Update app.py

app.py

@@ -3,8 +3,6 @@
 
 # # Araba Fiyatı Tahmin Eden Model ve Deployment
 
-# In[18]:
-
 
 #import libraries
 import pandas as pd
@@ -16,41 +14,26 @@ from sklearn.compose import ColumnTransformer
 from sklearn.preprocessing import StandardScaler,OneHotEncoder
 
 
-# In[19]:
-
 
 #Load data
 df=pd.read_excel('cars.xls')
 
 
-# In[6]:
-
-
-df.head()
-
-
-# In[7]:
-
 
-#df.to_csv('cars.csv',index=False)
 
 
-# In[20]:
 
 
 X=df.drop('Price',axis=1)
 y=df[['Price']]
 
 
-# In[21]:
-
 
 X_train,X_test,y_train,y_test=train_test_split(X,y,
                                                test_size=0.2,
                                                random_state=42)
 
 
-# In[22]:
 
 
 preproccer=ColumnTransformer(transformers=[('num',StandardScaler(),
@@ -58,7 +41,6 @@ preproccer=ColumnTransformer(transformers=[('num',StandardScaler(),
                             ('cat',OneHotEncoder(),['Make','Model','Trim','Type'])])
 
 
-# In[23]:
 
 
 model=LinearRegression()
@@ -68,10 +50,6 @@ pipe.fit(X_train,y_train)
 y_pred=pipe.predict(X_test)
 mean_squared_error(y_test,y_pred)**0.5,r2_score(y_test,y_pred)
 
-
-# In[24]:
-
-
 import streamlit as st
 def price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather):
 	input_data=pd.DataFrame({
@@ -90,27 +68,24 @@ def price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,lea
 		})
 	prediction=pipe.predict(input_data)[0]
 	return prediction
-st.title("Car Price Prediction :red_car: @drmurataltun")
-st.write("Enter Car Details to predict the price of the car")
-make=st.selectbox("Make",df['Make'].unique())
+st.title("Araba Fiyatı Tahmin :red_car: @drmurataltun")
+st.write("Arabanın özelliklerini seçin")
+make=st.selectbox("Marka",df['Make'].unique())
 model=st.selectbox("Model",df[df['Make']==make]['Model'].unique())
 trim=st.selectbox("Trim",df[(df['Make']==make) & (df['Model']==model)]['Trim'].unique())
-mileage=st.number_input("Mileage",200,60000)
-car_type=st.selectbox("Type",df['Type'].unique())
-cylinder=st.selectbox("Cylinder",df['Cylinder'].unique())
+mileage=st.number_input("Kilometre",200,60000)
+car_type=st.selectbox("Tipi",df[(df['Make']==make) & (df['Model']==model & df['Trim']==trim )['Type'].unique())
+cylinder=st.selectbox("Silindir",df['Cylinder'].unique())
 liter=st.number_input("Liter",1,6)
-doors=st.selectbox("Doors",df['Doors'].unique())
-cruise=st.radio("Cruise",[True,False])
-sound=st.radio("Sound",[True,False])
-leather=st.radio("Leather",[True,False])
-if st.button("Predict"):
+doors=st.selectbox("Kapı",df['Doors'].unique())
+cruise=st.radio("Hız S.",[True,False])
+sound=st.radio("Ses Sistemi",[True,False])
+leather=st.radio("Deri döşeme",[True,False])
+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))
 
 
-# In[ ]:
-
-