Spaces:
Sleeping
Sleeping
| 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])) | |