main.py

# Load libraries
import pandas
from pandas.plotting import scatter_matrix
# import matplotlib.pyplot as plt


from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC

# Auto convert
from sklearn.preprocessing import LabelEncoder

column_names = ["id", "region", "winner", "queue", "map", "season", "patch", "creation", "duration", "rank"]
p_column_names = ["id","match_id","region","team_id","summoner_id","role","champion_id","kills","deaths","assists","cs","first_blood","first_tower","first_inhibitor","largest_kill","largest_spree","tower_kills","inhibitor_kills","gold_earned","last_season","spell_d","spell_f","item_0","item_1","item_2","item_3","item_4","item_5","item_6","gold_0_10","gold_10_20","xp_0_10","xp_10_20","double_kills","triple_kills","quadra_kills","penta_kills","level","vision"]
dataset = pandas.read_csv('./matches.csv', names=column_names).merge(
    pandas.read_csv('./matches_participants.csv', names=p_column_names),
    how='inner', left_on=['id','region'], right_on = ['match_id','region']
)

del dataset['id_y']
del dataset['id_x']
del dataset['creation']
del dataset['map']
del dataset['season']
# del dataset['patch']
del dataset['queue']

for name in ['region', 'rank', 'role', 'last_season', 'first_blood', 'first_tower', 'first_inhibitor']:
	dataset[name] = LabelEncoder().fit_transform(dataset[name].values) # convert names to numbers

print(dataset.head(20))

# start the things
array = dataset.values
X = array[:]
Y = array[:,1] # 4 = rank

validation_size = 0.20
seed = 7
X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)

seed = 7
scoring = 'accuracy'

# Spot Check Algorithms
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
# models.append(('SVM', SVC())) # this taking some time.

# evaluate each model in turn
results = []
names = []

for name, model in models:
	kfold = model_selection.KFold(n_splits=10, random_state=seed)
	cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
	results.append(cv_results)
	names.append(name)
	msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
	print(msg)

requirements.txt

numpy==1.14.5
pandas==0.23.3
python-dateutil==2.7.3
pytz==2018.5
scikit-learn==0.19.1
scipy==1.1.0
six==1.11.0