Update app.py

app.py

@@ -13,6 +13,7 @@ X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random
 
 from sklearn.compose import ColumnTransformer
 from sklearn.pipeline import Pipeline
+from sklearn.metrics import mean_absolute_error
 from sklearn.preprocessing import StandardScaler, OneHotEncoder
 from sklearn.ensemble import HistGradientBoostingRegressor
 hgbr = HistGradientBoostingRegressor()
@@ -22,7 +23,9 @@ transformer = ColumnTransformer(transformers=[('num', StandardScaler(), num_feat
 pipe = Pipeline(steps=[('preprocessor', transformer), ('model', hgbr)])
 
 pipe.fit(X_train, y_train)
-
+score = pipe.score(X_test,y_test)
+y_pred = pipe.predict(X_test)
+mae = mean_absolute_error(y_test,y_pred)
 import streamlit as st
 
 def price(companyName,modelName,modelYear,locaiton,mileage,engineType,engineCapacity,color,assembly,bodyType,transmissionType,registrationStatus):
@@ -43,6 +46,7 @@ def price(companyName,modelName,modelYear,locaiton,mileage,engineType,engineCapa
   prediction=pipe.predict(input_data)[0]
   return prediction
 st.title('Car Price Prediction :car: :arrow_forward: :dollar: @yusufenes')
+st.write(f'Accuracy : {score}  Mean Absolute Error : {mae}')
 st.write('Please Chose Car Specifications')
 companyName = st.selectbox('Company Name',df['Company Name'].unique())
 modelName = st.selectbox('Model Name',df[df['Company Name']==companyName]['Model Name'].unique())