Chaimae's Projects
This repository contains the code and resources for training a robot car in AWS DeepRacer using reinforcement learning and participating in a race.
Revolutionize customer feedback analysis with our NLP Insights Analyzer. Utilize cutting-edge text preprocessing to transform raw reviews into a machine-friendly format. Explore sentiment models, such as Logistic Regression and Naive Bayes, employing cross-validation for model robustness.
Email Classifier: A machine learning project using Python that categorizes emails into spam and ham (non-spam). Utilizes the Scikit-Learn library, employing logistic regression and TF-IDF (Term Frequency-Inverse Document Frequency) vectorization for text analysis and classification.
Naive Bayes Email Classifier: An implementation of a 'hard' Naive Bayes classifier in Python to categorize emails as spam or ham. This code performs extensive data preprocessing, probability calculations, and model training for email classification using the raw Naive Bayes algorithm.
Count Vectorizer Naive Bayes Email Classifier: This Python project utilizes a simple Naive Bayes approach with Count Vectorizer to classify emails as spam or ham. The implementation focuses on word frequency for classification.
Facial Emotion Recognition is a deep learning project focused on classifying facial expressions into different emotions. The project utilizes convolutional neural networks (CNNs) and is implemented using Keras.
This machine learning project focused on predicting food delivery times. The code emphasizes essential tasks such as data cleaning, feature engineering, categorical feature encoding, data splitting, and standardization to establish a solid foundation for building a robust predictive model.
This project aims to optimize greenhouse conditions for strawberry cultivation through a combination of IoT technologies and AI. By leveraging Node-RED, MQTT, MySQL, FastAPI, scikit-learn, and Python, we simulate an AIoT project to regulate temperature, humidity, and light levels within a greenhouse environment.
This project serves as a comprehensive tool for collecting Hadiths from online sources, preprocessing the textual data, and extracting valuable insights using NLP methodologies. By combining web scraping techniques with advanced text processing algorithms, the project facilitates the analysis and understanding of Hadiths in a structured manner.
🚀 Welcome to my Kaggle submission for "Natural Language Processing with Disaster Tweets." In this challenge, we explore tweets, using NLP to distinguish between those about real disasters and those that aren't. The goal is to build a robust model for accurate disaster-related tweet prediction. 🏆 Impressive F1 score of 0.79926 on the public leader
Hello ! 👋 I'm thrilled to share my debut in the world of Kaggle competitions with my solution for the Titanic: Machine Learning from Disaster competition. In this challenge, we are tasked with predicting which passengers were more likely to survive the tragic sinking of the Titanic. Public Score: 0.79186
This project focuses on utilizing Long Short-Term Memory (LSTM) neural networks for portfolio optimization in the context of Moroccan stock market data. By leveraging historical stock prices obtained from Yahoo Finance, the project aims to predict future price movements of selected Moroccan companies.
This repository serves as as a practical guide for understanding (NLP) through a Lab. It consists of two Jupyter notebooks, each dedicated to a specific part of the lab.
This repository serves as a comprehensive exploration of Natural Language Processing (NLP) language models using the Sklearn library. It delves into both regression and classification tasks, utilizing various techniques and algorithms to analyze text data.
This repository contains a collection of NLP experiments conducted using PyTorch library. The project explores various techniques such as regression, text generation, and BERT embeddings.
This is an online market place built using Django where people can buy and sell items. It includes authentification, communication between users, dashboard for items, form handling and customisations and more.
A versatile Python application using Streamlit for hands-on experience in programming and machine learning. OptiML-Analyzer enables qualitative and quantitative data analysis using various machine learning algorithms through a user-friendly interface.
This project introduces a powerful image classification model to distinguish between pizza and steak images. Leveraging advanced techniques, the model achieves robust performance in handling complex visual features. With an accuracy of 87.40% on the validation dataset.
This is the First Project part of the AI-programming with Python Nanodegree by Udacity.
This repository contains the data processing components of a real-time sentiment analysis application for Twitter data, utilizing Apache Kafka, PySpark, and MongoDB. It also includes the frontend and backend as a submodule.
This repository contains the frontend and backend components of a real-time sentiment analysis application for Twitter data. The application is built using Angular, ExpressJS, Bootstrap and MongoDB.
This repository hosts a proof-of-concept (POC) project aimed at developing a domain expert model tailored for the Information Technology (IT) domain. The project employs advanced NLP techniques to train and deploy in AWS SageMaker a large language model, capable of generating informative and contextually relevant text responses in the IT domain.
This project is an ETL Pipeline using Dbt (dbt-core) for transformation, Snowflake for data warehousing and Airflow for orchestration.
This is a desktop application that helps manage delivery men, products and commands. It's build using JavaFX and demonstrates skills in desktop application and database integration.
This repository contains a dual-part project focusing on T-SQL query solutions and an interactive application for B-Tree and Hashing data structures, implemented using PySimpleGUI.
This is my first website that provides informations about the 2022 World Cup. it's build using HTML, CSS and JavaScript, it gives informations about groups, teams, matchs, technical sheets of players and more.