randomForest.py

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler 
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
import random
from tqdm import tqdm
from sklearn.model_selection import GridSearchCV


# regressionData = pd.read_csv('feedToModelData.csv') 
regressionData = pd.read_csv('feedToModelData2.csv', index_col = 0) 
regressionData = regressionData.dropna()
regressionData = shuffle(regressionData)

x = regressionData[regressionData.columns[2:]]
x = x.loc[:, x.columns != 'Team1Win']


standardScalerX = StandardScaler()

x = standardScalerX.fit_transform(x)

y = regressionData['Team1Win']


X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=random.randint(0, 100))

forest=RandomForestClassifier()

n_estimators = [100, 300, 500, 800, 1200]
max_depth = [5, 8, 15, 25, 30]
min_samples_split = [2, 5, 10, 15, 100]
min_samples_leaf = [1, 2, 5, 10] 

hyperF = dict(n_estimators = n_estimators, max_depth = max_depth,  
              min_samples_split = min_samples_split, 
             min_samples_leaf = min_samples_leaf)

gridF = GridSearchCV(forest, hyperF, cv = 3, verbose = 1, 
                      n_jobs = -1)
clf = gridF.fit(X_train, y_train)



#Train the model using the training sets y_pred=clf.predict(X_test)
clf.fit(X_train,y_train)

y_pred=clf.predict(X_test)

print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

randomForestPredictions = pd.DataFrame(X_test)
randomForestPredictions['Prediction'] = clf.predict(X_test)
randomForestPredictions['Actual'] = y_test.values
randomForestPredictions['Correct'] = (randomForestPredictions['Prediction'] == randomForestPredictions['Actual'])
randomForestPredictions['Model'] = 'Random Forest' 

randomForestPredictions.to_csv('randomForestPredictions.csv')

randomForestPredictions.to_csv('modelPredictions.csv', mode = 'a', header = False)