requirements.txt

scikit-learn==1.4.1.post1
pandas==2.1.0

app.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # Car prediction model training and serving with streamlit
+
+#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
+
+
+
+#Load data
+df=pd.read_csv('cars.csv')
+
+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("Car Price Prediction :green_car: @sbgonenc")
+st.write("Select Car Specs")
+make=st.selectbox("Brand",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("Milage",200,60000)
+car_type=st.selectbox("Type",df[(df['Make']==make) & (df['Model']==model) & (df['Trim']==trim )]['Type'].unique())
+cylinder=st.selectbox("Cylinders",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 System",[True,False])
+leather=st.radio("Leather",[True,False])
+if st.button("Prediction"):
+	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))