To model the probability of a soccer coach leave his/her team during Campeonato Brasileiro for 10 chosen teams and considering years 2018, 2019 and 2020.

Last update: Jan 20, 2022

Related tags

Deep Learning quemrodarodada

Overview

quemrodarodada

Main aim

To model the probability of a soccer coach leave his/her team during Campeonato Brasileiro for 10 chosen teams and considering years 2018, 2019 and 2020.

How to use it

Run 01_Manipul_dados.ipynb and 02_Manipul_dados.ipynb to process and organize data.
Run 03_descritivas.ipynb for descriptive analysis.
Run 04_Modelo.ipynb to fit a logistic regression model to the data.

Python Libraries

pandas
numpy
seaborn
sklearn
matplotlib
altair

DATA

Brasileirao Série A: from https://basedosdados.org/dataset/mundo-transfermarkt-competicoes
Tecnicos: gotten manually from https://interativos.globoesporte.globo.com/futebol/rotatividade-dos-tecnicos/

Remarks

This project was developed in Google colab and hence the code asks for Google Drive access.

Last update

December 13, 2021

Author

Larissa Sayuri Futino Castro dos Santos
@amandaaudi
@carinadourado
@ingridyrs
Meyrele Torres
@victorfariassb

Acknowledment

This project is part of the Machine Learning subject from Master em Jornalismo de Dados, Automação e Data Storytelling from INSPER.

Many thanks to @BurgosNY and Eduardo Colagrossi for the assistance.

To model the probability of a soccer coach leave his/her team during Campeonato Brasileiro for 10 chosen teams and considering years 2018, 2019 and 2020.

Related tags

Overview

quemrodarodada

Main aim

How to use it

Python Libraries

DATA

Remarks

Last update

Author

Acknowledment

Owner

Larissa Sayuri Futino Castro dos Santos

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To model the probability of a soccer coach leave his/her team during Campeonato Brasileiro for 10 chosen teams and considering years 2018, 2019 and 2020.

Related tags

Overview

quemrodarodada

Main aim

How to use it

Python Libraries

DATA

Remarks

Last update

Author

Acknowledment

Owner

Larissa Sayuri Futino Castro dos Santos

Adversarial Attacks on Probabilistic Autoregressive Forecasting Models.

Voice of Pajlada with model and weights.

Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).

🐸STT integration examples

The all new way to turn your boring vector meshes into the new fad in town; Voxels!

MPRNet-Cloud-removal: Progressive cloud removal

I will implement Fastai in each projects present in this repository.

Code for TIP 2017 paper --- Illumination Decomposition for Photograph with Multiple Light Sources.

GANimation: Anatomically-aware Facial Animation from a Single Image (ECCV'18 Oral) [PyTorch]

ChainerRL is a deep reinforcement learning library built on top of Chainer.

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This repository contains code to run experiments in the paper "Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers."

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