Pinned Repositories
A-mathematical-logistic-regression-model-From-Scratch-for-the-binary-classification-
1.a). Realize a mathematical logistic regression model for the binary classification of 'Occupancy' according to 'CO2' and 'Light'. b). Draw the decision boundary. 2. Perform a mathematical logistic regression model for the binary classification of 'Occupancy' as a function of 'Temperature', 'Humidity', 'Light', 'CO2' and 'HumidityRatio'.
A-mathematical-model-of-linear-regression-from-scratch-for-the-prediction-of-CO2
Realize a linear regression mathematical model (from scratch) for the prediction of CO2 emission as a function of 'Temperature', 'Humidity', 'Light' and 'HumidityRatio'.
ADMIN_UX
Angular_ebank-ui
app-oauth2-keycloak
bank-account-config-repo
Bank-account-service-Spring-Boot-Rest-GraphQL
billing-service_Spring
This service is built using Java and the Spring Framework and uses Spring Data JPA to interact with a H2 database. The repository contains code for the billing microservice, as well as configuration files for database connectivity
BlockChain-Workshop
Polynomiale_Regression_Machine_Learning
Yassine-Karimi's Repositories
Yassine-Karimi/A-mathematical-logistic-regression-model-From-Scratch-for-the-binary-classification-
1.a). Realize a mathematical logistic regression model for the binary classification of 'Occupancy' according to 'CO2' and 'Light'. b). Draw the decision boundary. 2. Perform a mathematical logistic regression model for the binary classification of 'Occupancy' as a function of 'Temperature', 'Humidity', 'Light', 'CO2' and 'HumidityRatio'.
Yassine-Karimi/A-mathematical-model-of-linear-regression-from-scratch-for-the-prediction-of-CO2
Realize a linear regression mathematical model (from scratch) for the prediction of CO2 emission as a function of 'Temperature', 'Humidity', 'Light' and 'HumidityRatio'.
Yassine-Karimi/ADMIN_UX
Yassine-Karimi/Angular_ebank-ui
Yassine-Karimi/app-oauth2-keycloak
Yassine-Karimi/bank-account-config-repo
Yassine-Karimi/Big_Data_Spark_SQL
Yassine-Karimi/Classification-using-logistic-regression-from-scratch-and-using-Scikitlearn
Classification using logistic regression from scratch and using Scikitlearn
Yassine-Karimi/config-repo
Yassine-Karimi/Distributed_Radar_System
Yassine-Karimi/docker-spring-angular-oauth2-oidc-keycloak
Yassine-Karimi/dynamic-gateway-service
Yassine-Karimi/E-Bank-Backend-ES-CQRS
Yassine-Karimi/E-Commerce-App-Spring-thymeleaf-Angular-Keycloak-Oauth2
Yassine-Karimi/ecom-app-spring-boot
Yassine-Karimi/Ecom_App
Yassine-Karimi/Immatriculation_service
Yassine-Karimi/infractions-service
Yassine-Karimi/kafka-spring-cloud-stream
Yassine-Karimi/migration-module
Yassine-Karimi/NLP_Vectoriel_Representation
Yassine-Karimi/parallel_and_distributed_GA_using_Island_Model
Yassine-Karimi/QLearning_with_MAS-master
Yassine-Karimi/Radar-service
Yassine-Karimi/Responsive-Web-Application--Front-End-
Yassine-Karimi/secured-decorized-resources-reservation-management-App
Yassine-Karimi/Spark_MLlib
Yassine-Karimi/SPARK_STREAMING
Yassine-Karimi/sparkSQL-with-CSVfile
Yassine-Karimi/yassine-karimi