Pinned Repositories
classification_binaire_MNIST_NN
Création d'un modèle (réseau de neurones) de classification pour la base MNIST sur un chiffre uniquement. En d'autres termes ce modèle pourra nous aider a prédire si X image contient le chiffre ou non
CNN_Encoder_Decoder_PokemonDB
Exploring efficient image compression and decompression techniques for the Pokemon database. This project explores the implementation of CNN and RNN encoders/decoders, aiming to enhance image storage and retrieval. Computing valuable insights related to compression performance and quality metrics.
Facial-expression-classification-using-deep-transfer-learning
The purpose of this project is to classify images from the “Natural human face dataset” using a pre-trained deep convolutional neural network (CNN) model such as VGG16, and transfer learning techniques. Only three emotion categories are being used: fear, happiness, and sadness.
Hypothesis-testing-on-Boston-housing-Data
This project focuses on conducting a comprehensive data analysis for a Boston-based housing agency. Using a dataset sourced from the U.S. Census Service, the analysis aims to provide valuable insights into housing prices
KNN-on-Diabetes-Data-KAGGLE-
This project utilizes the KNN classification algorithm to predict the likelihood of a patient developing diabetes. Using data from the National Institute of Diabetes and Digestive and Kidney Diseases dataset to provide insights into diabetes risk assessment.
Loan-data-Hypothesis-testing-Preprocessing-KNN-
Exploring and analyzing the dataset to draw meaningful insights. Conducted hypothesis testing, resolved data anomalies, and crafted a predictive model using KNN Classifier. Addressed key questions, such as demographic-based variations in loan status and predicting loan-to-value ratios. Ensured data integrity for informed decision-making.
Revenue_Manager
Flutter app that helps hourly workers keep tabs on their earnings effortlessly. Whether you're calculating your income per second or setting financial goals, this app has you covered. You can tweak your hourly pay rate whenever needed and get real-time updates on your progress.
Solving-TSP-with-PyGad
Revolutionizing Route Optimization with PyGad: An In-Depth Study of the Traveling Salesman Problem
Store-API-BackEnd
This project is a backend application built with Node.js, Express.js, and MongoDB. It provides a RESTful API for product management. The API allows users to retrieve products with flexibility by using different query parameters.
Task-Manager-Back-End
This project is a simple web application that provides a RESTful API for managing tasks. It utilizes Node.js, Express.js, and MongoDB for the backend implementation. The application allows users to create, read, update, and delete tasks through the provided API endpoints.
DHwass's Repositories
DHwass/KNN-on-Diabetes-Data-KAGGLE-
This project utilizes the KNN classification algorithm to predict the likelihood of a patient developing diabetes. Using data from the National Institute of Diabetes and Digestive and Kidney Diseases dataset to provide insights into diabetes risk assessment.
DHwass/Revenue_Manager
Flutter app that helps hourly workers keep tabs on their earnings effortlessly. Whether you're calculating your income per second or setting financial goals, this app has you covered. You can tweak your hourly pay rate whenever needed and get real-time updates on your progress.
DHwass/classification_binaire_MNIST_NN
Création d'un modèle (réseau de neurones) de classification pour la base MNIST sur un chiffre uniquement. En d'autres termes ce modèle pourra nous aider a prédire si X image contient le chiffre ou non
DHwass/CNN_Encoder_Decoder_PokemonDB
Exploring efficient image compression and decompression techniques for the Pokemon database. This project explores the implementation of CNN and RNN encoders/decoders, aiming to enhance image storage and retrieval. Computing valuable insights related to compression performance and quality metrics.
DHwass/Facial-expression-classification-using-deep-transfer-learning
The purpose of this project is to classify images from the “Natural human face dataset” using a pre-trained deep convolutional neural network (CNN) model such as VGG16, and transfer learning techniques. Only three emotion categories are being used: fear, happiness, and sadness.
DHwass/Hypothesis-testing-on-Boston-housing-Data
This project focuses on conducting a comprehensive data analysis for a Boston-based housing agency. Using a dataset sourced from the U.S. Census Service, the analysis aims to provide valuable insights into housing prices
DHwass/Loan-data-Hypothesis-testing-Preprocessing-KNN-
Exploring and analyzing the dataset to draw meaningful insights. Conducted hypothesis testing, resolved data anomalies, and crafted a predictive model using KNN Classifier. Addressed key questions, such as demographic-based variations in loan status and predicting loan-to-value ratios. Ensured data integrity for informed decision-making.
DHwass/Solving-TSP-with-PyGad
Revolutionizing Route Optimization with PyGad: An In-Depth Study of the Traveling Salesman Problem
DHwass/Store-API-BackEnd
This project is a backend application built with Node.js, Express.js, and MongoDB. It provides a RESTful API for product management. The API allows users to retrieve products with flexibility by using different query parameters.
DHwass/Task-Manager-Back-End
This project is a simple web application that provides a RESTful API for managing tasks. It utilizes Node.js, Express.js, and MongoDB for the backend implementation. The application allows users to create, read, update, and delete tasks through the provided API endpoints.
DHwass/To-do-App-Flask
DHwass/XGBoost-on-Home-Data-KAGGLE
In this project, XGBoost is applied to forecast real estate prices using the Boston Housing Dataset. The primary aim is to create an effective predictive model, assess its accuracy through metrics like Mean Absolute Error (MAE), and refine its performance by tuning hyperparameters with HYPEROPT.
DHwass/Spotifly