Update app.py

app.py

@@ -1,85 +1,83 @@
-Python 3.10.5 (tags/v3.10.5:f377153, Jun  6 2022, 16:14:13) [MSC v.1929 64 bit (AMD64)] on win32
-Type "help", "copyright", "credits" or "license()" for more information.
-#!/usr/bin/env python
-# coding: utf-8
-
-
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LinearRegression
-from sklearn.metrics import r2_score,mean_squared_error
-from sklearn.pipeline import Pipeline
-from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import StandardScaler,OneHotEncoder
-
-
-
-df=pd.read_excel('cars.xls')
-
-
-
-
-
-
-
-X=df.drop('Price',axis=1)
-y=df[['Price']]
-
-
-
-X_train,X_test,y_train,y_test=train_test_split(X,y,
-                                               test_size=0.2,
-                                               random_state=42)
-
-
-
-
-preproccer=ColumnTransformer(transformers=[('num',StandardScaler(),
-                                           ['Mileage','Cylinder','Liter','Doors']),
-                            ('cat',OneHotEncoder(),['Make','Model','Trim','Type'])])
-
-
-
-
-model=LinearRegression()
-pipe=Pipeline(steps=[('preprocessor',preproccer),
-                    ('model',model)])
-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)
-
-import streamlit as st
-def price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather):
-	input_data=pd.DataFrame({
-		'Make':[make],
-		'Model':[model],
-		'Trim':[trim],
-		'Mileage':[mileage],
-		'Type':[car_type],
-		'Car_type':[car_type],
-		'Cylinder':[cylinder],
-		'Liter':[liter],
-		'Doors':[doors],
-		'Cruise':[cruise],
-		'Sound':[sound],
-		'Leather':[leather]
-		})
-	prediction=pipe.predict(input_data)[0]
-	return prediction
-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("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("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("11062024:Predicted Price :red_car:  $",round(pred[0],2))
+#!/usr/bin/env python
+# coding: utf-8
+
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import r2_score,mean_squared_error
+from sklearn.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler,OneHotEncoder
+
+
+
+df=pd.read_excel('cars.xls')
+
+
+
+
+
+
+
+X=df.drop('Price',axis=1)
+y=df[['Price']]
+
+
+
+X_train,X_test,y_train,y_test=train_test_split(X,y,
+                                               test_size=0.2,
+                                               random_state=42)
+
+
+
+
+preproccer=ColumnTransformer(transformers=[('num',StandardScaler(),
+                                           ['Mileage','Cylinder','Liter','Doors']),
+                            ('cat',OneHotEncoder(),['Make','Model','Trim','Type'])])
+
+
+
+
+model=LinearRegression()
+pipe=Pipeline(steps=[('preprocessor',preproccer),
+                    ('model',model)])
+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)
+
+import streamlit as st
+def price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather):
+	input_data=pd.DataFrame({
+		'Make':[make],
+		'Model':[model],
+		'Trim':[trim],
+		'Mileage':[mileage],
+		'Type':[car_type],
+		'Car_type':[car_type],
+		'Cylinder':[cylinder],
+		'Liter':[liter],
+		'Doors':[doors],
+		'Cruise':[cruise],
+		'Sound':[sound],
+		'Leather':[leather]
+		})
+	prediction=pipe.predict(input_data)[0]
+	return prediction
+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("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("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("11062024:Predicted Price :red_car:  $",round(pred[0],2))