Aspiring AI Engineer · Machine Learning Engineer · Data Scientist

Building data-driven applications and intelligent systems with Machine Learning, Deep Learning, NLP, and Generative AI.

I'm a Software Engineering graduate transitioning into Data Science and Artificial Intelligence, with a focus on building practical, end-to-end machine learning systems.

I enjoy working across the complete AI lifecycle — from data preparation and feature engineering to model development, evaluation, deployment, and visualization.

• Transitioning into Data Science & Artificial Intelligence
• Building practical Machine Learning and AI projects
• Exploring Data Analytics, NLP, Computer Vision, and Predictive Modeling
• Learning Deep Learning, Transformers, LLMs, RAG, and AI Agents
• Interested in MLOps, AI system design, and cloud deployment
• Combining software engineering with AI to build deployable applications

End-to-end credit scoring project for Probability of Default (PD) prediction using feature selection, explainability, and machine learning.

Tech: Python · Pandas · Scikit-learn · SHAP · Machine Learning

Focus:

• Credit risk modeling
• Probability of Default prediction
• Correlation-based feature selection
• Information Value (IV)
• SHAP-based model explainability
• Machine learning model evaluation

End-to-end NLP application for classifying emails as Spam or Ham using TF-IDF feature extraction and a Perceptron classifier.

Tech: Python · Scikit-learn · NLP · TF-IDF · Perceptron · Streamlit

Highlights:

• 193K+ email records
• TF-IDF with unigram and bigram features
• ~97.7% classification accuracy
• Interactive Streamlit application

Machine learning project exploring ensemble learning techniques for heart disease prediction.

Tech: Python · Scikit-learn · Random Forest · Bagging · Ensemble Learning

Focus:

• Data preprocessing
• Feature analysis
• Ensemble model development
• Model comparison
• Accuracy, F1, and ROC-AUC evaluation

Time-series forecasting project focused on predicting future restaurant sales from historical transaction data.

Tech: Python · Pandas · Scikit-learn · Random Forest · Feature Engineering

Focus:

• Historical sales analysis
• Lag features
• Rolling statistics
• Short-term forecasting
• Model comparison and evaluation

A production-oriented computer vision project for automated employee attendance using live camera input and facial recognition.

Tech: Python · OpenCV · Computer Vision · Face Recognition · Machine Learning

Focus:

• Live camera capture
• Face detection and recognition
• Employee registration
• Attendance tracking
• Modular AI system architecture

Python
   ↓
Data Science & Statistics
   ↓
Machine Learning
   ↓
Deep Learning
   ↓
Computer Vision & NLP
   ↓
Transformers
   ↓
Large Language Models
   ↓
RAG & AI Applications
   ↓
AI Agents
   ↓
MLOps & Deployment
   ↓
AI System Design

• Advanced Python for Data Science
• Statistics & Probability
• Machine Learning
• Deep Learning
• Computer Vision
• Natural Language Processing
• TensorFlow & PyTorch
• Transformers & Hugging Face
• Large Language Models
• Retrieval-Augmented Generation
• AI Agents
• MLOps & Model Deployment
• Cloud Computing

My goal is to grow into an AI Engineer and Machine Learning Engineer capable of designing and deploying intelligent systems that solve real-world problems.

Software Engineering + Data Science + Machine Learning + Artificial Intelligence

"The best way to predict the future is to invent it." — Alan Kay