from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import seaborn as sb
import pandas as pd
from sklearn.metrics import accuracy_score



data_ = np.array(data, dtype=np.int16)
target_ = np.array(target, dtype=np.int16)

scaled_Data = StandardScaler().fit_transform(data_)


#clients_p = pd.DataFrame(data_.data, columns = [data_.columns])


train_data, test_data, train_target, test_target = train_test_split(data_, target_,test_size=0.2, random_state=101)

scaled_Data_train = StandardScaler().fit_transform(train_data)
scaled_Data_test = StandardScaler().fit_transform(test_data)

logistic_regression = LogisticRegression()
logistic_regression.fit(scaled_Data_train, train_target)


conf_matrix = confusion_matrix(test_target, logistic_regression.predict(scaled_Data_test))
print(conf_matrix)


acc = accuracy_score(test_target, logistic_regression.predict(scaled_Data_test))
print(acc)

print(data_.shape)
print(target_.shape)