app.py

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

# In[1]:


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.preprocessing import OneHotEncoder,StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer


# In[2]:


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


# In[3]:


df.head()


# In[4]:


X=df.drop('Price',axis=1)
y=df['Price']


# In[5]:


X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.2,random_state=42)


# In[6]:


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


# In[7]:


my_model=LinearRegression()
pipe=Pipeline(steps=[('preprocessor',preprocessor),('model',my_model)])
pipe.fit(X_train,y_train)
y_pred=pipe.predict(X_test)
mean_squared_error(y_test,y_pred)**.5, r2_score(y_test,y_pred)


# In[8]:


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("Predict Car Prices @KenanAvşar")
st.write("Enter Car Details to predict the price of the car")
make=st.selectbox("Marka",df['Make'].unique())
model=st.selectbox("Model",df[df['Make']==make]['Model'].unique())
trim=st.selectbox("Versiyon",df[(df['Make']==make)&(df['Model']==model)]['Trim'].unique())
mileage=st.number_input("Kilometre",100,df['Mileage'].max())
car_type=st.selectbox("Araç Tipi",df[(df['Make']==make)&(df['Model']==model)&(df['Trim']==trim)]['Type'].unique())
cylinder=st.selectbox("Silindir",df[(df['Make']==make)&(df['Model']==model)&(df['Trim']==trim)]['Cylinder'].unique())
liter=st.selectbox("Depo Hacmi",df[(df['Make']==make)&(df['Model']==model)&(df['Trim']==trim)]['Liter'].unique())
doors=st.selectbox("Kapı Sayısı",df[(df['Make']==make)&(df['Model']==model)&(df['Trim']==trim)]['Doors'].unique())
cruise=st.radio("Hız Sabitleyici",[True,False])
sound=st.radio("Ses Sistemi",[True,False])
leather=st.radio("Deri Döşeme",[True,False])

if st.button('Tahmin Et'):
    pred=price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)
    st.write('Fiyat:$',round(pred,2))


# In[ ]: