Update app.py

app.py

@@ -3,7 +3,6 @@
 
 # # Araba Fiyatı Tahmin Eden Model ve Deployment
 
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 #import libraries
 import pandas as pd
 from sklearn.model_selection import train_test_split
@@ -13,36 +12,20 @@ from sklearn.pipeline import Pipeline
 from sklearn.compose import ColumnTransformer
 from sklearn.preprocessing import StandardScaler,OneHotEncoder
 
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 #Load data
 df=pd.read_excel('cars.xls')
 
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 X=df.drop('Price',axis=1)
 y=df[['Price']]
 
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 X_train,X_test,y_train,y_test=train_test_split(X,y,
                                                test_size=0.2,
                                                random_state=42)
 
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 preproccer=ColumnTransformer(transformers=[('num',StandardScaler(),
                                            ['Mileage','Cylinder','Liter','Doors']),
                             ('cat',OneHotEncoder(),['Make','Model','Trim','Type'])])
 
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 model=LinearRegression()
 pipe=Pipeline(steps=[('preprocessor',preproccer),
                     ('model',model)])
@@ -68,7 +51,7 @@ def price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,lea
 		})
 	prediction=pipe.predict(input_data)[0]
 	return prediction
-st.title("Araba Fiyatı Tahmin :red_car: @drmurataltun")
+st.title("Araba Fiyatı Tahmin :red_car: demethantas")
 st.write("Arabanın özelliklerini seçin")
 make=st.selectbox("Marka",df['Make'].unique())
 model=st.selectbox("Model",df[df['Make']==make]['Model'].unique())
@@ -85,7 +68,3 @@ if st.button("Tahmin"):
 	pred=price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)
 
 	st.write("Predicted Price :red_car:  $",round(pred[0],2))
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