GitHub - sakatass/Rent-forecast-in-UA: Collection of actual data on apartment rent in all cities of Ukraine and training of regression models

Load libraries

import pandas as pd
import requests
import os
import warnings
from selenium import webdriver
from webdriver_manager.chrome import ChromeDriverManager
from bs4 import BeautifulSoup
import time
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import requests 
import os 
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
from sklearn.model_selection import train_test_split, GridSearchCV, KFold
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import numpy as np
from scipy.stats import zscore
from sklearn.ensemble import RandomForestRegressor
import xgboost as xgb
import joblib
from lightgbm import LGBMRegressor
from catboost import CatBoostRegressor, cv

Dataset Creation

def create_directory(directory_path):
    # Проверяем, существует ли директория
    if not os.path.exists(directory_path):
        # Создаем директорию, если она не существует
        os.makedirs(directory_path)
        print(f"Директория {directory_path} успешно создана.")
    else:
        print(f"Директория {directory_path} уже существует.")

# Пример использования:
directory_path = "HousePrice_excels"
create_directory(directory_path)

Директория HousePrice_excels уже существует.

Download excel files

def download_excel_selenim(download_directory, n_page, state_id):
    files_count = os.listdir(download_directory)
    # Set up ChromeOptions
    chrome_options = webdriver.ChromeOptions()

    # Set the download directory
    prefs = {
       "download.default_directory": download_directory,
       "savefile.default_directory": download_directory
    }
    chrome_options.add_experimental_option('prefs', prefs)
    #chrome_options.add_argument('--headless=new')

    # Instantiate the Chrome driver with the configured options
    driver = webdriver.Chrome(ChromeDriverManager().install(),
                              chrome_options=chrome_options
                             )
    driver.get(f'https://dom.ria.com/uk/search/xls/?excludeSold=1&category=1&realty_type=2&operation=3&state_id={state_id}&price_cur=1&wo_dupl=1&sort=inspected_sort&firstIteraction=false&client=searchV2&limit=20&page={n_page}&type=list&ch=246_244&xls=1')
    time.sleep(1.2)
    if os.listdir(download_directory) == files_count:
        input()
    else:
        pass

for state_id in range(1, 25):
    for n_page in range(1, 30):
        download_excel_selenim(os.path.join(os.getcwd(), directory_path), state_id, n_page)

C:\Users\MLZZZDS\AppData\Local\Temp\ipykernel_19048\2558725661.py:15: DeprecationWarning: executable_path has been deprecated, please pass in a Service object
  driver = webdriver.Chrome(ChromeDriverManager().install(),
C:\Users\MLZZZDS\AppData\Local\Temp\ipykernel_19048\2558725661.py:15: DeprecationWarning: use options instead of chrome_options
  driver = webdriver.Chrome(ChromeDriverManager().install(),



---------------------------------------------------------------------------

KeyboardInterrupt                         Traceback (most recent call last)

Cell In[5], line 3
      1 for state_id in range(1, 25):
      2     for n_page in range(1, 30):
----> 3         download_excel_selenim(os.path.join(os.getcwd(), directory_path), state_id, n_page)


Cell In[3], line 15, in download_excel_selenim(download_directory, n_page, state_id)
     11 chrome_options.add_experimental_option('prefs', prefs)
     12 #chrome_options.add_argument('--headless=new')
     13 
     14 # Instantiate the Chrome driver with the configured options
---> 15 driver = webdriver.Chrome(ChromeDriverManager().install(),
     16                           chrome_options=chrome_options
     17                          )
     18 driver.get(f'https://dom.ria.com/uk/search/xls/?excludeSold=1&category=1&realty_type=2&operation=3&state_id={state_id}&price_cur=1&wo_dupl=1&sort=inspected_sort&firstIteraction=false&client=searchV2&limit=20&page={n_page}&type=list&ch=246_244&xls=1')
     19 time.sleep(1.2)


File ~\AppData\Roaming\Python\Python310\site-packages\webdriver_manager\chrome.py:40, in ChromeDriverManager.install(self)
     39 def install(self) -> str:
---> 40     driver_path = self._get_driver_binary_path(self.driver)
     41     os.chmod(driver_path, 0o755)
     42     return driver_path


File ~\AppData\Roaming\Python\Python310\site-packages\webdriver_manager\core\manager.py:35, in DriverManager._get_driver_binary_path(self, driver)
     34 def _get_driver_binary_path(self, driver):
---> 35     binary_path = self._cache_manager.find_driver(driver)
     36     if binary_path:
     37         return binary_path


File ~\AppData\Roaming\Python\Python310\site-packages\webdriver_manager\core\driver_cache.py:105, in DriverCacheManager.find_driver(self, driver)
    102 if not browser_version:
    103     return None
--> 105 driver_version = self.get_cache_key_driver_version(driver)
    106 metadata = self.load_metadata_content()
    108 key = self.__get_metadata_key(driver)


File ~\AppData\Roaming\Python\Python310\site-packages\webdriver_manager\core\driver_cache.py:152, in DriverCacheManager.get_cache_key_driver_version(self, driver)
    150 if self._cache_key_driver_version:
    151     return self._cache_key_driver_version
--> 152 return driver.get_driver_version_to_download()


File ~\AppData\Roaming\Python\Python310\site-packages\webdriver_manager\core\driver.py:48, in Driver.get_driver_version_to_download(self)
     45 if self._driver_version_to_download:
     46     return self._driver_version_to_download
---> 48 return self.get_latest_release_version()


File ~\AppData\Roaming\Python\Python310\site-packages\webdriver_manager\drivers\chrome.py:59, in ChromeDriver.get_latest_release_version(self)
     57 if determined_browser_version is not None and version.parse(determined_browser_version) >= version.parse("115"):
     58     url = "https://googlechromelabs.github.io/chrome-for-testing/latest-patch-versions-per-build.json"
---> 59     response = self._http_client.get(url)
     60     response_dict = json.loads(response.text)
     61     determined_browser_version = response_dict.get("builds").get(determined_browser_version).get("version")


File ~\AppData\Roaming\Python\Python310\site-packages\webdriver_manager\core\http.py:32, in WDMHttpClient.get(self, url, **kwargs)
     30 def get(self, url, **kwargs) -> Response:
     31     try:
---> 32         resp = requests.get(
     33             url=url, verify=self._ssl_verify, stream=True, **kwargs)
     34     except exceptions.ConnectionError:
     35         raise exceptions.ConnectionError(f"Could not reach host. Are you offline?")


File C:\ProgramData\anaconda3\lib\site-packages\requests\api.py:73, in get(url, params, **kwargs)
     62 def get(url, params=None, **kwargs):
     63     r"""Sends a GET request.
     64 
     65     :param url: URL for the new :class:`Request` object.
   (...)
     70     :rtype: requests.Response
     71     """
---> 73     return request("get", url, params=params, **kwargs)


File C:\ProgramData\anaconda3\lib\site-packages\requests\api.py:59, in request(method, url, **kwargs)
     55 # By using the 'with' statement we are sure the session is closed, thus we
     56 # avoid leaving sockets open which can trigger a ResourceWarning in some
     57 # cases, and look like a memory leak in others.
     58 with sessions.Session() as session:
---> 59     return session.request(method=method, url=url, **kwargs)


File C:\ProgramData\anaconda3\lib\site-packages\requests\sessions.py:587, in Session.request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)
    582 send_kwargs = {
    583     "timeout": timeout,
    584     "allow_redirects": allow_redirects,
    585 }
    586 send_kwargs.update(settings)
--> 587 resp = self.send(prep, **send_kwargs)
    589 return resp


File C:\ProgramData\anaconda3\lib\site-packages\requests\sessions.py:701, in Session.send(self, request, **kwargs)
    698 start = preferred_clock()
    700 # Send the request
--> 701 r = adapter.send(request, **kwargs)
    703 # Total elapsed time of the request (approximately)
    704 elapsed = preferred_clock() - start


File C:\ProgramData\anaconda3\lib\site-packages\requests\adapters.py:489, in HTTPAdapter.send(self, request, stream, timeout, verify, cert, proxies)
    487 try:
    488     if not chunked:
--> 489         resp = conn.urlopen(
    490             method=request.method,
    491             url=url,
    492             body=request.body,
    493             headers=request.headers,
    494             redirect=False,
    495             assert_same_host=False,
    496             preload_content=False,
    497             decode_content=False,
    498             retries=self.max_retries,
    499             timeout=timeout,
    500         )
    502     # Send the request.
    503     else:
    504         if hasattr(conn, "proxy_pool"):


File C:\ProgramData\anaconda3\lib\site-packages\urllib3\connectionpool.py:703, in HTTPConnectionPool.urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)
    700     self._prepare_proxy(conn)
    702 # Make the request on the httplib connection object.
--> 703 httplib_response = self._make_request(
    704     conn,
    705     method,
    706     url,
    707     timeout=timeout_obj,
    708     body=body,
    709     headers=headers,
    710     chunked=chunked,
    711 )
    713 # If we're going to release the connection in ``finally:``, then
    714 # the response doesn't need to know about the connection. Otherwise
    715 # it will also try to release it and we'll have a double-release
    716 # mess.
    717 response_conn = conn if not release_conn else None


File C:\ProgramData\anaconda3\lib\site-packages\urllib3\connectionpool.py:386, in HTTPConnectionPool._make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)
    384 # Trigger any extra validation we need to do.
    385 try:
--> 386     self._validate_conn(conn)
    387 except (SocketTimeout, BaseSSLError) as e:
    388     # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.
    389     self._raise_timeout(err=e, url=url, timeout_value=conn.timeout)


File C:\ProgramData\anaconda3\lib\site-packages\urllib3\connectionpool.py:1042, in HTTPSConnectionPool._validate_conn(self, conn)
   1040 # Force connect early to allow us to validate the connection.
   1041 if not getattr(conn, "sock", None):  # AppEngine might not have  `.sock`
-> 1042     conn.connect()
   1044 if not conn.is_verified:
   1045     warnings.warn(
   1046         (
   1047             "Unverified HTTPS request is being made to host '%s'. "
   (...)
   1052         InsecureRequestWarning,
   1053     )


File C:\ProgramData\anaconda3\lib\site-packages\urllib3\connection.py:358, in HTTPSConnection.connect(self)
    356 def connect(self):
    357     # Add certificate verification
--> 358     self.sock = conn = self._new_conn()
    359     hostname = self.host
    360     tls_in_tls = False


File C:\ProgramData\anaconda3\lib\site-packages\urllib3\connection.py:174, in HTTPConnection._new_conn(self)
    171     extra_kw["socket_options"] = self.socket_options
    173 try:
--> 174     conn = connection.create_connection(
    175         (self._dns_host, self.port), self.timeout, **extra_kw
    176     )
    178 except SocketTimeout:
    179     raise ConnectTimeoutError(
    180         self,
    181         "Connection to %s timed out. (connect timeout=%s)"
    182         % (self.host, self.timeout),
    183     )


File C:\ProgramData\anaconda3\lib\site-packages\urllib3\util\connection.py:85, in create_connection(address, timeout, source_address, socket_options)
     83     if source_address:
     84         sock.bind(source_address)
---> 85     sock.connect(sa)
     86     return sock
     88 except socket.error as e:


KeyboardInterrupt:

for state_id in range(21, 25):
    for n_page in range(1, 30):
        download_excel_selenim(os.path.join(os.getcwd(), directory_path), state_id, n_page)

Combine excel files to a DataFrame

combined_df = pd.DataFrame()

for filename in os.listdir(directory_path):
    file_path = os.path.join(directory_path, filename)
    try:
        df = pd.read_excel(file_path)
        combined_df = pd.concat([combined_df, df], ignore_index=True)
    except Exception as e:
        print(f"An error occurred while reading a file {file_path}: {e}")

WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_1 (24).xls: unpack requires a buffer of 2 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_1 (29).xls: list index out of range
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_1 (32).xls: list index out of range
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_1 (46).xls: 'utf-16-le' codec can't decode bytes in position 908-909: illegal UTF-16 surrogate
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_1 (53).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_1 (58).xls: list index out of range
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_10 (24).xls: list index out of range
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_10 (4).xls: list index out of range
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_11 (24).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_12 (2).xls: unpack requires a buffer of 2 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_12 (24).xls: list index out of range
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_13.xls: 
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_14 (2).xls: 
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_14 (4).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_15 (4).xls: unpack requires a buffer of 2 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_16 (2).xls: 
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_16 (4).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_17 (11).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_17 (2).xls: unpack requires a buffer of 2 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_17.xls: list index out of range
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_18 (11).xls: 
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_18 (2).xls: 
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_18 (4).xls: 
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_2 (17).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_2 (23).xls: list index out of range
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_2 (24).xls: unpack requires a buffer of 2 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_2 (32).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_2 (33).xls: unpack requires a buffer of 2 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_20 (4).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_20.xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_22 (9).xls: unpack requires a buffer of 2 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_23 (11).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_23 (2).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_23 (4).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_24 (2).xls: unpack requires a buffer of 2 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_3 (3).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_5 (19).xls: list index out of range
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_5 (24).xls: list index out of range
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_5.xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_6 (11).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_6 (24).xls: unpack requires a buffer of 2 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_6 (3).xls: 
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_7 (24).xls: unpack requires a buffer of 2 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_7 (8).xls: list index out of range
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_8 (24).xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_8 (3).xls: unpack requires a buffer of 2 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_8.xls: unpack requires a buffer of 4 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_9 (24).xls: unpack requires a buffer of 2 bytes
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
An error occurred while reading a file HousePrice_excels\DOM.RIA.com_page_9 (3).xls: list index out of range
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero
WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero

combined_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5803 entries, 0 to 5802
Data columns (total 22 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   Realty ID         5803 non-null   object 
 1   Вулиця            5801 non-null   object 
 2   Район             5398 non-null   object 
 3   Місто             5803 non-null   object 
 4   Область           5803 non-null   object 
 5   Дата              5803 non-null   object 
 6   Тип нерухомості   5803 non-null   object 
 7   Ціна              5803 non-null   object 
 8   Тип ціни          5803 non-null   object 
 9   Кімнат            5803 non-null   object 
 10  Поверх            5803 non-null   object 
 11  Поверховість      5803 non-null   object 
 12  Загальна площа    5803 non-null   object 
 13  Житлова площа     3728 non-null   object 
 14  Кухня             4935 non-null   object 
 15  Стіни             5798 non-null   object 
 16  Назва             0 non-null      float64
 17  Соток             0 non-null      float64
 18  Агенція           5116 non-null   object 
 19  Користувач        5758 non-null   object 
 20  Телефон           0 non-null      float64
 21  Опис              4396 non-null   object 
dtypes: float64(3), object(19)
memory usage: 997.5+ KB

Dataset Preprocessing

combined_df.drop_duplicates(inplace=True)

Drop useless columns

combined_df.columns = df.columns.str.strip()

for col in combined_df.columns:
    if combined_df[col].isna().sum() > combined_df.shape[0] * 0.5:
        combined_df.drop(col, axis=1, inplace=True)
        
useless_columns = ['Realty ID',
                  'Дата',
                  'Агенція',
                  'Користувач',
                  'Опис',
                  'Тип нерухомості'
                  ]
combined_df.drop(useless_columns, axis=1, inplace=True)

combined_df.head(2)

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	Вулиця	Район	Місто	Область	Ціна	Тип ціни	Кімнат	Поверх	Поверховість	Загальна площа	Житлова площа	Кухня	Стіни
0	Польский бульвар	Богунский	Житомир	Житомирська	9000	грн	2	1	9	60	50.0	9.0	панель
1	Киевская улица	Вокзал	Житомир	Житомирська	12000	грн	2	1	9	53	30.0	8.0	цегла

Change columns dtype

int_cols = ['Ціна', 'Кімнат', 'Поверх', 'Поверховість']
float_cols = ['Загальна площа', 'Житлова площа', 'Кухня']

for col_name in int_cols:
    combined_df[col_name] = combined_df[col_name].astype(int)
for col_name in float_cols:
    combined_df[col_name] = combined_df[col_name].apply(lambda x: str(x).replace(',', '.')).astype(float)

Preprocessing Price column

combined_df['Тип ціни'].unique()

array(['грн', '$', '€'], dtype=object)

price_ratio = {'грн': 1,
                '$': 37,
                '€': 40,}

combined_df['Ціна'] = combined_df \
        .apply(lambda x: x['Ціна'] * price_ratio[x['Тип ціни']], axis=1)

combined_df.drop('Тип ціни', axis=1, inplace=True)

Drop outliers using Z-score

num_cols = combined_df.select_dtypes(exclude=object).columns

for col_name in num_cols:
    plt.figure(figsize=(6, 4))
    sns.boxplot(x=combined_df[col_name])
    plt.title(f'Column name: {col_name}')
    plt.show()

for col_name in num_cols:
    z_scores = zscore(combined_df[col_name])
    z_score_threshold = 3
    outliers_mask = (abs(z_scores) > z_score_threshold)
    combined_df = combined_df[~outliers_mask]
    plt.figure(figsize=(6, 4))
    sns.boxplot(x=combined_df[col_name])
    plt.title(f'Column name: {col_name}')
    plt.show()

combined_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 5349 entries, 0 to 5802
Data columns (total 12 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Вулиця          5347 non-null   object 
 1   Район           4971 non-null   object 
 2   Місто           5349 non-null   object 
 3   Область         5349 non-null   object 
 4   Ціна            5349 non-null   int64  
 5   Кімнат          5349 non-null   int32  
 6   Поверх          5349 non-null   int32  
 7   Поверховість    5349 non-null   int32  
 8   Загальна площа  5349 non-null   float64
 9   Житлова площа   3380 non-null   float64
 10  Кухня           4543 non-null   float64
 11  Стіни           5344 non-null   object 
dtypes: float64(3), int32(3), int64(1), object(5)
memory usage: 480.6+ KB

Fill NaN-value

missing_values = combined_df.isnull().sum()

percent_missing = (missing_values / len(combined_df)) * 100

missing_info = pd.DataFrame({'Missing Values': missing_values, 'Percent Missing': percent_missing})

print("Информация о пропущенных значениях:")
print(missing_info)

plt.figure(figsize=(10, 6))
sns.barplot(x=missing_info.index, y='Percent Missing', data=missing_info)
plt.title('Процент пропущенных значений по колонкам')
plt.xlabel('Колонки')
plt.ylabel('Процент пропущенных значений')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

Информация о пропущенных значениях:
                Missing Values  Percent Missing
Вулиця                       2         0.037390
Район                      378         7.066741
Місто                        0         0.000000
Область                      0         0.000000
Ціна                         0         0.000000
Кімнат                       0         0.000000
Поверх                       0         0.000000
Поверховість                 0         0.000000
Загальна площа               0         0.000000
Житлова площа             1969        36.810619
Кухня                      806        15.068237
Стіни                        5         0.093475

mode_value = combined_df['Район'].mode().iloc[0]
combined_df['Район'] = combined_df['Район'].fillna(value=mode_value)

mode_value = combined_df['Вулиця'].mode().iloc[0]
combined_df['Вулиця'] = combined_df['Вулиця'].fillna(value=mode_value)

mode_value = combined_df['Стіни'].mode().iloc[0]
combined_df['Стіни'] = combined_df['Стіни'].fillna(value=mode_value)

Fill 'Житлова площа' & 'Кухня'

livArea_kitchen_df = combined_df[['Кімнат', 'Загальна площа', 'Житлова площа', 'Кухня']]
livArea_kitchen_df.dropna(inplace=True)
#livArea_kitchen_df = livArea_kitchen_df[livArea_kitchen_df['Кухня'] < 60]

C:\Users\MLZZZDS\AppData\Local\Temp\ipykernel_19048\4126488395.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  livArea_kitchen_df.dropna(inplace=True)

livArea_kitchen_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 3336 entries, 0 to 5801
Data columns (total 4 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Кімнат          3336 non-null   int32  
 1   Загальна площа  3336 non-null   float64
 2   Житлова площа   3336 non-null   float64
 3   Кухня           3336 non-null   float64
dtypes: float64(3), int32(1)
memory usage: 117.3 KB

Models training

import math
X = livArea_kitchen_df.drop(['Житлова площа', 'Кухня'], axis=1)
y = livArea_kitchen_df['Житлова площа']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model_lArea = LinearRegression()
model_lArea.fit(X_train, y_train)
predictions = model_lArea.predict(X_test)

mse = mean_squared_error(y_test, predictions)
mae = mean_absolute_error(y_test, predictions)
r2 = r2_score(y_test, predictions)


print(f'lArea Root Mean Squared Error: {math.sqrt(mse)}')
print(f'lArea Mean Absolute Error: {mae}')
print(f'lArea R-squared: {r2}')
print('')


X = livArea_kitchen_df.drop(['Житлова площа', 'Кухня'], axis=1)
y = livArea_kitchen_df['Кухня']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model_kitchen = LinearRegression()
model_kitchen.fit(X_train, y_train)
predictions = model_kitchen.predict(X_test)

mse = mean_squared_error(y_test, predictions)
mae = mean_absolute_error(y_test, predictions)
r2 = r2_score(y_test, predictions)

print(f'kitchen Root Mean Squared Error: {math.sqrt(mse)}')
print(f'kitchen Mean Absolute Error: {mae}')
print(f'kitchen R-squared: {r2}')

lArea Root Mean Squared Error: 9.340870992604554
lArea Mean Absolute Error: 6.657544671223893
lArea R-squared: 0.7059133195229013

kitchen Root Mean Squared Error: 4.842392028425681
kitchen Mean Absolute Error: 3.2974729664073634
kitchen R-squared: 0.42195546767054304

print(model_lArea.feature_names_in_)
print(model_kitchen.feature_names_in_)

['Кімнат' 'Загальна площа']
['Кімнат' 'Загальна площа']

combined_df_V2 = combined_df.copy()

Fill

column_with_nan = 'Житлова площа'

rows_with_nan = combined_df_V2[combined_df_V2[column_with_nan].isna()]

X_for_prediction = rows_with_nan[model_lArea.feature_names_in_]

predictions_lArea = model_lArea.predict(X_for_prediction)
rows_with_nan[column_with_nan] = predictions_lArea

combined_df_V2 = pd.concat([combined_df_V2.drop('Кухня', axis=1).dropna(), rows_with_nan])



column_with_nan = 'Кухня'

rows_with_nan = combined_df_V2[combined_df_V2[column_with_nan].isna()]

X_for_prediction = rows_with_nan[model_lArea.feature_names_in_]

predictions_lArea = model_lArea.predict(X_for_prediction)
rows_with_nan[column_with_nan] = predictions_lArea

combined_df_V2 = pd.concat([combined_df_V2.dropna(), rows_with_nan])

C:\Users\MLZZZDS\AppData\Local\Temp\ipykernel_19048\2608047449.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  rows_with_nan[column_with_nan] = predictions_lArea
C:\Users\MLZZZDS\AppData\Local\Temp\ipykernel_19048\2608047449.py:21: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  rows_with_nan[column_with_nan] = predictions_lArea

missing_values = combined_df_V2.isnull().sum()

percent_missing = (missing_values / len(combined_df_V2)) * 100

missing_info = pd.DataFrame({'Missing Values': missing_values, 'Percent Missing': percent_missing})

print("Информация о пропущенных значениях:")
print(missing_info)

plt.figure(figsize=(10, 6))
sns.barplot(x=missing_info.index, y='Percent Missing', data=missing_info)
plt.title('Процент пропущенных значений по колонкам')
plt.xlabel('Колонки')
plt.ylabel('Процент пропущенных значений')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()

Информация о пропущенных значениях:
                Missing Values  Percent Missing
Вулиця                       0              0.0
Район                        0              0.0
Місто                        0              0.0
Область                      0              0.0
Ціна                         0              0.0
Кімнат                       0              0.0
Поверх                       0              0.0
Поверховість                 0              0.0
Загальна площа               0              0.0
Житлова площа                0              0.0
Стіни                        0              0.0
Кухня                        0              0.0

combined_df.to_csv('HousePrice_df_versions/combined_df.csv', index=False)
combined_df_V2.to_csv('HousePrice_df_versions/combined_df_V2.csv', index=False)

combined_df_V2 = pd.read_csv('C:/Users/MLZZZDS/HousePrice_df_versions/combined_df_V2.csv')

combined_df_V3 = pd.get_dummies(combined_df_V2)

Models Price prediction tuning

combined_df_V3.shape

(5349, 2475)

X = combined_df_V3.drop('Ціна', axis=1)
X.columns = X.columns.str.replace('[^a-zA-Z0-9А-Яа-яіїЇІЄє]', '_')
y = combined_df_V3['Ціна']

'Місто_Київ' in X.columns

True

RandomForestRegressor

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)

predictions = rf_model.predict(X_test)

mse = mean_squared_error(y_test, predictions)
mae = mean_absolute_error(y_test, predictions)
r2 = r2_score(y_test, predictions)

print(f'Price Root Mean Squared Error: {math.sqrt(mse)}')
print(f'Price Mean Absolute Error: {mae}')
print(f'Price R-squared: {r2}')

Price Root Mean Squared Error: 5350.68889786027
Price Mean Absolute Error: 2937.321319537161
Price R-squared: 0.7327547264754519

train_pred = rf_model.predict(X_train)

plt.figure(figsize=(10,6))

plt.scatter(train_pred,train_pred - y_train,
          c = 'black', marker = 'o', s = 35, alpha = 0.5,
          label = 'Train data')
plt.scatter(predictions,predictions - y_test,
          c = 'c', marker = 'o', s = 35, alpha = 0.7,
          label = 'Test data')
plt.xlabel('Predicted values')
plt.ylabel('Tailings')
plt.legend(loc = 'upper left')
plt.hlines(y = 0, xmin = 0, xmax = 60000, lw = 2, color = 'red')
plt.show()

xgb

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Создание объекта DMatrix для эффективной работы с данными в XGBoost
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)

# Определение параметров модели
params = {
    'objective': 'reg:squarederror',  # Функция потерь для задачи регрессии
    'eval_metric': 'rmse',  # Метрика оценки качества (среднеквадратичное отклонение)
    'max_depth': 3,  # Максимальная глубина дерева
    'learning_rate': 0.1,  # Скорость обучения (шаг градиентного спуска)
    'n_estimators': 300  # Количество деревьев в ансамбле
}

# Обучение модели
model = xgb.train(params, dtrain, num_boost_round=params['n_estimators'])

# Предсказание на тестовом наборе
predictions = model.predict(dtest)

# Оценка модели
mse = mean_squared_error(y_test, predictions)
mae = mean_absolute_error(y_test, predictions)
r2 = r2_score(y_test, predictions)

print(f'Price Root Mean Squared Error: {math.sqrt(mse)}')
print(f'Price Mean Absolute Error: {mae}')
print(f'Price R-squared: {r2}')

[18:55:38] WARNING: C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-07593ffd91cd9da33-1\xgboost\xgboost-ci-windows\src\learner.cc:767: 
Parameters: { "n_estimators" } are not used.

Price Root Mean Squared Error: 5795.626268473332
Price Mean Absolute Error: 3329.0631594078563
Price R-squared: 0.6864611393254245

Tuning params

%%time

param_grid = {
    'max_depth': [3, 5, 7, 9, 11],
    'learning_rate': [0.01, 0.1, 0.2, 0.5, 1],
    'n_estimators': [100, 300, 500, 700],
    'tree_method': ['hist']
}

# Создание модели XGBRegressor
xgb_model = xgb.XGBRegressor(objective='reg:squarederror')

# Определение метрики для оценки
scorer = make_scorer(mean_squared_error, greater_is_better=False, squared=False)

# Создание объекта GridSearchCV
grid_search = GridSearchCV(
    xgb_model,
    param_grid,
    scoring=scorer,
    cv=KFold(n_splits=5, shuffle=True, random_state=42),  # Пример использования 5-кратной кросс-валидации
    verbose=1,
    n_jobs=-1  # Используйте все доступные ядра процессора
)

# Выполнение поиска по сетке
grid_result = grid_search.fit(X_train, y_train)

# Вывод лучших параметров и оценки
print("Best Parameters: ", grid_result.best_params_)
print("Best RMSE: ", math.sqrt(-grid_result.best_score_))

# Обучение модели с лучшими параметрами
best_model = grid_result.best_estimator_
best_model.fit(X_train, y_train)

# Предсказание на тестовом наборе
predictions = best_model.predict(X_test)

# Оценка модели
mse = mean_squared_error(y_test, predictions)
mae = mean_absolute_error(y_test, predictions)
r2 = r2_score(y_test, predictions)

print(f'Price Root Mean Squared Error: {math.sqrt(mse)}')
print(f'Price Mean Absolute Error: {mae}')
print(f'Price R-squared: {r2}')

---------------------------------------------------------------------------

NameError                                 Traceback (most recent call last)

File <timed exec>:12


NameError: name 'make_scorer' is not defined

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

xgb_model = xgb.XGBRegressor(objective='reg:squarederror',
                            learning_rate=0.2, max_depth=3, n_estimators=500, tree_method='hist')
xgb_model.fit(X_train, y_train)
predictions = xgb_model.predict(X_test)
# Оценка модели
mse = mean_squared_error(y_test, predictions)
mae = mean_absolute_error(y_test, predictions)
r2 = r2_score(y_test, predictions)

print(f'Price Root Mean Squared Error: {math.sqrt(mse)}')
print(f'Price Mean Absolute Error: {mae}')
print(f'Price R-squared: {r2}')

Price Root Mean Squared Error: 5676.951711605583
Price Mean Absolute Error: 3200.2685480706045
Price R-squared: 0.699170077546494

joblib.dump(xgb_model, 'xgboost_model.pkl')

['xgboost_model.pkl']

'Місто_Київ' in xgb_model.feature_names_in_

True

import pickle
with open('xgboost_model.pkl', 'wb') as model_file:
    pickle.dump(xgb_model, model_file)

import pickle

with open('xgboost_model.pkl', 'rb') as model_file:
    xgbr_model = pickle.load(model_file)

%%time

from sklearn.metrics import make_scorer


param_grid = {
    'max_depth': [3, 5],
    'learning_rate': [0.001, 0.01, 0.1, 0.2],
    'n_estimators': [100, 300, 500, 700],
    'tree_method': ['hist'],
}

# Создание модели XGBRegressor
xgb_model = xgb.XGBRegressor(objective='reg:squarederror')

# Определение метрики для оценки
scorer = make_scorer(mean_squared_error, greater_is_better=False, squared=False)

# Создание объекта GridSearchCV
grid_search = GridSearchCV(
    xgb_model,
    param_grid,
    scoring=scorer,
    cv=KFold(n_splits=5, shuffle=True, random_state=42),  # Пример использования 5-кратной кросс-валидации
    verbose=1,
    n_jobs=-1  # Используйте все доступные ядра процессора
)

# Выполнение поиска по сетке
grid_result = grid_search.fit(X_train, y_train)

# Вывод лучших параметров и оценки
print("Best Parameters: ", grid_result.best_params_)
print("Best RMSE: ", math.sqrt(-grid_result.best_score_))

# Обучение модели с лучшими параметрами
best_model = grid_result.best_estimator_
best_model.fit(X_train, y_train)

# Предсказание на тестовом наборе
predictions = best_model.predict(X_test)

# Оценка модели
mse = mean_squared_error(y_test, predictions)
mae = mean_absolute_error(y_test, predictions)
r2 = r2_score(y_test, predictions)

print(f'Price Root Mean Squared Error: {math.sqrt(mse)}')
print(f'Price Mean Absolute Error: {mae}')
print(f'Price R-squared: {r2}')

Fitting 5 folds for each of 32 candidates, totalling 160 fits
Best Parameters:  {'learning_rate': 0.2, 'max_depth': 3, 'n_estimators': 700, 'tree_method': 'hist'}
Best RMSE:  69.8661476023613
Price Root Mean Squared Error: 5661.579644013591
Price Mean Absolute Error: 3174.6731036890333
Price R-squared: 0.7007970482109578
CPU times: total: 8min 2s
Wall time: 7min 31s

LGBM

model = LGBMRegressor(
    objective='regression',  
    max_depth=3, 
    learning_rate=0.1,  
    n_estimators=300  
)

# Обучение модели
model.fit(X_train, y_train)

# Предсказание на тестовом наборе
predictions = model.predict(X_test)

mse = mean_squared_error(y_test, predictions)
mae = mean_absolute_error(y_test, predictions)
r2 = r2_score(y_test, predictions)

print(f'Price Root Mean Squared Error: {math.sqrt(mse)}')
print(f'Price Mean Absolute Error: {mae}')
print(f'Price R-squared: {r2}')

Price Root Mean Squared Error: 6067.107449097583
Price Mean Absolute Error: 3426.288094960851
Price R-squared: 0.6563993281459335

Tuning params

%%time

param_grid = {
    'max_depth': [3, 5, 7, 9, 11],
    'learning_rate': [0.01, 0.1, 0.2, 0.5, 1],
    'n_estimators': [100, 300, 500, 700],
    'reg_alpha': [0.01, 0.1, 1, 10, 100],
    'reg_lambda': [0.01, 0.1, 1, 10, 100]
}


model = LGBMRegressor(
    objective='regression',  
)

# Определение метрики для оценки
scorer = make_scorer(mean_squared_error, greater_is_better=False, squared=False)

# Создание объекта GridSearchCV
grid_search = GridSearchCV(
    model,
    param_grid,
    scoring=scorer,
    cv=KFold(n_splits=5, shuffle=True, random_state=42),  # Пример использования 5-кратной кросс-валидации
    verbose=1,
    n_jobs=-1  # Используйте все доступные ядра процессора
)

# Выполнение поиска по сетке
grid_result = grid_search.fit(X_train, y_train)

# Вывод лучших параметров и оценки
print("Best Parameters: ", grid_result.best_params_)
print("Best RMSE: ", math.sqrt(-grid_result.best_score_))

# Обучение модели с лучшими параметрами
best_model = grid_result.best_estimator_
best_model.fit(X_train, y_train)

# Предсказание на тестовом наборе
predictions = best_model.predict(X_test)

# Оценка модели
mse = mean_squared_error(y_test, predictions)
mae = mean_absolute_error(y_test, predictions)
r2 = r2_score(y_test, predictions)

print(f'Price Root Mean Squared Error: {math.sqrt(mse)}')
print(f'Price Mean Absolute Error: {mae}')
print(f'Price R-squared: {r2}')

Fitting 5 folds for each of 2500 candidates, totalling 12500 fits
Best Parameters:  {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 300, 'reg_alpha': 100, 'reg_lambda': 1}
Best RMSE:  73.50429862985898
Price Root Mean Squared Error: 6054.932663585321
Price Mean Absolute Error: 3395.967401768939
Price R-squared: 0.6577769425249678
CPU times: total: 1min 33s
Wall time: 16min 2s

DNN

from keras import models, layers

# Function to create the DNN model
def create_dnn_model(input_shape):
    model = models.Sequential()
    model.add(layers.Dense(32, activation='relu', input_shape=(input_shape,)))
    model.add(layers.Dense(64, activation='relu'))
    model.add(layers.Dropout(0.7))
    model.add(layers.Dense(32, activation='relu'))
    model.add(layers.Dense(16, activation='relu'))
    model.add(layers.Dense(1, activation='linear'))  

    model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mse'])
    return model

# Load the data
combined_df_V2 = pd.read_csv('HousePrice_df_versions/combined_df_V2.csv')
combined_df_V3 = pd.get_dummies(combined_df_V2)

X = combined_df_V3.drop('Ціна', axis=1)
X.columns = X.columns.str.replace('[^a-zA-Z0-9А-Яа-яіІЄє]', '_')
y = combined_df_V3['Ціна']

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.35, random_state=42)
X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size=0.5, random_state=42)

# Create DNN model
dnn_model = create_dnn_model(X_train.shape[1])

# Train the model and get the history
history = dnn_model.fit(X_train, y_train, epochs=400, batch_size=128, validation_data=(X_val, y_val), verbose=0)

# Plot MSE over training epochs
plt.plot(history.history['mse'], label='Training MSE')
plt.plot(history.history['val_mse'], label='Validation MSE')
plt.title('Training and Validation MSE over Epochs')
plt.xlabel('Epochs')
plt.ylabel('Mean Squared Error')
plt.legend()
plt.show()

# Evaluate the model on the test set
dnn_predictions = dnn_model.predict(X_test)
dnn_mse = mean_squared_error(y_test, dnn_predictions)
dnn_r2 = r2_score(y_test, dnn_predictions)

print(f'DNN Mean Squared Error: {dnn_mse}')
print(f'DNN Root Mean Squared Error: {math.sqrt(dnn_mse)}')
print(f'DNN R-squared: {dnn_r2}')

30/30 [==============================] - 0s 1ms/step
DNN Mean Squared Error: 91517999.4233715
DNN Root Mean Squared Error: 9566.504033520892
DNN R-squared: 0.2013581416586554

# Function to create the DNN model
def create_dnn_model(input_shape):
    model = models.Sequential()
    model.add(layers.Dense(24, activation='relu', input_shape=(input_shape,)))
    model.add(layers.Dense(64, activation='relu'))
    model.add(layers.Dropout(0.6))
    model.add(layers.Dense(24, activation='relu'))
    model.add(layers.Dropout(0.1))
    model.add(layers.Dense(8, activation='relu'))
    model.add(layers.Dropout(0.1))
    model.add(layers.Dense(4, activation='relu'))
    model.add(layers.Dropout(0.1))
    model.add(layers.Dense(1, activation='linear'))  

    model.compile(optimizer='adam', loss='mean_squared_error', metrics=['mse'])
    return model

# Load the data
combined_df_V2 = pd.read_csv('HousePrice_df_versions/combined_df_V2.csv')
combined_df_V3 = pd.get_dummies(combined_df_V2)

X = combined_df_V3.drop('Ціна', axis=1)
X.columns = X.columns.str.replace('[^a-zA-Z0-9А-Яа-яіІЄє]', '_')
y = combined_df_V3['Ціна']

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.35, random_state=42)
X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size=0.5, random_state=42)

# Create DNN model
dnn_model = create_dnn_model(X_train.shape[1])

# Train the model and get the history
history = dnn_model.fit(X_train, y_train, epochs=400, batch_size=128, validation_data=(X_val, y_val), verbose=0)

# Plot MSE over training epochs
plt.plot(history.history['mse'], label='Training MSE')
plt.plot(history.history['val_mse'], label='Validation MSE')
plt.title('Training and Validation MSE over Epochs')
plt.xlabel('Epochs')
plt.ylabel('Mean Squared Error')
plt.legend()
plt.show()

# Evaluate the model on the test set
dnn_predictions = dnn_model.predict(X_test)
dnn_mse = mean_squared_error(y_test, dnn_predictions)
dnn_r2 = r2_score(y_test, dnn_predictions)

print(f'DNN Mean Squared Error: {dnn_mse}')
print(f'DNN Root Mean Squared Error: {math.sqrt(dnn_mse)}')
print(f'DNN R-squared: {dnn_r2}')

30/30 [==============================] - 0s 1ms/step
DNN Mean Squared Error: 32493751.440369897
DNN Root Mean Squared Error: 5700.329064218126
DNN R-squared: 0.7164397146099379

Name		Name	Last commit message	Last commit date
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README.md		README.md
combined_df_V2.csv		combined_df_V2.csv
main.py		main.py
output_23_0.png		output_23_0.png
output_23_1.png		output_23_1.png
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Load libraries

Dataset Creation

Download excel files

Combine excel files to a DataFrame

Dataset Preprocessing

Drop useless columns

Change columns dtype

Preprocessing Price column

Drop outliers using Z-score

Fill NaN-value

Fill 'Житлова площа' & 'Кухня'

Models training

Fill

Models Price prediction tuning

RandomForestRegressor

xgb

Tuning params

LGBM

Tuning params

DNN

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sakatass/Rent-forecast-in-UA

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Repository files navigation

Load libraries

Dataset Creation

Download excel files

Combine excel files to a DataFrame

Dataset Preprocessing

Drop useless columns

Change columns dtype

Preprocessing Price column

Drop outliers using Z-score

Fill NaN-value

Fill 'Житлова площа' & 'Кухня'

Models training

Fill

Models Price prediction tuning

RandomForestRegressor

xgb

Tuning params

LGBM

Tuning params

DNN

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