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car_predict_app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[15]:
+
+
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import warnings
+warnings.filterwarnings("ignore")
+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
+from sklearn.model_selection import train_test_split
+
+
+# In[4]:
+
+
+pip install xldr
+
+
+# In[6]:
+
+
+ls
+
+
+# In[7]:
+
+
+df=pd.read_excel("cars.xls")
+
+
+# In[8]:
+
+
+df.head()
+
+
+# In[9]:
+
+
+#veri ön işleme,standartlıştırma
+
+
+# In[10]:
+
+
+X=df.drop("Price",axis=1)
+
+
+# In[13]:
+
+
+y=df["Price"]
+
+
+# In[16]:
+
+
+X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)
+
+
+# In[17]:
+
+
+preprocess=ColumnTransformer(
+        transformers=[
+            ("num",StandardScaler(),["Mileage","Cylinder","Liter","Doors"]),
+            ("cat",OneHotEncoder(),["Make","Model","Trim","Type"])
+        ]
+)
+
+
+# In[20]:
+
+
+my_model=LinearRegression()
+pipe=Pipeline(steps=[("preprocessor",preprocess),("model",my_model)])
+
+
+# In[21]:
+
+
+pipe.fit(X_train,y_train)
+
+
+# In[24]:
+
+
+y_pred=pipe.predict(X_test)
+rmse=mean_squared_error(y_test,y_pred)**0.5
+r2=r2_score(y_test,y_pred)
+r2,rmse
+
+
+# In[25]:
+
+
+#modeli yayma, kullanıma sunma
+
+
+# ### Streamlit
+
+# In[28]:
+
+
+get_ipython().system('pip install streamlit')
+
+
+# In[29]:
+
+
+import streamlit as st
+
+
+# In[34]:
+
+
+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("Car Price Prediction:blue_car:@neslisahozturk")
+st.write("Select the features of the car")
+make=st.selectbox("Brand",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("Km",100,df["Mileage"].max())
+car_type=st.selectbox("Car Type",df["Type"].unique())
+cylinder=st.selectbox("Cylinder",df["Cylinder"].unique())
+liter=st.number_input("Fuel Volume",df["Liter"].min(),df["Liter"].max())
+doors=st.selectbox("Number of Doors",df["Doors"].unique())
+cruise=st.radio("Velocity Constant",[True,False])
+sound=st.radio("Sound System",[True,False])
+leather=st.radio("Leather Seats",[True,False])
+
+if st.button("Predict"):
+    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