#!/usr/bin/env python
# coding: utf-8

#  # Car Price Prediction

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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.pipeline import Pipeline


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#pip install xlrd==2.0.1 


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df = pd.read_excel("cars.xls")
df.head()


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df.info()


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# Veri Ön İşleme
X = df.drop("Price", axis=1)
y = df["Price"]


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X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)


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preprocess = ColumnTransformer(
    transformers = [
        ("num",StandardScaler(),["Mileage","Cylinder","Liter","Doors"]),
        ("cat",OneHotEncoder(),["Make","Model","Trim","Type"])
    ])


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my_model = LinearRegression()


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# Pipeline tanımla
pipe = Pipeline(steps=[("preprocessor",preprocess),("model",my_model)])


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pipe.fit(X_train,y_train)


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y_pred = pipe.predict(X_test)
print("RMSE", mean_squared_error(y_test,y_pred)**0.50)
print("R2", r2_score(y_test,y_pred))


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#pip install streamlit


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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 @ROO")
st.write("Arabanın özelliklerini 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('Silindir',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('Fiyat:$', round(pred,2))


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