{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "54f20002-11a1-4a11-88c4-f3b5bef89a08",
   "metadata": {},
   "source": [
    "Potrzebne importy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0318fb7f-f6df-4d0d-9d92-61e17793e31a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report, accuracy_score \n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f3e1deb-f7f1-4108-8662-7ad1dc6d6485",
   "metadata": {},
   "source": [
    "Odczytywanie danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "522d753b-fb82-4b9b-8d2d-20c7089d1b9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('bodyPerformance.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a75cd10b-f1de-460f-b4e3-0b99502d2d11",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>body fat_%</th>\n",
       "      <th>diastolic</th>\n",
       "      <th>systolic</th>\n",
       "      <th>gripForce</th>\n",
       "      <th>sit and bend forward_cm</th>\n",
       "      <th>sit-ups counts</th>\n",
       "      <th>broad jump_cm</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "      <td>172.3</td>\n",
       "      <td>75.24</td>\n",
       "      <td>21.3</td>\n",
       "      <td>80.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>54.9</td>\n",
       "      <td>18.4</td>\n",
       "      <td>60.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.0</td>\n",
       "      <td>M</td>\n",
       "      <td>165.0</td>\n",
       "      <td>55.80</td>\n",
       "      <td>15.7</td>\n",
       "      <td>77.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>36.4</td>\n",
       "      <td>16.3</td>\n",
       "      <td>53.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.0</td>\n",
       "      <td>M</td>\n",
       "      <td>179.6</td>\n",
       "      <td>78.00</td>\n",
       "      <td>20.1</td>\n",
       "      <td>92.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>44.8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>174.5</td>\n",
       "      <td>71.10</td>\n",
       "      <td>18.4</td>\n",
       "      <td>76.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>41.4</td>\n",
       "      <td>15.2</td>\n",
       "      <td>53.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.0</td>\n",
       "      <td>M</td>\n",
       "      <td>173.8</td>\n",
       "      <td>67.70</td>\n",
       "      <td>17.1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>27.1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age gender  height_cm  weight_kg  body fat_%  diastolic  systolic  \\\n",
       "0  27.0      M      172.3      75.24        21.3       80.0     130.0   \n",
       "1  25.0      M      165.0      55.80        15.7       77.0     126.0   \n",
       "2  31.0      M      179.6      78.00        20.1       92.0     152.0   \n",
       "3  32.0      M      174.5      71.10        18.4       76.0     147.0   \n",
       "4  28.0      M      173.8      67.70        17.1       70.0     127.0   \n",
       "\n",
       "   gripForce  sit and bend forward_cm  sit-ups counts  broad jump_cm class  \n",
       "0       54.9                     18.4            60.0          217.0     C  \n",
       "1       36.4                     16.3            53.0          229.0     A  \n",
       "2       44.8                     12.0            49.0          181.0     C  \n",
       "3       41.4                     15.2            53.0          219.0     B  \n",
       "4       43.5                     27.1            45.0          217.0     B  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d218c58-3d86-4007-9ec4-253b51f5e003",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13393 entries, 0 to 13392\n",
      "Data columns (total 12 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   age                      13393 non-null  float64\n",
      " 1   gender                   13393 non-null  object \n",
      " 2   height_cm                13393 non-null  float64\n",
      " 3   weight_kg                13393 non-null  float64\n",
      " 4   body fat_%               13393 non-null  float64\n",
      " 5   diastolic                13393 non-null  float64\n",
      " 6   systolic                 13393 non-null  float64\n",
      " 7   gripForce                13393 non-null  float64\n",
      " 8   sit and bend forward_cm  13393 non-null  float64\n",
      " 9   sit-ups counts           13393 non-null  float64\n",
      " 10  broad jump_cm            13393 non-null  float64\n",
      " 11  class                    13393 non-null  object \n",
      "dtypes: float64(10), object(2)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce463e56-68c0-4a09-a452-d3b50e836eb7",
   "metadata": {},
   "source": [
    "Przygotowanie danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1565dcf0-7fb0-4a56-902c-aabd3a5739a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                        0\n",
       "gender                     0\n",
       "height_cm                  0\n",
       "weight_kg                  0\n",
       "body fat_%                 0\n",
       "diastolic                  0\n",
       "systolic                   0\n",
       "gripForce                  0\n",
       "sit and bend forward_cm    0\n",
       "sit-ups counts             0\n",
       "broad jump_cm              0\n",
       "class                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "23c1fa61-45b3-45d6-9f7a-af4eaca34501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     13393\n",
       "unique        2\n",
       "top           M\n",
       "freq       8467\n",
       "Name: gender, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c52e6cd1-7175-4a54-bfb1-865494ce1eae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns=['gender'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9236fdb8-feb6-4ad4-a5ed-9262ae76b66c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>172.3</td>\n",
       "      <td>75.24</td>\n",
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       "      <td>80.0</td>\n",
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       "      <td>25.0</td>\n",
       "      <td>165.0</td>\n",
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       "      <td>77.0</td>\n",
       "      <td>126.0</td>\n",
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       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>31.0</td>\n",
       "      <td>179.6</td>\n",
       "      <td>78.00</td>\n",
       "      <td>20.1</td>\n",
       "      <td>92.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>44.8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>C</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>174.5</td>\n",
       "      <td>71.10</td>\n",
       "      <td>18.4</td>\n",
       "      <td>76.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>41.4</td>\n",
       "      <td>15.2</td>\n",
       "      <td>53.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>B</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.0</td>\n",
       "      <td>173.8</td>\n",
       "      <td>67.70</td>\n",
       "      <td>17.1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>27.1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>B</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  height_cm  weight_kg  body fat_%  diastolic  systolic  gripForce  \\\n",
       "0  27.0      172.3      75.24        21.3       80.0     130.0       54.9   \n",
       "1  25.0      165.0      55.80        15.7       77.0     126.0       36.4   \n",
       "2  31.0      179.6      78.00        20.1       92.0     152.0       44.8   \n",
       "3  32.0      174.5      71.10        18.4       76.0     147.0       41.4   \n",
       "4  28.0      173.8      67.70        17.1       70.0     127.0       43.5   \n",
       "\n",
       "   sit and bend forward_cm  sit-ups counts  broad jump_cm class  gender_F  \\\n",
       "0                     18.4            60.0          217.0     C     False   \n",
       "1                     16.3            53.0          229.0     A     False   \n",
       "2                     12.0            49.0          181.0     C     False   \n",
       "3                     15.2            53.0          219.0     B     False   \n",
       "4                     27.1            45.0          217.0     B     False   \n",
       "\n",
       "   gender_M  \n",
       "0      True  \n",
       "1      True  \n",
       "2      True  \n",
       "3      True  \n",
       "4      True  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63fdc3e1-2388-4f5b-9a11-5f87c88a28e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['gender_F'] = df['gender_F'].map({True:1, False:0}) \n",
    "df['gender_M'] = df['gender_M'].map({True:1, False:0}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "91f4116a-45f7-4d49-8d88-d8b4c3173019",
   "metadata": {},
   "outputs": [
    {
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       "      <td>31.0</td>\n",
       "      <td>179.6</td>\n",
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       "      <td>20.1</td>\n",
       "      <td>92.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>44.8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>181.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>174.5</td>\n",
       "      <td>71.10</td>\n",
       "      <td>18.4</td>\n",
       "      <td>76.0</td>\n",
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       "      <td>41.4</td>\n",
       "      <td>15.2</td>\n",
       "      <td>53.0</td>\n",
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       "      <td>B</td>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>28.0</td>\n",
       "      <td>173.8</td>\n",
       "      <td>67.70</td>\n",
       "      <td>17.1</td>\n",
       "      <td>70.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>27.1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>B</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "    age  height_cm  weight_kg  body fat_%  diastolic  systolic  gripForce  \\\n",
       "0  27.0      172.3      75.24        21.3       80.0     130.0       54.9   \n",
       "1  25.0      165.0      55.80        15.7       77.0     126.0       36.4   \n",
       "2  31.0      179.6      78.00        20.1       92.0     152.0       44.8   \n",
       "3  32.0      174.5      71.10        18.4       76.0     147.0       41.4   \n",
       "4  28.0      173.8      67.70        17.1       70.0     127.0       43.5   \n",
       "\n",
       "   sit and bend forward_cm  sit-ups counts  broad jump_cm class  gender_F  \\\n",
       "0                     18.4            60.0          217.0     C         0   \n",
       "1                     16.3            53.0          229.0     A         0   \n",
       "2                     12.0            49.0          181.0     C         0   \n",
       "3                     15.2            53.0          219.0     B         0   \n",
       "4                     27.1            45.0          217.0     B         0   \n",
       "\n",
       "   gender_M  \n",
       "0         1  \n",
       "1         1  \n",
       "2         1  \n",
       "3         1  \n",
       "4         1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00518d17-a95d-491b-8926-cddc73360928",
   "metadata": {},
   "source": [
    "Podział na zbiory uczące i testowe oraz skalowanie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "efe47699-b999-4432-973a-e6141bb21a76",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['class']\n",
    "X = df.drop('class', axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c252d459-a639-4610-a742-901381c30e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e75d7f4-acc5-44d7-8798-64f9db2c5100",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "81a2c2d5-9c03-45d4-b12d-f843f52a908a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10714"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8413b009-25c2-4c18-94ce-96f66262b43b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2679"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f281ecc-292a-4c4b-a4ac-e73055e48323",
   "metadata": {},
   "source": [
    "Regresja logistyczna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c10b7f26-5759-4045-b613-033c0f5ccc2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3a64150f-f098-4c1e-a089-81bfab9af398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c8768890-e826-41d1-a923-6da63f16db6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "logistic_predicts = logistic_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "28456695-9a70-4b63-b92b-1313ee817307",
   "metadata": {},
   "outputs": [],
   "source": [
    "cr = classification_report(y_test, logistic_predicts) \n",
    "accuracy = accuracy_score(y_test, logistic_predicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "985d5347-e9fa-4921-b70b-2d72faceb31d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       0.70      0.71      0.71       685\n",
      "           B       0.45      0.44      0.45       662\n",
      "           C       0.52      0.53      0.53       650\n",
      "           D       0.79      0.78      0.78       682\n",
      "\n",
      "    accuracy                           0.62      2679\n",
      "   macro avg       0.62      0.62      0.62      2679\n",
      "weighted avg       0.62      0.62      0.62      2679\n",
      "\n",
      "accuracy:  0.6188876446435237\n"
     ]
    }
   ],
   "source": [
    "print(cr)\n",
    "print('accuracy: ',accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5705678-5aa0-47b2-ba0d-e8892abb6b0a",
   "metadata": {},
   "source": [
    "Naiwny klasyfikator Bayesa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "58be3b38-3a2f-45b8-a24c-89fedf302a2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "bayes_model = GaussianNB().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "21138174-4740-4ab9-91ed-5b5514e1d6a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "bayes_predicts = bayes_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9e60479c-8985-424f-aae3-5af4e9da67c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(y_test, bayes_predicts)\n",
    "accuracy = accuracy_score(y_test, bayes_predicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "643ab5d3-175f-4f2e-9da4-9ebfb0104a36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       0.59      0.74      0.66       685\n",
      "           B       0.41      0.30      0.34       662\n",
      "           C       0.46      0.46      0.46       650\n",
      "           D       0.68      0.69      0.68       682\n",
      "\n",
      "    accuracy                           0.55      2679\n",
      "   macro avg       0.53      0.55      0.54      2679\n",
      "weighted avg       0.54      0.55      0.54      2679\n",
      "\n",
      "accuracy:  0.5487122060470325\n"
     ]
    }
   ],
   "source": [
    "print(report)\n",
    "print('accuracy: ',accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b19e79-ada4-4a65-a35d-68c492585d8c",
   "metadata": {},
   "source": [
    "Klasyfikator najbliższych sąsiadów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3f482536-8898-4eea-8c85-15a73039a913",
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN = KNeighborsClassifier(n_neighbors=8).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6bfaf6c4-8e63-4839-9c71-76befae6d82f",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_predicts = KNN.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4e150a72-110a-4e45-a99f-10f7d93af94e",
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(y_test, knn_predicts)\n",
    "accuracy = accuracy_score(y_test, knn_predicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b6a28ccf-a0f5-427c-89e8-8b0bef12519d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       0.62      0.82      0.71       685\n",
      "           B       0.43      0.47      0.45       662\n",
      "           C       0.56      0.51      0.54       650\n",
      "           D       0.91      0.64      0.75       682\n",
      "\n",
      "    accuracy                           0.61      2679\n",
      "   macro avg       0.63      0.61      0.61      2679\n",
      "weighted avg       0.64      0.61      0.61      2679\n",
      "\n",
      "accuracy:  0.6114221724524076\n"
     ]
    }
   ],
   "source": [
    "print(report)\n",
    "print('accuracy: ',accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "972dccde-74fe-4353-accd-847eb210c58f",
   "metadata": {},
   "source": [
    "Drzewo decyzyjne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e132911c-7b1d-40c1-9ab6-6383b679fd47",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "y_train = label_encoder.fit_transform(y_train)\n",
    "y_test = label_encoder.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ff196743-c2c5-4947-bbb8-4b6bad6e9afc",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_model = DecisionTreeClassifier(max_depth=9).fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d2b9f937-94b0-4a07-b9e4-77607649dee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_predicts = tree_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0f47c596-6ff5-4808-abab-870b7746e2c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(y_test, tree_predicts)\n",
    "accuracy = accuracy_score(y_test, tree_predicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d89eae25-77d9-4f60-b954-743dcbc7535b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.69      0.82      0.75       685\n",
      "           1       0.54      0.57      0.56       662\n",
      "           2       0.66      0.59      0.62       650\n",
      "           3       0.88      0.76      0.81       682\n",
      "\n",
      "    accuracy                           0.69      2679\n",
      "   macro avg       0.69      0.69      0.69      2679\n",
      "weighted avg       0.70      0.69      0.69      2679\n",
      "\n",
      "accuracy:  0.6875699888017918\n"
     ]
    }
   ],
   "source": [
    "print(report)\n",
    "print('accuracy: ',accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6972b651-25a0-4a4d-9d26-ae8b0a3e42f8",
   "metadata": {},
   "source": [
    "Sieć neuronowa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "80945e15-a154-49c2-917c-3c830616b02a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 128)               1664      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               16512     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 128)               16512     \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 4)                 516       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 35,204\n",
      "Trainable params: 35,204\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential(\n",
    "    [\n",
    "        keras.Input(shape=(12,)),\n",
    "        layers.Dense(128, activation=\"relu\"),\n",
    "        layers.Dense(128, activation=\"relu\"),\n",
    "        layers.Dense(128, activation=\"relu\"),\n",
    "        layers.Dense(4, activation=\"softmax\"),\n",
    "    ]\n",
    ")\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b9263592-8fec-48bf-b232-0e1a90903cbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "76/76 [==============================] - 1s 7ms/step - loss: 0.9884 - accuracy: 0.5528 - val_loss: 0.8428 - val_accuracy: 0.6287\n",
      "Epoch 2/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.8034 - accuracy: 0.6459 - val_loss: 0.7855 - val_accuracy: 0.6670\n",
      "Epoch 3/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.7376 - accuracy: 0.6876 - val_loss: 0.7341 - val_accuracy: 0.6931\n",
      "Epoch 4/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.6971 - accuracy: 0.7119 - val_loss: 0.7143 - val_accuracy: 0.7146\n",
      "Epoch 5/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.6715 - accuracy: 0.7206 - val_loss: 0.6984 - val_accuracy: 0.7201\n",
      "Epoch 6/50\n",
      "76/76 [==============================] - 0s 2ms/step - loss: 0.6511 - accuracy: 0.7301 - val_loss: 0.6901 - val_accuracy: 0.7164\n",
      "Epoch 7/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.6375 - accuracy: 0.7359 - val_loss: 0.6782 - val_accuracy: 0.7285\n",
      "Epoch 8/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.6239 - accuracy: 0.7410 - val_loss: 0.7001 - val_accuracy: 0.7062\n",
      "Epoch 9/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.6144 - accuracy: 0.7483 - val_loss: 0.6578 - val_accuracy: 0.7304\n",
      "Epoch 10/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.6059 - accuracy: 0.7492 - val_loss: 0.6606 - val_accuracy: 0.7183\n",
      "Epoch 11/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.6000 - accuracy: 0.7541 - val_loss: 0.6597 - val_accuracy: 0.7341\n",
      "Epoch 12/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5942 - accuracy: 0.7582 - val_loss: 0.6617 - val_accuracy: 0.7183\n",
      "Epoch 13/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5856 - accuracy: 0.7586 - val_loss: 0.6732 - val_accuracy: 0.7285\n",
      "Epoch 14/50\n",
      "76/76 [==============================] - 0s 5ms/step - loss: 0.5807 - accuracy: 0.7627 - val_loss: 0.6709 - val_accuracy: 0.7369\n",
      "Epoch 15/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5731 - accuracy: 0.7659 - val_loss: 0.6618 - val_accuracy: 0.7416\n",
      "Epoch 16/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5665 - accuracy: 0.7694 - val_loss: 0.6483 - val_accuracy: 0.7463\n",
      "Epoch 17/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5620 - accuracy: 0.7694 - val_loss: 0.6635 - val_accuracy: 0.7220\n",
      "Epoch 18/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5555 - accuracy: 0.7709 - val_loss: 0.6493 - val_accuracy: 0.7425\n",
      "Epoch 19/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5523 - accuracy: 0.7715 - val_loss: 0.6649 - val_accuracy: 0.7313\n",
      "Epoch 20/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5489 - accuracy: 0.7771 - val_loss: 0.6862 - val_accuracy: 0.7164\n",
      "Epoch 21/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5409 - accuracy: 0.7801 - val_loss: 0.6567 - val_accuracy: 0.7435\n",
      "Epoch 22/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5373 - accuracy: 0.7784 - val_loss: 0.6638 - val_accuracy: 0.7285\n",
      "Epoch 23/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5316 - accuracy: 0.7838 - val_loss: 0.6582 - val_accuracy: 0.7407\n",
      "Epoch 24/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5308 - accuracy: 0.7857 - val_loss: 0.6762 - val_accuracy: 0.7285\n",
      "Epoch 25/50\n",
      "76/76 [==============================] - 0s 2ms/step - loss: 0.5279 - accuracy: 0.7844 - val_loss: 0.6775 - val_accuracy: 0.7229\n",
      "Epoch 26/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5165 - accuracy: 0.7884 - val_loss: 0.6599 - val_accuracy: 0.7257\n",
      "Epoch 27/50\n",
      "76/76 [==============================] - 0s 2ms/step - loss: 0.5186 - accuracy: 0.7899 - val_loss: 0.6610 - val_accuracy: 0.7369\n",
      "Epoch 28/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5117 - accuracy: 0.7903 - val_loss: 0.6590 - val_accuracy: 0.7388\n",
      "Epoch 29/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5162 - accuracy: 0.7883 - val_loss: 0.6800 - val_accuracy: 0.7211\n",
      "Epoch 30/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.5048 - accuracy: 0.7945 - val_loss: 0.6595 - val_accuracy: 0.7379\n",
      "Epoch 31/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7955 - val_loss: 0.6911 - val_accuracy: 0.7146\n",
      "Epoch 32/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4927 - accuracy: 0.8012 - val_loss: 0.6630 - val_accuracy: 0.7332\n",
      "Epoch 33/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4943 - accuracy: 0.8029 - val_loss: 0.6822 - val_accuracy: 0.7285\n",
      "Epoch 34/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4825 - accuracy: 0.8061 - val_loss: 0.6837 - val_accuracy: 0.7285\n",
      "Epoch 35/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4813 - accuracy: 0.8067 - val_loss: 0.6821 - val_accuracy: 0.7267\n",
      "Epoch 36/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4830 - accuracy: 0.8034 - val_loss: 0.6730 - val_accuracy: 0.7379\n",
      "Epoch 37/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4725 - accuracy: 0.8099 - val_loss: 0.7114 - val_accuracy: 0.7295\n",
      "Epoch 38/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4729 - accuracy: 0.8085 - val_loss: 0.7065 - val_accuracy: 0.7183\n",
      "Epoch 39/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4679 - accuracy: 0.8136 - val_loss: 0.6859 - val_accuracy: 0.7248\n",
      "Epoch 40/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4683 - accuracy: 0.8111 - val_loss: 0.7185 - val_accuracy: 0.7108\n",
      "Epoch 41/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4571 - accuracy: 0.8174 - val_loss: 0.7018 - val_accuracy: 0.7276\n",
      "Epoch 42/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4459 - accuracy: 0.8218 - val_loss: 0.7098 - val_accuracy: 0.7127\n",
      "Epoch 43/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4492 - accuracy: 0.8227 - val_loss: 0.7279 - val_accuracy: 0.7248\n",
      "Epoch 44/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4493 - accuracy: 0.8253 - val_loss: 0.7087 - val_accuracy: 0.7211\n",
      "Epoch 45/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4405 - accuracy: 0.8242 - val_loss: 0.7006 - val_accuracy: 0.7155\n",
      "Epoch 46/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4327 - accuracy: 0.8279 - val_loss: 0.7278 - val_accuracy: 0.7155\n",
      "Epoch 47/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4324 - accuracy: 0.8272 - val_loss: 0.7120 - val_accuracy: 0.7267\n",
      "Epoch 48/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4275 - accuracy: 0.8313 - val_loss: 0.7302 - val_accuracy: 0.7090\n",
      "Epoch 49/50\n",
      "76/76 [==============================] - 0s 4ms/step - loss: 0.4230 - accuracy: 0.8335 - val_loss: 0.7484 - val_accuracy: 0.7183\n",
      "Epoch 50/50\n",
      "76/76 [==============================] - 0s 3ms/step - loss: 0.4182 - accuracy: 0.8354 - val_loss: 0.7354 - val_accuracy: 0.7164\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x260d1488370>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
    "model.fit(X_train, y_train, batch_size=128, epochs=50, validation_split=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "28fb38de-bff7-4a37-8184-f82078378427",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84/84 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "neural_predicts = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2517e17d-522e-455a-a9bd-3872b44a256a",
   "metadata": {},
   "outputs": [],
   "source": [
    "neural_predicts_labels = np.argmax(neural_predicts, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d2030824-2a46-494e-973c-7913536cb651",
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(y_test, neural_predicts_labels)\n",
    "accuracy = accuracy_score(y_test, neural_predicts_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "43a2b294-ea59-4dbb-9515-1496e3ade233",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.84      0.79       685\n",
      "           1       0.60      0.65      0.62       662\n",
      "           2       0.72      0.62      0.67       650\n",
      "           3       0.89      0.84      0.86       682\n",
      "\n",
      "    accuracy                           0.74      2679\n",
      "   macro avg       0.74      0.74      0.74      2679\n",
      "weighted avg       0.74      0.74      0.74      2679\n",
      "\n",
      "accuracy:  0.7375886524822695\n"
     ]
    }
   ],
   "source": [
    "print(report)\n",
    "print('accuracy: ',accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba943951-ad7c-4a1a-b08f-a9a1613d8379",
   "metadata": {},
   "outputs": [],
   "source": [
    "    "
   ]
  }
 ],
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