Wardah Haq

I’m a Computer Science undergraduate developing practical, end-to-end projects across machine learning, web applications, and relational database design. I’ve strengthened my problem-solving approach through hands-on work with Python, JavaScript, and SQL, along with foundational knowledge in machine learning, data mining, and data science.

In my ML work, I reproduced a research-based study for student performance prediction using SVM, Decision Tree, KNN, and Naïve Bayes, including GridSearchCV tuning and SMOTE. I also built a telecom customer churn pipeline with an ensemble of Random Forest, LightGBM, and XGBoost, running a full ML workflow (EDA, imputation, feature engineering, stratified 5-fold cross-validation, and threshold optimization) to reach a public leaderboard F1 score of 0.7967 on a 24,000+ record dataset.

Alongside ML, I created a responsive Next.js website with server-side rendering and sub-2s load time, using optimized component architecture and Framer Motion animations. I designed a normalized NADRA-style relational database schema across 5+ entity tables with enforced referential integrity, and developed a 3-level Unity horror game in C# with collision, enemy behavior, and scene transitions—while gaining real-world collaboration experience through internships in social media marketing and HR.