wassima-manssour's Projects
Using the Multiple Correspondance Analysis to study the relationships between study methods, personality and the psychology and performance of INSEA students.
Comparaison de state-of-art ML models pour la detection des maladie des feuilles de pommier.
This repository contains best profile readme's for your reference.
Building a simple chat bot to interact with, using NLP and DL techniques.
Homeworks conducted during the Big Data Course.
Comparing different ML models for regression to predict the house prices using the California dataset provided by scikit learn.sc
In this notebook we will try to process image data for Deep Learning. Exploring Grayscale images and RGB images and how to process these images. Then we will try to apply that on a real dataset from kaggle of a Breast Cancer Histopathology images.
Building a web app with Flask to pick films from the top rated films on imdb website using a web scraping algorithm with BeautifulSoup.
Comparison of exact and approximate methods for single-machine scheduling minimization problems
Build a predictive system for digits based on Neural Network applied on the MNIST dataset.
Building a movie recommandation system based on content using the disney-tv-shows-and-movies Kaggle dataset.
Deployed Streamlit web app on Heroku, to predicti disease using Machine Learning
Building an Ontology using the AMine platforme for the oncology domain.
Scraping Marackech hotel reviews for sentiment analysis using selenium.
The Sentiment Checker automates sentiment analysis of reviews, comparing it to given ratings to assess alignment. It collects data from up to 100 articles and comments, stores it in a structured database, detects the language, and identifies discussed topics.
Config files for my GitHub profile.
Scraping different web pages usinf Beautiful Soup, for internships lists, billionairs list and top selling books.
In this project I will train a custom dataset (wearing mask dataset from roboflow) with YOLOv5 in colab. The input is an image or a video, and the output is the detection wearing a mask or not. In next project I will try it for a real time detection.