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Introduction Machine Learning is a branch of computer science. You can use your data to predict outcomes. In this case we look at a soccer team. We will be looking at the points scored in the top league by each team. If you look at the league table, it is not hard to understand why the most popular predictor is the most popular team. If you are not familiar with Machine Learning, it is recommended that you read the Python Machine Learning documentation and other resources. Import libraries We will be importing the libraries provided by Kaggle. import pandas as pd import numpy as np import tensorflow as tf import scipy as sp import matplotlib.pyplot as plt import seaborn as sns from sklearn import model_selection from sklearn.metrics import confusion_matrix import h2o import h2o_svm import h2o_gbm from sklearn.naive_bayes import GaussianNB from sklearn.neural_network import MLPClassifier from sklearn.neural_network import SVC from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import GradientBoostingClassifier import sklearn from sklearn.linear_model import LogisticRegression from sklearn.decomposition import PCA from sklearn.metrics import classification_report from sklearn.metrics import roc_curve from sklearn.metrics import auc_score import matplotlib.pyplot as plt

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