Abdul Haseeb (hase3b)

hase3b

User data from Github https://github.com/hase3b

Location:Karachi, Pakistan

GitHub:@hase3b

Abdul Haseeb's repositories

BONBID-HIE-Segmentation

This repository implements a 3D U-Net model for segmenting HIE lesions in neonatal MRI scans, exploring various loss functions including Dice, Dice-Focal, Tversky, Hausdorff Distance, and hybrid loss functions. BONBID-HIE dataset is used for this study.

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Class-Imbalance-Classification-Performance-Analysis

This repository contains the code, documentation, and datasets for a comprehensive exploration of machine learning techniques to address class imbalance. The project investigates the impact of various methods, like ADASYN, KMeansSMOTE, and Deep Learning Generator, on classification performance while effectively demonstrating benefits of pipelining.

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End-to-End-DWH-Pipeline

This repository contains the end-to-end pipeline for building a data warehouse for a real estate management company. The pipeline includes data generation, ETL process, creation of star schema dimensions and fact table, visualization using Power BI, and automation with Pabbly Connect.

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Deep-Learning-Fall2024

This repository contains assignment submissions for the Deep Learning (96943) course, Fall 2024, at Institute of Business Administration (IBA) Karachi.

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DiaCausal

DiaCausal: AI-driven causal analysis of diabetes risk factors.

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Flask-Dash-Interactive-Dashboard

An interactive data visualization dashboard created using Flask and Dash. This project includes comprehensive data preparation, exploratory data analysis (EDA), and dynamic visualizations with Seaborn and Plotly. Explore the multi-page Dash app with features like dropdowns and callbacks for updated plots.

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KNN-Pipeline

This repository features a comprehensive ML pipeline for supervised learning tasks, covering data collection, cleaning, EDA, feature engineering, model training, and evaluation.

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SCPRAG

This repository implements a Retrieval-Augmented Generation (RAG) system for the Supreme Court of Pakistan, utilizing different LLMs, embedding models, and retrieval and generation enhancement strategies. It processes SCP judgments, applies chunking, and generates legal summaries and answers based on relevant case data.

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