Update app.py

app.py

@@ -1,84 +1,87 @@
-# -*- coding: utf-8 -*-
-"""Car_predict.ipynb
+#!/usr/bin/env python
+# coding: utf-8
 
-Automatically generated by Colab.
+# # Araba Fiyatı Tahmin Eden Model ve Deployment
 
-Original file is located at
-    https://colab.research.google.com/drive/1ydYFB1nlbvDcEQ4QKJFLLlV9JAhiinwU
-
-# Car Prediction
-"""
 
+#import libraries
 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.compose import ColumnTransformer
-from sklearn.preprocessing import OneHotEncoder, StandardScaler
+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
 
-# pip install xlrd
 
-# ls
 
-df=pd.read_excel("cars.xls")
-# df
+#Load data
+df=pd.read_excel('cars.xls')
 
-# df.info()
 
-"""## Veri Ön işleme"""
 
-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)
 
-preprocess = ColumnTransformer(transformers=[("num",StandardScaler(),["Mileage","Cylinder","Liter","Doors"]),
-                                             ("cat",OneHotEncoder(),["Make","Model","Trim","Type"])])
 
-my_model = LinearRegression()
 
-# Pipeline tanımla
-pipe = Pipeline(steps=[("preprocessor",preprocess),("model",my_model)])
+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)
 
-pipe.fit(X_train,y_train)
 
-y_pred=pipe.predict(X_test)
-print("RMSE",mean_squared_error(y_test,y_pred)**0.5)
-print("R2",r2_score(y_test,y_pred))
 
-# pip install streamlit
+
+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],
-                             'Cylinder':[cylinder],
-                             'Liter':[liter],
-                             'Doors':[doors],
-                             'Cruise':[cruise],
-                             'Sound':[sound],
-                             'Leather':[leather]})
-    prediction=pipe.predict(input_data)[0]
-    return prediction
-
-st.title("2. El Araba Fiyat Tahmin @Beyza Nur Sarıkaya")
-st.write("Arabanın öselliklerini seçiniz")
-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',100,200000)
-car_type=st.selectbox('Araç Tipi',df[(df['Make']==make) &(df['Model']==model)&(df['Trim']==trim)]['Type'].unique())
-cylinder=st.selectbox('Cylinder',df['Cylinder'].unique())
-liter=st.number_input('Motor hacmi',1,10)
-doors=st.selectbox('Kapı sayısı',df['Doors'].unique())
-cruise=st.radio('Hız Sbt.',[True,False])
-sound=st.radio('Ses Sis.',[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))
-
+	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: @Beyza Nur Sarıkaya")
+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("Predicted Price :red_car:  $",round(pred[0],2))