Sign-Language-Detection-Using-Python/train_classifier.py at main · bot041/Sign-Language-Detection-Using-Python · GitHub

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import pickle

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np


data_dict = pickle.load(open('./data.pickle', 'rb'))

# Convert data into NumPy format
data = np.asarray(data_dict['data'])
labels = np.asarray(data_dict['labels'])

x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, shuffle=True, stratify=labels)

model = RandomForestClassifier()

model.fit(x_train, y_train)

y_predict = model.predict(x_test)

score = accuracy_score(y_predict, y_test)

print('{}% of samples were classified correctly !'.format(score * 100))

f = open('model.p', 'wb')
pickle.dump({'model': model}, f)
f.close()


# import pickle

# from sklearn.ensemble import RandomForestClassifier
# from sklearn.model_selection import train_test_split
# from sklearn.metrics import accuracy_score
# import numpy as np


# data_dict = pickle.load(open('./data.pickle', 'rb'))

# data = np.asarray(data_dict['data'])
# labels = np.asarray(data_dict['labels'])

# x_train, x_test, y_train, y_test = train_test_split(data, labels, test_size=0.2, shuffle=True, stratify=labels)

# model = RandomForestClassifier()

# model.fit(x_train, y_train)

# y_predict = model.predict(x_test)

# score = accuracy_score(y_predict, y_test)

# print('{}% of samples were classified correctly !'.format(score * 100))

# f = open('model.p', 'wb')
# pickle.dump({'model': model}, f)
# f.close()