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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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import tensorflow as tf |
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from sklearn.preprocessing import MinMaxScaler |
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import tensorflow_probability as tfp |
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tfd = tfp.distributions |
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from pickle import dump |
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from pickle import load |
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scaler = MinMaxScaler() |
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tf.random.set_seed(42) |
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np.random.seed(42) |
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st.markdown("<body style ='color:#E2E0D9;'></body>", unsafe_allow_html=True) |
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st.markdown("<h4 style='text-align: center; color: #1B9E91;'>House Price Prediction in Ames,Iowa</h4>", unsafe_allow_html=True) |
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st.markdown("<h5 style='text-align: center; color: #1B9E91;'>A multi-step process is used to estimate the range of house prices based on your selection. The modeling process is done using the data found on Kaggle(link at left bottom corner of page)</h5>", unsafe_allow_html=True) |
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name_list = ['MSSubClass', |
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'OverallQual', |
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'YearBuilt', |
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'YearRemodAdd', |
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'BsmtUnfSF', |
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'TotalBsmtSF', |
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'FstFlrSF', |
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'SndFlrSF', |
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'GrLivArea', |
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'FullBath', |
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'HalfBath', |
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'TotRmsAbvGrd', |
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'Fireplaces', |
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'GarageCars', |
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'GarageArea', |
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'MoSold', |
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'YrSold'] |
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name_list_train = ['MSSubClass', |
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'OverallQual', |
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'YearBuilt', |
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'YearRemodAdd', |
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'BsmtUnfSF', |
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'TotalBsmtSF', |
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'1stFlrSF', |
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'2ndFlrSF', |
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'GrLivArea', |
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'FullBath', |
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'HalfBath', |
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'TotRmsAbvGrd', |
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'Fireplaces', |
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'GarageCars', |
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'GarageArea', |
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'MoSold', |
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'YrSold'] |
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data = pd.read_csv(filepath_or_buffer = 'train.csv', on_bad_lines = 'skip') |
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data = data[name_list_train].values |
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scaler.fit(data) |
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description_list = [ |
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'What is the building class?', |
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'What is the Overall material and finish quality?', |
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'In which year was the Original construction date?', |
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'In which year was it remodelled?', |
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'What is the Unfinished square feet of basement area?', |
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'What is the Total square feet of basement area?', |
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'What is the First Floor square feet?', |
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'What is the Second floor square feet?', |
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'What is the Above grade (ground) living area square feet?', |
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'What is the number of full bathrooms?', |
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'What is the number of Half baths?', |
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'What is the number of Total rooms above grade (does not include bathrooms)?', |
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'What is the number of fireplaces?', |
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'What is the garage capacity in car sizes?', |
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'What is the size of garage in square feet?', |
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'In which month was it sold?', |
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'In which year was it sold?' |
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] |
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min_list = [20.0,1.0,1872.0, |
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1950.0, |
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0.0, |
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0.0, |
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334.0, |
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0.0, |
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334.0, |
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0.0, |
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0.0, |
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2.0, |
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0.0, |
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0.0, |
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0.0, |
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1.0, |
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2006.0] |
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max_list = [190.0, |
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10.0, |
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2010.0, |
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2010.0, |
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2336.0, |
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6110.0, |
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4692.0, |
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2065.0, |
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5642.0, |
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3.0, |
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2.0, |
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14.0, |
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3.0, |
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4.0, |
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1418.0, |
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12.0, |
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2010.0] |
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count = 0 |
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with st.sidebar: |
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for i in range(len(name_list)): |
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variable_name = name_list[i] |
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globals()[variable_name] = st.slider(description_list[i] ,min_value=int(min_list[i]), max_value =int(max_list[i]),step=1) |
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st.write("[Kaggle Link to Data Set](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques)") |
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data_df = { |
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'MSSubClass': [MSSubClass], |
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'OverallQual': [OverallQual], |
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'YearBuilt': [YearBuilt], |
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'YearRemodAdd': [YearRemodAdd], |
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'BsmtUnfSF': [BsmtUnfSF], |
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'TotalBsmtSF': [TotalBsmtSF], |
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'1stFlrSF': [FstFlrSF], |
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'2ndFlrSF': [SndFlrSF], |
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'GrLivArea':[GrLivArea], |
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'FullBath': [FullBath], |
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'HalfBath': [HalfBath], |
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'TotRmsAbvGrd':[TotRmsAbvGrd], |
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'Fireplaces': [Fireplaces], |
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'GarageCars': [GarageCars], |
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'GarageArea':[GarageArea], |
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'MoSold': [MoSold], |
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'YrSold' : [YrSold] |
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} |
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negloglik = lambda y, p_y: -p_y.log_prob(y) |
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model1 = tf.keras.models.load_model('model_files/my_keras_model1.h5') |
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model1 = tf.keras.models.Sequential(model1.layers[:5]) |
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data_df = pd.DataFrame.from_dict(data_df) |
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data_df_normal = scaler.transform(data_df) |
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latent_var = model1.predict(data_df_normal) |
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model2 = tf.keras.models.load_model('model_files/keras_2.h5',compile=False) |
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yhat = model2(latent_var) |
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col1, col2, col3 , col4, col5 = st.columns(5) |
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with col1: |
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pass |
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with col2: |
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pass |
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with col4: |
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pass |
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with col5: |
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pass |
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with col3 : |
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center_button = st.button('Calculate range of house price') |
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if center_button: |
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import time |
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with st.spinner('Calculating....'): |
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time.sleep(2) |
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st.markdown("<h5 style='text-align: center; color: #1B9E91;'>The price range of your house is between:</h5>", unsafe_allow_html=True) |
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col1, col2 = st.columns([3, 3]) |
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lower_number = "{:,.2f}".format(int(yhat.mean().numpy()-1.95*yhat.stddev().numpy())) |
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higher_number = "{:,.2f}".format(int(yhat.mean().numpy()+1.95*yhat.stddev().numpy())) |
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col1, col2, col3 = st.columns(3) |
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with col1: |
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st.write("") |
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with col2: |
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st.subheader("USD "+ str(lower_number)) |
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st.subheader(" AND ") |
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st.subheader(" USD "+str(higher_number)) |
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with col3: |
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st.write("") |
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import base64 |
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file_ = open("kramer_gif.gif", "rb") |
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contents = file_.read() |
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data_url = base64.b64encode(contents).decode("utf-8") |
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file_.close() |
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st.markdown( |
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f'<center><img src="data:image/gif;base64,{data_url}" alt="cat gif"></center>', |
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unsafe_allow_html=True, |
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) |
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