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app.py

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+#!/usr/bin/env python
+# coding: utf-8
+# author : @ Ali Cemil Özdemir
+
+# Model that predicts car prices.
+
+#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.pipeline import Pipeline
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import StandardScaler,OneHotEncoder
+
+# upload data
+df=pd.read_excel('cars.xls')
+
+# create axis
+X=df.drop('Price',axis=1)
+y=df[['Price']]
+
+# split data as train and test data.
+X_train,X_test,y_train,y_test=train_test_split(X,y,
+                                               test_size=0.2,
+                                               random_state=42)
+
+
+# preprocessing
+preproccer=ColumnTransformer(transformers=[('num',StandardScaler(),
+                                           ['Mileage','Cylinder','Liter','Doors']),
+                            ('cat',OneHotEncoder(),['Make','Model','Trim','Type'])])
+
+# modelling
+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)
+
+# web interface
+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("Car Price Prediction :red_car: @Ali Cemil Özdemir")
+st.write("Enter Car Details to predict the price of the car")
+st.image("ozellikler_CarPred.png", caption="Car Features", use_column_width=True)
+make=st.selectbox("Make",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())
+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"):
+	pred=price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)
+
+	st.write("Predicted Price :red_car:  $",round(pred[0],2))