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Second-year Data Science student actively pursuing Data Science & Machine Learning Internships.
Second-year Data Science student at NUST with a profound interest in understanding the data by transforming messy datasets into useful insights to help the decision-making process. My work mainly sits at the intersection of data science and Machine Learning. I have implemented complete machine learning pipelines after preprocessing multiple datasets using different tools. My main focus has mostly been around healthcare and other fields. Lately, I've been intrigued by intricacies of data in other fields mainly economics and finance. I am looking for internship/job opportunities so I can gain hands-on expertise in my field and understand the real intricacies of the real-world data, working in a collaborative environment under the supervision of domain experts to gain useful skillset.
National University of Science & Technology (NUST)
B.S. · Data Science
N/A – June 30, 2028
Health Data Dashboard & EDA Platform
April 1, 2026 – May 1, 2026
Performed comprehensive EDA on health-related datasets, cleaning and transforming raw records into analysis-ready structures. Designed multi-page Streamlit dashboards featuring heatmaps, confusion matrices, distribution plots, and correlation matrices for non-technical stakeholders. Surfaced clinically relevant patterns (feature correlations, class imbalances) that informed downstream modelling decisions.
View ProjectGlobal AI Adoption in Education - Statistical Analysis & Forecasting
April 1, 2026 – May 1, 2026
Analyzed a global AI-in-education dataset (10 countries, 2015-2026) to study adoption trends. Applied One-way, Two-way ANOVA and nonparametric tests (Kruskal-Wallis, Mann-Whitney), finding no significant regional or policy-based differences. Built a Multiple Linear Regression model identifying urban AI usage and teacher adoption as key predictors. Performed probability distribution fitting (Normal Gamma, Exponential) with goodness-of-ערך. Developed ARIMA models for time-series forecasting
Heart Disease Prediction & Streamlit Application
March 1, 2026 – May 1, 2026
Built a hybrid (supervised+ unsupervised) ML pipeline (data cleaning → feature engineering → model training) on a Cleveland Heart Disease dataset. Benchmarked Logistic Regression, Random Forest, SVM, PCA and K-means via 5-fold cross-validation and confusion matrix analysis, achieving 93% accuracy on the test set. Built an interactive Streamlit application enabling a real-time cardiac prediction.
View ProjectAI-Powered Student Habit Tracker
November 1, 2025 – December 1, 2025
Developed a productivity-tracking application that ingests student habit data and applies ML algorithms to detect performance trends. Used Pearson Correlation to identify the strongest habit predictors of academic performance, and Decision Trees / Random Forest for personalized improvement suggestions.
Disaster-Relief Database Management System
April 1, 2025 – May 1, 2025
Designed a normalized relational schema to manage resource allocation, volunteer coordination, and incident tracking for NGOs operating during natural disasters. Wrote complex SQL queries (joins, aggregations, stored procedures) to generate real-time operational reports under high-pressure data conditions.
Certificate of Participation – Elva Tech Workshop
AIESEC Pakistan
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in applying data science to diverse fields such as healthcare, education, and disaster relief, indicating adaptability and a broad perspective. The focus on collaborative environments mentioned in the professional summary suggests a team-oriented mindset. The academic nature of all projects, however, means there is no direct evidence of experience in a corporate or startup culture.
Soft Skills & Operational Fit
The candidate's resume highlights 'Strong Communication, Leadership skills, Lucidity' as soft skills. The project descriptions indicate an ability to present complex data to non-technical stakeholders, suggesting good communication. However, without direct assessment data, the operational fit and depth of these soft skills cannot be fully validated.