X = df.drop(['label'], axis=1)
y = df['label']
training_count = 0

X_train, test_data, y_train, test_label = train_test_split(X, y, test_size=0.1, random_state=7)
model = XGBClassifier(learning_rate=0.5, n_estimators=250, max_depth= 5)
model.fit(X_train, y_train)
model.save_model('trained_model_full')

#validation
from collections import  OrderedDict
from operator import itemgetter
import csv


model = XGBClassifier()
booster = xgb.Booster()
booster.load_model('trained_model_full')
model._Booster = booster
model._le = LabelEncoder().fit(test_label)
start = time.time()
pred = model.predict(test_data)
end = time.time()

X = df.drop(['label'], axis=1)
y = df['label']
training_count = 0

X_train, test_data, y_train, test_label = train_test_split(X, y, test_size=0.1, random_state=7)

#validation
from collections import  OrderedDict
from operator import itemgetter
import csv


model = XGBClassifier()
booster = xgb.Booster()
booster.load_model('trained_model_full')
model._Booster = booster
model._le = LabelEncoder().fit(test_label)
start = time.time()
pred = model.predict(test_data)
end = time.time()