BS - AI
National University of Sciences & Technology (NUST) • 2024 - 2028
About
Eesha Raees
I am an Artificial Intelligence student at NUST SEECS with a strong interest in machine learning, software development, and data-driven systems. I enjoy designing and building practical solutions that combine AI techniques with modern software engineering practices.
Skills
P
PythonJ
JavaC
C++J
JavaScriptS
SQLML
Machine LearningDL
Deep LearningNN
Neural NetworksX
XGBoostL
LightGBMS
Scikit-learnN
NLPDA
Data AnalysisR
ReactN
Node.jsE
Express.jsF
FastAPIF
FlaskRA
REST APIsM
MySQLM
MongoDBD
DockerG
GitS
StreamlitJN
Jupyter NotebookC
CanvaExperience
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Education
Certifications
Certification details will appear here once added in the editor.
Projects
URD
Urdu News Article Classifier
- Built a multi-class Urdu news classification system using 1,139 scraped Urdu news articles. - Implemented Multinomial Naive Bayes, One-vs-All Logistic Regression, and Neural Network models. - Applied Bag-of-Words feature engineering with bigrams and Lidstone smoothing. - Compared multiple machine learning approaches to identify the best-performing model.
TRO
Trojan Detection & Network Intrusion Prevention System
- Developed a real-time network intrusion detection and prevention system - Captured and analyzed live network traffic using Scapy. - Engineered 80 network flow features for malware detection. - Integrated XGBoost-based classification for threat identification. - Created an interactive Streamlit dashboard for monitoring and analysis. - Achieved sub-second detection for RAT beaconing and data exfiltration attacks.
PIN
Pinterestify — Image Sharing & Discovery Platform
- Developed a Pinterest-inspired full-stack web application. - Implemented secure user authentication and authorization. - Created image upload and categorized board management features. - Built responsive masonry-style layouts for content discovery. - Integrated REST APIs for efficient frontend-backend communication. - Optimized user experience across desktop and mobile devices.
RUL
Rule-Augmented Credit Risk Decision System
- Built a credit risk assessment platform using LightGBM and Random Forest models. - Integrated business-rule overrides alongside machine learning predictions. - Logged conflicts between business logic and model outputs. - Containerized the application using Docker. - Developed RESTful APIs with FastAPI. - Performed stress testing for reliability and scalability.
Contact Me
Feel free to reach out for collaborations or just a friendly hello!