Update app.py

app.py

@@ -3,6 +3,19 @@ import pandas as pd
 import xgboost as xgb
 import pandas as pd
 
+import random
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split,cross_val_score
+from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor
+from sklearn.metrics import classification_report,confusion_matrix,accuracy_score
+from sklearn.neighbors import KNeighborsClassifier,KNeighborsRegressor
+from sklearn.svm import SVC,SVR
+from sklearn import datasets
+
+
+
+
 df_strength=pd.read_csv("data_strength.csv")
 
 import random
@@ -11,9 +24,13 @@ x_strength=df_strength.iloc[:,:-1]
 y_strength=df_strength.iloc[:,-1]
 
 
+x_strength=df_strength.iloc[:,:-1]
+y_strength=df_strength.iloc[:,-1]
+from sklearn.model_selection import train_test_split
+x_train, x_test, y_train, y_test=train_test_split(x_strength,y_strength, test_size=0.15, random_state=2)
 
 clf = xgb.XGBRegressor(learning_rate=0.1788, max_depth=27, subsample=0.5146, n_estimators=100)
-clf.fit(x_strength, y_strength)
+clf.fit(x_train, y_train)