File size: 1,042 Bytes
f7a33d3 85a5865 80df98a bb1644c 80df98a 85a5865 80df98a ad1c9cd ec1c3e0 d3da53d 85a5865 ec1c3e0 d3da53d ad1c9cd ec1c3e0 | 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 | import streamlit as st
import pickle
# Load the model
#with open('house_price_model.pkl', 'rb') as f:
# model = pickle.load(f)
#st.title('House Price Prediction 🏡')
# User inputs
#living_area = st.number_input('Living Area (sqft)')
#bedrooms = st.number_input('Number of Bedrooms')
# Predict house price
#if st.button('Predict Price'):
# prediction = model.predict([[living_area, bedrooms]])
# st.write(f'Predicted House Price: ${prediction[0]:.2f}')
#Load the model
with open('best_model.pkl', 'rb') as f:
model = pickle.load(f)
previous_rating = st.text_input("Previous Rating", "")
kpi_met = st.text_input("KPI Met 80", "")
awards_won = st.text_input("Awards Won", "")
avg_train_score = st.text_input("AVG Train Score", "")
# Predict house price
if st.button('Predict Promosi'):
prediction = model.predict([[previous_rating, kpi_met, awards_won, avg_train_score]])
st.write(f'Predicted Result: {prediction[0]}')
#import sklearn
#st.text('The scikit-learn version is {}.'.format(sklearn.__version__)) |