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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.
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.
Analyzing the most affecting factors that deteriorate the health of a person and predicting the risk of developing dreadful diseases using Machine Learning.
Use Python and Scikit-learn and Imbalanced-learn to predict credit risk. Compare the strengths and weaknesses of machine learning models. Assess how well a model works.