Prediction of Match results by running Naive Bayes

predict.py

"""
    Labels : Lost, Draw, Won [-1,0,1]
    Features
    ==========
        Toss(Lost,Won) = [-1,1]
        Bat(First, Second) = [-1,1]
"""

# Import Library of Gaussian Naive Bayes model
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
import numpy as np
from sklearn.metrics import precision_recall_fscore_support as score


# Assigning Features
features = np.genfromtxt('train.csv',delimiter=',',usecols=(1,2),dtype=int)
labels = np.genfromtxt('train.csv',delimiter=',',usecols=(0),dtype=int)

features_test = np.genfromtxt('test.csv',delimiter=',',usecols=(1,2),dtype=int)
labels_test = np.genfromtxt('test.csv',delimiter=',',usecols=(0),dtype=int)

# Create a Gaussian Classifier
model = GaussianNB()
#
# # Train the model using the training sets
model.fit(features, labels)
#
# # Predict Output
predicted = model.predict(features_test)
# print(predicted)
acc = accuracy_score(labels_test,predicted)
print(acc)