Delete app.py

app.py

@@ -1,77 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-import pandas as pd
-from sklearn.model_selection import train_test_split #veri setini bölme işlemleri
-from sklearn.linear_model import LinearRegression #Doğrusal regresyon
-from sklearn.metrics import r2_score,mean_squared_error #modelimizin performansını ölçmek için
-from sklearn.compose import ColumnTransformer #Sütun dönüşüm işlemleri
-from sklearn.preprocessing import OneHotEncoder, StandardScaler # kategori - sayısal dönüşüm  ve ölçeklendirme
-from sklearn.pipeline import Pipeline #Veri işleme hattı
-
-df=pd.read_excel('cars.xls')
-
-df
-
-X=df.drop('Price',axis=1) #fiyat sütunu çıkar fiyata etki edenler kalsın
-y=df['Price'] #tahmin
-
-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()
-
-
-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))
-
-df['Mileage'].max()
-
-df['Type'].unique()
-
-df['Liter'].max()
-
-import streamlit as st
-#price tahmin fonksiyonu tanımla
-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("Used Car Price Estimation:red_car: @jameswhitecookjr90")
-st.write('Select the features 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',100,200000)
-car_type=st.selectbox('Vehicle Type',df[(df['Make']==make) &(df['Model']==model)&(df['Trim']==trim)]['Type'].unique())
-cylinder=st.selectbox('Cylinder',df['Cylinder'].unique())
-liter=st.number_input('Engine Displacement',1,10)
-doors=st.selectbox('Number of Doors',df['Doors'].unique())
-cruise=st.radio('Cruise Control',[True,False])
-sound=st.radio('Audio System',[True,False])
-leather=st.radio('Leather Seat',[True,False])
-if st.button('Predict'):
-    pred=price(make,model,trim,mileage,car_type,cylinder,liter,doors,cruise,sound,leather)
-    st.write('Price:$', round(pred[0],2))