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Built a fraud detection system combining supervised (Tabular Transformers, Neural Networks) and semi-supervised
(GraphSAGE, Graph Attention Networks) models. Engineered a hybrid loss function for graph-based learning with
only 15% labeled data, achieving competitive F1 scores under realistic label scarcity.
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Analytics pipeline for detecting fraudulent bikers in ride-sharing. Applied statistical hypothesis testing and
anomaly detection to identify suspicious trip patterns, simulating production-level fraud analysis for mobility platforms.
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Essay Agent is an intelligent assistant for writing essays, built with LangGraph and Streamlit.
It guides you through the full cycle of essay creation: planning, researching, drafting, and critiquing.
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This project investigates what factors contribute to a movie becoming a blockbuster.
Using the Kaggle Movies Dataset, we analyze how genre, budget, runtime, release date, cast & crew, and production companies influence box office revenue and audience reception.
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This project focuses on the development and application of an advanced deep learning model, specifically YOLOv9, for the detection
and segmentation of potholes in both images and video streams. Leveraging a custom dataset, the model has been fine-tuned to achieve high precision and recall
rates through the use of optimized hyperparameters, data augmentation techniques, and advanced training strategies such as mixed precision training and gradient accumulation.
Key achievements include a mAP50 of 0.807 and a Mask mAP50 of 0.825, underscoring its effectiveness in practical applications.
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A machine learning model was created and trained on Amazon Reviews to perform sentiment analysis. The dataset underwent comprehensive
preprocessing steps, including tokenization, removal of stopwords, lemmatization, stemming, elimination of tags and emojis,
and normalization. Logistic Regression was utilized for the sentiment analysis task on Amazon Reviews.
The repository includes resources and code for implementing various sentiment classification models, such as Embedding Models, BERT, and Simple Neural Networks.
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A computer vision project for medical image classification. AlzMRI-Net classifies brain MRI scans into four stages of Alzheimer's disease: MildDemented,
ModerateDemented, NonDemented, and VeryMildDemented.
Built in PyTorch using transfer learning on a pre-trained EfficientNet-V2-L image backbone, fine-tuned on a specialized medical imaging dataset.
Advanced techniques such as mixed precision training and gradient accumulation are employed to enhance performance and efficiency. The model achieves
a test accuracy of 99.19%,
with comprehensive evaluation metrics, including precision, recall, and AUC, underscoring its effectiveness in Alzheimer's disease classification.
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The goal of this project is to simplify the process of reviewing the latest research papers by providing concise summaries generated using BART,
a state-of-the-art large language model (LLM). The tool fetches the most recent papers from PubMed and arXiv based on a user-defined query and
presents them through an interactive interface. Users can easily select papers and view detailed summaries, making it easier to stay updated with current research.
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This project involves clustering and segmenting around 9,000 active credit card users based on their behavior over a six-month period, using 18
behavioral variables. Customer data is preprocessed and reduced to two dimensions via Principal Component Analysis (PCA).
K-Means clustering is then applied to group customers into four distinct clusters, enabling analysis of their behaviors and identification of
cluster-specific characteristics.
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In this project, I had access to a bank dataset to predict which individuals the bank should target with personal loan offers based on historical data.
I also employed machine learning models like Logistic Regression, Naive Bayes, and K-Nearest Neighbors, and assessed loan acceptance probabilities.