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import pickle as pkl
import streamlit as st
from sklearn.linear_model import LinearRegression
import pandas as pd
# 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 )
population = st.sidebar.slider('population', 170,70000, 5000 )
user_report_data = {
'Income':Income,
'House_age':House_age,
'No_rooms':No_rooms,
'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)
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)
# 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))
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