Zain Waheed

Final-year AI Engineering student skilled in statistical analysis, data-driven modeling, and end-to-end ML deployment. Built and deployed systems including a 1,500+ job posting recommender (cosine similarity, 93 engineered features), a churn prediction API (F1 improved 0.50→0.62), and an NLP classifier (~90% accuracy). Proficient in Python, Scikit-learn, XGBoost, FastAPI, and Docker, with live deployments on Streamlit and Hugging Face.

NLP Intern - Elevo Pathway

(2025-10 - 2025-11)

• Designed and deployed an end-to-end sentiment analysis pipeline on real-world customer text data, achieving ~90% classification accuracy through systematic model tuning and validation.
• Engineered a complete NLP preprocessing pipeline — tokenization, stopword removal, lemmatization, and TF-IDF feature extraction — reducing noise and transforming raw text into structured, model-ready features.
• Evaluated five classifiers (Logistic Regression, SVM, Naive Bayes, Random Forest, XGBoost), selecting the optimal model based on F1-score performance under class imbalance constraints.

Bachelor of Science - Artificial Intelligence - COMSATS University Islamabad, Lahore Campus (2023 - 2027)