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app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+#  # Car Price Prediction
+
+# 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.compose import ColumnTransformer
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.pipeline import Pipeline
+
+
+# In[4]:
+
+
+#pip install xlrd==2.0.1 
+
+
+# In[5]:
+
+
+df = pd.read_excel("cars.xls")
+df.head()
+
+
+# In[6]:
+
+
+df.info()
+
+
+# In[7]:
+
+
+# Veri Ön İşleme
+X = df.drop("Price", axis=1)
+y = df["Price"]
+
+
+# In[8]:
+
+
+X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)
+
+
+# In[9]:
+
+
+preprocess = ColumnTransformer(
+    transformers = [
+        ("num",StandardScaler(),["Mileage","Cylinder","Liter","Doors"]),
+        ("cat",OneHotEncoder(),["Make","Model","Trim","Type"])
+    ])
+
+
+# In[10]:
+
+
+my_model = LinearRegression()
+
+
+# In[11]:
+
+
+# Pipeline tanımla
+pipe = Pipeline(steps=[("preprocessor",preprocess),("model",my_model)])
+
+
+# In[12]:
+
+
+pipe.fit(X_train,y_train)
+
+
+# In[13]:
+
+
+y_pred = pipe.predict(X_test)
+print("RMSE", mean_squared_error(y_test,y_pred)**0.50)
+print("R2", r2_score(y_test,y_pred))
+
+
+# In[15]:
+
+
+#pip install streamlit
+
+
+# In[16]:
+
+
+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("2. El araba Fiyat Tahmin @ROO")
+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('Silindir',df['Cylinder'].unique())
+liter=st.number_input('Motor Hacmi',1,10)
+doors=st.selectbox('Kapı sayısı',df['Doors'].unique())
+cruise=st.radio('Hız Sbt.',[True,False])
+sound=st.radio('Ses Sis.',[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[ ]:
+
+
+
+