Update app.py

app.py

@@ -2,9 +2,26 @@ import pickle as pkl
 import streamlit as st
 from sklearn.linear_model import LinearRegression
 import pandas as pd
+from sklearn.model_selection import train_test_split
 
+# loading the data
+df = pd.read_csv('/content/drive/MyDrive/housing.csv')
+df.rename(columns = {'Avg. Area Income':'Income','Avg. Area House Age':'House_age', 'Avg. Area Number of Rooms':'No_rooms',
+       'Avg. Area Number of Bedrooms':'No_bedrooms', 'Area Population':'population'},inplace = True)
 
 
+X_train,X_test,y_train,y_test = train_test_split(df.drop(columns =['Price']),
+                                                 df['Price'],
+                                                 test_size = 0.2,
+                                                 random_state =2)
+#model building
+from sklearn.linear_model import LinearRegression
+
+lr = LinearRegression()
+# training the model or fitting the model
+
+lr.fit(X_train,y_train)
+
 # FUNCTION
 def user_report():
   Income = st.sidebar.slider('Income', 17795,107702, 18000 )
@@ -30,11 +47,6 @@ user_data = user_report()
 st.subheader('Housing Data')
 st.write(user_data)
 
-lr = LinearRegression()
-#Open the saved file with read-binary mode
-lr_pickle = pkl.load(open('linear_saved_model', 'rb'))
-
-
 # MODEL
 user_result = lr.predict(user_data)