Delete app.py

app.py

@@ -1,111 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# # Araba Fiyatı Tahmin Eden Model ve Deployment
-
-# In[18]:
-
-
-#import libraries
-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.pipeline import Pipeline
-from sklearn.compose import ColumnTransformer
-from sklearn.preprocessing import StandardScaler,OneHotEncoder
-
-
-# In[19]:
-
-
-#Load data
-df=pd.read_excel('cars.xls')
-
-
-# In[6]:
-
-
-df.head()
-
-
-# In[7]:
-
-
-#df.to_csv('cars.csv',index=False)
-
-
-# In[20]:
-
-
-X=df.drop('Price',axis=1)
-y=df[['Price']]
-
-
-# In[21]:
-
-
-X_train,X_test,y_train,y_test=train_test_split(X,y,
-                                               test_size=0.2,
-                                               random_state=42)
-
-
-# In[22]:
-
-
-preproccer=ColumnTransformer(transformers=[('num',StandardScaler(),
-                                           ['Mileage','Cylinder','Liter','Doors']),
-                            ('cat',OneHotEncoder(),['Make','Model','Trim','Type'])])
-
-
-# In[23]:
-
-
-model=LinearRegression()
-pipe=Pipeline(steps=[('preprocessor',preproccer),
-                    ('model',model)])
-pipe.fit(X_train,y_train)
-y_pred=pipe.predict(X_test)
-mean_squared_error(y_test,y_pred)**0.5,r2_score(y_test,y_pred)
-
-
-# In[24]:
-
-
-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],
-		'Car_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("Used Car Price Estimation :red_car: by JamesWhiteCookJr90")
-st.write("Enter Car Details to predict the price of the car")
-make=st.selectbox("Make",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("Mileage",200,60000)
-car_type=st.selectbox("Type",df['Type'].unique())
-cylinder=st.selectbox("Cylinder",df['Cylinder'].unique())
-liter=st.number_input("Liter",1,6)
-doors=st.selectbox("Doors",df['Doors'].unique())
-cruise=st.radio("Cruise",[True,False])
-sound=st.radio("Sound",[True,False])
-leather=st.radio("Leather",[True,False])
-if st.button("Predict"):
-	pred=price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)
-
-	st.write("Predicted Price :red_car:  $",round(pred[0],2))
-
-