Update app.py

app.py

@@ -14,8 +14,26 @@ df = pd.read_csv('train2.csv')
 
 
 # Renaming columns
-#df.rename(columns = {'MSSubClass':'Income','LotFrontage':'House_age', 'Avg. Area Number of Rooms':'No_rooms',
- #      'Avg. Area Number of Bedrooms':'No_bedrooms', 'Area Population':'population'},inplace = True)
+#df.rename(columns = {'MSSubClass':'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('train2.csv')
+
+
+# Renaming columns
+#df.rename(columns = {'MSSubClass':'MSSubClass','LotArea':'LotArea', 'OverallQual':'OverallQual',
+    'OverallCond':'OverallCond', 'YearBuilt':'YearBuilt', 
+	'MasVnrArea':'MasVnrArea', 'BsmtFinSF1':'BsmtFinSF1', 'BsmtFinSF2':'BsmtFinSF2',
+	'BsmtUnfSF':'BsmtUnfSF','TotalBsmtSF':'TotalBsmtSF'},inplace = True)
 
 
 # HEADINGS
@@ -33,20 +51,30 @@ x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2, random
 
 # 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 )
+  MSSubClass = st.sidebar.slider('MSSubClass', 17795,107702, 18000 )
+  LotArea = st.sidebar.slider('LotArea', 2,10, 4 )
+  OverallQual = st.sidebar.slider('OverallQual', 3,11, 5 )
+  OverallCond = st.sidebar.slider('OverallCond', 2,7, 3 )
+  YearBuilt = st.sidebar.slider('YearBuilt', 170,70000, 5000 )
+  MasVnrArea = st.sidebar.slider('MasVnrArea', 170,70000, 5000 )
+  BsmtFinSF1 = st.sidebar.slider('BsmtFinSF1', 170,70000, 5000 )
+  BsmtUnfSF = st.sidebar.slider('BsmtUnfSF', 170,70000, 5000 )
+  BsmtFinSF2 = st.sidebar.slider('BsmtFinSF2', 170,70000, 5000 )
+  TotalBsmtSF = st.sidebar.slider('TotalBsmtSF', 170,70000, 5000 )
   
 
   user_report_data = {
-      'Income':Income,
-      'House_age':House_age,
-      'No_rooms':No_rooms,
-      'No_bedrooms':No_bedrooms,
-      'population':population
+      'MSSubClass':MSSubClass,
+      'LotArea':LotArea,
+      'OverallQual':OverallQual
+      'YearBuilt':YearBuilt,
+      'MasVnrArea':MasVnrArea
+	  'BsmtFinSF1': BsmtFinSF1
+	  'BsmtFinSF': BsmtFinSF
+	  'BsmtFinSF2': BsmtFinSF2
+	  'TotalBsmtSF': TotalBsmtSF
   }
+  
   report_data = pd.DataFrame(user_report_data, index=[0])
   return report_data
 
@@ -84,6 +112,83 @@ 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)+'%')
+st.write(str(r2_score(y_test, lr.predict(x_test))*100)+'%')','LotFrontage':'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(['SalePrice'], 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():
+    MSSubClass = st.sidebar.slider('MSSubClass', 17795,107702, 18000 )
+  LotArea = st.sidebar.slider('LotArea', 2,10, 4 )
+  OverallQual = st.sidebar.slider('OverallQual', 3,11, 5 )
+  OverallCond = st.sidebar.slider('OverallCond', 2,7, 3 )
+  YearBuilt = st.sidebar.slider('YearBuilt', 170,70000, 5000 )
+  MasVnrArea = st.sidebar.slider('MasVnrArea', 170,70000, 5000 )
+  BsmtFinSF1 = st.sidebar.slider('BsmtFinSF1', 170,70000, 5000 )
+  BsmtUnfSF = st.sidebar.slider('BsmtUnfSF', 170,70000, 5000 )
+  BsmtFinSF2 = st.sidebar.slider('BsmtFinSF2', 170,70000, 5000 )
+  TotalBsmtSF = st.sidebar.slider('TotalBsmtSF', 170,70000, 5000 )
+  
+
+  user_report_data = {
+      'MSSubClass':MSSubClass,
+      'LotArea':LotArea,
+      'OverallQual':OverallQual
+      'YearBuilt':YearBuilt,
+      'MasVnrArea':MasVnrArea
+	  'BsmtFinSF1': BsmtFinSF1
+	  'BsmtFinSF': BsmtFinSF
+	  'BsmtFinSF2': BsmtFinSF2
+	  'TotalBsmtSF': TotalBsmtSF
+  }
+  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)+'%')