1609 lines
48 KiB
Plaintext
1609 lines
48 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 316,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from statistics import mean,median\n",
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"import re\n",
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"import numpy as np\n",
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" "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Wczytanie datasetów"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 223,
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"metadata": {},
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"outputs": [],
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"source": [
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"train_dataset = pd.read_csv(\"./train/train.tsv\", sep = \"\\t\", header=None)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Data exploration "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 188,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>0</th>\n",
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" <th>1</th>\n",
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" <th>2</th>\n",
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" <th>3</th>\n",
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" <th>4</th>\n",
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" <th>5</th>\n",
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" <th>6</th>\n",
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" <th>7</th>\n",
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" <th>8</th>\n",
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" <th>9</th>\n",
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" <th>...</th>\n",
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" <th>16</th>\n",
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" <th>17</th>\n",
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" <th>18</th>\n",
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" <th>19</th>\n",
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" <th>20</th>\n",
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" <th>21</th>\n",
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" <th>22</th>\n",
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" <th>23</th>\n",
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" <th>24</th>\n",
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" <th>25</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>309000.0</td>\n",
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" <td>do zamieszkania</td>\n",
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" <td>390 zł</td>\n",
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" <td>spółdzielcze własnościowe</td>\n",
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" <td>7113</td>\n",
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" <td>https://www.otodom.pl/oferta/niezalezny-uklad-...</td>\n",
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" <td>2</td>\n",
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" <td>NaN</td>\n",
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" <td>43.44</td>\n",
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" <td>wtórny</td>\n",
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" <td>...</td>\n",
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" <td>NaN</td>\n",
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" <td>gazowe</td>\n",
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" <td>plastikowe</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>cegła</td>\n",
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" <td>Polecamy na sprzedaż dwupokojowe mieszkanie p...</td>\n",
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" <td>NaN</td>\n",
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" <td>telewizja kablowa, internet, meble, piwnica, g...</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>1 rows × 26 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" 0 1 2 3 4 \\\n",
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"0 309000.0 do zamieszkania 390 zł spółdzielcze własnościowe 7113 \n",
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"\n",
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" 5 6 7 8 9 \\\n",
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"0 https://www.otodom.pl/oferta/niezalezny-uklad-... 2 NaN 43.44 wtórny \n",
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"\n",
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" ... 16 17 18 19 20 21 22 \\\n",
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"0 ... NaN gazowe plastikowe NaN NaN NaN cegła \n",
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"\n",
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" 23 24 \\\n",
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"0 Polecamy na sprzedaż dwupokojowe mieszkanie p... NaN \n",
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"\n",
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" 25 \n",
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"0 telewizja kablowa, internet, meble, piwnica, g... \n",
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"\n",
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"[1 rows x 26 columns]"
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]
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},
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||
"execution_count": 188,
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||
"metadata": {},
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"output_type": "execute_result"
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||
}
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||
],
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"source": [
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"train_dataset.head(1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Wczytywanie danych testowych i preprocessing jak na treningu"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 243,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"COLUMN 15:\n",
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"Value counts before changes:\n",
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" 15\n",
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" 1 569\n",
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" 2 527\n",
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" 0 452\n",
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||
" 4 357\n",
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" 3 321\n",
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||
" 5 117\n",
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" 6 51\n",
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||
" 7 42\n",
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||
" 8 32\n",
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||
" 10 29\n",
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||
" 11 24\n",
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" 9 21\n",
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"-1 5\n",
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"Name: count, dtype: int64\n",
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"Value counts after changes:\n",
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" 15\n",
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" 1 569\n",
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" 2 527\n",
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" 0 452\n",
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||
" 4 357\n",
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||
" 3 321\n",
|
||
" 5 117\n",
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||
" 6 51\n",
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||
" 7 42\n",
|
||
" 8 32\n",
|
||
" 10 29\n",
|
||
" 11 24\n",
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||
" 9 21\n",
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||
"-1 5\n",
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||
"Name: count, dtype: int64\n",
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"COLUMN 8:\n",
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"0 43.44\n",
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||
"1 42.60\n",
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||
"2 44.30\n",
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"3 88.00\n",
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||
"4 77.00\n",
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||
" ... \n",
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||
"2542 94.00\n",
|
||
"2543 53.50\n",
|
||
"2544 55.25\n",
|
||
"2545 62.00\n",
|
||
"2546 392.00\n",
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||
"Name: 8, Length: 2547, dtype: float64\n",
|
||
"COLUMN 6:\n",
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||
"Value counts before changes:\n",
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||
" 6\n",
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||
"2 1014\n",
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||
"3 878\n",
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||
"4 293\n",
|
||
"1 271\n",
|
||
"5 64\n",
|
||
"6 13\n",
|
||
"7 7\n",
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||
"10 6\n",
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||
"9 1\n",
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||
"Name: count, dtype: int64\n",
|
||
"Value counts after changes:\n",
|
||
" 6\n",
|
||
"2 1014\n",
|
||
"3 878\n",
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||
"4 293\n",
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||
"1 271\n",
|
||
"5 64\n",
|
||
"6 13\n",
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||
"7 7\n",
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||
"10 6\n",
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||
"9 1\n",
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||
"Name: count, dtype: int64\n"
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||
]
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||
}
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||
],
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"source": [
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"\n",
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"# Preprocessing column 15:\n",
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"print(\"COLUMN 15:\")\n",
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"# Count the occurrence of unique values in column before preprocessing:\n",
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"print(\"Value counts before changes:\\n\",train_dataset[15].value_counts())\n",
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"\n",
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"# Replace string to int or NaN:\n",
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"train_dataset[15] = train_dataset[15].replace({\"parter\": 0, \"suterena\": -1, \"> 10\": 11, \"poddasze\": np.nan})\n",
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"train_dataset[15] = train_dataset[15].apply(float)\n",
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"\n",
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"# Fill Nans with median:\n",
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"train_dataset[15].fillna(train_dataset[15].median(), inplace=True)\n",
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"train_dataset[15]= train_dataset[15].apply(int)\n",
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"\n",
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"# Count the occurrence of unique values in column after preprocessing:\n",
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"print(\"Value counts after changes:\\n\",train_dataset[15].value_counts())\n",
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"\n",
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"# Preprocessing column 8:\n",
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"print(\"COLUMN 8:\")\n",
|
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"# Replace strings containing space to NaN:\n",
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"train_dataset[8] = train_dataset[8].replace(' ', np.nan, regex=True)\n",
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"\n",
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||
"# Fill Nans with median:\n",
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"train_dataset[8] = train_dataset[8].apply(float)\n",
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"train_dataset[8].fillna(train_dataset[8].median(), inplace=True)\n",
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"\n",
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"print(train_dataset[8])\n",
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"\n",
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"# Preprocessing column 6:\n",
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"print(\"COLUMN 6:\")\n",
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"# Count the occurrence of unique values in column before preprocessing:\n",
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"print(\"Value counts before changes:\\n\",train_dataset[6].value_counts())\n",
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"\n",
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||
"# Change string to 10:\n",
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"train_dataset[6] = train_dataset[6].replace({\"więcej niż 10\": 10})\n",
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"train_dataset[6] = train_dataset[6].apply(int)\n",
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"\n",
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||
"# Count the occurrence of unique values in column after preprocessing:\n",
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||
"print(\"Value counts after changes:\\n\",train_dataset[6].value_counts())\n",
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||
"\n",
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||
"train_dataset[10].fillna(train_dataset[10].median(), inplace=True)\n",
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||
"train_dataset[10] = train_dataset[10].apply(float)\n",
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||
"\n",
|
||
"train_dataset = train_dataset[[0,6,8,10,15]]\n",
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||
"\n"
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||
]
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},
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{
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||
"cell_type": "code",
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||
"execution_count": 275,
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
|
||
"test_dataset = pd.read_csv(\"./dev-0/in.tsv\", sep= \"\\t\", header=None)"
|
||
]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 278,
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||
"metadata": {},
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"outputs": [
|
||
{
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||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"COLUMN 15:\n",
|
||
"Value counts before changes:\n",
|
||
" 14\n",
|
||
" 1 108\n",
|
||
" 2 89\n",
|
||
" 0 82\n",
|
||
" 4 65\n",
|
||
" 3 54\n",
|
||
" 5 22\n",
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||
" 6 12\n",
|
||
" 7 9\n",
|
||
" 11 9\n",
|
||
" 10 5\n",
|
||
" 8 3\n",
|
||
"-1 2\n",
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||
" 9 2\n",
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||
"Name: count, dtype: int64\n",
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||
"Value counts after changes:\n",
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||
" 14\n",
|
||
" 1 108\n",
|
||
" 2 89\n",
|
||
" 0 82\n",
|
||
" 4 65\n",
|
||
" 3 54\n",
|
||
" 5 22\n",
|
||
" 6 12\n",
|
||
" 7 9\n",
|
||
" 11 9\n",
|
||
" 10 5\n",
|
||
" 8 3\n",
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||
"-1 2\n",
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||
" 9 2\n",
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||
"Name: count, dtype: int64\n",
|
||
"COLUMN 8:\n",
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||
"0 59.10\n",
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||
"1 38.00\n",
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||
"2 63.84\n",
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||
"3 50.00\n",
|
||
"4 65.62\n",
|
||
" ... \n",
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||
"457 72.78\n",
|
||
"458 51.23\n",
|
||
"459 54.16\n",
|
||
"460 90.10\n",
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||
"461 71.90\n",
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||
"Name: 7, Length: 462, dtype: float64\n",
|
||
"COLUMN 6:\n",
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||
"Value counts before changes:\n",
|
||
" 5\n",
|
||
"2 196\n",
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||
"3 152\n",
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||
"1 51\n",
|
||
"4 50\n",
|
||
"5 9\n",
|
||
"6 4\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Value counts after changes:\n",
|
||
" 5\n",
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||
"2 196\n",
|
||
"3 152\n",
|
||
"1 51\n",
|
||
"4 50\n",
|
||
"5 9\n",
|
||
"6 4\n",
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||
"Name: count, dtype: int64\n"
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||
]
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||
}
|
||
],
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||
"source": [
|
||
"\n",
|
||
"# Preprocessing column 15:\n",
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||
"print(\"COLUMN 15:\")\n",
|
||
"# Count the occurrence of unique values in column before preprocessing:\n",
|
||
"print(\"Value counts before changes:\\n\",test_dataset[14].value_counts())\n",
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||
"\n",
|
||
"# Replace string to int or NaN:\n",
|
||
"test_dataset[14] = test_dataset[14].replace({\"parter\": 0, \"suterena\": -1, \"> 10\": 11, \"poddasze\": np.nan})\n",
|
||
"test_dataset[14] = test_dataset[14].apply(float)\n",
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||
"\n",
|
||
"# Fill Nans with median:\n",
|
||
"test_dataset[14].fillna(test_dataset[14].median(), inplace=True)\n",
|
||
"test_dataset[14]= test_dataset[14].apply(int)\n",
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"\n",
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"# Count the occurrence of unique values in column after preprocessing:\n",
|
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"print(\"Value counts after changes:\\n\",test_dataset[14].value_counts())\n",
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"\n",
|
||
"# Preprocessing column 8:\n",
|
||
"print(\"COLUMN 8:\")\n",
|
||
"# Replace strings containing space to NaN:\n",
|
||
"test_dataset[7] = test_dataset[7].replace(' ', np.nan, regex=True)\n",
|
||
"\n",
|
||
"# Fill Nans with median:\n",
|
||
"test_dataset[7] = test_dataset[7].apply(float)\n",
|
||
"test_dataset[7].fillna(test_dataset[7].median(), inplace=True)\n",
|
||
"\n",
|
||
"print(test_dataset[7])\n",
|
||
"\n",
|
||
"# Preprocessing column 6:\n",
|
||
"print(\"COLUMN 6:\")\n",
|
||
"# Count the occurrence of unique values in column before preprocessing:\n",
|
||
"print(\"Value counts before changes:\\n\",test_dataset[5].value_counts())\n",
|
||
"\n",
|
||
"# Change string to 10:\n",
|
||
"test_dataset[5] = test_dataset[5].replace({\"więcej niż 10\": 10})\n",
|
||
"test_dataset[5] = test_dataset[5].apply(int)\n",
|
||
"\n",
|
||
"# Count the occurrence of unique values in column after preprocessing:\n",
|
||
"print(\"Value counts after changes:\\n\",test_dataset[5].value_counts())\n",
|
||
"\n",
|
||
"test_dataset[9].fillna(test_dataset[9].median(), inplace=True)\n",
|
||
"test_dataset[9] = test_dataset[9].apply(float)\n",
|
||
"\n",
|
||
"test_dataset = test_dataset[[5,7,9,14]]\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 305,
|
||
"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>5</th>\n",
|
||
" <th>7</th>\n",
|
||
" <th>9</th>\n",
|
||
" <th>14</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>59.1</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 5 7 9 14\n",
|
||
"0 3 59.1 4.0 2"
|
||
]
|
||
},
|
||
"execution_count": 305,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"test_dataset.head(1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Model"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 234,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.linear_model import LinearRegression\n",
|
||
"from sklearn.preprocessing import StandardScaler"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 291,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"X_train = train_dataset.drop(0,axis=1)\n",
|
||
"y_train = train_dataset[[0]]\n",
|
||
"\n",
|
||
"scaler = StandardScaler()\n",
|
||
"trans_data = scaler.fit_transform(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 292,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"X_test = test_dataset"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 293,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"reg = LinearRegression()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 294,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"reg.fit(X_train, y_train)\n",
|
||
"results = reg.predict(X_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 265,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pickle\n",
|
||
"from sklearn.metrics import r2_score\n",
|
||
"# pickle.dump(reg, open(\"model.pkl\", \"wb\"))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 295,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
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" [ 444493.62218294],\n",
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" [ 514644.81461941],\n",
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" [ 608248.81678902],\n",
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" [1162131.46450679],\n",
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" [ 344572.38851392],\n",
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" [ 641419.64152483],\n",
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||
" [ 406890.71039238],\n",
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||
" [ 330256.77930708],\n",
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" [ 453663.96080749],\n",
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" [1656917.6302292 ],\n",
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" [ 449408.85843111],\n",
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||
" [ 258707.30278287],\n",
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" [ 345745.18314357],\n",
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" [ 310109.76692089],\n",
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" [ 424505.72665604],\n",
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" [ 310095.0475672 ],\n",
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" [ 401125.77300418],\n",
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" [ 457988.13524458],\n",
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" [ 550575.62607344],\n",
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" [ 523303.70234502],\n",
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||
" [ 644515.6686963 ],\n",
|
||
" [ 537987.4698391 ]])"
|
||
]
|
||
},
|
||
"execution_count": 295,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"results"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 301,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
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"<div>\n",
|
||
"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
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|
||
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|
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|
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|
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" <th>0</th>\n",
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||
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|
||
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|
||
" <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",
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||
" </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",
|
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"2 365000.00\n",
|
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"3 369000.00\n",
|
||
"4 483791.00\n",
|
||
".. ...\n",
|
||
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|
||
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|
||
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|
||
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|
||
"461 850000.00\n",
|
||
"\n",
|
||
"[462 rows x 1 columns]"
|
||
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|
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},
|
||
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|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"y_test = pd.read_csv(\"./dev-0/expected.tsv\", header=None)\n",
|
||
"y_test"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 302,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.6393762535622007"
|
||
]
|
||
},
|
||
"execution_count": 302,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"r2_score(y_test, results)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 303,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"71559.96181964973"
|
||
]
|
||
},
|
||
"execution_count": 303,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"mean_absolute_error(y_test, results)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Predykcja dla zbioru testowego"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 317,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"final_test_dataset = pd.read_csv(\"./test-A/in.tsv\", sep= \"\\t\", header=None)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 318,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"COLUMN 15:\n",
|
||
"Value counts before changes:\n",
|
||
" 14\n",
|
||
"1 92\n",
|
||
"parter 70\n",
|
||
"3 68\n",
|
||
"4 64\n",
|
||
"2 61\n",
|
||
"5 15\n",
|
||
"6 11\n",
|
||
"7 7\n",
|
||
"10 5\n",
|
||
"> 10 5\n",
|
||
"9 4\n",
|
||
"8 2\n",
|
||
"suterena 1\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Value counts after changes:\n",
|
||
" 14\n",
|
||
" 1 92\n",
|
||
" 2 74\n",
|
||
" 0 70\n",
|
||
" 3 68\n",
|
||
" 4 64\n",
|
||
" 5 15\n",
|
||
" 6 11\n",
|
||
" 7 7\n",
|
||
" 10 5\n",
|
||
" 11 5\n",
|
||
" 9 4\n",
|
||
" 8 2\n",
|
||
"-1 1\n",
|
||
"Name: count, dtype: int64\n",
|
||
"COLUMN 8:\n",
|
||
"0 61.99\n",
|
||
"1 64.00\n",
|
||
"2 51.15\n",
|
||
"3 45.77\n",
|
||
"4 44.36\n",
|
||
" ... \n",
|
||
"413 34.97\n",
|
||
"414 49.06\n",
|
||
"415 76.71\n",
|
||
"416 72.63\n",
|
||
"417 65.84\n",
|
||
"Name: 7, Length: 418, dtype: float64\n",
|
||
"COLUMN 6:\n",
|
||
"Value counts before changes:\n",
|
||
" 5\n",
|
||
"2 175\n",
|
||
"3 143\n",
|
||
"4 50\n",
|
||
"1 40\n",
|
||
"5 6\n",
|
||
"6 2\n",
|
||
"więcej niż 10 1\n",
|
||
"8 1\n",
|
||
"Name: count, dtype: int64\n",
|
||
"Value counts after changes:\n",
|
||
" 5\n",
|
||
"2 175\n",
|
||
"3 143\n",
|
||
"4 50\n",
|
||
"1 40\n",
|
||
"5 6\n",
|
||
"6 2\n",
|
||
"10 1\n",
|
||
"8 1\n",
|
||
"Name: count, dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# Preprocessing column 15:\n",
|
||
"print(\"COLUMN 15:\")\n",
|
||
"# Count the occurrence of unique values in column before preprocessing:\n",
|
||
"print(\"Value counts before changes:\\n\",final_test_dataset[14].value_counts())\n",
|
||
"\n",
|
||
"# Replace string to int or NaN:\n",
|
||
"final_test_dataset[14] = final_test_dataset[14].replace({\"parter\": 0, \"suterena\": -1, \"> 10\": 11, \"poddasze\": np.nan})\n",
|
||
"final_test_dataset[14] = final_test_dataset[14].apply(float)\n",
|
||
"\n",
|
||
"# Fill Nans with median:\n",
|
||
"final_test_dataset[14].fillna(final_test_dataset[14].median(), inplace=True)\n",
|
||
"final_test_dataset[14]= final_test_dataset[14].apply(int)\n",
|
||
"\n",
|
||
"# Count the occurrence of unique values in column after preprocessing:\n",
|
||
"print(\"Value counts after changes:\\n\",final_test_dataset[14].value_counts())\n",
|
||
"\n",
|
||
"# Preprocessing column 8:\n",
|
||
"print(\"COLUMN 8:\")\n",
|
||
"# Replace strings containing space to NaN:\n",
|
||
"final_test_dataset[7] = final_test_dataset[7].replace(' ', np.nan, regex=True)\n",
|
||
"\n",
|
||
"# Fill Nans with median:\n",
|
||
"final_test_dataset[7] = final_test_dataset[7].apply(float)\n",
|
||
"final_test_dataset[7].fillna(final_test_dataset[7].median(), inplace=True)\n",
|
||
"\n",
|
||
"print(final_test_dataset[7])\n",
|
||
"\n",
|
||
"# Preprocessing column 6:\n",
|
||
"print(\"COLUMN 6:\")\n",
|
||
"# Count the occurrence of unique values in column before preprocessing:\n",
|
||
"print(\"Value counts before changes:\\n\",final_test_dataset[5].value_counts())\n",
|
||
"\n",
|
||
"# Change string to 10:\n",
|
||
"final_test_dataset[5] = final_test_dataset[5].replace({\"więcej niż 10\": 10})\n",
|
||
"final_test_dataset[5] = final_test_dataset[5].apply(int)\n",
|
||
"\n",
|
||
"# Count the occurrence of unique values in column after preprocessing:\n",
|
||
"print(\"Value counts after changes:\\n\",final_test_dataset[5].value_counts())\n",
|
||
"\n",
|
||
"final_test_dataset[9].fillna(final_test_dataset[9].median(), inplace=True)\n",
|
||
"final_test_dataset[9] = final_test_dataset[9].apply(float)\n",
|
||
"\n",
|
||
"final_test_dataset = final_test_dataset[[5,7,9,14]]\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 319,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>5</th>\n",
|
||
" <th>7</th>\n",
|
||
" <th>9</th>\n",
|
||
" <th>14</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>61.99</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>64.00</td>\n",
|
||
" <td>4.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>51.15</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>45.77</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>44.36</td>\n",
|
||
" <td>13.0</td>\n",
|
||
" <td>5</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>413</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>34.97</td>\n",
|
||
" <td>8.0</td>\n",
|
||
" <td>4</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>414</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>49.06</td>\n",
|
||
" <td>3.0</td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>415</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>76.71</td>\n",
|
||
" <td>5.0</td>\n",
|
||
" <td>3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>416</th>\n",
|
||
" <td>3</td>\n",
|
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