afafsaedabdlrahman,Afaf_Saed,github

-buliding-an-e-commerece-multi-language-arabic-and-english-conversational-ai-chatbot.

Created a multilingual e-commerce chatbot (Arabic/English) using deep learning and NLP, standing out by learning patterns instead of relying on predefined responses.

-earthquake-damage-in-nepal

In this project, you'll work with data from Open Data Nepal to build a model to predict building damage from the Nepal 2015 Earthquake. You'll work primarily with data from the Gorkha district, with additional examples from Ramechhap.

-meta-_kaggle

Build a project called Meta Kaggle to predict who will be a grand master in the future. ,How many medals are required to be a grand master? And how does being a grand master on competitions diﬀer from being a grand master on notebooks? and achieved quantifable success with the Gradient Boosting Regressor, Random Forest Regressor, and XGB Classifer.

-speed-dating-match-prediction

Built a dating compatibility predictor using profile data and XGBoost for precise relationship potential predictions. Employed a robust data preprocessing pipeline with feature engineering for superior accuracy in speed dating scenarios. Successfully quantified project success by emphasizing XGBoost's pivotal role in optimal predictive performance.

-wish.com-product-rating-prediction

The goal is to predict product ratings on Wish.com based on other known features. Ratings are in categories from 1 to 5. The higher the rating for one product, the more customers like the product using machine learning models like Naive Bayes, SVM,k-nearest neighbors and decision tree algorithms.

afafsaedabdlrahman--market-forecasting-in-india

Predict volatility on the Bombay Stock Exchange.

air-quality-in-nairobi

In this project, I worked with data from one of Africa's largest open data platforms openAfrica. I looked at air quality data from Nairobi, Lagos, and Dar es Salaam; and build a time series model to predict PM 2.5 readings throughout the day.

airbnb-price-category-prediction

Airbnb-price-category-prediction,a multi-output model where both textual and image data contribute to predicting the type and price of the listing. The use of diverse layers and regularization techniques indicates an effort to build a robust and generalizable model ..

bankruptcy-in-poland

In this project, you'll work with financial data from the Emerging Markets Information Service. You'll look at financial indicators from Poland and Taiwan, and build a model that predicts bankruptcy.

chat-with-multiple-pdfs.

Extracted Key Performance Indicators (KPIs) from real PDFs. # Skills: langchain · chroma vector · Natural Language Processing (NLP) · Large Language Models (LLM)

covid-19-outcome-prediction

Build a project that identifes whether a person is going to recover from coronavirus symptoms or not based on on some pre-defned standard symptoms. and i applied different supervised machine learning algorithms like naive bayes, logistic regression, decision trees,k-nearest neighbors, and SVM.

customer-segmentation-in-the-us

In this project, you'll work with consumer finance data from the US Federal Reserve. You'll build an unsupervised model to segment households that fear they will be unable to get credit.

fashion-minst-deep-learning.

Build a project whose goal is to identify (predict) diﬀerent fashion products from the given images and i applied three diﬀerent transferable learning models (LeNet 5, VGG19, and AlexNet) with some preprocessing for each of them.and the best result is obtained using VGG19.

housing-in-buenos-aires

In this project, you'll build on those skills and move from descriptive to predictive data science. Your focus is still real estate, but now you need to create a machine learning model that predicts apartment prices in Buenos Aires, Argentina.

housing-in-mexico

In this project, I worked with a dataset with 21,000 properties for sale in Mexico through the real estate website Properati.com. My goal is to determine whether sale prices are influenced more by property size or location.

language-modeling

A language model can predict the probability of the next word in the sequence, based on the words already observed in the sequence. and had quantifed success using the RNN, LSTM, and GRU models.but the best result is obtained using GRU model.

mountain-car-problem

Mountain car problem is a problem in which an under-powered car must drive up a steep hill. Since gravity is stronger than the car’s engine, even at full throttle, the car cannot simply accelerate up the steep slope. and had quantifed success using reinforcement learning algorithm( queue learning approximation algorithm).

object-detection-model-for-people-that-are-not-wearing-mask

Create an ai object detection model for people that are not wearing masks.Check code from here

reddit-fake-post-detection-by-looking-only-at-the-title

Is model is able to predict to Whether specifc reddit post is fake news or not, by looking at its title.and had quantifed success by applying different preprocessing , multiple estimators, and then automating the machine learning process. and the best results are obtained by applying random forest classifier.

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