Analysis of Job Market Data in Germany

Collecting and structuring 22K+ job listings from a German job portal using a Python web scraping pipeline, enabling large-scale analysis of sector demand, job distribution, and geographic employment patterns across Germany.

data exploration web scraping
Open Code
Applied Data Science: Analytics, Visualization, and Machine Learning

Applied Data Science: Analytics, Visualization, and Machine Learning

Projects exploring data analysis, machine learning, and interactive visualization using R. The work focuses on analyzing film industry trends, streaming platform datasets, and healthcare data to uncover patterns and meaningful insights.

Data Science & Analytics Projects R
Open Code
Why MLOps Feels Like a Basketball Court at First (Until the Patterns Appear)

Why MLOps Feels Like a Basketball Court at First (Until the Patterns Appear)

Exploring MLOps through simple analogies drawn from everyday learning experiences. Using the ideas of pattern recognition, a crowded basketball court, and Stephen Covey’s “sharpen the saw” principle, it shows how complex systems become understandable once their structure appears.

Open Demo