- 🎓 ICS (Statistics) Student
- 📍 Based in Rawalpindi, Pakistan
- 🤖 Focused on Machine Learning and Artificial Intelligence
- 🐍 Strong foundation in Python and Data Science tools
- 📊 Interested in predictive modeling and data-driven solutions
- 🧩 Completed Deep Learning fundamentals — ANNs, CNNs, and RNNs
- 🌱 Currently learning Transformer Architecture
- 🎯 Goal: Become a Machine Learning Engineer and build AI solutions that solve real-world problems
| Category | Skills |
|---|---|
| Regression | Linear Regression, Logistic Regression |
| Model Selection | Cross Validation (CV), GridSearchCV, RandomizedSearchCV |
| Workflow | Pipelines, Feature Engineering, Data Preprocessing |
| Evaluation | Model Evaluation Metrics, Classification & Regression Models |
| Deep Learning | ANN, CNN, RNN · 🌱 Currently learning: Transformer Architecture |
Programming Language
Data Analysis & Visualization
Machine Learning
Tools & Platforms
Deep Learning
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Deployment & Backend
|
Age & gender estimation, emotion classification, and real-vs-AI-generated image detection — all served through one FastAPI backend with an interactive web UI. Stack: |
End-to-end customer churn prediction comparing a tuned Scikit-Learn pipeline against a Keras ANN, wrapped in a dark/orange Streamlit dashboard and a FastAPI backend. Docker-ready. Stack: |
Production-style fraud detection pipeline — SMOTE vs class-weighting for imbalance, Pydantic-validated FastAPI service, a Streamlit UI, and a database-backed retraining loop. Fully Dockerized. Stack: |
⭐ Note: Repo links above are my best.
"Turning data into decisions, one model at a time." 🤖📈
"Turning data into decisions, one model at a time." 🤖📈