End-to-end machine learning pipeline to classify disaster messages into 36 categories and a web app to deploy the trained model.
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Updated
Mar 25, 2021 - Jupyter Notebook
End-to-end machine learning pipeline to classify disaster messages into 36 categories and a web app to deploy the trained model.
Machine learning (ML) pipelines consist of several steps to train a model.
Machine Learning based web application which helps users to choose an appropriate insurance premuim for subscription by predicting it based on user's details like living style, gender, smoker or not etc.
Projeto de ensino para o curso Ciência de Dados ministrado por mim na Hashtag
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
Predicting developer's salary from Stack Overflow Annual Developer Survey (https://insights.stackoverflow.com/survey)
Scikit-Learn useful pre-defined Pipelines Hub
Using Scikit-Learn Pipelines with GridSearchCV to train and tune multiple regression models at once on account transaction data.
Predict loan approval status using machine learning models. Explore data, build, tune, and evaluate models for accurate predictions.
Predict the number of passengers per plane on some flights
⚡ Code for machine Learning Pipeline with Scikit-learn ⚡
An collection of machine learning projects implemented based on IEEE papers.
This is a very brief notebook on NLP, it contains a "Disaster Analysis" project in which all the possible architectures were shown and described briefly.
A car price prediction model based on regressor task.
Projeto de ensino para o curso Ciência de Dados ministrado por mim na Hashtag
Notebook modelo de dados com análise descritiva
This repository includes projects using datasets of structured data (non-Spark). The projects use Python, NumPy, Pandas, Matplotlib, Seaborn, TensorFlow, Pytorch, and Sklearn.
Demo for "Pandas In, Pandas Out" scheme based scikit-learn pipeline.
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