| import streamlit as st | |
| import joblib | |
| import numpy as np | |
| from sklearn.preprocessing import StandardScaler | |
| with open("src/housing_linear","rb") as f: | |
| model = joblib.load(f) | |
| with open("src/X_model","rb") as f: | |
| X_model = joblib.load(f) | |
| st.title(":green[HOUSE] PRICE QUOTATION(USA) :house:") | |
| bedrooms = st.number_input("Bedroom : ",min_value=1,max_value=10,step=1) | |
| bathrooms = st.number_input("Bathroom : ",min_value=1.0,max_value=10.0,step=0.5) | |
| floors = st.number_input("Floor :",min_value=1,max_value=10,step=1) | |
| sqft_living = st.number_input("Sqft_Living :",min_value=370.0,max_value=4310.0,step=100.0) | |
| if st.button("Estimate"): | |
| model_inputs = np.array([[bedrooms,bathrooms,floors,sqft_living]]) | |
| model_input=X_model.transform(model_inputs) | |
| prediction = model.predict(model_input) | |
| formatted_price = round(prediction[0],2) | |
| st.success(f"Quoted_Price : ${formatted_price}") | |