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Hi , I'm Umm e Hani

A dedicated Data Science student

ummehani-ds

ummehani-ds

  • šŸŒ± Iā€™m currently learning Generative AI.

  • šŸ¤ Iā€™m looking for help with causal inference with machine learning.

  • šŸ“« How to reach me [email protected]

  • āš” Fun fact Data scientists spend about 80% of their time cleaning and preparing data, and only 20% actually analyzing it. This crucial yet time-consuming process is often humorously referred to as "data wrangling" or "data munging.

Connect with me:

Umm e Hani

Languages and Tools:

bash bootstrap c chartjs cplusplus css3 d3js docker express flask git hadoop html5 java javascript kafka linux mongodb mysql nodejs opencv pandas postgresql python pytorch react scikit_learn seaborn selenium tensorflow

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Ā ummehani-ds

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Umm e Hani's Projects

-multi-core-neural-network-operating-system icon -multi-core-neural-network-operating-system

This project implements a neural network architecture using separate processes and threads on a multi-core processor. It utilizes inter-process communication through pipes for exchanging weights and biases between processes. Implemented in C++, it leverages multi-core processing for efficient training. Designed for Linux environments.

amazon-personalized-recommendation-system icon amazon-personalized-recommendation-system

This project creates a live product recommender system using the Amazon dataset. It involves data processing with Apache Spark, MongoDB, and exploratory analysis. A recommendation model (ALS) is trained and integrated into a Flask web application, with real-time updates via Apache Kafka.

image-processing-using-dsa icon image-processing-using-dsa

A comprehensive C++ project implementing image processing operations such as statistics computation, connected component extraction, run-length coding, quad tree representation, and shape recognition. Utilizing data structures like arrays, queues, stacks, and linked lists.

kafka-stock-analysis-pipeline icon kafka-stock-analysis-pipeline

This project implements a real-time data processing pipeline using Apache Kafka, Python, and MongoDB. It processes Microsoft and Apple stock data, with Kafka consumers analyzing price differences, risk, and percentage changes, then storing results in MongoDB.

meme-sentiment-classifier icon meme-sentiment-classifier

Web application for meme sentiment analysis using machine learning classifiers (text & image) with Flask deployment.

multiplayer-game-using-threads icon multiplayer-game-using-threads

This is an implementation of a multiplayer game where players navigate a two-dimensional grid to collect items. Players can move concurrently on the game board and collect items to earn points. The game is implemented using threads to handle player movement and item collection, ensuring a seamless and engaging gameplay experience.

multiple-source-personalized-pagerank icon multiple-source-personalized-pagerank

This project implements a scalable algorithm to rank nodes in a network based on their proximity to user-specified source nodes. It leverages Hadoop's distributed processing power to handle large networks efficiently. The core algorithm, implemented in Python using MapReduce, personalizes traditional PageRank.

os-concurrency-challenges-solutions icon os-concurrency-challenges-solutions

The repository provides solutions to two concurrency challenges in C/C++. The first involves implementing a lock and unlock mechanism using the Bakery algorithm for mutual exclusion. The second addresses traffic management on a bridge, ensuring limited capacity is maintained using mutex synchronization.

os-process-ipc-scripting-assignment icon os-process-ipc-scripting-assignment

This repository contains solutions to operating system assignments covering process management, inter-process communication, and system administration tasks using shell scripting. Explore for practical implementations and explanations.

sentimental-analysis-web-application icon sentimental-analysis-web-application

sentriX is a web application that leverages Flask and React to perform sentiment analysis on text. Utilizing the TextBlob library, the application analyzes user-input text to determine whether it is positive, negative, or neutral. The application features four main pages: Home, Tool, About, and Contact.

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