Projeto de ensino para o curso Ciência de Dados ministrado por mim na Hashtag

An collection of machine learning projects implemented based on IEEE papers.

Simple End-to-End Machine Learning Project

Predicting the Residential Energy Usage across 113.6 million U.S. households using Machine Learning Algorithms (Regression and Ensemble)

This repository includes projects using datasets of structured data (non-Spark). The projects use Python, NumPy, Pandas, Matplotlib, Seaborn, TensorFlow, Pytorch, and Sklearn.

Visual tool to explore variants of Polis-like data pipelines.

Simple Application for predicting price of the flight. It uses sklearn pipeline to perform preprocessing , feature selection and feature engineering and model building .The pipeline object is saved in a pickle file and used in the flask application for prediction

Here are some fun projects to learn ML using Handson approach

Scikit-Learn useful pre-defined Pipelines Hub

this repo will include all my work regarding NLP

Regression Problem

Predicting developer's salary from Stack Overflow Annual Developer Survey (https://insights.stackoverflow.com/survey)

End-to-end machine learning pipeline to classify disaster messages into 36 categories and a web app to deploy the trained model.

[COMMUNITY] — Spatial Join & Enrich any urban layer given any external urban dataset of interest, streamline your urban analysis with Scikit-Learn-Like pipelines, and share your insights with the urban research community!

⚡ Code for machine Learning Pipeline with Scikit-learn ⚡

Projeto de ensino para o curso Ciência de Dados ministrado por mim na Hashtag

Logistic regression pipeline by using sklearn, feature_engine, joblib

The Telco Customer Churn dataset, the project involves collecting, cleaning, and analyzing customer data to uncover key factors influencing churn.

ChurnIQ is a Streamlit app that predicts customer churn based on inputted customer details. Using a trained machine learning model, it helps businesses anticipate whether customers will stay or leave

A deployable end to end ML model for predicting loan default risk of new customers for a financial institution

:house_with_garden: Built linear regression model to predict house prices in Ames dataset with applied tools such as scikit-learn pipeline

No description provided.

Machine learning (ML) pipelines consist of several steps to train a model.

Automated sklearn pipelines using LangChain and LangGraph.

AutoAD - A framework for the rapid detection of anomalies in (big) datasets

This model will evaluate either a passenger will survive in titanic.

Built Random Forest classifier from scratch on top of Scikit Learn decision trees. Using Scikit Learn to create data cleaning pipelines, perform grid searches for hyper parameter tuning, and decision tree modeling

A PyQt6-based GUI application for venomic data analysis, spectral deconvolution, and statistical similarity indexing of neogastropod peptidomes. Uses a Sequential Analysis Pipeline (SAP)-structured, classical ML algorithm built with sklearn modules as its backend for the actual computational engine.

Project No.2 (Data Engineering) in the Data Scientist Nanodegree program. Build a machine learning pipeline to categorize emergency messages based on the need communicated by the sender.

Predict loan approval status using machine learning models. Explore data, build, tune, and evaluate models for accurate predictions.