{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0063a986",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%python` not found (But cell magic `%%python` exists, did you mean that instead?).\n"
     ]
    }
   ],
   "source": [
    "%python -m kaggle datasets download -d ulrikthygepedersen/diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5bc46bfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diamonds.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
     ]
    }
   ],
   "source": [
    "!kaggle datasets download -d shivam2503/diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "75024e0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "!tar -xf diamonds.zip\n",
    "## rozpakowanie archiwum .zip w windowsie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99c20a95",
   "metadata": {},
   "outputs": [
    {
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       "       Unnamed: 0  carat        cut color clarity  depth  table  price     x  \\\n",
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       "...           ...    ...        ...   ...     ...    ...    ...    ...   ...   \n",
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       "53939       53940   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83   \n",
       "\n",
       "          y     z  \n",
       "0      3.98  2.43  \n",
       "1      3.84  2.31  \n",
       "2      4.07  2.31  \n",
       "3      4.23  2.63  \n",
       "4      4.35  2.75  \n",
       "...     ...   ...  \n",
       "53935  5.76  3.50  \n",
       "53936  5.75  3.61  \n",
       "53937  5.68  3.56  \n",
       "53938  6.12  3.74  \n",
       "53939  5.87  3.64  \n",
       "\n",
       "[53940 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "diamonds = pd.read_csv('diamonds.csv')\n",
    "#Wyświetlenie zbioru danych\n",
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "122b0b57",
   "metadata": {},
   "outputs": [
    {
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       "          id  carat        cut color clarity  depth  table  price     x     y  \\\n",
       "0          1   0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98   \n",
       "1          2   0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
       "2          3   0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07   \n",
       "3          4   0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23   \n",
       "4          5   0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35   \n",
       "...      ...    ...        ...   ...     ...    ...    ...    ...   ...   ...   \n",
       "53935  53936   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76   \n",
       "53936  53937   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75   \n",
       "53937  53938   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68   \n",
       "53938  53939   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12   \n",
       "53939  53940   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87   \n",
       "\n",
       "          z  \n",
       "0      2.43  \n",
       "1      2.31  \n",
       "2      2.31  \n",
       "3      2.63  \n",
       "4      2.75  \n",
       "...     ...  \n",
       "53935  3.50  \n",
       "53936  3.61  \n",
       "53937  3.56  \n",
       "53938  3.74  \n",
       "53939  3.64  \n",
       "\n",
       "[53940 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#przydzielanie nazwy kolumny z id\n",
    "diamonds = diamonds.rename(columns={diamonds.columns[0]: 'id'})\n",
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a489dab8",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.23</td>\n",
       "      <td>good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.29</td>\n",
       "      <td>premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.31</td>\n",
       "      <td>good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>53936</td>\n",
       "      <td>0.72</td>\n",
       "      <td>ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>53937</td>\n",
       "      <td>0.72</td>\n",
       "      <td>good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>53938</td>\n",
       "      <td>0.70</td>\n",
       "      <td>very good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>53939</td>\n",
       "      <td>0.86</td>\n",
       "      <td>premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>53940</td>\n",
       "      <td>0.75</td>\n",
       "      <td>ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  carat        cut color clarity  depth  table  price     x     y  \\\n",
       "0          1   0.23      ideal     E     SI2   61.5   55.0    326  3.95  3.98   \n",
       "1          2   0.21    premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
       "2          3   0.23       good     E     VS1   56.9   65.0    327  4.05  4.07   \n",
       "3          4   0.29    premium     I     VS2   62.4   58.0    334  4.20  4.23   \n",
       "4          5   0.31       good     J     SI2   63.3   58.0    335  4.34  4.35   \n",
       "...      ...    ...        ...   ...     ...    ...    ...    ...   ...   ...   \n",
       "53935  53936   0.72      ideal     D     SI1   60.8   57.0   2757  5.75  5.76   \n",
       "53936  53937   0.72       good     D     SI1   63.1   55.0   2757  5.69  5.75   \n",
       "53937  53938   0.70  very good     D     SI1   62.8   60.0   2757  5.66  5.68   \n",
       "53938  53939   0.86    premium     H     SI2   61.0   58.0   2757  6.15  6.12   \n",
       "53939  53940   0.75      ideal     D     SI2   62.2   55.0   2757  5.83  5.87   \n",
       "\n",
       "          z  \n",
       "0      2.43  \n",
       "1      2.31  \n",
       "2      2.31  \n",
       "3      2.63  \n",
       "4      2.75  \n",
       "...     ...  \n",
       "53935  3.50  \n",
       "53936  3.61  \n",
       "53937  3.56  \n",
       "53938  3.74  \n",
       "53939  3.64  \n",
       "\n",
       "[53940 rows x 11 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert to lowerCase\n",
    "\n",
    "diamonds['cut'] = diamonds['cut'].str.lower()\n",
    "diamonds\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1836b2a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fcf6448a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#podział danych na train/test/dev w proporcji 4:1:1\n",
    "#losować ustawiona na 10\n",
    "\n",
    "#1. Dzielimy na zbiór treningowy 80 % i resztę danych\n",
    "diamonds_train, diamonds_test_dev = sklearn.model_selection.train_test_split(diamonds, test_size=0.2, random_state=10)\n",
    "\n",
    "#2. Podział reszty danych na zbiór testowy 10% i walidacyjny 10%\n",
    "diamonds_test, diamonds_dev = train_test_split(diamonds_test_dev, test_size=0.5, random_state=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9476846a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rozmiar diamonds:  (53940, 11)\n",
      "Rozmiar diamonds_train:  (43152, 11)\n",
      "Rozmiar diamonds_test:  (5394, 11)\n",
      "Rozmiar diamonds_dev:  (5394, 11)\n"
     ]
    }
   ],
   "source": [
    "#Wyświetlenie rozmiarów zbiorów danych train/test/dev\n",
    "print(\"Rozmiar diamonds: \", diamonds.shape)\n",
    "print(\"Rozmiar diamonds_train: \", diamonds_train.shape)\n",
    "print(\"Rozmiar diamonds_test: \", diamonds_test.shape)\n",
    "print(\"Rozmiar diamonds_dev: \", diamonds_dev.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e1f11cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 id         carat         depth         table         price  \\\n",
      "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
      "mean   26970.500000      0.797940     61.749405     57.457184   3932.799722   \n",
      "std    15571.281097      0.474011      1.432621      2.234491   3989.439738   \n",
      "min        1.000000      0.200000     43.000000     43.000000    326.000000   \n",
      "25%    13485.750000      0.400000     61.000000     56.000000    950.000000   \n",
      "50%    26970.500000      0.700000     61.800000     57.000000   2401.000000   \n",
      "75%    40455.250000      1.040000     62.500000     59.000000   5324.250000   \n",
      "max    53940.000000      5.010000     79.000000     95.000000  18823.000000   \n",
      "\n",
      "                  x             y             z  \n",
      "count  53940.000000  53940.000000  53940.000000  \n",
      "mean       5.731157      5.734526      3.538734  \n",
      "std        1.121761      1.142135      0.705699  \n",
      "min        0.000000      0.000000      0.000000  \n",
      "25%        4.710000      4.720000      2.910000  \n",
      "50%        5.700000      5.710000      3.530000  \n",
      "75%        6.540000      6.540000      4.040000  \n",
      "max       10.740000     58.900000     31.800000  \n"
     ]
    }
   ],
   "source": [
    "# średnią, minimum, maksimum, odchylenia standardowe, medianę wartości poszczególnych parametrów)\n",
    "print(diamonds.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88a89b38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 id         carat         depth         table         price  \\\n",
      "count  43152.000000  43152.000000  43152.000000  43152.000000  43152.000000   \n",
      "mean   26971.712111      0.795979     61.748241     57.448355   3920.786939   \n",
      "std    15565.585777      0.472184      1.426394      2.224297   3975.894633   \n",
      "min        3.000000      0.200000     43.000000     44.000000    327.000000   \n",
      "25%    13469.750000      0.400000     61.000000     56.000000    946.000000   \n",
      "50%    27019.500000      0.700000     61.800000     57.000000   2400.000000   \n",
      "75%    40439.250000      1.040000     62.500000     59.000000   5313.250000   \n",
      "max    53938.000000      5.010000     79.000000     76.000000  18823.000000   \n",
      "\n",
      "                  x             y             z  \n",
      "count  43152.000000  43152.000000  43152.000000  \n",
      "mean       5.726933      5.731011      3.535791  \n",
      "std        1.119635      1.147069      0.693846  \n",
      "min        0.000000      0.000000      0.000000  \n",
      "25%        4.710000      4.720000      2.910000  \n",
      "50%        5.690000      5.710000      3.520000  \n",
      "75%        6.540000      6.530000      4.030000  \n",
      "max       10.740000     58.900000      8.060000  \n"
     ]
    }
   ],
   "source": [
    "print(diamonds_train.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "80b5060f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 id        carat        depth        table         price  \\\n",
      "count   5394.000000  5394.000000  5394.000000  5394.000000   5394.000000   \n",
      "mean   26951.351316     0.802666    61.760808    57.470189   3970.308676   \n",
      "std    15565.740253     0.482062     1.464893     2.309900   4083.195823   \n",
      "min        1.000000     0.210000    52.300000    43.000000    326.000000   \n",
      "25%    13519.750000     0.400000    61.000000    56.000000    958.000000   \n",
      "50%    27013.500000     0.700000    61.900000    57.000000   2375.500000   \n",
      "75%    40342.250000     1.050000    62.500000    59.000000   5273.750000   \n",
      "max    53930.000000     3.510000    78.200000    95.000000  18806.000000   \n",
      "\n",
      "                 x            y            z  \n",
      "count  5394.000000  5394.000000  5394.000000  \n",
      "mean      5.738817     5.739106     3.542097  \n",
      "std       1.132069     1.123925     0.701446  \n",
      "min       3.840000     3.780000     0.000000  \n",
      "25%       4.710000     4.710000     2.900000  \n",
      "50%       5.690000     5.700000     3.530000  \n",
      "75%       6.550000     6.540000     4.040000  \n",
      "max       9.660000     9.630000     6.030000  \n"
     ]
    }
   ],
   "source": [
    "print(diamonds_test.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "31f4af56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 id        carat        depth        table         price  \\\n",
      "count   5394.000000  5394.000000  5394.000000  5394.000000   5394.000000   \n",
      "mean   26979.951798     0.808901    61.747312    57.514813   3991.393029   \n",
      "std    15625.161644     0.480344     1.449816     2.238671   4002.742530   \n",
      "min        2.000000     0.200000    53.200000    51.000000    326.000000   \n",
      "25%    13525.500000     0.400000    61.000000    56.000000    961.000000   \n",
      "50%    26529.500000     0.710000    61.850000    57.000000   2484.500000   \n",
      "75%    40665.500000     1.050000    62.500000    59.000000   5465.250000   \n",
      "max    53940.000000     3.040000    73.600000    68.000000  18779.000000   \n",
      "\n",
      "                 x            y            z  \n",
      "count  5394.000000  5394.000000  5394.000000  \n",
      "mean      5.757290     5.758066     3.558910  \n",
      "std       1.128191     1.120344     0.797759  \n",
      "min       3.790000     3.750000     0.000000  \n",
      "25%       4.730000     4.740000     2.930000  \n",
      "50%       5.710000     5.730000     3.540000  \n",
      "75%       6.560000     6.540000     4.040000  \n",
      "max       9.510000     9.460000    31.800000  \n"
     ]
    }
   ],
   "source": [
    "print(diamonds_dev.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eab3e1f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ideal        17292\n",
       "premium      10954\n",
       "very good     9708\n",
       "good          3929\n",
       "fair          1269\n",
       "Name: cut, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Wyświetlenie częstości przykładów dla poszczególnych klas diamentów\n",
    "diamonds_train[\"cut\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2e7c37d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ideal        2184\n",
       "premium      1385\n",
       "very good    1183\n",
       "good          473\n",
       "fair          169\n",
       "Name: cut, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_test[\"cut\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a7ccece5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ideal        2075\n",
       "premium      1452\n",
       "very good    1191\n",
       "good          504\n",
       "fair          172\n",
       "Name: cut, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_dev[\"cut\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "17223f54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "diamonds['cut'].value_counts().plot(kind='bar')\n",
    "plt.title('Rozkład częstości dla szlifów diamentów dla zbioru diamonds')\n",
    "plt.xlabel('Szlif')\n",
    "plt.ylabel('Liczba wystąpień')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8633ea7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "diamonds_train['cut'].value_counts().plot(kind='bar')\n",
    "plt.title('Rozkład częstości dla szlifów diamentów dla zbioru diamonds tranującego')\n",
    "plt.xlabel('Szlif')\n",
    "plt.ylabel('Liczba wystąpień')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ab567b6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "diamonds_test['cut'].value_counts().plot(kind='bar')\n",
    "plt.title('Rozkład częstości dla szlifów diamentów dla zbioru diamonds testowego')\n",
    "plt.xlabel('Szlif')\n",
    "plt.ylabel('Liczba wystąpień')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "18e61963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "diamonds_dev['cut'].value_counts().plot(kind='bar')\n",
    "plt.title('Rozkład częstości dla szlifów diamentów dla zbioru diamonds walidacyjnego')\n",
    "plt.xlabel('Szlif')\n",
    "plt.ylabel('Liczba wystąpień')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1bf608c2",
   "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>carat</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cut</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fair</th>\n",
       "      <td>0.516404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>good</th>\n",
       "      <td>0.454054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ideal</th>\n",
       "      <td>0.432876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>premium</th>\n",
       "      <td>0.515262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>very good</th>\n",
       "      <td>0.459435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              carat\n",
       "cut                \n",
       "fair       0.516404\n",
       "good       0.454054\n",
       "ideal      0.432876\n",
       "premium    0.515262\n",
       "very good  0.459435"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds[[\"cut\",\"carat\"]].groupby(\"cut\").std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0d6e54d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cut'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diamonds[[\"cut\",\"carat\"]].groupby(\"cut\").mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4598d9cf",
   "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>id</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.006237</td>\n",
       "      <td>ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>0.513889</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.367784</td>\n",
       "      <td>0.067572</td>\n",
       "      <td>0.076415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.002079</td>\n",
       "      <td>premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.362197</td>\n",
       "      <td>0.065195</td>\n",
       "      <td>0.072642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.006237</td>\n",
       "      <td>good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>0.386111</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.377095</td>\n",
       "      <td>0.069100</td>\n",
       "      <td>0.072642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.018711</td>\n",
       "      <td>premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>0.538889</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>0.391061</td>\n",
       "      <td>0.071817</td>\n",
       "      <td>0.082704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.022869</td>\n",
       "      <td>good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>0.563889</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.000487</td>\n",
       "      <td>0.404097</td>\n",
       "      <td>0.073854</td>\n",
       "      <td>0.086478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>53936</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>0.494444</td>\n",
       "      <td>0.269231</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.535382</td>\n",
       "      <td>0.097793</td>\n",
       "      <td>0.110063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>53937</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>0.558333</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.529795</td>\n",
       "      <td>0.097623</td>\n",
       "      <td>0.113522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>53938</td>\n",
       "      <td>0.103950</td>\n",
       "      <td>very good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.326923</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.527002</td>\n",
       "      <td>0.096435</td>\n",
       "      <td>0.111950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>53939</td>\n",
       "      <td>0.137214</td>\n",
       "      <td>premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.572626</td>\n",
       "      <td>0.103905</td>\n",
       "      <td>0.117610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>53940</td>\n",
       "      <td>0.114345</td>\n",
       "      <td>ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.542831</td>\n",
       "      <td>0.099660</td>\n",
       "      <td>0.114465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id     carat        cut color clarity     depth     table     price  \\\n",
       "0          1  0.006237      ideal     E     SI2  0.513889  0.230769  0.000000   \n",
       "1          2  0.002079    premium     E     SI1  0.466667  0.346154  0.000000   \n",
       "2          3  0.006237       good     E     VS1  0.386111  0.423077  0.000054   \n",
       "3          4  0.018711    premium     I     VS2  0.538889  0.288462  0.000433   \n",
       "4          5  0.022869       good     J     SI2  0.563889  0.288462  0.000487   \n",
       "...      ...       ...        ...   ...     ...       ...       ...       ...   \n",
       "53935  53936  0.108108      ideal     D     SI1  0.494444  0.269231  0.131427   \n",
       "53936  53937  0.108108       good     D     SI1  0.558333  0.230769  0.131427   \n",
       "53937  53938  0.103950  very good     D     SI1  0.550000  0.326923  0.131427   \n",
       "53938  53939  0.137214    premium     H     SI2  0.500000  0.288462  0.131427   \n",
       "53939  53940  0.114345      ideal     D     SI2  0.533333  0.230769  0.131427   \n",
       "\n",
       "              x         y         z  \n",
       "0      0.367784  0.067572  0.076415  \n",
       "1      0.362197  0.065195  0.072642  \n",
       "2      0.377095  0.069100  0.072642  \n",
       "3      0.391061  0.071817  0.082704  \n",
       "4      0.404097  0.073854  0.086478  \n",
       "...         ...       ...       ...  \n",
       "53935  0.535382  0.097793  0.110063  \n",
       "53936  0.529795  0.097623  0.113522  \n",
       "53937  0.527002  0.096435  0.111950  \n",
       "53938  0.572626  0.103905  0.117610  \n",
       "53939  0.542831  0.099660  0.114465  \n",
       "\n",
       "[53940 rows x 11 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#normalizacja wartości typu float do zakrsu 0.0 - 1.0\n",
    "#Powyżej wykonano jeszcze konwersję danych typu string na lowerCase\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "diamonds[['carat', 'depth', 'table', 'price', 'x', 'y', 'z']] = scaler.fit_transform(diamonds[['carat', 'depth', 'table', 'price', 'x', 'y', 'z']])\n",
    "\n",
    "#wyświetlenie zbioru\n",
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "97350bed",
   "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>id</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.006237</td>\n",
       "      <td>ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>0.513889</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.367784</td>\n",
       "      <td>0.067572</td>\n",
       "      <td>0.076415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.002079</td>\n",
       "      <td>premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.362197</td>\n",
       "      <td>0.065195</td>\n",
       "      <td>0.072642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.006237</td>\n",
       "      <td>good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>0.386111</td>\n",
       "      <td>0.423077</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.377095</td>\n",
       "      <td>0.069100</td>\n",
       "      <td>0.072642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.018711</td>\n",
       "      <td>premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>0.538889</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>0.391061</td>\n",
       "      <td>0.071817</td>\n",
       "      <td>0.082704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.022869</td>\n",
       "      <td>good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>0.563889</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.000487</td>\n",
       "      <td>0.404097</td>\n",
       "      <td>0.073854</td>\n",
       "      <td>0.086478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>53936</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>0.494444</td>\n",
       "      <td>0.269231</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.535382</td>\n",
       "      <td>0.097793</td>\n",
       "      <td>0.110063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>53937</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>0.558333</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.529795</td>\n",
       "      <td>0.097623</td>\n",
       "      <td>0.113522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>53938</td>\n",
       "      <td>0.103950</td>\n",
       "      <td>very good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.326923</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.527002</td>\n",
       "      <td>0.096435</td>\n",
       "      <td>0.111950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>53939</td>\n",
       "      <td>0.137214</td>\n",
       "      <td>premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.572626</td>\n",
       "      <td>0.103905</td>\n",
       "      <td>0.117610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>53940</td>\n",
       "      <td>0.114345</td>\n",
       "      <td>ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.542831</td>\n",
       "      <td>0.099660</td>\n",
       "      <td>0.114465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id     carat        cut color clarity     depth     table     price  \\\n",
       "0          1  0.006237      ideal     E     SI2  0.513889  0.230769  0.000000   \n",
       "1          2  0.002079    premium     E     SI1  0.466667  0.346154  0.000000   \n",
       "2          3  0.006237       good     E     VS1  0.386111  0.423077  0.000054   \n",
       "3          4  0.018711    premium     I     VS2  0.538889  0.288462  0.000433   \n",
       "4          5  0.022869       good     J     SI2  0.563889  0.288462  0.000487   \n",
       "...      ...       ...        ...   ...     ...       ...       ...       ...   \n",
       "53935  53936  0.108108      ideal     D     SI1  0.494444  0.269231  0.131427   \n",
       "53936  53937  0.108108       good     D     SI1  0.558333  0.230769  0.131427   \n",
       "53937  53938  0.103950  very good     D     SI1  0.550000  0.326923  0.131427   \n",
       "53938  53939  0.137214    premium     H     SI2  0.500000  0.288462  0.131427   \n",
       "53939  53940  0.114345      ideal     D     SI2  0.533333  0.230769  0.131427   \n",
       "\n",
       "              x         y         z  \n",
       "0      0.367784  0.067572  0.076415  \n",
       "1      0.362197  0.065195  0.072642  \n",
       "2      0.377095  0.069100  0.072642  \n",
       "3      0.391061  0.071817  0.082704  \n",
       "4      0.404097  0.073854  0.086478  \n",
       "...         ...       ...       ...  \n",
       "53935  0.535382  0.097793  0.110063  \n",
       "53936  0.529795  0.097623  0.113522  \n",
       "53937  0.527002  0.096435  0.111950  \n",
       "53938  0.572626  0.103905  0.117610  \n",
       "53939  0.542831  0.099660  0.114465  \n",
       "\n",
       "[53940 rows x 11 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Usuwanie artefaktów\n",
    "diamonds = diamonds.dropna() ## usuwanie pustych wierszy, które posiadają przynajmniej jedno wystąpienie NULL or NaN\n",
    "diamonds"
   ]
  }
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