Machine Learning
Data Analysis & Model Development
AI & Machine Learning
2 min read
March 2026
Introduction
Machine learning enables computers to learn patterns from data and make intelligent predictions. I've built end to end ML pipelines including data preprocessing, feature engineering, model training, evaluation, and deployment. My work spans classification, regression, and deep learning tasks.
Key Learnings
Data Preprocessing
Learned Cleaning, normalizing, and transforming raw data with handling of missing values, outliers, and feature scaling.
Feature Engineering
Learned Creating meaningful features from raw data using domain knowledge and statistical techniques.
Model Training & Tuning
Learned Training machine learning models with hyperparameter optimization and cross-validation for robust performance.
Model Evaluation
Learned Comprehensive evaluation using appropriate metrics like precision, recall, F1 score, and confusion matrices.
Tools & Technologies
Python
Primary language for ML development with extensive library ecosystem.
Pandas
Data manipulation library for loading, cleaning, and transforming datasets.
NumPy
Numerical computing library for array operations and mathematical functions.
Scikit-Learn
Machine learning library with algorithms for classification, regression, and clustering.
PyCaret
Low-code machine learning library that simplifies model training, comparison, and deployment workflows.
Matplotlib
Data visualization library used to create static, animated, and interactive charts and graphs.
How I Used This in Projects
→DementiaInsight - Non-Medical Dementia Risk Classifier (Dec 2025)
Developed an automated ML pipeline to predict dementia risk using non medical features. Implemented data processing, model training and evaluation, and a CLI prediction tool. Achieved strong performance with LightGBM and was a finalist at the ModelX Inter University Hackathon.
→MedPredict - Medical Cost Prediction Model (Nov 2025)
Developed a Random Forest regressor to predict medical insurance costs using lifestyle and demographic indicators. Deployed via Streamlit to provide a lightweight interactive web interface.
Skills & Tags
Want to explore more?
← Back to Portfolio