My attempt at the introduction to machine learning Kaggle competition: "Titanic: Machine Learning from Disaster"
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Updated
May 2, 2017 - Jupyter Notebook
My attempt at the introduction to machine learning Kaggle competition: "Titanic: Machine Learning from Disaster"
Made it using Streamlit and deployed it on AWS EC2 and Streamlit Share
Automatically find the optimal Machine Learning model using TPOT
program that uses vote data inputted manually from bracketcounter to generate a COBEN list (due to bracketcounter not being 100% accurate). this is a reverse engineer of code made by "teedster" in htwins central
Classification models to predict the churn rate of bank customers.
This day I wanna share my task about Automated Machine Learning using TPOT, Tree-based Pipeline Optimization Tool, is a Python library for automated machine learning. TPOT uses a tree-based structure to represent a model pipeline for a predictive modeling problem, including data preparation and modeling algorithms and model hyperparameters.
Starter repository for learning how to make machine learning models with voice data.
This project implements functions for building an ensemble of classifiers and regressors using the TPOT and auto-sklearn libraries with the help of a genetic algorithm evolved using the deap library.
TP Final de 75.70, FIUBA
Use of different regression models to predict bike demand.
It's a data science project for classification of Fraudulent and Non-Fraudulent transactions.
TPOT is meant to be an assistant that gives you ideas on how to solve a particular machine learning problem by exploring pipeline configurations that you might have never considered, then leaves the fine-tuning to more constrained parameter tuning techniques such as grid search.
Using TPOT AutoML for the Pima Diabetes dataset
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