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app.py

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+#!/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)
+print('RMSE :', mean_squared_error(y_test,y_pred)**.5)
+print('R2 :', 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[0],2))
+
+
+# In[ ]:
+
+
+
+

requirements.txt

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+streamlit==1.31.1
+scikit-learn==1.4.1.post1
+pandas==2.1.0
+xlrd == 2.0.1