Projeto realizado para a matéria de Introdução ao Aprendizado de Máquina, onde foi feito um modelo regressor utilizando algoritmos de Machine Learning com a biblioteca Scikit-Learn em Python.

Full-Stack application that allows client to use a predictive model to determine which user is more likely to have tweeted a given text. This project covers everything from API's to Predictive Modeling, SQLAlchemy database storage, Flask, along with other full-stack components. In the end it is deployed for online usage using Heroku.

This consists of various machine learning algorithms like Linear regression, logistic regression, SVM, Decision tree, kNN etc. This will provide you basic knowledge of Machine learning algorithms using python. You'll learn PyTorch, pandas, numpy, matplotlib, seaborn, and various libraries.

Explore the basics of linear regression, gradient descent, and AI using Python. Get hands-on with NumPy, pandas, Matplotlib, and scikit-learn for practical learning.

A jupyter notebook which trains a model with scikit-learn

Spotify Playlist Music Recommendation System

simple classification of hanzi by scikit-learn

A web app that assists doctors in prescribing right medicine to patients in order to avoid drug side effects

A trained ML model for prediction of house prices in IOWA using Random Forest Regression technique

The project analyzed Asana user data to determine adoption rate and factors influencing adoption. After data cleaning, an adoption rate of 12% was calculated. Predictor variables were extracted and modeled using Random Forest and Decision Tree classifiers. Both models performed well, with Random Forest achieving 87% accuracy.

Scikit-learn (sklearn) projects in form of Jupyter Notebooks

Repositório de códigos de teste do Python feito por Rafael da Silva Braga

Criação de modelo de classificação capaz de identificar documentos como petições iniciais com base de treinamento em dados do CRETA

This is the basic introductory project of machine learning for predicting the survivals category based on the data set available,which is implemented using different inbuilt models available in scikit learn

24/01/2024 Jeyfrey J. Calero R. Aplicación de Redes Neuronales con scikit-learn streamlit, pandas, seaborn y matplolib

Winter24AICohort

Machine Learning Tutorials