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import pandas as pd |
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from sklearn.model_selection import train_test_split |
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from sklearn.linear_model import LinearRegression |
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from sklearn.metrics import r2_score,mean_squared_error |
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from sklearn.pipeline import Pipeline |
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from sklearn.compose import ColumnTransformer |
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from sklearn.preprocessing import StandardScaler,OneHotEncoder |
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df=pd.read_excel('cars.xls') |
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X=df.drop('Price',axis=1) |
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y=df[['Price']] |
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X_train,X_test,y_train,y_test=train_test_split(X,y, |
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test_size=0.2, |
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random_state=42) |
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preproccer=ColumnTransformer(transformers=[('num',StandardScaler(), |
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['Mileage','Cylinder','Liter','Doors']), |
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('cat',OneHotEncoder(),['Make','Model','Trim','Type'])]) |
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model=LinearRegression() |
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pipe=Pipeline(steps=[('preprocessor',preproccer), |
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('model',model)]) |
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pipe.fit(X_train,y_train) |
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y_pred=pipe.predict(X_test) |
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mean_squared_error(y_test,y_pred)**0.5,r2_score(y_test,y_pred) |
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import streamlit as st |
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def price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather): |
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input_data=pd.DataFrame({ |
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'Make':[make], |
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'Model':[model], |
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'Trim':[trim], |
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'Mileage':[mileage], |
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'Type':[car_type], |
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'Car_type':[car_type], |
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'Cylinder':[cylinder], |
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'Liter':[liter], |
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'Doors':[doors], |
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'Cruise':[cruise], |
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'Sound':[sound], |
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'Leather':[leather] |
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}) |
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prediction=pipe.predict(input_data)[0] |
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return prediction |
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st.title("Used Car Price Estimation :red_car: by JamesWhiteCookJr90") |
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st.write("Enter features of the car") |
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make=st.selectbox("Make",df['Make'].unique()) |
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model=st.selectbox("Model",df[df['Make']==make]['Model'].unique()) |
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trim=st.selectbox("Trim",df[(df['Make']==make) & (df['Model']==model)]['Trim'].unique()) |
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mileage=st.number_input("Mileage",200,60000) |
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car_type=st.selectbox("Vehicle Type",df[(df['Make']==make) & (df['Model']==model) & (df['Trim']==trim )]['Type'].unique()) |
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cylinder=st.selectbox("Cylinder",df['Cylinder'].unique()) |
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liter=st.number_input("Engine Displacement",1,6) |
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doors=st.selectbox("Number of Doors",df['Doors'].unique()) |
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cruise=st.radio("Cruise Control",[True,False]) |
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sound=st.radio("Audio System",[True,False]) |
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leather=st.radio("Leather Seat",[True,False]) |
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if st.button("Predict"): |
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pred=price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather) |
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st.write("11062024:Predicted Price :red_car: $",round(pred[0],2)) |
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