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Paula Ehab Alfy's Projects

boston_housing_price_prediction icon boston_housing_price_prediction

This is a machine learning model train to predict the price of house in boston city based on information gathered in 1978 about the following data 1-number of rooms 2-percent of lower class workers 3-ratio of students to teachers

customer-sgement-project icon customer-sgement-project

In this project you will apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data. You will first explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, you will preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, you will apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, you will compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.

deep-learning icon deep-learning

This repo is personal one to help me study deep learning by doing separate projects and try examples

finding-donors-for-charity-ml icon finding-donors-for-charity-ml

CharityML is a fictitious charity organization located in the heart of Silicon Valley that was established to provide financial support for people eager to learn machine learning. After nearly 32,000 letters were sent to people in the community, CharityML determined that every donation they received came from someone that was making more than $50,000 annually. To expand their potential donor base, CharityML has decided to send letters to residents of California, but to only those most likely to donate to the charity. With nearly 15 million working Californians, CharityML has brought you on board to help build an algorithm to best identify potential donors and reduce overhead cost of sending mail. Your goal will be evaluate and optimize several different supervised learners to determine which algorithm will provide the highest donation yield while also reducing the total number of letters being sent.

haystack icon haystack

:mag: Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, ChatGPT and alike). Haystack offers production-ready tools to quickly build complex question answering, semantic search, text generation applications, and more.

imdb-data-in-keras icon imdb-data-in-keras

we will analyze a dataset from IMDB and use it to predict the sentiment analysis of a review.

ivy icon ivy

The Unified Machine Learning Framework

multilingual-named-entity-recognition icon multilingual-named-entity-recognition

This project was implemented to if we want to perform NER for a customer based in Switzerland, where there are four national languages ( German (62.9%), French (22.9%), Italian (8.4%), and English (5.9%). with English often serving as a bridge between them),

natours-theme icon natours-theme

It is the first project of advanced-css-course using html and css for making these theme

nlp-projects icon nlp-projects

This repo. is for my projects to learn NLP concepts and state of art

project-write-an-algorithm-for-a-dog-breed-identification-app icon project-write-an-algorithm-for-a-dog-breed-identification-app

In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).

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