This repository shows the implementation of machine learning algorithms, data pipelines and data visualization with scikit-learn and python.

Flight Price Prediction Regression using Scikit-Learn

Explore machine learning models and optimization techniques.

Spotify Playlist Music Recommendation System

Employee Attrition Classification using Scikit-Learn

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 project predicts the burned area of forest fires using historical data and weather reports. It employs a neural network model built with TensorFlow, utilizing data preprocessing with Scikit-learn and analysis with Pandas and Matplotlib. The goal is to provide accurate, real-time predictions to aid in fire management and prevention.

simple classification of hanzi by scikit-learn

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.

Decision tree implementation using python scikit-learn

Winter24AICohort

Prediction of a cholesterol exam based in some features about the patient

A collection of documents and programs to learn about Data Science and ML using Scikit-learn.

Mobile Price Prediction

Data Science and Machine Learning project for a recipe website that wishes to increase their traffic.