Update app.py

app.py

@@ -1,89 +1,57 @@
-import pandas as pd
-import streamlit as st
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.metrics import r2_score
-from sklearn.linear_model import LinearRegression
-from sklearn.model_selection import train_test_split
-import seaborn as sns
-
-
-
-# loading the data
-df = pd.read_csv('housing.csv')
-
-
-# Renaming columns
-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)
-
-
-# HEADINGS
-st.title('House Price Prediction')
-st.sidebar.header('Housing Data')
-st.subheader('Training Data Stats')
-st.write(df.describe())
-
-
-# X AND Y DATA
-x = df.drop(['Price'], axis = 1)
-y = df.iloc[:, -1]
-x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2, random_state = 0)
-
-
-# FUNCTION
-def user_report():
-  Income = st.sidebar.slider('Income', 17795,107702, 18000 )
-  House_age = st.sidebar.slider('House_age', 2,10, 4 )
-  No_rooms = st.sidebar.slider('No_rooms', 3,11, 5 )
-  No_bedrooms = st.sidebar.slider('No_bedrooms', 2,7, 3 )
-  population = st.sidebar.slider('population', 170,70000, 5000 )
-  
-
-  user_report_data = {
-      'Income':Income,
-      'House_age':House_age,
-      'No_rooms':No_rooms,
-      'No_bedrooms':No_bedrooms,
-      'population':population
-  }
-  report_data = pd.DataFrame(user_report_data, index=[0])
-  return report_data
-
-
-
-
-# Housing Data
-user_data = user_report()
-st.subheader('Housing Data')
-st.write(user_data)
-
-
-
-
-# MODEL
-lr  = LinearRegression()
-lr.fit(x_train, y_train)
-user_result = lr.predict(user_data)
-
-
-
-# VISUALISATIONS
-st.title('Visualised Housing Data')
-
-
-
-# COLOR FUNCTION
-if user_result[0]==0:
-  color = 'blue'
-else:
-  color = 'red'
-
-
-
-# OUTPUT
-st.subheader('Price of House is : ')
-st.write(str(user_result))
-st.title('output')
-st.subheader('r2_score: ')
-st.write(str(r2_score(y_test, lr.predict(x_test))*100)+'%')
+import pickle as pkl
+
+#Step 2: Open the saved file with read-binary mode
+lr_pickle = pkl.load(open('linear_saved_model', 'rb'))
+
+# FUNCTION
+def user_report():
+  Income = st.sidebar.slider('Income', 17795,107702, 18000 )
+  House_age = st.sidebar.slider('House_age', 2,10, 4 )
+  No_rooms = st.sidebar.slider('No_rooms', 3,11, 5 )
+  No_bedrooms = st.sidebar.slider('No_bedrooms', 2,7, 3 )
+  population = st.sidebar.slider('population', 170,70000, 5000 )
+  
+
+  user_report_data = {
+      'Income':Income,
+      'House_age':House_age,
+      'No_rooms':No_rooms,
+      'No_bedrooms':No_bedrooms,
+      'population':population
+  }
+  report_data = pd.DataFrame(user_report_data, index=[0])
+  return report_data
+
+
+
+
+# Housing Data
+user_data = user_report()
+st.subheader('Housing Data')
+st.write(user_data)
+
+
+
+
+# MODEL
+user_result = lr_pickle.predict(user_data)
+
+
+
+# VISUALISATIONS
+st.title('Visualised Housing Data')
+
+
+
+# COLOR FUNCTION
+if user_result[0]==0:
+  color = 'blue'
+else:
+  color = 'red'
+
+
+
+# OUTPUT
+st.subheader('Price of House is : ')
+st.write(str(user_result))
+