Data Scientist & ML Engineer passionate about building intelligent systems that drive real business value.
"The goal is to turn data into information, and information into insight."
- 🔭 Currently building Recommendation Engines using Python, Flask, Hadoop & SQL
- 🤖 Specialised in Machine Learning, Deep Learning, Recommender Systems & Neural Networks
- 📊 Strong business acumen — translating data into decisions across Banking, Retail, and Technology & Media sectors
- 🎬 Content creator on YouTube — teaching Data Science & ML concepts
- 💞️ Open to collaboration on ML, deep learning, recommendation systems, and neural network projects
Languages
 Python |
 JavaScript |
 React |
 HTML5 |
 CSS3 |
 Go |
ML / AI Frameworks
 TensorFlow |
 PyTorch |
 Scikit-learn |
 Keras |
Data & Databases
 PostgreSQL |
 Hadoop |
 Node.js |
Visualisation & Tools
 Power BI |
 Tableau |
 Streamlit |
 Docker |
| Domain | Skills |
|---|---|
| Machine Learning | Supervised & Unsupervised Learning, Ensemble Methods, XGBoost, CatBoost |
| Deep Learning | CNNs, Neural Networks, Transfer Learning, TensorFlow, PyTorch, Keras |
| Recommendation Systems | Apriori, FP-Growth, Naive Bayes, Collaborative Filtering, Association Rules |
| Statistical Analysis | A/B Testing, Hypothesis Testing, Time Series, Survival Analysis, Churn Analysis |
| Data Engineering | ETL/ELT Pipelines, Hadoop, Impala, SQL, Oracle, MLflow |
| APIs & Backend | FastAPI, REST API, Flask, Docker |
| Visualisation | Power BI, Tableau, Matplotlib, Seaborn, Plotly |
| Project | Description | Stack |
|---|---|---|
| 🛒 Recommendation Systems | Apriori, FP-Growth & Naive Bayes recommendation engine on Global Superstore data | Python, mlxtend, scikit-learn |
| 🌸 PyTorch Image Classifier | Transfer learning with ResNet50 to classify 102 flower species | PyTorch, torchvision |
| 🐾 CNN Pet Classifier | Benchmarks ResNet, AlexNet & VGG16 for pet image classification | PyTorch, argparse |
| 🏨 Booking.com Analytics Suite | Hotel cancellation analysis, ratings classification & Tableau dashboards | Python, Tableau, PyCaret |
| 🧪 A/B Test Results Analysis | Bootstrapped hypothesis testing + logistic regression for conversion rate analysis | pandas, statsmodels |
| 💼 Users Churning Predictions | Forecasts the likelihood of customer churn. | Python, Scikit-learn, CatBoost |
| 🚲 US Bikeshare Analysis — Python Command-Line Application | An interactive command-line application that lets users explore US bikeshare trip data across three cities — Chicago, New York City, and Washington. | Python, Pandas, Numpy |
 |
 YouTube |