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

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import pandas as pd
import numpy as np
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
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
from sklearn.model_selection import train_test_split


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pip install xldr


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ls


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


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df.head()


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#veri ön işleme,standartlıştırma


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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,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()
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)
rmse=mean_squared_error(y_test,y_pred)**0.5
r2=r2_score(y_test,y_pred)
r2,rmse


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#modeli yayma, kullanıma sunma


# ### Streamlit

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get_ipython().system('pip install streamlit')


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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):
    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("Car Price Prediction:blue_car:@neslisahozturk")
st.write("Select the features of the car")
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("Km",100,df["Mileage"].max())
car_type=st.selectbox("Car Type",df["Type"].unique())
cylinder=st.selectbox("Cylinder",df["Cylinder"].unique())
liter=st.number_input("Fuel Volume",df["Liter"].min(),df["Liter"].max())
doors=st.selectbox("Number of Doors",df["Doors"].unique())
cruise=st.radio("Velocity Constant",[True,False])
sound=st.radio("Sound System",[True,False])
leather=st.radio("Leather Seats",[True,False])

if st.button("Predict"):
    pred=price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)
    st.write("Fiyat:$",round(pred[0],2))


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