Jorge Antonio Payà Albero's Projects
EDA including for the New York Airbnb data from Kaggle. Step-by-step process and conclusions of each step including the results on the relationships between the variables.
Applying boosting techniques such as GradientBoostingClassifier, GradientBoostingRegressor (eXtreme Gradient Boosting (XGBClassifier and XGBRegressor functions). This repo contains class examples and the project.
This is the dataset used in the second chapter of Aurélien Géron's recent book 'Hands-On Machine learning with Scikit-Learn and TensorFlow'. It serves as an excellent introduction to implementing machine learning algorithms
Create a SQL database locally using the psql command and understand and get used to the most basic profesional python project structure with PIP and .env file
Dataset analysis exercises using data from different sources such as Data is Plural, Kaggle...
Very simple template to work on machine learning projects with Python
Using a Decision Tree algorithm we have to predict based on diagnostic measures whether or not a patient has diabetes. The dataset originally comes from the National Institute of Diabetes and Digestive and Kidney Diseases.
We embark on a journey through various deep learning projects, delving into diverse models, techniques, methods, and frameworks. From convolutional neural networks (CNNs) to recurrent neural networks (RNNs), from TensorFlow to PyTorch, we uncover the intricacies of cutting-edge AI technologies.
Final Project on how to detect domains that were generated using "Domain Generation Algorithm" (DGA). The idea is to tell DGA-generated and non-DGA-generated domains apart using a combination of linguistic features by transforming raw domain strings to ML features.
Step-by-step guide on how to detect domains that were generated using "Domain Generation Algorithm" (DGA). This is based on Sidi Trainings (November 18th, 2020) presented by GTK Cyber.
Improving DGA classification models
README profile
This repository contains the exercises and projects completed during the 16 weeks of the Data Science and Machine Learning bootcamp at 4Geeks Academy.
The most basic boilerplate template to start an HTML/CSS website in just 30 seconds. Compatible with Gitpod.
Module 1 first project
Exploring Unsupervised ML models. For this project we'll classify houses according to their region and median income from the well-known California Housing dataset.
Building a KNN model. The dataset for this project collects part of the knowledge from the API TMDB, which contains only 5000 movies out of the total number. Model the data using a KNN, analyze the results and optimize the model.
Building a KNN model. The dataset for this project is the diabetes prediction used in previous projects.
This repository contains the lab for agile planning
Linear Regression project for predicting the cost of health insurance for a person
Logistic Regression project for Banking Marketing Campaign.
Data Science and Machine Learning - 16 weeks
Implementation of popular ML algorithms from scratch