{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Systemy informatyczne - 1. Organizacja pracy zespołowej"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Klonowanie dostępnych danych uczących"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'mieszkania5' already exists and is not an empty directory.\n"
     ]
    }
   ],
   "source": [
    "!git clone git://gonito.net/mieszkania5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import danych treningowych, walidacyjnych i testowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dane treningowe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(r'mieszkania5\\train\\train.tsv', sep='\\t', header=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>309000.0</td>\n",
       "      <td>do zamieszkania</td>\n",
       "      <td>390 zł</td>\n",
       "      <td>spółdzielcze własnościowe</td>\n",
       "      <td>7113</td>\n",
       "      <td>https://www.otodom.pl/oferta/niezalezny-uklad-...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.44</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>plastikowe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cegła</td>\n",
       "      <td>Polecamy na sprzedaż dwupokojowe mieszkanie p...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>314900.0</td>\n",
       "      <td>do wykończenia</td>\n",
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       "      <td>pełna własność</td>\n",
       "      <td>7392</td>\n",
       "      <td>https://www.otodom.pl/oferta/urokliwe-mieszkan...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.60</td>\n",
       "      <td>pierwotny</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gazowe</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cegła</td>\n",
       "      <td>Kameralne 2 pokojowe mieszkanie z aneksem kuc...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, drzwi / ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>249000.0</td>\n",
       "      <td>do remontu</td>\n",
       "      <td>300 zł</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>5621</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkanie-do-rem...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.30</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miejskie</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>cegła</td>\n",
       "      <td>Oferta bezpośrednio od właściciela - bez pośr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, domofon ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>419000.0</td>\n",
       "      <td>do zamieszkania</td>\n",
       "      <td>490 zł</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>4761</td>\n",
       "      <td>https://www.otodom.pl/oferta/w-szeregowcu-4-po...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>plastikowe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cegła</td>\n",
       "      <td>Drodzy Państwo Zapraszam do zapoznania się z ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, zmywarka, lodówka...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>499000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>850 zł</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6481</td>\n",
       "      <td>https://www.otodom.pl/oferta/komfortowe-przest...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biuro Immohouse ma przyjemność proponować do ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>balkon, piwnica, winda</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2542</th>\n",
       "      <td>507600.0</td>\n",
       "      <td>do wykończenia</td>\n",
       "      <td>1 zł</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>5400</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkanie-do-spr...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gazowe</td>\n",
       "      <td>drewniane</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cegła</td>\n",
       "      <td>Do sprzedaży mieszkanie na drugim piętrze w s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2543</th>\n",
       "      <td>342400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>spółdzielcze własnościowe</td>\n",
       "      <td>6400</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkanie-53-50-...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.50</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miejskie</td>\n",
       "      <td>drewniane</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cegła</td>\n",
       "      <td>| Świetnie zlokalizowane, przestronne, czyste...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, monitori...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2544</th>\n",
       "      <td>335000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>280 zł</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>6063</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkanie-55-25-...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.25</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cegła</td>\n",
       "      <td>MIESZKANIE ŁAZARZ Przedmiotem sprzedaży jest ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, monitori...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2545</th>\n",
       "      <td>260000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4194</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkanie-udzial...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>piece kaflowe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-07-05</td>\n",
       "      <td>cegła</td>\n",
       "      <td>Polecam do sprzedaży mieszkanie w udziale kam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>piwnica, oddzielna kuchnia, pom. użytkowe</td>\n",
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       "    <tr>\n",
       "      <th>2546</th>\n",
       "      <td>1990000.0</td>\n",
       "      <td>do zamieszkania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5077</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkania-w-kami...</td>\n",
       "      <td>więcej niż 10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>392</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gazowe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cegła</td>\n",
       "      <td>Sprzedam 7 mieszkań w centrum Poznania przy u...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lodówka, meble, piekarnik, kuchenka, pralka</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2547 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0                1       2                          3     4   \\\n",
       "0      309000.0  do zamieszkania  390 zł  spółdzielcze własnościowe  7113   \n",
       "1      314900.0   do wykończenia     NaN             pełna własność  7392   \n",
       "2      249000.0       do remontu  300 zł             pełna własność  5621   \n",
       "3      419000.0  do zamieszkania  490 zł             pełna własność  4761   \n",
       "4      499000.0              NaN  850 zł                        NaN  6481   \n",
       "...         ...              ...     ...                        ...   ...   \n",
       "2542   507600.0   do wykończenia    1 zł             pełna własność  5400   \n",
       "2543   342400.0              NaN     NaN  spółdzielcze własnościowe  6400   \n",
       "2544   335000.0              NaN  280 zł             pełna własność  6063   \n",
       "2545   260000.0              NaN     NaN                        NaN  4194   \n",
       "2546  1990000.0  do zamieszkania     NaN                        NaN  5077   \n",
       "\n",
       "                                                     5              6   7   \\\n",
       "0     https://www.otodom.pl/oferta/niezalezny-uklad-...              2 NaN   \n",
       "1     https://www.otodom.pl/oferta/urokliwe-mieszkan...              2 NaN   \n",
       "2     https://www.otodom.pl/oferta/mieszkanie-do-rem...              2 NaN   \n",
       "3     https://www.otodom.pl/oferta/w-szeregowcu-4-po...              4 NaN   \n",
       "4     https://www.otodom.pl/oferta/komfortowe-przest...              3 NaN   \n",
       "...                                                 ...            ...  ..   \n",
       "2542  https://www.otodom.pl/oferta/mieszkanie-do-spr...              4 NaN   \n",
       "2543  https://www.otodom.pl/oferta/mieszkanie-53-50-...              2 NaN   \n",
       "2544  https://www.otodom.pl/oferta/mieszkanie-55-25-...              3 NaN   \n",
       "2545  https://www.otodom.pl/oferta/mieszkanie-udzial...              3 NaN   \n",
       "2546  https://www.otodom.pl/oferta/mieszkania-w-kami...  więcej niż 10 NaN   \n",
       "\n",
       "         8          9   ...  16             17          18      19  20  \\\n",
       "0     43.44     wtórny  ... NaN         gazowe  plastikowe     NaN NaN   \n",
       "1     42.60  pierwotny  ... NaN         gazowe  plastikowe     NaN NaN   \n",
       "2     44.30     wtórny  ... NaN       miejskie  plastikowe  1960.0 NaN   \n",
       "3        88     wtórny  ... NaN         gazowe  plastikowe     NaN NaN   \n",
       "4        77     wtórny  ... NaN            NaN  plastikowe     NaN NaN   \n",
       "...     ...        ...  ...  ..            ...         ...     ...  ..   \n",
       "2542     94     wtórny  ... NaN         gazowe   drewniane  1914.0 NaN   \n",
       "2543  53.50     wtórny  ... NaN       miejskie   drewniane  1999.0 NaN   \n",
       "2544  55.25     wtórny  ... NaN            NaN  plastikowe  1910.0 NaN   \n",
       "2545     62     wtórny  ... NaN  piece kaflowe         NaN  1930.0 NaN   \n",
       "2546    392     wtórny  ... NaN         gazowe         NaN     NaN NaN   \n",
       "\n",
       "              21     22                                                 23  \\\n",
       "0            NaN  cegła   Polecamy na sprzedaż dwupokojowe mieszkanie p...   \n",
       "1            NaN  cegła   Kameralne 2 pokojowe mieszkanie z aneksem kuc...   \n",
       "2     2019-06-30  cegła   Oferta bezpośrednio od właściciela - bez pośr...   \n",
       "3            NaN  cegła   Drodzy Państwo Zapraszam do zapoznania się z ...   \n",
       "4            NaN    NaN   Biuro Immohouse ma przyjemność proponować do ...   \n",
       "...          ...    ...                                                ...   \n",
       "2542         NaN  cegła   Do sprzedaży mieszkanie na drugim piętrze w s...   \n",
       "2543         NaN  cegła   | Świetnie zlokalizowane, przestronne, czyste...   \n",
       "2544         NaN  cegła   MIESZKANIE ŁAZARZ Przedmiotem sprzedaży jest ...   \n",
       "2545  2011-07-05  cegła   Polecam do sprzedaży mieszkanie w udziale kam...   \n",
       "2546         NaN  cegła   Sprzedam 7 mieszkań w centrum Poznania przy u...   \n",
       "\n",
       "      24                                                 25  \n",
       "0    NaN  telewizja kablowa, internet, meble, piwnica, g...  \n",
       "1    NaN  telewizja kablowa, internet, telefon, drzwi / ...  \n",
       "2    NaN  telewizja kablowa, internet, telefon, domofon ...  \n",
       "3    NaN  telewizja kablowa, internet, zmywarka, lodówka...  \n",
       "4    NaN                             balkon, piwnica, winda  \n",
       "...   ..                                                ...  \n",
       "2542 NaN                                                NaN  \n",
       "2543 NaN  telewizja kablowa, internet, telefon, monitori...  \n",
       "2544 NaN  telewizja kablowa, internet, telefon, monitori...  \n",
       "2545 NaN          piwnica, oddzielna kuchnia, pom. użytkowe  \n",
       "2546 NaN        lodówka, meble, piekarnik, kuchenka, pralka  \n",
       "\n",
       "[2547 rows x 26 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dane walidacyjne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_valid = pd.read_csv(r'mieszkania5\\dev-0\\in.tsv', sep='\\t', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>do zamieszkania</td>\n",
       "      <td>250 zł</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>6311</td>\n",
       "      <td>https://www.otodom.pl/oferta/idealne-mieszkani...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.10</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miejskie</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wielka płyta</td>\n",
       "      <td>Zamieszkaj od zaraz – idealna lokalizacja OFE...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, domofon ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>do zamieszkania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>7868</td>\n",
       "      <td>https://www.otodom.pl/oferta/2-pokoje-38m2-po-...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.00</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>inne</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Na sprzedaż mieszkanie dwupokojowe na osiedlu...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>balkon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>do zamieszkania</td>\n",
       "      <td>650 zł</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>5717</td>\n",
       "      <td>https://www.otodom.pl/oferta/3-pokoje-na-jezyc...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63.84</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miejskie</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cegła</td>\n",
       "      <td>!! Oferta dostępna tylko u nas !! Polecam ofe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, piwnica,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>do zamieszkania</td>\n",
       "      <td>359 zł</td>\n",
       "      <td>spółdzielcze własnościowe</td>\n",
       "      <td>7380</td>\n",
       "      <td>https://www.otodom.pl/oferta/uniwersytet-przyr...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.00</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miejskie</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-06-30</td>\n",
       "      <td>wielka płyta</td>\n",
       "      <td>Oferta  bezpośrednio od właściciela  - bez po...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, drzwi / ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7373</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkanie-blisko...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.62</td>\n",
       "      <td>pierwotny</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MIESZKANIE 3-POKOJOWE O POW.65,62M2 Mieszkani...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>garaż/miejsce parkingowe, taras, pom. użytkowe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>do wykończenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>9007</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkanie-zajezd...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.78</td>\n",
       "      <td>pierwotny</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miejskie</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cegła</td>\n",
       "      <td>Zapraszam do zapoznania się z nową atrakcyjną...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, monitoring / ochr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>do wykończenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>9202</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkanie-zajezd...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.23</td>\n",
       "      <td>pierwotny</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zapraszam do zapoznania się z nową atrakcyjną...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>garaż/miejsce parkingowe, winda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>do wykończenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>5723</td>\n",
       "      <td>https://www.otodom.pl/oferta/nowe-mieszkanie-o...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.16</td>\n",
       "      <td>pierwotny</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kotłownia</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>silikat</td>\n",
       "      <td>Mieszkanie składa się z 3 pokoi, aneksu kuche...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, domofon ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>do zamieszkania</td>\n",
       "      <td>1 102.05 zł</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>7758</td>\n",
       "      <td>https://www.otodom.pl/oferta/apartament-w-atan...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.10</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kotłownia</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-04-13</td>\n",
       "      <td>cegła</td>\n",
       "      <td>Proponuję bardzo atrakcyjny, przestronny, w p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, drzwi / okna antywłamaniowe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11822</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkanie-71-90-...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.90</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>drewniane</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cegła</td>\n",
       "      <td>Link do oferty: -wielkopolskie-poznan-grunwal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>balkon, garaż/miejsce parkingowe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>462 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0            1                          2      3   \\\n",
       "0    do zamieszkania       250 zł             pełna własność   6311   \n",
       "1    do zamieszkania          NaN             pełna własność   7868   \n",
       "2    do zamieszkania       650 zł             pełna własność   5717   \n",
       "3    do zamieszkania       359 zł  spółdzielcze własnościowe   7380   \n",
       "4                NaN          NaN                        NaN   7373   \n",
       "..               ...          ...                        ...    ...   \n",
       "457   do wykończenia          NaN             pełna własność   9007   \n",
       "458   do wykończenia          NaN             pełna własność   9202   \n",
       "459   do wykończenia          NaN             pełna własność   5723   \n",
       "460  do zamieszkania  1 102.05 zł             pełna własność   7758   \n",
       "461              NaN          NaN                        NaN  11822   \n",
       "\n",
       "                                                    4   5   6      7   \\\n",
       "0    https://www.otodom.pl/oferta/idealne-mieszkani...   3 NaN  59.10   \n",
       "1    https://www.otodom.pl/oferta/2-pokoje-38m2-po-...   2 NaN  38.00   \n",
       "2    https://www.otodom.pl/oferta/3-pokoje-na-jezyc...   3 NaN  63.84   \n",
       "3    https://www.otodom.pl/oferta/uniwersytet-przyr...   4 NaN  50.00   \n",
       "4    https://www.otodom.pl/oferta/mieszkanie-blisko...   3 NaN  65.62   \n",
       "..                                                 ...  ..  ..    ...   \n",
       "457  https://www.otodom.pl/oferta/mieszkanie-zajezd...   3 NaN  72.78   \n",
       "458  https://www.otodom.pl/oferta/mieszkanie-zajezd...   2 NaN  51.23   \n",
       "459  https://www.otodom.pl/oferta/nowe-mieszkanie-o...   3 NaN  54.16   \n",
       "460  https://www.otodom.pl/oferta/apartament-w-atan...   3 NaN  90.10   \n",
       "461  https://www.otodom.pl/oferta/mieszkanie-71-90-...   2 NaN  71.90   \n",
       "\n",
       "            8     9   ...  15         16          17      18  19          20  \\\n",
       "0       wtórny   4.0  ... NaN   miejskie  plastikowe     NaN NaN         NaN   \n",
       "1       wtórny  12.0  ... NaN       inne  plastikowe     NaN NaN         NaN   \n",
       "2       wtórny   4.0  ... NaN   miejskie  plastikowe  1958.0 NaN         NaN   \n",
       "3       wtórny  10.0  ... NaN   miejskie  plastikowe  1975.0 NaN  2019-06-30   \n",
       "4    pierwotny   3.0  ... NaN        NaN         NaN  2020.0 NaN  2020-01-01   \n",
       "..         ...   ...  ...  ..        ...         ...     ...  ..         ...   \n",
       "457  pierwotny   5.0  ... NaN   miejskie  plastikowe  2019.0 NaN         NaN   \n",
       "458  pierwotny   5.0  ... NaN        NaN         NaN     NaN NaN         NaN   \n",
       "459  pierwotny   3.0  ... NaN  kotłownia  plastikowe  2019.0 NaN  2019-12-31   \n",
       "460     wtórny   5.0  ... NaN  kotłownia  plastikowe  2001.0 NaN  2018-04-13   \n",
       "461     wtórny   3.0  ... NaN        NaN   drewniane  2016.0 NaN         NaN   \n",
       "\n",
       "               21                                                 22  23  \\\n",
       "0    wielka płyta   Zamieszkaj od zaraz – idealna lokalizacja OFE... NaN   \n",
       "1             NaN   Na sprzedaż mieszkanie dwupokojowe na osiedlu... NaN   \n",
       "2           cegła   !! Oferta dostępna tylko u nas !! Polecam ofe... NaN   \n",
       "3    wielka płyta   Oferta  bezpośrednio od właściciela  - bez po... NaN   \n",
       "4             NaN   MIESZKANIE 3-POKOJOWE O POW.65,62M2 Mieszkani... NaN   \n",
       "..            ...                                                ...  ..   \n",
       "457         cegła   Zapraszam do zapoznania się z nową atrakcyjną... NaN   \n",
       "458           NaN   Zapraszam do zapoznania się z nową atrakcyjną... NaN   \n",
       "459       silikat   Mieszkanie składa się z 3 pokoi, aneksu kuche... NaN   \n",
       "460         cegła   Proponuję bardzo atrakcyjny, przestronny, w p... NaN   \n",
       "461         cegła   Link do oferty: -wielkopolskie-poznan-grunwal... NaN   \n",
       "\n",
       "                                                    24  \n",
       "0    telewizja kablowa, internet, telefon, domofon ...  \n",
       "1                                               balkon  \n",
       "2    telewizja kablowa, internet, telefon, piwnica,...  \n",
       "3    telewizja kablowa, internet, telefon, drzwi / ...  \n",
       "4       garaż/miejsce parkingowe, taras, pom. użytkowe  \n",
       "..                                                 ...  \n",
       "457  telewizja kablowa, internet, monitoring / ochr...  \n",
       "458                    garaż/miejsce parkingowe, winda  \n",
       "459  telewizja kablowa, internet, telefon, domofon ...  \n",
       "460  telewizja kablowa, drzwi / okna antywłamaniowe...  \n",
       "461                   balkon, garaż/miejsce parkingowe  \n",
       "\n",
       "[462 rows x 25 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_valid_cena = pd.read_csv(r'mieszkania5\\dev-0\\expected.tsv', sep='\\t', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>373000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>299000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>365000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>369000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>483791.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>655544.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>471397.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>309958.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>699000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>850000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>462 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0\n",
       "0    373000.00\n",
       "1    299000.00\n",
       "2    365000.00\n",
       "3    369000.00\n",
       "4    483791.00\n",
       "..         ...\n",
       "457  655544.02\n",
       "458  471397.97\n",
       "459  309958.00\n",
       "460  699000.00\n",
       "461  850000.00\n",
       "\n",
       "[462 rows x 1 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_valid_cena"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dane testowe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.read_csv(r'mieszkania5\\test-A\\in.tsv', sep='\\t', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>do wykończenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>6938</td>\n",
       "      <td>https://www.otodom.pl/oferta/nowe-mieszkanie-z...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.99</td>\n",
       "      <td>pierwotny</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Prezentujemy naszą najnowszą inwestycje zloka...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, drzwi / ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>do zamieszkania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>6078</td>\n",
       "      <td>https://www.otodom.pl/oferta/3-pok-parter-nisk...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64.00</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miejskie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wielka płyta</td>\n",
       "      <td>Polecam na sprzedaż duże atrakcyjnie zlokaliz...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>domofon / wideofon, balkon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>do wykończenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>6150</td>\n",
       "      <td>https://www.otodom.pl/oferta/hot-oferta-od-dew...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.15</td>\n",
       "      <td>pierwotny</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miejskie</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cegła</td>\n",
       "      <td>&amp;gt...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, domofon ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>do zamieszkania</td>\n",
       "      <td>350 zł</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>9373</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkanie-z-ogro...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.77</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>!! OFERTA BEZPOŚREDNIA !! Na sprzedaż mieszkan...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, drzwi / ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7200</td>\n",
       "      <td>https://www.otodom.pl/oferta/osiedle-nowych-ko...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.36</td>\n",
       "      <td>pierwotny</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-08-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Inwestycja NOWYCH KOSMONAUTÓW powstaje u zbie...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, drzwi / ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>do wykończenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7150</td>\n",
       "      <td>https://www.otodom.pl/oferta/zielony-marcelin-...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.97</td>\n",
       "      <td>pierwotny</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miejskie</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zielony Marcelin I etap mieszkanie nr 407, 1 ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, drzwi / ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>do wykończenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>6499</td>\n",
       "      <td>https://www.otodom.pl/oferta/nowe-mieszkanie-p...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.06</td>\n",
       "      <td>pierwotny</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>kotłownia</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>silikat</td>\n",
       "      <td>Mieszkanie składa się z 3 pokoi z aneksem kuc...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, domofon ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>do wykończenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>9451</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkanie-76-71-...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.71</td>\n",
       "      <td>pierwotny</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zapraszam do zapoznania się z nową atrakcyjną...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>monitoring / ochrona, garaż/miejsce parkingowe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>do wykończenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>9322</td>\n",
       "      <td>https://www.otodom.pl/oferta/mieszkanie-zajezd...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.63</td>\n",
       "      <td>pierwotny</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zapraszam do zapoznania się z nową atrakcyjną...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>garaż/miejsce parkingowe, winda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>do wykończenia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pełna własność</td>\n",
       "      <td>6500</td>\n",
       "      <td>https://www.otodom.pl/oferta/nowe-mieszkanie-w...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.84</td>\n",
       "      <td>wtórny</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>miejskie</td>\n",
       "      <td>plastikowe</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pustak</td>\n",
       "      <td>Super mieszkanie 2-pokojowe 65,84m2. wykończo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>telewizja kablowa, internet, telefon, monitori...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0       1               2     3   \\\n",
       "0     do wykończenia     NaN  pełna własność  6938   \n",
       "1    do zamieszkania     NaN  pełna własność  6078   \n",
       "2     do wykończenia     NaN  pełna własność  6150   \n",
       "3    do zamieszkania  350 zł  pełna własność  9373   \n",
       "4                NaN     NaN             NaN  7200   \n",
       "..               ...     ...             ...   ...   \n",
       "413   do wykończenia     NaN             NaN  7150   \n",
       "414   do wykończenia     NaN  pełna własność  6499   \n",
       "415   do wykończenia     NaN  pełna własność  9451   \n",
       "416   do wykończenia     NaN  pełna własność  9322   \n",
       "417   do wykończenia     NaN  pełna własność  6500   \n",
       "\n",
       "                                                    4  5   6      7   \\\n",
       "0    https://www.otodom.pl/oferta/nowe-mieszkanie-z...  3 NaN  61.99   \n",
       "1    https://www.otodom.pl/oferta/3-pok-parter-nisk...  4 NaN  64.00   \n",
       "2    https://www.otodom.pl/oferta/hot-oferta-od-dew...  3 NaN  51.15   \n",
       "3    https://www.otodom.pl/oferta/mieszkanie-z-ogro...  2 NaN  45.77   \n",
       "4    https://www.otodom.pl/oferta/osiedle-nowych-ko...  2 NaN  44.36   \n",
       "..                                                 ... ..  ..    ...   \n",
       "413  https://www.otodom.pl/oferta/zielony-marcelin-...  1 NaN  34.97   \n",
       "414  https://www.otodom.pl/oferta/nowe-mieszkanie-p...  3 NaN  49.06   \n",
       "415  https://www.otodom.pl/oferta/mieszkanie-76-71-...  3 NaN  76.71   \n",
       "416  https://www.otodom.pl/oferta/mieszkanie-zajezd...  3 NaN  72.63   \n",
       "417  https://www.otodom.pl/oferta/nowe-mieszkanie-w...  2 NaN  65.84   \n",
       "\n",
       "            8     9   ...  15         16          17      18  19          20  \\\n",
       "0    pierwotny   7.0  ... NaN        NaN  plastikowe  2020.0 NaN         NaN   \n",
       "1       wtórny   4.0  ... NaN   miejskie         NaN  1982.0 NaN         NaN   \n",
       "2    pierwotny   5.0  ... NaN   miejskie  plastikowe  2020.0 NaN         NaN   \n",
       "3       wtórny   7.0  ... NaN        NaN         NaN     NaN NaN         NaN   \n",
       "4    pierwotny  13.0  ... NaN        NaN  plastikowe     NaN NaN  2020-08-01   \n",
       "..         ...   ...  ...  ..        ...         ...     ...  ..         ...   \n",
       "413  pierwotny   8.0  ... NaN   miejskie  plastikowe     NaN NaN         NaN   \n",
       "414  pierwotny   3.0  ... NaN  kotłownia  plastikowe  2018.0 NaN  2018-12-31   \n",
       "415  pierwotny   5.0  ... NaN        NaN  plastikowe  2019.0 NaN         NaN   \n",
       "416  pierwotny   5.0  ... NaN        NaN         NaN     NaN NaN         NaN   \n",
       "417     wtórny  10.0  ... NaN   miejskie  plastikowe  2012.0 NaN         NaN   \n",
       "\n",
       "               21                                                 22  23  \\\n",
       "0             NaN   Prezentujemy naszą najnowszą inwestycje zloka... NaN   \n",
       "1    wielka płyta   Polecam na sprzedaż duże atrakcyjnie zlokaliz... NaN   \n",
       "2           cegła                                             &gt... NaN   \n",
       "3             NaN  !! OFERTA BEZPOŚREDNIA !! Na sprzedaż mieszkan... NaN   \n",
       "4             NaN   Inwestycja NOWYCH KOSMONAUTÓW powstaje u zbie... NaN   \n",
       "..            ...                                                ...  ..   \n",
       "413           NaN   Zielony Marcelin I etap mieszkanie nr 407, 1 ... NaN   \n",
       "414       silikat   Mieszkanie składa się z 3 pokoi z aneksem kuc... NaN   \n",
       "415           NaN   Zapraszam do zapoznania się z nową atrakcyjną... NaN   \n",
       "416           NaN   Zapraszam do zapoznania się z nową atrakcyjną... NaN   \n",
       "417        pustak   Super mieszkanie 2-pokojowe 65,84m2. wykończo... NaN   \n",
       "\n",
       "                                                    24  \n",
       "0    telewizja kablowa, internet, telefon, drzwi / ...  \n",
       "1                           domofon / wideofon, balkon  \n",
       "2    telewizja kablowa, internet, telefon, domofon ...  \n",
       "3    telewizja kablowa, internet, telefon, drzwi / ...  \n",
       "4    telewizja kablowa, internet, telefon, drzwi / ...  \n",
       "..                                                 ...  \n",
       "413  telewizja kablowa, internet, telefon, drzwi / ...  \n",
       "414  telewizja kablowa, internet, telefon, domofon ...  \n",
       "415  monitoring / ochrona, garaż/miejsce parkingowe...  \n",
       "416                    garaż/miejsce parkingowe, winda  \n",
       "417  telewizja kablowa, internet, telefon, monitori...  \n",
       "\n",
       "[418 rows x 25 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wybranie interesujących nas kolumn (cena, ilość pokoi, metraż, rok budowy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train[[0, 6, 8, 19]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_valid[0] = df_valid_cena"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_valid = df_valid[[0, 5, 7, 18]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test[[5, 7, 18]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zamiana nazw kolumn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.rename(columns = {0:\"cena\", 6:\"pokoje\", 8:\"metraz\", 19:\"rok\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_valid = df_valid.rename(columns = {0:\"cena\", 5:\"pokoje\", 7:\"metraz\", 18:\"rok\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test.rename(columns = {5:\"pokoje\", 7:\"metraz\", 18:\"rok\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Czyszczenie danych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Czyszczenie danych treningowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.replace( \"więcej niż 10\", '11')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train[(df_train['rok'] >= 1800) & (df_train['rok'] <= 2023)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train[df_train[\"metraz\"] != '6 909']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train[\"pokoje\"] = df_train[\"pokoje\"].astype(float)\n",
    "df_train[\"metraz\"] = df_train[\"metraz\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cena</th>\n",
       "      <th>pokoje</th>\n",
       "      <th>metraz</th>\n",
       "      <th>rok</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.763000e+03</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1763.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.262096e+05</td>\n",
       "      <td>2.609756</td>\n",
       "      <td>60.068327</td>\n",
       "      <td>1992.024957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.478208e+05</td>\n",
       "      <td>1.038289</td>\n",
       "      <td>29.652713</td>\n",
       "      <td>37.968540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.500000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.100000e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1976.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.671000e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>52.300000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.700000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000e+06</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>2022.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               cena       pokoje       metraz          rok\n",
       "count  1.763000e+03  1763.000000  1763.000000  1763.000000\n",
       "mean   4.262096e+05     2.609756    60.068327  1992.024957\n",
       "std    2.478208e+05     1.038289    29.652713    37.968540\n",
       "min    8.500000e+04     1.000000    19.000000  1840.000000\n",
       "25%    3.100000e+05     2.000000    43.000000  1976.000000\n",
       "50%    3.671000e+05     2.000000    52.300000  2010.000000\n",
       "75%    4.700000e+05     3.000000    68.000000  2019.000000\n",
       "max    6.000000e+06    11.000000   404.000000  2022.000000"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cena</th>\n",
       "      <th>pokoje</th>\n",
       "      <th>metraz</th>\n",
       "      <th>rok</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>249000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>44.30</td>\n",
       "      <td>1960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>449000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>92.00</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>389000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.60</td>\n",
       "      <td>1985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>260000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44.20</td>\n",
       "      <td>1970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>330682.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>48.99</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2541</th>\n",
       "      <td>383680.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>70.40</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2542</th>\n",
       "      <td>507600.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>94.00</td>\n",
       "      <td>1914.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2543</th>\n",
       "      <td>342400.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>53.50</td>\n",
       "      <td>1999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2544</th>\n",
       "      <td>335000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>55.25</td>\n",
       "      <td>1910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2545</th>\n",
       "      <td>260000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "      <td>1930.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1763 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cena  pokoje  metraz     rok\n",
       "2     249000.0     2.0   44.30  1960.0\n",
       "6     449000.0     4.0   92.00  1930.0\n",
       "7     389000.0     3.0   63.60  1985.0\n",
       "9     260000.0     3.0   44.20  1970.0\n",
       "10    330682.0     3.0   48.99  2019.0\n",
       "...        ...     ...     ...     ...\n",
       "2541  383680.0     3.0   70.40  2016.0\n",
       "2542  507600.0     4.0   94.00  1914.0\n",
       "2543  342400.0     2.0   53.50  1999.0\n",
       "2544  335000.0     3.0   55.25  1910.0\n",
       "2545  260000.0     3.0   62.00  1930.0\n",
       "\n",
       "[1763 rows x 4 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Czyszczenie danych walidacyjnych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cena</th>\n",
       "      <th>pokoje</th>\n",
       "      <th>metraz</th>\n",
       "      <th>rok</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.620000e+02</td>\n",
       "      <td>462.000000</td>\n",
       "      <td>462.000000</td>\n",
       "      <td>322.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.108530e+05</td>\n",
       "      <td>2.528139</td>\n",
       "      <td>57.329113</td>\n",
       "      <td>1988.605590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.049347e+05</td>\n",
       "      <td>0.953512</td>\n",
       "      <td>25.300686</td>\n",
       "      <td>113.363432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.597610e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.984000e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.520765e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.510000</td>\n",
       "      <td>2013.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.687800e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>66.315000</td>\n",
       "      <td>2019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.318580e+06</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>234.200000</td>\n",
       "      <td>2021.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               cena      pokoje      metraz          rok\n",
       "count  4.620000e+02  462.000000  462.000000   322.000000\n",
       "mean   4.108530e+05    2.528139   57.329113  1988.605590\n",
       "std    2.049347e+05    0.953512   25.300686   113.363432\n",
       "min    1.597610e+05    1.000000    1.000000    70.000000\n",
       "25%    2.984000e+05    2.000000   42.000000  1980.000000\n",
       "50%    3.520765e+05    2.000000   50.510000  2013.000000\n",
       "75%    4.687800e+05    3.000000   66.315000  2019.000000\n",
       "max    2.318580e+06    6.000000  234.200000  2021.000000"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_valid.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_valid = df_valid.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_valid = df_valid[df_valid['metraz'] > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_valid = df_valid[(df_valid['rok'] >= 1800) & (df_valid['rok'] <= 2023)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_valid = df_valid.replace( \"więcej niż 10\", '11')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_valid[\"pokoje\"] = df_valid[\"pokoje\"].astype(float)\n",
    "df_valid[\"metraz\"] = df_valid[\"metraz\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cena</th>\n",
       "      <th>pokoje</th>\n",
       "      <th>metraz</th>\n",
       "      <th>rok</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>365000.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.84</td>\n",
       "      <td>1958.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>369000.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>50.00</td>\n",
       "      <td>1975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>483791.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.62</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>430000.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>96.00</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>325000.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>49.00</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>455</th>\n",
       "      <td>641395.58</td>\n",
       "      <td>2.0</td>\n",
       "      <td>72.78</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>655544.02</td>\n",
       "      <td>3.0</td>\n",
       "      <td>72.78</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>309958.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>54.16</td>\n",
       "      <td>2019.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>699000.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>90.10</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>850000.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>71.90</td>\n",
       "      <td>2016.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>320 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          cena  pokoje  metraz     rok\n",
       "2    365000.00     3.0   63.84  1958.0\n",
       "3    369000.00     4.0   50.00  1975.0\n",
       "4    483791.00     3.0   65.62  2020.0\n",
       "5    430000.00     5.0   96.00  2019.0\n",
       "9    325000.00     2.0   49.00  2001.0\n",
       "..         ...     ...     ...     ...\n",
       "455  641395.58     2.0   72.78  2019.0\n",
       "457  655544.02     3.0   72.78  2019.0\n",
       "459  309958.00     3.0   54.16  2019.0\n",
       "460  699000.00     3.0   90.10  2001.0\n",
       "461  850000.00     2.0   71.90  2016.0\n",
       "\n",
       "[320 rows x 4 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_valid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Czyszczenie danych testowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test.replace( \"więcej niż 10\", '11')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test[\"pokoje\"] = df_test[\"pokoje\"].astype(float)\n",
    "df_test[\"metraz\"] = df_test[\"metraz\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>cena</th>\n",
       "      <th>pokoje</th>\n",
       "      <th>metraz</th>\n",
       "      <th>rok</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.763000e+03</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1763.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.262096e+05</td>\n",
       "      <td>2.609756</td>\n",
       "      <td>60.068327</td>\n",
       "      <td>1992.024957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.478208e+05</td>\n",
       "      <td>1.038289</td>\n",
       "      <td>29.652713</td>\n",
       "      <td>37.968540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.500000e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1840.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.100000e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1976.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.671000e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>52.300000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.700000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000e+06</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>2022.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               cena       pokoje       metraz          rok\n",
       "count  1.763000e+03  1763.000000  1763.000000  1763.000000\n",
       "mean   4.262096e+05     2.609756    60.068327  1992.024957\n",
       "std    2.478208e+05     1.038289    29.652713    37.968540\n",
       "min    8.500000e+04     1.000000    19.000000  1840.000000\n",
       "25%    3.100000e+05     2.000000    43.000000  1976.000000\n",
       "50%    3.671000e+05     2.000000    52.300000  2010.000000\n",
       "75%    4.700000e+05     3.000000    68.000000  2019.000000\n",
       "max    6.000000e+06    11.000000   404.000000  2022.000000"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model regresji liniowej"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Podział danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_train[[\"pokoje\", \"metraz\", \"rok\"]]\n",
    "y_train = df_train[[\"cena\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = df_valid[[\"pokoje\", \"metraz\", \"rok\"]]\n",
    "y_val = df_valid[[\"cena\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = df_test[[\"pokoje\", \"metraz\", \"rok\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uczenie modelu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Walidacja modelu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6264197107092035"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_true=y_train, y_pred=y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22930468691.882328"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_true=y_train, y_pred=y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_val_pred = model.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5872189061243203"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_true=y_val, y_pred=y_val_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12603444942.321444"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_true=y_val, y_pred=y_val_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predykcja cen mieszkań"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cena</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>445999.679659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>391984.112513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>361680.683534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>634737.891029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>334716.539450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>639199.416015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>493459.955384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>343927.359472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>559751.151754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>511183.577622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>292 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              cena\n",
       "0    445999.679659\n",
       "1    391984.112513\n",
       "2    361680.683534\n",
       "3    634737.891029\n",
       "4    334716.539450\n",
       "..             ...\n",
       "287  639199.416015\n",
       "288  493459.955384\n",
       "289  343927.359472\n",
       "290  559751.151754\n",
       "291  511183.577622\n",
       "\n",
       "[292 rows x 1 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_pred_df = pd.DataFrame(y_test_pred, columns=['cena'])\n",
    "y_test_pred_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wizualizacja regresji liniowej"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cena nieruchomości ze względna na liczbę pokoi dla danych treningowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef = np.polyfit(X_train['pokoje'], y_train['cena'], 1)\n",
    "poly1d_fn = np.poly1d(coef)\n",
    "\n",
    "plt.plot(X_train['pokoje'], y_train['cena'], 'o', X_train['pokoje'], poly1d_fn(X_train['pokoje']), '--k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cena nieruchomości ze względna na rok wybudowania dla danych treningowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef = np.polyfit(X_train['rok'], y_train['cena'], 1)\n",
    "poly1d_fn = np.poly1d(coef)\n",
    "\n",
    "plt.plot(X_train['rok'], y_train['cena'], 'o', X_train['rok'], poly1d_fn(X_train['rok']), '--k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cena nieruchomości ze względna na metraż dla danych treningowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef = np.polyfit(X_train['metraz'], y_train['cena'], 1)\n",
    "poly1d_fn = np.poly1d(coef)\n",
    "\n",
    "plt.plot(X_train['metraz'], y_train['cena'], 'o', X_train['metraz'], poly1d_fn(X_train['metraz']), '--k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cena nieruchomości ze względna na liczbę pokoi dla danych testowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef = np.polyfit(X_train['metraz'], y_train['cena'], 1)\n",
    "poly1d_fn = np.poly1d(coef)\n",
    "\n",
    "plt.plot(X_test['pokoje'], y_test_pred_df['cena'], 'yo', X_train['pokoje'], poly1d_fn(X_train['pokoje']), '--k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cena nieruchomości ze względna na rok wybudowania dla danych testowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef = np.polyfit(X_train['metraz'], y_train['cena'], 1)\n",
    "poly1d_fn = np.poly1d(coef)\n",
    "\n",
    "plt.plot(X_test['rok'], y_test_pred_df['cena'], 'yo', X_train['rok'], poly1d_fn(X_train['rok']), '--k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cena nieruchomości ze względna na metraż dla danych testowych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coef = np.polyfit(X_train['metraz'], y_train['cena'], 1)\n",
    "poly1d_fn = np.poly1d(coef)\n",
    "\n",
    "plt.plot(X_test['metraz'], y_test_pred_df['cena'], 'yo', X_train['metraz'], poly1d_fn(X_train['metraz']), '--k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funkcja predykcji ceny mieszkania"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cena_mieszkania(pokoje, metraz, rok):\n",
    "    mieszkanie = pd.DataFrame([[pokoje, metraz, rok]], columns=['pokoje', 'metraz', 'rok'])\n",
    "    cena = model.predict(mieszkanie).item()\n",
    "    return np.round(cena, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184531.94"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cena_mieszkania(pokoje=2, metraz=25, rok=2000)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
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