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| # importing libraries | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import pickle | |
| np.random.seed(42) | |
| st.markdown("<body style ='color:#E2E0D9;'></body>", unsafe_allow_html=True) | |
| st.markdown("<h4 style='text-align: center; color: #1B9E91;'>House Price Prediction in Ames, Iowa</h4>", unsafe_allow_html=True) | |
| st.markdown("<h5 style='text-align: center; color: #1B9E91;'>Optuna optimized LGBM model to estimate the range of house prices based on your selection. </h5>", unsafe_allow_html=True) | |
| st.write("If you want to know the numbers that you picked for some of the features such as Overall Quality, Sale Conditions etc., please check the following link") | |
| st.write("[link to the categorical encoding](https://github.com/aye-thuzar/CS634Project/edit/main/docs.md)") | |
| #setting up the sliders and getting the input the sliders | |
| name_list = [ | |
| 'OverallQual', | |
| 'YearBuilt', | |
| 'TotalBsmtSF', | |
| 'GrLivArea', | |
| 'MasVnrArea', | |
| 'BsmtFinType1', | |
| 'Neighborhood', | |
| 'GarageType', | |
| 'SaleCondition', | |
| 'BsmtExposure'] | |
| description_list = [ | |
| 'What is the Overall material and finish quality?', | |
| 'In which year was the Original construction date?', | |
| 'What is the Total square feet of basement area?', | |
| 'What is the Above grade (ground) living area in square feet?', | |
| 'What is the Masonry veneer area in square feet?', | |
| 'What is the Quality of the basement finished area?', | |
| 'Where are the physical locations within Ames city limits?', | |
| 'Where is the location of the Garage?', | |
| 'What is the condition of the sale?', | |
| 'What is the basement exposure: walkout or garden-level basement walls?' | |
| ] | |
| min_list = [ | |
| 1.0, | |
| 1950.0, | |
| 0.0, | |
| 0.0, | |
| 334.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 0.0 | |
| ] | |
| max_list = [ | |
| 10.0, | |
| 2010.0, | |
| 2336.0, | |
| 6110.0, | |
| 4692.0, | |
| 7.0, | |
| 25.0, | |
| 7.0, | |
| 6.0, | |
| 5.0, | |
| ] | |
| count = 0 | |
| with st.sidebar: | |
| for i in range(len(name_list)): | |
| variable_name = name_list[i] | |
| globals()[variable_name] = st.slider(description_list[i] ,min_value=int(min_list[i]), max_value =int(max_list[i]),step=1) | |
| st.write("[Kaggle Link to Data Set](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques)") | |
| data_df = { | |
| 'OverallQual': [OverallQual], | |
| 'YearBuilt': [YearBuilt], | |
| 'TotalBsmtSF': [TotalBsmtSF], | |
| 'GrLivArea':[GrLivArea], | |
| 'MasVnrArea': [MasVnrArea], | |
| 'BsmtFinType1': [BsmtFinType1], | |
| 'Neighborhood': [Neighborhood], | |
| 'GarageType': [GarageType], | |
| 'SaleCondition': [SaleCondition], | |
| 'BsmtExposure': [BsmtExposure] | |
| } | |
| data_df = pd.DataFrame.from_dict(data_df) | |
| st.write("Please adjust the feature values using the slides on the left: ") | |
| st.write(data_df.head()) | |
| #normalizing the data | |
| diff = np.array(max_list)-np.array(min_list) | |
| data_df = (data_df.values - np.array(min_list)) / diff | |
| st.write("Normalized input data") | |
| st.write(data_df) | |
| # load trained model | |
| lgbm_base = pickle.load(open('lgbm_base.pkl', 'rb')) | |
| lgbm_opt = pickle.load(open('lgbm_opt_test.pkl', 'rb')) | |
| xgb = pickle.load(open('xgb_model.pkl', 'rb')) | |
| y_pred_xgb = xgb.predict(data_df) | |
| y_pred_optimized = lgbm_opt.predict(data_df) | |
| col1, col2, col3 , col4, col5 = st.columns(5) | |
| with col1: | |
| pass | |
| with col2: | |
| pass | |
| with col4: | |
| pass | |
| with col5: | |
| pass | |
| with col3 : | |
| center_button = st.button('Calculate range of house price') | |
| if center_button: | |
| import time | |
| #my_bar = st.progress(0) | |
| with st.spinner('Calculating....'): | |
| time.sleep(2) | |
| st.markdown("<h5 style='text-align: center; color: #1B9E91;'>The price range of your house is between:</h5>", unsafe_allow_html=True) | |
| col1, col2 = st.columns([3, 3]) | |
| lower_number = "{:,.2f}".format(y_pred_xgb[0]) | |
| higher_number = "{:,.2f}".format(y_pred_xgb[0]) | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.write("") | |
| with col2: | |
| st.subheader("USD "+ str(lower_number)) | |
| st.subheader(" AND ") | |
| st.subheader(" USD "+str(higher_number)) | |
| with col3: | |
| st.write("") | |