Upload Araba_fiyat_tahmin.py

Araba_fiyat_tahmin.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[20]:
+
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.compose import ColumnTransformer
+from sklearn.preprocessing import OneHotEncoder,StandardScaler
+from sklearn.pipeline import Pipeline
+
+
+# In[21]:
+
+
+df=pd.read_excel('cars.xls')
+df.head()
+
+
+# In[22]:
+
+
+pip install xlrd
+
+
+# In[23]:
+
+
+X=df.drop('Price', axis=1)
+y=df['Price']
+
+
+# In[24]:
+
+
+X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)
+
+
+# In[25]:
+
+
+#!pip install ydata-profiling
+
+
+# In[26]:
+
+
+#import ydata_profiling
+
+
+# In[27]:
+
+
+#df.profile_report()
+
+
+# In[28]:
+
+
+preprocess=ColumnTransformer(transformers=[
+    ('num',StandardScaler(),['Mileage','Cylinder','Liter','Doors']),
+    ('cat',OneHotEncoder(),['Make','Model','Trim','Type'])])
+
+
+# Veri önişlemedeki standartlaşma ve one-hot kodlama işlemlerini otomatikleştiriyoruz. 
+# Artık preprocess kullanarak kullanıcıdan gelen veriyi modelimize uygun girdi haline dçnüştürebiliriz. 
+
+# In[31]:
+
+
+model=LinearRegression()
+pipe=Pipeline(steps=[('preprocesor', preprocess), ('model', model)])
+
+
+# In[32]:
+
+
+pipe.fit(X_train, y_train)
+
+
+# In[33]:
+
+
+y_pred=pipe.predict(X_test)
+mean_squared_error(y_test,y_pred)**0.5,r2_score(y_test,y_pred)
+
+
+# In[ ]:
+
+
+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("Car Price Prediction :red_car: @drmurataltun")
+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))
+
+
+# In[ ]:
+
+
+
+