Spaces:
Build error
Build error
File size: 2,579 Bytes
cf5dd5c 1041f5d 4b9b283 aa7428d 2726493 8960813 aa7428d cf5dd5c 386d509 cf5dd5c d1ed6db cf5dd5c 0627a0d bd68159 6983588 cf5dd5c aa7428d 7560521 a9ce1d9 7560521 e03ac5b 7560521 a305603 7560521 56d893f 6983588 cf5dd5c aa7428d cf5dd5c c7ddfd2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
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('train5.csv')
# Renaming columns
df.rename(columns = {'MSSubClass':'MSSubClass','LotArea':'LotArea', 'OverallQual':'OverallQual','OverallCond':'OverallCond', 'YearBuilt':'YearBuilt',
'BsmtFinSF1':'BsmtFinSF1', 'BsmtFinSF2':'BsmtFinSF2',
'BsmtUnfSF':'BsmtUnfSF','TotalBsmtSF':'TotalBsmtSF'},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', 0,60, 200 )
LotArea = st.sidebar.slider('LotArea', 1300,10000,22000 )
OverallQual = st.sidebar.slider('OverallQual', 1,5, 10 )
OverallCond = st.sidebar.slider('OverallCond', 1,5, 9 )
YearBuilt = st.sidebar.slider('YearBuilt', 1872,1975, 2010 )
YearRemodAdd = st.sidebar.slider('YearRemodAdd', 1950,1975, 2010 )
BsmtFinSF1 = st.sidebar.slider('BsmtFinSF1', 0,2500, 5000 )
BsmtUnfSF = st.sidebar.slider('BsmtUnfSF', 0,2500, 5000 )
BsmtFinSF2 = st.sidebar.slider('BsmtFinSF2', 0,2500, 5000 )
TotalBsmtSF = st.sidebar.slider('TotalBsmtSF', 0,2500, 6000 )
#SalePrice = st.sidebar.slider('SalePrice', 0,300000, 800000 )
user_report_data = {
'MSSubClass':MSSubClass,
'LotArea':LotArea,
'OverallQual':OverallQual,
'OverallCond': OverallCond,
'YearBuilt':YearBuilt,
'YearRemodAdd': YearRemodAdd,
'BsmtFinSF1': BsmtFinSF1,
'BsmtUnfSF': BsmtUnfSF,
'BsmtFinSF2': BsmtFinSF2,
'TotalBsmtSF': TotalBsmtSF
#'SalePrice': SalePrice,
}
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)+'%') |