app.py

# -*- coding: utf-8 -*-
"""d7.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1Z4csahRmsC2REaymzcm_UZxrUpVgUsIv
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
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

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

df.info()

#VERİ ÖN İŞLEME
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)

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

my_model=LinearRegression()

#Pipeline Tanımla
pipe=Pipeline(steps=[('preprocessor',preprocess),('model',my_model)])

pipe.fit(X_train,y_train)

y_pred=pipe.predict(X_test)
print('RMSE',mean_squared_error(y_test,y_pred)**0.5)
print('R2',r2_score(y_test,y_pred))

#pip install streamlit

import streamlit as st
def price(make, model,trim,mileage,car_type,cylinder,liter,doors,
          curise,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]})
    # The following line was indented too far, causing the error.
    prediction=pipe.predict(input_data)[0]
    return prediction
st.title('2.El Araba Fiyat Tahmin @tugbaksu')
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('Cylinder',df['Cylinder'].unique())
liter=st.number_input('Yakıt 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[0],2))