About Me
A Data Science aspirant with hands-on experience building, optimizing, and deploying machine learning models using Python and Flask. My background includes working across the complete ML lifecycle—from Eexploratory Data Analysis and feature engineering to model training, evaluation, and real-time deployment. During my internship as a Machine Learning Engineer, I contributed to developing production-ready ML solutions, achieving measurable improvements in model accuracy, performance, and reliability. I have worked with structured and unstructured datasets, applied supervised and unsupervised learning techniques, and built scalable pipelines to convert raw data into actionable insights. Alongside machine learning, I actively work on analytics and visualization projects using Power BI, SQL, and Python to support data-driven decision-making in business and financial domains. I enjoy solving real-world problems, collaborating with teams, and continuously learning new technologies. I am currently seeking opportunities where I can grow as a Data Scientist or Graduate Engineer, contribute to impactful projects, and learn from experienced professionals in a collaborative environment. 📌 Core Interests: Machine Learning • Data Science • Data Analytics • Predictive Modeling • Business Intelligence • Real-Time ML Applications 🌐 Portfolio: azamkhan5.github.io
Skills & Technologies
Work Experience
Machine Learning Engineer Intern
Engineered Multi-Disease Health Risk Prediction System achieving 93% accuracy across five disease categories using SVM, KNN, and Random Forest. Improved model accuracy by 15% and reduced false positives by 10% through hyperparameter optimization. Reduced overfitting by 20% by implementing regularization and feature selection techniques. Performed exploratory data analysis on 50,000+ records, uncovering key patterns in customer behavior that optimized marketing ROI Deployed ML models via Flask REST APIs, enabling real-time health risk predictions through a user-friendly interface.
Projects
Multi-Disease Health Risk Prediction System
Built an ML-based early risk prediction system (93% accuracy) across five diseases, improving reliability by 15%. Designed the end-to-end pipeline (EDA, feature engineering, tuning) and deployed scalable Flask APIs for real-time screening.
Language Detection System
Engineered a 22-language text classification model (95%+ accuracy) to automate multilingual content routing. Implemented efficient preprocessing and vectorization pipelines and deployed a real-time Streamlit inference app.
Customer Churn Forecasting with Deep Learning
Developed a deep learning model (85% accuracy) to proactively identify high-risk telecom customers and enable targeted retention strategies. Applied regularization and performance optimization to improve generalization and deployed a real-time inference API for operational use.
Banking Risk Analytics Dashboard
Created a centralized risk analytics dashboard to enhance loan monitoring and credit decision-making. Developed DAX-based KPIs and customer segmentation models for improved portfolio visibility.
Education
Bachelor of Technology
Full Time