{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## IUM_02"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "da5635319c1475f3"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Wymagane zależności"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "5c88bd65c24cfc75"
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [],
   "source": [
    "# Instalacja wymaganych zależności\n",
    "!pip install kaggle\n",
    "!pip install pandas\n",
    "!pip install scikit-learn"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:06:25.947706400Z",
     "start_time": "2024-03-15T19:06:25.928113300Z"
    }
   },
   "id": "ae6cca2241835fba"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Import bibliotek"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "ba9581e73648e5c3"
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# Import bibliotek\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:35.877258600Z",
     "start_time": "2024-03-15T19:05:35.821429600Z"
    }
   },
   "id": "5db08fde342b5463"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 1. Pobieranie zbioru danych"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2000b14bbb95a446"
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading breast-cancer-wisconsin-data.zip to C:\\Users\\broke\\PycharmProjects\\ium_464863\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0.00/48.6k [00:00<?, ?B/s]\n",
      "100%|##########| 48.6k/48.6k [00:00<00:00, 270kB/s]\n",
      "100%|##########| 48.6k/48.6k [00:00<00:00, 268kB/s]\n"
     ]
    }
   ],
   "source": [
    "# Pobranie zbioru danych\n",
    "!kaggle datasets download -d uciml/breast-cancer-wisconsin-data\n",
    "\n",
    "# Wypakowanie archiwum\n",
    "!tar -xf breast-cancer-wisconsin-data.zip"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:44.855185900Z",
     "start_time": "2024-03-15T19:05:35.842341200Z"
    }
   },
   "id": "679faee0f0f27fa1"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 2. Wczytanie danych oraz wstępne przetworzenie"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "2aa06e9443f948c9"
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "# Wczytanie danych, ustawienie kolumny 'id' jako indeks\n",
    "df = pd.read_csv('data.csv', index_col='id')\n",
    "\n",
    "# Usunięcie niepotrzebnych/błędnych kolumn\n",
    "df = df.drop(columns=['Unnamed: 32'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:44.915248600Z",
     "start_time": "2024-03-15T19:05:44.855185900Z"
    }
   },
   "id": "2bc8c4be37c8fa69"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "diagnosis                  0\nradius_mean                0\ntexture_mean               0\nperimeter_mean             0\narea_mean                  0\nsmoothness_mean            0\ncompactness_mean           0\nconcavity_mean             0\nconcave points_mean        0\nsymmetry_mean              0\nfractal_dimension_mean     0\nradius_se                  0\ntexture_se                 0\nperimeter_se               0\narea_se                    0\nsmoothness_se              0\ncompactness_se             0\nconcavity_se               0\nconcave points_se          0\nsymmetry_se                0\nfractal_dimension_se       0\nradius_worst               0\ntexture_worst              0\nperimeter_worst            0\narea_worst                 0\nsmoothness_worst           0\ncompactness_worst          0\nconcavity_worst            0\nconcave points_worst       0\nsymmetry_worst             0\nfractal_dimension_worst    0\ndtype: int64"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sprawdzenie czy istnieją wartości brakujące (NaN)\n",
    "df.isnull().sum()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:44.961022900Z",
     "start_time": "2024-03-15T19:05:44.916665400Z"
    }
   },
   "id": "abcf61d13e9b47f1"
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "         diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\nid                                                                         \n842302           M        17.99         10.38          122.80     1001.0   \n842517           M        20.57         17.77          132.90     1326.0   \n84300903         M        19.69         21.25          130.00     1203.0   \n84348301         M        11.42         20.38           77.58      386.1   \n84358402         M        20.29         14.34          135.10     1297.0   \n\n          smoothness_mean  compactness_mean  concavity_mean  \\\nid                                                            \n842302            0.11840           0.27760          0.3001   \n842517            0.08474           0.07864          0.0869   \n84300903          0.10960           0.15990          0.1974   \n84348301          0.14250           0.28390          0.2414   \n84358402          0.10030           0.13280          0.1980   \n\n          concave points_mean  symmetry_mean  ...  radius_worst  \\\nid                                            ...                 \n842302                0.14710         0.2419  ...         25.38   \n842517                0.07017         0.1812  ...         24.99   \n84300903              0.12790         0.2069  ...         23.57   \n84348301              0.10520         0.2597  ...         14.91   \n84358402              0.10430         0.1809  ...         22.54   \n\n          texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\nid                                                                       \n842302            17.33           184.60      2019.0            0.1622   \n842517            23.41           158.80      1956.0            0.1238   \n84300903          25.53           152.50      1709.0            0.1444   \n84348301          26.50            98.87       567.7            0.2098   \n84358402          16.67           152.20      1575.0            0.1374   \n\n          compactness_worst  concavity_worst  concave points_worst  \\\nid                                                                   \n842302               0.6656           0.7119                0.2654   \n842517               0.1866           0.2416                0.1860   \n84300903             0.4245           0.4504                0.2430   \n84348301             0.8663           0.6869                0.2575   \n84358402             0.2050           0.4000                0.1625   \n\n          symmetry_worst  fractal_dimension_worst  \nid                                                 \n842302            0.4601                  0.11890  \n842517            0.2750                  0.08902  \n84300903          0.3613                  0.08758  \n84348301          0.6638                  0.17300  \n84358402          0.2364                  0.07678  \n\n[5 rows x 31 columns]",
      "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>diagnosis</th>\n      <th>radius_mean</th>\n      <th>texture_mean</th>\n      <th>perimeter_mean</th>\n      <th>area_mean</th>\n      <th>smoothness_mean</th>\n      <th>compactness_mean</th>\n      <th>concavity_mean</th>\n      <th>concave points_mean</th>\n      <th>symmetry_mean</th>\n      <th>...</th>\n      <th>radius_worst</th>\n      <th>texture_worst</th>\n      <th>perimeter_worst</th>\n      <th>area_worst</th>\n      <th>smoothness_worst</th>\n      <th>compactness_worst</th>\n      <th>concavity_worst</th>\n      <th>concave points_worst</th>\n      <th>symmetry_worst</th>\n      <th>fractal_dimension_worst</th>\n    </tr>\n    <tr>\n      <th>id</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>842302</th>\n      <td>M</td>\n      <td>17.99</td>\n      <td>10.38</td>\n      <td>122.80</td>\n      <td>1001.0</td>\n      <td>0.11840</td>\n      <td>0.27760</td>\n      <td>0.3001</td>\n      <td>0.14710</td>\n      <td>0.2419</td>\n      <td>...</td>\n      <td>25.38</td>\n      <td>17.33</td>\n      <td>184.60</td>\n      <td>2019.0</td>\n      <td>0.1622</td>\n      <td>0.6656</td>\n      <td>0.7119</td>\n      <td>0.2654</td>\n      <td>0.4601</td>\n      <td>0.11890</td>\n    </tr>\n    <tr>\n      <th>842517</th>\n      <td>M</td>\n      <td>20.57</td>\n      <td>17.77</td>\n      <td>132.90</td>\n      <td>1326.0</td>\n      <td>0.08474</td>\n      <td>0.07864</td>\n      <td>0.0869</td>\n      <td>0.07017</td>\n      <td>0.1812</td>\n      <td>...</td>\n      <td>24.99</td>\n      <td>23.41</td>\n      <td>158.80</td>\n      <td>1956.0</td>\n      <td>0.1238</td>\n      <td>0.1866</td>\n      <td>0.2416</td>\n      <td>0.1860</td>\n      <td>0.2750</td>\n      <td>0.08902</td>\n    </tr>\n    <tr>\n      <th>84300903</th>\n      <td>M</td>\n      <td>19.69</td>\n      <td>21.25</td>\n      <td>130.00</td>\n      <td>1203.0</td>\n      <td>0.10960</td>\n      <td>0.15990</td>\n      <td>0.1974</td>\n      <td>0.12790</td>\n      <td>0.2069</td>\n      <td>...</td>\n      <td>23.57</td>\n      <td>25.53</td>\n      <td>152.50</td>\n      <td>1709.0</td>\n      <td>0.1444</td>\n      <td>0.4245</td>\n      <td>0.4504</td>\n      <td>0.2430</td>\n      <td>0.3613</td>\n      <td>0.08758</td>\n    </tr>\n    <tr>\n      <th>84348301</th>\n      <td>M</td>\n      <td>11.42</td>\n      <td>20.38</td>\n      <td>77.58</td>\n      <td>386.1</td>\n      <td>0.14250</td>\n      <td>0.28390</td>\n      <td>0.2414</td>\n      <td>0.10520</td>\n      <td>0.2597</td>\n      <td>...</td>\n      <td>14.91</td>\n      <td>26.50</td>\n      <td>98.87</td>\n      <td>567.7</td>\n      <td>0.2098</td>\n      <td>0.8663</td>\n      <td>0.6869</td>\n      <td>0.2575</td>\n      <td>0.6638</td>\n      <td>0.17300</td>\n    </tr>\n    <tr>\n      <th>84358402</th>\n      <td>M</td>\n      <td>20.29</td>\n      <td>14.34</td>\n      <td>135.10</td>\n      <td>1297.0</td>\n      <td>0.10030</td>\n      <td>0.13280</td>\n      <td>0.1980</td>\n      <td>0.10430</td>\n      <td>0.1809</td>\n      <td>...</td>\n      <td>22.54</td>\n      <td>16.67</td>\n      <td>152.20</td>\n      <td>1575.0</td>\n      <td>0.1374</td>\n      <td>0.2050</td>\n      <td>0.4000</td>\n      <td>0.1625</td>\n      <td>0.2364</td>\n      <td>0.07678</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 31 columns</p>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wyświetlenie 5 pierwszych wierszy\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:45.015712300Z",
     "start_time": "2024-03-15T19:05:44.947489400Z"
    }
   },
   "id": "db9cfc5e73a4da57"
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "# Normalizacja cech do wartości z przedziału [0, 1]\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "df[df.columns[1:]] = scaler.fit_transform(df[df.columns[1:]])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:45.023766200Z",
     "start_time": "2024-03-15T19:05:44.995971700Z"
    }
   },
   "id": "f8513c47a4a1f844"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### 3. Podział danych na zbiór treningowy, walidacyjny i testowy"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "7d74496029e594b1"
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "# Podział zbioru na zbiór treningowy, walidacyjny i testowy w proporcji 80/10/10\n",
    "df_train, df_val_test = train_test_split(df, test_size=0.2, random_state=1234)\n",
    "df_val, df_test = train_test_split(df_val_test, test_size=0.5, random_state=1234)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:45.111265300Z",
     "start_time": "2024-03-15T19:05:45.028046500Z"
    }
   },
   "id": "651b6bf8d1dd8e6d"
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cały zbiór: 569 wierszy, 31 kolumn\n",
      "Zbiór treningowy: 455 wierszy, 31 kolumn\n",
      "Zbiór walidacyjny: 57 wierszy, 31 kolumn\n",
      "Zbiór testowy: 57 wierszy, 31 kolumn\n"
     ]
    }
   ],
   "source": [
    "# Wymiary zbiorów i podzbiorów\n",
    "print(f\"Cały zbiór: {df.shape[0]} wierszy, {df.shape[1]} kolumn\")\n",
    "print(f\"Zbiór treningowy: {df_train.shape[0]} wierszy, {df_train.shape[1]} kolumn\")\n",
    "print(f\"Zbiór walidacyjny: {df_val.shape[0]} wierszy, {df_val.shape[1]} kolumn\")\n",
    "print(f\"Zbiór testowy: {df_test.shape[0]} wierszy, {df_test.shape[1]} kolumn\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:45.168725700Z",
     "start_time": "2024-03-15T19:05:45.058794500Z"
    }
   },
   "id": "97f1bdbc7597c39f"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Statystyki dla cech numerycznych (średnia, odchylenie standardowe, min, max, kwantyle)"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "9014307b7d26b73f"
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "       radius_mean  texture_mean  perimeter_mean   area_mean  smoothness_mean  \\\ncount   569.000000    569.000000      569.000000  569.000000       569.000000   \nmean      0.338222      0.323965        0.332935    0.216920         0.394785   \nstd       0.166787      0.145453        0.167915    0.149274         0.126967   \nmin       0.000000      0.000000        0.000000    0.000000         0.000000   \n25%       0.223342      0.218465        0.216847    0.117413         0.304595   \n50%       0.302381      0.308759        0.293345    0.172895         0.390358   \n75%       0.416442      0.408860        0.416765    0.271135         0.475490   \nmax       1.000000      1.000000        1.000000    1.000000         1.000000   \n\n       compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\ncount        569.000000      569.000000           569.000000     569.000000   \nmean           0.260601        0.208058             0.243137       0.379605   \nstd            0.161992        0.186785             0.192857       0.138456   \nmin            0.000000        0.000000             0.000000       0.000000   \n25%            0.139685        0.069260             0.100944       0.282323   \n50%            0.224679        0.144189             0.166501       0.369697   \n75%            0.340531        0.306232             0.367793       0.453030   \nmax            1.000000        1.000000             1.000000       1.000000   \n\n       fractal_dimension_mean  ...  radius_worst  texture_worst  \\\ncount              569.000000  ...    569.000000     569.000000   \nmean                 0.270379  ...      0.296663       0.363998   \nstd                  0.148702  ...      0.171940       0.163813   \nmin                  0.000000  ...      0.000000       0.000000   \n25%                  0.163016  ...      0.180719       0.241471   \n50%                  0.243892  ...      0.250445       0.356876   \n75%                  0.340354  ...      0.386339       0.471748   \nmax                  1.000000  ...      1.000000       1.000000   \n\n       perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\ncount       569.000000  569.000000        569.000000         569.000000   \nmean          0.283138    0.170906          0.404138           0.220212   \nstd           0.167352    0.139932          0.150779           0.152649   \nmin           0.000000    0.000000          0.000000           0.000000   \n25%           0.167837    0.081130          0.300007           0.116337   \n50%           0.235320    0.123206          0.397081           0.179110   \n75%           0.373475    0.220901          0.494156           0.302520   \nmax           1.000000    1.000000          1.000000           1.000000   \n\n       concavity_worst  concave points_worst  symmetry_worst  \\\ncount       569.000000            569.000000      569.000000   \nmean          0.217403              0.393836        0.263307   \nstd           0.166633              0.225884        0.121954   \nmin           0.000000              0.000000        0.000000   \n25%           0.091454              0.223127        0.185098   \n50%           0.181070              0.343402        0.247782   \n75%           0.305831              0.554639        0.318155   \nmax           1.000000              1.000000        1.000000   \n\n       fractal_dimension_worst  \ncount               569.000000  \nmean                  0.189596  \nstd                   0.118466  \nmin                   0.000000  \n25%                   0.107700  \n50%                   0.163977  \n75%                   0.242949  \nmax                   1.000000  \n\n[8 rows x 30 columns]",
      "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>radius_mean</th>\n      <th>texture_mean</th>\n      <th>perimeter_mean</th>\n      <th>area_mean</th>\n      <th>smoothness_mean</th>\n      <th>compactness_mean</th>\n      <th>concavity_mean</th>\n      <th>concave points_mean</th>\n      <th>symmetry_mean</th>\n      <th>fractal_dimension_mean</th>\n      <th>...</th>\n      <th>radius_worst</th>\n      <th>texture_worst</th>\n      <th>perimeter_worst</th>\n      <th>area_worst</th>\n      <th>smoothness_worst</th>\n      <th>compactness_worst</th>\n      <th>concavity_worst</th>\n      <th>concave points_worst</th>\n      <th>symmetry_worst</th>\n      <th>fractal_dimension_worst</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>...</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.338222</td>\n      <td>0.323965</td>\n      <td>0.332935</td>\n      <td>0.216920</td>\n      <td>0.394785</td>\n      <td>0.260601</td>\n      <td>0.208058</td>\n      <td>0.243137</td>\n      <td>0.379605</td>\n      <td>0.270379</td>\n      <td>...</td>\n      <td>0.296663</td>\n      <td>0.363998</td>\n      <td>0.283138</td>\n      <td>0.170906</td>\n      <td>0.404138</td>\n      <td>0.220212</td>\n      <td>0.217403</td>\n      <td>0.393836</td>\n      <td>0.263307</td>\n      <td>0.189596</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.166787</td>\n      <td>0.145453</td>\n      <td>0.167915</td>\n      <td>0.149274</td>\n      <td>0.126967</td>\n      <td>0.161992</td>\n      <td>0.186785</td>\n      <td>0.192857</td>\n      <td>0.138456</td>\n      <td>0.148702</td>\n      <td>...</td>\n      <td>0.171940</td>\n      <td>0.163813</td>\n      <td>0.167352</td>\n      <td>0.139932</td>\n      <td>0.150779</td>\n      <td>0.152649</td>\n      <td>0.166633</td>\n      <td>0.225884</td>\n      <td>0.121954</td>\n      <td>0.118466</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.223342</td>\n      <td>0.218465</td>\n      <td>0.216847</td>\n      <td>0.117413</td>\n      <td>0.304595</td>\n      <td>0.139685</td>\n      <td>0.069260</td>\n      <td>0.100944</td>\n      <td>0.282323</td>\n      <td>0.163016</td>\n      <td>...</td>\n      <td>0.180719</td>\n      <td>0.241471</td>\n      <td>0.167837</td>\n      <td>0.081130</td>\n      <td>0.300007</td>\n      <td>0.116337</td>\n      <td>0.091454</td>\n      <td>0.223127</td>\n      <td>0.185098</td>\n      <td>0.107700</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.302381</td>\n      <td>0.308759</td>\n      <td>0.293345</td>\n      <td>0.172895</td>\n      <td>0.390358</td>\n      <td>0.224679</td>\n      <td>0.144189</td>\n      <td>0.166501</td>\n      <td>0.369697</td>\n      <td>0.243892</td>\n      <td>...</td>\n      <td>0.250445</td>\n      <td>0.356876</td>\n      <td>0.235320</td>\n      <td>0.123206</td>\n      <td>0.397081</td>\n      <td>0.179110</td>\n      <td>0.181070</td>\n      <td>0.343402</td>\n      <td>0.247782</td>\n      <td>0.163977</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.416442</td>\n      <td>0.408860</td>\n      <td>0.416765</td>\n      <td>0.271135</td>\n      <td>0.475490</td>\n      <td>0.340531</td>\n      <td>0.306232</td>\n      <td>0.367793</td>\n      <td>0.453030</td>\n      <td>0.340354</td>\n      <td>...</td>\n      <td>0.386339</td>\n      <td>0.471748</td>\n      <td>0.373475</td>\n      <td>0.220901</td>\n      <td>0.494156</td>\n      <td>0.302520</td>\n      <td>0.305831</td>\n      <td>0.554639</td>\n      <td>0.318155</td>\n      <td>0.242949</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 30 columns</p>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cały zbiór\n",
    "df.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:45.568275400Z",
     "start_time": "2024-03-15T19:05:45.073564100Z"
    }
   },
   "id": "4f0c97b4de052a0c"
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "       radius_mean  texture_mean  perimeter_mean   area_mean  smoothness_mean  \\\ncount   455.000000    455.000000      455.000000  455.000000       455.000000   \nmean      0.338949      0.326381        0.333644    0.217261         0.395892   \nstd       0.165349      0.145664        0.166246    0.147801         0.126845   \nmin       0.000000      0.022658        0.000000    0.000000         0.000000   \n25%       0.225235      0.219817        0.219128    0.119321         0.304776   \n50%       0.300961      0.310450        0.295833    0.170859         0.389636   \n75%       0.415259      0.411397        0.414346    0.271113         0.476393   \nmax       1.000000      0.815015        1.000000    0.999152         0.831182   \n\n       compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\ncount        455.000000      455.000000           455.000000     455.000000   \nmean           0.260580        0.209922             0.243100       0.381150   \nstd            0.160494        0.187617             0.192341       0.136984   \nmin            0.000000        0.000000             0.000000       0.000000   \n25%            0.136096        0.068030             0.100497       0.283081   \n50%            0.230262        0.145150             0.168191       0.369697   \n75%            0.340991        0.310098             0.357952       0.453030   \nmax            0.895712        1.000000             1.000000       1.000000   \n\n       fractal_dimension_mean  ...  radius_worst  texture_worst  \\\ncount              455.000000  ...    455.000000     455.000000   \nmean                 0.270577  ...      0.297118       0.369023   \nstd                  0.147338  ...      0.170654       0.166060   \nmin                  0.000000  ...      0.000000       0.012527   \n25%                  0.168176  ...      0.182675       0.248801   \n50%                  0.241786  ...      0.250445       0.358742   \n75%                  0.340354  ...      0.377090       0.481343   \nmax                  1.000000  ...      0.896478       1.000000   \n\n       perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\ncount       455.000000  455.000000        455.000000         455.000000   \nmean          0.284164    0.171085          0.407890           0.221950   \nstd           0.166564    0.138560          0.153644           0.156748   \nmin           0.000000    0.000000          0.000000           0.000000   \n25%           0.169929    0.081781          0.303308           0.116871   \n50%           0.235370    0.123206          0.396421           0.181244   \n75%           0.369740    0.209964          0.496467           0.299318   \nmax           0.890931    0.797975          1.000000           1.000000   \n\n       concavity_worst  concave points_worst  symmetry_worst  \\\ncount       455.000000            455.000000      455.000000   \nmean          0.221686              0.394672        0.263384   \nstd           0.172039              0.226663        0.119284   \nmin           0.000000              0.000000        0.000000   \n25%           0.092212              0.221753        0.188252   \n50%           0.184505              0.347079        0.248571   \n75%           0.309265              0.558935        0.317465   \nmax           1.000000              1.000000        1.000000   \n\n       fractal_dimension_worst  \ncount               455.000000  \nmean                  0.191552  \nstd                   0.122769  \nmin                   0.000000  \n25%                   0.107110  \n50%                   0.164305  \n75%                   0.242785  \nmax                   1.000000  \n\n[8 rows x 30 columns]",
      "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>radius_mean</th>\n      <th>texture_mean</th>\n      <th>perimeter_mean</th>\n      <th>area_mean</th>\n      <th>smoothness_mean</th>\n      <th>compactness_mean</th>\n      <th>concavity_mean</th>\n      <th>concave points_mean</th>\n      <th>symmetry_mean</th>\n      <th>fractal_dimension_mean</th>\n      <th>...</th>\n      <th>radius_worst</th>\n      <th>texture_worst</th>\n      <th>perimeter_worst</th>\n      <th>area_worst</th>\n      <th>smoothness_worst</th>\n      <th>compactness_worst</th>\n      <th>concavity_worst</th>\n      <th>concave points_worst</th>\n      <th>symmetry_worst</th>\n      <th>fractal_dimension_worst</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>...</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n      <td>455.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.338949</td>\n      <td>0.326381</td>\n      <td>0.333644</td>\n      <td>0.217261</td>\n      <td>0.395892</td>\n      <td>0.260580</td>\n      <td>0.209922</td>\n      <td>0.243100</td>\n      <td>0.381150</td>\n      <td>0.270577</td>\n      <td>...</td>\n      <td>0.297118</td>\n      <td>0.369023</td>\n      <td>0.284164</td>\n      <td>0.171085</td>\n      <td>0.407890</td>\n      <td>0.221950</td>\n      <td>0.221686</td>\n      <td>0.394672</td>\n      <td>0.263384</td>\n      <td>0.191552</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.165349</td>\n      <td>0.145664</td>\n      <td>0.166246</td>\n      <td>0.147801</td>\n      <td>0.126845</td>\n      <td>0.160494</td>\n      <td>0.187617</td>\n      <td>0.192341</td>\n      <td>0.136984</td>\n      <td>0.147338</td>\n      <td>...</td>\n      <td>0.170654</td>\n      <td>0.166060</td>\n      <td>0.166564</td>\n      <td>0.138560</td>\n      <td>0.153644</td>\n      <td>0.156748</td>\n      <td>0.172039</td>\n      <td>0.226663</td>\n      <td>0.119284</td>\n      <td>0.122769</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.022658</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.012527</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.225235</td>\n      <td>0.219817</td>\n      <td>0.219128</td>\n      <td>0.119321</td>\n      <td>0.304776</td>\n      <td>0.136096</td>\n      <td>0.068030</td>\n      <td>0.100497</td>\n      <td>0.283081</td>\n      <td>0.168176</td>\n      <td>...</td>\n      <td>0.182675</td>\n      <td>0.248801</td>\n      <td>0.169929</td>\n      <td>0.081781</td>\n      <td>0.303308</td>\n      <td>0.116871</td>\n      <td>0.092212</td>\n      <td>0.221753</td>\n      <td>0.188252</td>\n      <td>0.107110</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.300961</td>\n      <td>0.310450</td>\n      <td>0.295833</td>\n      <td>0.170859</td>\n      <td>0.389636</td>\n      <td>0.230262</td>\n      <td>0.145150</td>\n      <td>0.168191</td>\n      <td>0.369697</td>\n      <td>0.241786</td>\n      <td>...</td>\n      <td>0.250445</td>\n      <td>0.358742</td>\n      <td>0.235370</td>\n      <td>0.123206</td>\n      <td>0.396421</td>\n      <td>0.181244</td>\n      <td>0.184505</td>\n      <td>0.347079</td>\n      <td>0.248571</td>\n      <td>0.164305</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.415259</td>\n      <td>0.411397</td>\n      <td>0.414346</td>\n      <td>0.271113</td>\n      <td>0.476393</td>\n      <td>0.340991</td>\n      <td>0.310098</td>\n      <td>0.357952</td>\n      <td>0.453030</td>\n      <td>0.340354</td>\n      <td>...</td>\n      <td>0.377090</td>\n      <td>0.481343</td>\n      <td>0.369740</td>\n      <td>0.209964</td>\n      <td>0.496467</td>\n      <td>0.299318</td>\n      <td>0.309265</td>\n      <td>0.558935</td>\n      <td>0.317465</td>\n      <td>0.242785</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>0.815015</td>\n      <td>1.000000</td>\n      <td>0.999152</td>\n      <td>0.831182</td>\n      <td>0.895712</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>0.896478</td>\n      <td>1.000000</td>\n      <td>0.890931</td>\n      <td>0.797975</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 30 columns</p>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Zbiór treningowy\n",
    "df_train.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:45.578805Z",
     "start_time": "2024-03-15T19:05:45.179260600Z"
    }
   },
   "id": "a6594457c1209a45"
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "       radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\ncount    57.000000     57.000000       57.000000  57.000000        57.000000   \nmean      0.334850      0.315985        0.331800   0.216332         0.399432   \nstd       0.176956      0.119165        0.181708   0.171264         0.144361   \nmin       0.089782      0.106865        0.089489   0.041357         0.167193   \n25%       0.225709      0.226581        0.223205   0.117413         0.293220   \n50%       0.282976      0.290159        0.279110   0.161909         0.396678   \n75%       0.413129      0.389922        0.405017   0.260912         0.471879   \nmax       0.967343      0.623267        0.988943   1.000000         1.000000   \n\n       compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\ncount         57.000000       57.000000            57.000000      57.000000   \nmean           0.279956        0.216035             0.254466       0.387684   \nstd            0.184302        0.208557             0.203610       0.160009   \nmin            0.046500        0.003622             0.027793       0.078283   \n25%            0.165235        0.076406             0.112326       0.282323   \n50%            0.222195        0.127413             0.154026       0.381818   \n75%            0.359242        0.296626             0.371918       0.464141   \nmax            1.000000        0.879569             0.839463       0.932323   \n\n       fractal_dimension_mean  ...  radius_worst  texture_worst  \\\ncount               57.000000  ...     57.000000      57.000000   \nmean                 0.281045  ...      0.294395       0.357428   \nstd                  0.161084  ...      0.180500       0.130661   \nmin                  0.047810  ...      0.072963       0.116205   \n25%                  0.169545  ...      0.180719       0.267058   \n50%                  0.258214  ...      0.225187       0.348348   \n75%                  0.342249  ...      0.355034       0.463486   \nmax                  0.949031  ...      1.000000       0.619670   \n\n       perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\ncount        57.000000   57.000000         57.000000          57.000000   \nmean          0.281028    0.170796          0.391354           0.222054   \nstd           0.179389    0.161083          0.140480           0.133225   \nmin           0.074008    0.028264          0.085320           0.019239   \n25%           0.162757    0.082653          0.301327           0.126233   \n50%           0.228597    0.109836          0.416232           0.182893   \n75%           0.343593    0.197454          0.484911           0.308632   \nmax           1.000000    1.000000          0.786040           0.571557   \n\n       concavity_worst  concave points_worst  symmetry_worst  \\\ncount        57.000000             57.000000       57.000000   \nmean          0.209888              0.405999        0.276614   \nstd           0.152149              0.214849        0.143775   \nmin           0.006176              0.095670        0.066233   \n25%           0.107827              0.248179        0.198502   \n50%           0.164537              0.352234        0.234969   \n75%           0.312380              0.517182        0.323674   \nmax           0.613498              0.902062        0.829687   \n\n       fractal_dimension_worst  \ncount                57.000000  \nmean                  0.187256  \nstd                   0.103423  \nmin                   0.028073  \n25%                   0.107700  \n50%                   0.155910  \n75%                   0.260724  \nmax                   0.460186  \n\n[8 rows x 30 columns]",
      "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>radius_mean</th>\n      <th>texture_mean</th>\n      <th>perimeter_mean</th>\n      <th>area_mean</th>\n      <th>smoothness_mean</th>\n      <th>compactness_mean</th>\n      <th>concavity_mean</th>\n      <th>concave points_mean</th>\n      <th>symmetry_mean</th>\n      <th>fractal_dimension_mean</th>\n      <th>...</th>\n      <th>radius_worst</th>\n      <th>texture_worst</th>\n      <th>perimeter_worst</th>\n      <th>area_worst</th>\n      <th>smoothness_worst</th>\n      <th>compactness_worst</th>\n      <th>concavity_worst</th>\n      <th>concave points_worst</th>\n      <th>symmetry_worst</th>\n      <th>fractal_dimension_worst</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>...</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.334850</td>\n      <td>0.315985</td>\n      <td>0.331800</td>\n      <td>0.216332</td>\n      <td>0.399432</td>\n      <td>0.279956</td>\n      <td>0.216035</td>\n      <td>0.254466</td>\n      <td>0.387684</td>\n      <td>0.281045</td>\n      <td>...</td>\n      <td>0.294395</td>\n      <td>0.357428</td>\n      <td>0.281028</td>\n      <td>0.170796</td>\n      <td>0.391354</td>\n      <td>0.222054</td>\n      <td>0.209888</td>\n      <td>0.405999</td>\n      <td>0.276614</td>\n      <td>0.187256</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.176956</td>\n      <td>0.119165</td>\n      <td>0.181708</td>\n      <td>0.171264</td>\n      <td>0.144361</td>\n      <td>0.184302</td>\n      <td>0.208557</td>\n      <td>0.203610</td>\n      <td>0.160009</td>\n      <td>0.161084</td>\n      <td>...</td>\n      <td>0.180500</td>\n      <td>0.130661</td>\n      <td>0.179389</td>\n      <td>0.161083</td>\n      <td>0.140480</td>\n      <td>0.133225</td>\n      <td>0.152149</td>\n      <td>0.214849</td>\n      <td>0.143775</td>\n      <td>0.103423</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.089782</td>\n      <td>0.106865</td>\n      <td>0.089489</td>\n      <td>0.041357</td>\n      <td>0.167193</td>\n      <td>0.046500</td>\n      <td>0.003622</td>\n      <td>0.027793</td>\n      <td>0.078283</td>\n      <td>0.047810</td>\n      <td>...</td>\n      <td>0.072963</td>\n      <td>0.116205</td>\n      <td>0.074008</td>\n      <td>0.028264</td>\n      <td>0.085320</td>\n      <td>0.019239</td>\n      <td>0.006176</td>\n      <td>0.095670</td>\n      <td>0.066233</td>\n      <td>0.028073</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.225709</td>\n      <td>0.226581</td>\n      <td>0.223205</td>\n      <td>0.117413</td>\n      <td>0.293220</td>\n      <td>0.165235</td>\n      <td>0.076406</td>\n      <td>0.112326</td>\n      <td>0.282323</td>\n      <td>0.169545</td>\n      <td>...</td>\n      <td>0.180719</td>\n      <td>0.267058</td>\n      <td>0.162757</td>\n      <td>0.082653</td>\n      <td>0.301327</td>\n      <td>0.126233</td>\n      <td>0.107827</td>\n      <td>0.248179</td>\n      <td>0.198502</td>\n      <td>0.107700</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.282976</td>\n      <td>0.290159</td>\n      <td>0.279110</td>\n      <td>0.161909</td>\n      <td>0.396678</td>\n      <td>0.222195</td>\n      <td>0.127413</td>\n      <td>0.154026</td>\n      <td>0.381818</td>\n      <td>0.258214</td>\n      <td>...</td>\n      <td>0.225187</td>\n      <td>0.348348</td>\n      <td>0.228597</td>\n      <td>0.109836</td>\n      <td>0.416232</td>\n      <td>0.182893</td>\n      <td>0.164537</td>\n      <td>0.352234</td>\n      <td>0.234969</td>\n      <td>0.155910</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.413129</td>\n      <td>0.389922</td>\n      <td>0.405017</td>\n      <td>0.260912</td>\n      <td>0.471879</td>\n      <td>0.359242</td>\n      <td>0.296626</td>\n      <td>0.371918</td>\n      <td>0.464141</td>\n      <td>0.342249</td>\n      <td>...</td>\n      <td>0.355034</td>\n      <td>0.463486</td>\n      <td>0.343593</td>\n      <td>0.197454</td>\n      <td>0.484911</td>\n      <td>0.308632</td>\n      <td>0.312380</td>\n      <td>0.517182</td>\n      <td>0.323674</td>\n      <td>0.260724</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>0.967343</td>\n      <td>0.623267</td>\n      <td>0.988943</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.879569</td>\n      <td>0.839463</td>\n      <td>0.932323</td>\n      <td>0.949031</td>\n      <td>...</td>\n      <td>1.000000</td>\n      <td>0.619670</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.786040</td>\n      <td>0.571557</td>\n      <td>0.613498</td>\n      <td>0.902062</td>\n      <td>0.829687</td>\n      <td>0.460186</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 30 columns</p>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Zbiór walidacyjny\n",
    "df_val.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:45.583803900Z",
     "start_time": "2024-03-15T19:05:45.287918100Z"
    }
   },
   "id": "5ce8c6c658be60c1"
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "       radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\ncount    57.000000     57.000000       57.000000  57.000000        57.000000   \nmean      0.335794      0.312663        0.328412   0.214790         0.381297   \nstd       0.170785      0.167714        0.169927   0.139898         0.109590   \nmin       0.075252      0.000000        0.072904   0.033001         0.146249   \n25%       0.192106      0.199188        0.185751   0.097222         0.308658   \n50%       0.315159      0.310450        0.301223   0.180445         0.390990   \n75%       0.486961      0.396348        0.481031   0.329629         0.460143   \nmax       0.692366      1.000000        0.695253   0.535949         0.578406   \n\n       compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\ncount         57.000000       57.000000            57.000000      57.000000   \nmean           0.241410        0.185207             0.232105       0.359197   \nstd            0.150381        0.156133             0.188722       0.127273   \nmin            0.021839        0.002798             0.011948       0.072222   \n25%            0.129471        0.062910             0.095179       0.271717   \n50%            0.203239        0.119845             0.154573       0.361616   \n75%            0.330102        0.261246             0.383996       0.439899   \nmax            0.809214        0.658388             0.776342       0.674242   \n\n       fractal_dimension_mean  ...  radius_worst  texture_worst  \\\ncount               57.000000  ...     57.000000      57.000000   \nmean                 0.258133  ...      0.295299       0.330458   \nstd                  0.148563  ...      0.176543       0.173641   \nmin                  0.021061  ...      0.054891       0.000000   \n25%                  0.139217  ...      0.146211       0.199893   \n50%                  0.246420  ...      0.256492       0.325160   \n75%                  0.339090  ...      0.429740       0.425640   \nmax                  0.839090  ...      0.667022       0.875533   \n\n       perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\ncount        57.000000   57.000000         57.000000          57.000000   \nmean          0.277057    0.169590          0.386967           0.204497   \nstd           0.164054    0.130427          0.137458           0.138294   \nmin           0.047263    0.022046          0.111074           0.015504   \n25%           0.146023    0.064368          0.285478           0.098777   \n50%           0.245530    0.128048          0.396421           0.172318   \n75%           0.410827    0.256046          0.478307           0.280205   \nmax           0.627970    0.467902          0.674437           0.709327   \n\n       concavity_worst  concave points_worst  symmetry_worst  \\\ncount        57.000000             57.000000       57.000000   \nmean          0.190725              0.374998        0.249384   \nstd           0.132668              0.233099        0.120215   \nmin           0.002860              0.035808        0.081411   \n25%           0.086981              0.202131        0.159866   \n50%           0.155511              0.292509        0.234772   \n75%           0.275240              0.520619        0.304356   \nmax           0.563339              0.997595        0.622708   \n\n       fractal_dimension_worst  \ncount                57.000000  \nmean                  0.176326  \nstd                   0.096130  \nmin                   0.001115  \n25%                   0.113210  \n50%                   0.160042  \n75%                   0.231011  \nmax                   0.481175  \n\n[8 rows x 30 columns]",
      "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>radius_mean</th>\n      <th>texture_mean</th>\n      <th>perimeter_mean</th>\n      <th>area_mean</th>\n      <th>smoothness_mean</th>\n      <th>compactness_mean</th>\n      <th>concavity_mean</th>\n      <th>concave points_mean</th>\n      <th>symmetry_mean</th>\n      <th>fractal_dimension_mean</th>\n      <th>...</th>\n      <th>radius_worst</th>\n      <th>texture_worst</th>\n      <th>perimeter_worst</th>\n      <th>area_worst</th>\n      <th>smoothness_worst</th>\n      <th>compactness_worst</th>\n      <th>concavity_worst</th>\n      <th>concave points_worst</th>\n      <th>symmetry_worst</th>\n      <th>fractal_dimension_worst</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>...</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n      <td>57.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.335794</td>\n      <td>0.312663</td>\n      <td>0.328412</td>\n      <td>0.214790</td>\n      <td>0.381297</td>\n      <td>0.241410</td>\n      <td>0.185207</td>\n      <td>0.232105</td>\n      <td>0.359197</td>\n      <td>0.258133</td>\n      <td>...</td>\n      <td>0.295299</td>\n      <td>0.330458</td>\n      <td>0.277057</td>\n      <td>0.169590</td>\n      <td>0.386967</td>\n      <td>0.204497</td>\n      <td>0.190725</td>\n      <td>0.374998</td>\n      <td>0.249384</td>\n      <td>0.176326</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.170785</td>\n      <td>0.167714</td>\n      <td>0.169927</td>\n      <td>0.139898</td>\n      <td>0.109590</td>\n      <td>0.150381</td>\n      <td>0.156133</td>\n      <td>0.188722</td>\n      <td>0.127273</td>\n      <td>0.148563</td>\n      <td>...</td>\n      <td>0.176543</td>\n      <td>0.173641</td>\n      <td>0.164054</td>\n      <td>0.130427</td>\n      <td>0.137458</td>\n      <td>0.138294</td>\n      <td>0.132668</td>\n      <td>0.233099</td>\n      <td>0.120215</td>\n      <td>0.096130</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.075252</td>\n      <td>0.000000</td>\n      <td>0.072904</td>\n      <td>0.033001</td>\n      <td>0.146249</td>\n      <td>0.021839</td>\n      <td>0.002798</td>\n      <td>0.011948</td>\n      <td>0.072222</td>\n      <td>0.021061</td>\n      <td>...</td>\n      <td>0.054891</td>\n      <td>0.000000</td>\n      <td>0.047263</td>\n      <td>0.022046</td>\n      <td>0.111074</td>\n      <td>0.015504</td>\n      <td>0.002860</td>\n      <td>0.035808</td>\n      <td>0.081411</td>\n      <td>0.001115</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.192106</td>\n      <td>0.199188</td>\n      <td>0.185751</td>\n      <td>0.097222</td>\n      <td>0.308658</td>\n      <td>0.129471</td>\n      <td>0.062910</td>\n      <td>0.095179</td>\n      <td>0.271717</td>\n      <td>0.139217</td>\n      <td>...</td>\n      <td>0.146211</td>\n      <td>0.199893</td>\n      <td>0.146023</td>\n      <td>0.064368</td>\n      <td>0.285478</td>\n      <td>0.098777</td>\n      <td>0.086981</td>\n      <td>0.202131</td>\n      <td>0.159866</td>\n      <td>0.113210</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.315159</td>\n      <td>0.310450</td>\n      <td>0.301223</td>\n      <td>0.180445</td>\n      <td>0.390990</td>\n      <td>0.203239</td>\n      <td>0.119845</td>\n      <td>0.154573</td>\n      <td>0.361616</td>\n      <td>0.246420</td>\n      <td>...</td>\n      <td>0.256492</td>\n      <td>0.325160</td>\n      <td>0.245530</td>\n      <td>0.128048</td>\n      <td>0.396421</td>\n      <td>0.172318</td>\n      <td>0.155511</td>\n      <td>0.292509</td>\n      <td>0.234772</td>\n      <td>0.160042</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.486961</td>\n      <td>0.396348</td>\n      <td>0.481031</td>\n      <td>0.329629</td>\n      <td>0.460143</td>\n      <td>0.330102</td>\n      <td>0.261246</td>\n      <td>0.383996</td>\n      <td>0.439899</td>\n      <td>0.339090</td>\n      <td>...</td>\n      <td>0.429740</td>\n      <td>0.425640</td>\n      <td>0.410827</td>\n      <td>0.256046</td>\n      <td>0.478307</td>\n      <td>0.280205</td>\n      <td>0.275240</td>\n      <td>0.520619</td>\n      <td>0.304356</td>\n      <td>0.231011</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>0.692366</td>\n      <td>1.000000</td>\n      <td>0.695253</td>\n      <td>0.535949</td>\n      <td>0.578406</td>\n      <td>0.809214</td>\n      <td>0.658388</td>\n      <td>0.776342</td>\n      <td>0.674242</td>\n      <td>0.839090</td>\n      <td>...</td>\n      <td>0.667022</td>\n      <td>0.875533</td>\n      <td>0.627970</td>\n      <td>0.467902</td>\n      <td>0.674437</td>\n      <td>0.709327</td>\n      <td>0.563339</td>\n      <td>0.997595</td>\n      <td>0.622708</td>\n      <td>0.481175</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 30 columns</p>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Zbiór testowy\n",
    "df_test.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:45.588195700Z",
     "start_time": "2024-03-15T19:05:45.381735500Z"
    }
   },
   "id": "49acb8e5dfbda89f"
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Rozkład klas w zbiorze"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "1c07bc584a09d6b8"
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "diagnosis\nB    357\nM    212\nName: count, dtype: int64"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cały zbiór\n",
    "df['diagnosis'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:45.651344400Z",
     "start_time": "2024-03-15T19:05:45.482422700Z"
    }
   },
   "id": "1e655fa9f8e48ff9"
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "diagnosis\nB    288\nM    167\nName: count, dtype: int64"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Zbiór treningowy\n",
    "df_train['diagnosis'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:45.652345300Z",
     "start_time": "2024-03-15T19:05:45.493491600Z"
    }
   },
   "id": "d66c931287444033"
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "diagnosis\nB    35\nM    22\nName: count, dtype: int64"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Zbiór walidacyjny\n",
    "df_val['diagnosis'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:45.652345300Z",
     "start_time": "2024-03-15T19:05:45.508825500Z"
    }
   },
   "id": "e3f1504600da351b"
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "diagnosis\nB    34\nM    23\nName: count, dtype: int64"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Zbiór testowy\n",
    "df_test['diagnosis'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-03-15T19:05:45.653709400Z",
     "start_time": "2024-03-15T19:05:45.523627900Z"
    }
   },
   "id": "de5b287d4c68a1ec"
  }
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