areegtarek Goto Github PK

ai-for-healthcare

Explore AI projects transforming healthcare. From image analysis to predictive analytics, join us in revolutionizing patient care.

airbnb-price-category-prediction

This project aims to build machine learning models to predict the price range category (beginner, plus, premium) of Airbnb listings in Montreal based on their characteristics.

areegtarek

Config files for my GitHub profile.

cisc-886-cloud-computing-w23

contractnli-using-bert

Natural language inference (NLI): Document-level three-class classification (one of Entailment, Contradiction or NotMentioned). Evidence identification: Multi-label binary classification over span_s, where a _span is a sentence or a list item within a sentence. This is only defined when NLI label is either Entailment or Contradiction.

covid-19-outcome-prediction

This project applies different classifiers, such as logistic regression, random forest, and support vector machines, to predict the outcome of COVID-19 (death or recovery) for patients admitted to the hospital.

datacamp-projects

Data Insight's Scholarships for Data Scientist Program

elo-merchant-category-prediction

predict customer loyalty scores for Elo, one of the largest payment brands in Brazil. by using transactional and promotional data , which contains information about cardholders, merchants, and purchases. The project explores various aspects of data analysis, such as feature engineering, data preprocessing, model selection, and model evaluation.

emotion-recognition-using-bert

Emotion Recognition Task using BERT is a pretrained model offered by Huggingface, e.g., 'distilbert-base-uncased' to train a emotion classification model from train and report their performances on the validation dataset, in terms of accuracy, F1 score, precision and recall.

fake-reddit-prediction

both classical machine learning and deep learning techniques are applied to preprocessed text data to automatically learn linguistic patterns that differentiate fake from real news titles.

fashion_mnist_clothing_classification

Training a deep convolutional neural network (CNN) for image classification. The project explores different aspects of building and evaluating a CNN, such as data preprocessing, model architecture, hyperparameter tuning, and performance metrics.

financial-named-entity-recognition-using-bert

The goal of this project is to build a Named Entity Recognition (NER) model to extract key financial metrics like revenue, expenses, profit etc. from earnings call transcripts.

fwd-projects

gpt-llm-trainer

graph-convolutional-network-anti-cancer-drug-activity-prediction

The goal of the competition is to develop machine learning models that can accurately classify whether a given compound has activity against non-small cell lung cancer. Each sample is represented as a graph with atoms as nodes and bonds as edges.

grip-the-sparks-foundation

huawei-hcia-ai

leaf-classification

This project aims to build a machine learning model to classify leaf images into plant species based on their visual characteristics and Fine-tune the hyperparameters to get the best performance of the fully connected network.

lifesign

nlp-101-iti-course

NLP course for ITI AI-Pro track

nyc-rolling-sales

Analyzes rolling sales data from New York City to understand real estate trends in neighborhoods, property types, seasons, and unit features. Over 100,000 property records are explored using Python visualizations and statistics. Insights help buyers, sellers, and stakeholders make informed decisions in the NYC market.

predicting-stock-price-movement-using-bert

The goal of this project is to fine-tune a BERT NLP model to predict if a company's stock price will increase or decrease in the following quarter based on the text from their earnings call transcript.

pyspark-social-media-sentiment-analysis

performing sentiment analysis on social media data. The project uses the sentiment140 dataset from Kaggle, which contains 1.6 million tweets annotated with positive, negative, or neutral polarity. The project explores various aspects of data processing, such as data cleaning, tokenization, stopword removal, and feature extraction.

quantium-virtual-program

rfm-analysis-customer-segmentation

Performing customer segmentation and analysis for Elo, a Brazilian payment brand, using various techniques such as RFM analysis, rule-based segmentation, clustering, PCA, and frequent pattern mining.

rl-gridworld-example

This project uses reinforcement learning, a machine learning paradigm that learns from its own actions and rewards, to calculate the state value functions for all states in the GridWorld example.

sentiment-analysis-of-earnings-call-transcripts-using-finbert

The goal of this project is to build a sentiment analysis model using BERT to determine if management sentiment expressed in earnings call transcripts is positive or negative by using a fine-tuned BERT model

speed-dating-match-prediction

This project aims to build a machine learning model to predict the likelihood of a successful match occurring between two people during a speed dating session, based on their profile information.

students-adaptability-level-in-online-education

This project analyzes survey data from students to understand their adaptability levels to online education with Power BI Dashboard

topic-modeling-for-scientific-paper-abstract

This project uses topic modeling, a statistical technique for discovering latent topics in a collection of documents, to cluster scientific papers based on their abstracts. The project uses a subset of the arXiv dataset, which contains 50,000 randomly sampled papers from various fields of science.