| import numpy as np | |
| import pickle | |
| import pandas as pd | |
| import streamlit as st | |
| import json | |
| from PIL import Image | |
| import warnings | |
| warnings.filterwarnings('ignore') | |
| pickle_in = open("banglore_home_prices_model.pickle","rb") | |
| classifier=pickle.load(pickle_in) | |
| with open("columns.json", "r") as f: | |
| __data_columns = json.load(f)['data_columns'] | |
| __locations = __data_columns[3:] | |
| def welcome(): | |
| return "Welcome All" | |
| def predict_note_authentication(sqft,bhk,bath,loc): | |
| try: | |
| loc_index = __data_columns.index(loc.lower()) | |
| except: | |
| loc_index = -1 | |
| x = np.zeros(len(__data_columns)) | |
| x[0] = sqft | |
| x[1] = bath | |
| x[2] = bhk | |
| if loc_index>=0: | |
| x[loc_index] = 1 | |
| prediction=round(classifier.predict([x])[0],2) | |
| return round(classifier.predict([x])[0],2) | |
| def main(): | |
| html_temp = """ | |
| <div style="padding:10px"> | |
| <h2 style="color:white;text-align:center;">Banglore Home Price Predictor </h2> | |
| </div> | |
| """ | |
| st.markdown(html_temp,unsafe_allow_html=True) | |
| sqft = st.text_input("Area (Total Square Feet)","") | |
| bhk = st.selectbox("BHK",('1','2','3','4','5')) | |
| bath = st.selectbox("Bath",('1','2','3','4','5')) | |
| loc = st.selectbox("Location",__locations) | |
| result="" | |
| if st.button("Estimate Price"): | |
| result=predict_note_authentication(sqft,bhk,bath,loc) | |
| st.success('Estimated Price is {} lakhs'.format(result)) | |
| if __name__=='__main__': | |
| main() | |