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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('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)
# we will drop number of bedrooms column from the dataset
df.drop(columns= ['No_bedrooms'],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', 50000,107701, 58000 )
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', 30000,70000, 50000 )
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)
# 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.subheader(str(user_result[0]))
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