import pandas as pd
import xgboost as xgb
from sklearn.metrics import confusion_matrix, mean_squared_error
from sklearn.metrics import mean_absolute_error,mean_squared_error,median_absolute_error
df = pd.read_csv('zab_work2.csv',";",header=None)
X_train = df.drop(17,axis=1)
Y_train = df[17]
T_train_xgb = xgb.DMatrix(X_train, Y_train)
 
params = {"objective": "reg:linear", "booster":"gblinear"}
gbm = xgb.train(dtrain=T_train_xgb,params=params)
test_data = pd.read_csv('new_work2.csv',";",header=None)
print(test_data)
X_test = test_data.drop(17,axis=1)
Y_test = test_data[17]
Y_pred = gbm.predict(xgb.DMatrix(X_test))
test_erorr = mean_squared_error(Y_test,Y_pred);
print("Accuracy: %.2f%%" % (test_erorr * 100.0))    
accuracy = mean_absolute_error(Y_test,Y_pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))
accuracy2 = median_absolute_error(Y_test, Y_pred)
print("Accuracy: %.2f%%" % (accuracy2 * 100.0))

 import numpy
import sklearn
import xgboost as xgb
from sklearn.metrics import confusion_matrix, mean_squared_error
dataset = numpy.genfromtxt('zab_work2.csv', delimiter=";")
dataset2 = numpy.genfromtxt('new_work2.csv', delimiter=";")
# split data into X and y
X = dataset[:,[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]]
Y = dataset[:,[17]]
T_train_xgb = xgb.DMatrix(X, Y)
x_2 = dataset2[:,[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]]
y_2 = dataset2[:,[17]]
 
params = {"objective": "reg:linear", "booster":"gblinear"}
gbm = xgb.train(dtrain=T_train_xgb,params=params)
Y_pred = gbm.predict(xgb.DMatrix(x_2))
test_erorr = mean_squared_error(y_2,Y_pred);
print("Accuracy: %.2f%%" % (test_erorr * 100.0))    
accuracy = mean_absolute_error(y_2,Y_pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))
accuracy2 = median_absolute_error(y_2,Y_pred)
print("Accuracy: %.2f%%" % (accuracy2 * 100.0))