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app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# # Car Prediction
+
+# In[1]:
+
+
+#pip install pandas
+
+
+# In[2]:
+
+
+import pandas as pd
+
+
+# In[3]:
+
+
+#pip install -U scikit-learn
+
+
+# In[4]:
+
+
+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.compose import ColumnTransformer
+from sklearn.preprocessing import OneHotEncoder,StandardScaler
+from sklearn.pipeline import Pipeline
+
+
+# In[5]:
+
+
+#pip install xlrd
+
+
+# In[6]:
+
+
+ls
+
+
+# In[7]:
+
+
+#Veriyi Yükle
+df=pd.read_excel('cars.xls')
+df
+
+
+# In[8]:
+
+
+df.info()
+
+
+# ## Veri Önişleme
+
+# In[9]:
+
+
+x=df.drop('Price',axis=1)
+y=df['Price']
+
+
+# In[10]:
+
+
+x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2,random_state=42)
+
+
+# In[15]:
+
+
+preprocess=ColumnTransformer(
+    transformers=[
+        ('num',StandardScaler(),['Mileage','Cylinder','Liter','Doors']),
+        ('cat',OneHotEncoder(),['Make','Model','Trim','Type'])
+    ]
+)
+
+
+# In[16]:
+
+
+my_model=LinearRegression()
+
+
+# In[17]:
+
+
+# Pipeline Tanımlama
+pipe=Pipeline(steps=[('preprocessor',preprocess),('model',my_model)])
+
+
+# In[18]:
+
+
+pipe.fit(x_train,y_train)
+
+
+# In[20]:
+
+
+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))
+
+
+# In[21]:
+
+
+#pip install streamlit
+
+
+# In[22]:
+
+
+import streamlit as st
+
+
+# In[23]:
+
+
+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("II. El Araba Fiyatı Tahmin:red_car: @SenasuDemir")
+st.write('Arabanın özelliklerini seçiniz')
+make=st.selectbox('Marka',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('Kilometre',100,200000)
+car_type=st.selectbox('Araç Tipi',df[(df['Make']==make) &(df['Model']==model)&(df['Trim']==trim)]['Type'].unique())
+cylinder=st.selectbox('Cylinder',df['Cylinder'].unique())
+liter=st.number_input('Motor Hacmi',1,10)
+doors=st.selectbox('Kapı sayısı',df['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'):
+    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