A_C_SH_SD-Model1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **FINAL PROJECT : SLEEP DISORDER CLASSIFICATION  👨🏽‍💻**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Name         : Lintang Nagari\n",
    "* Email        : unggullintangg@gmail.com\n",
    "* Linkedin     : <a href='https://www.linkedin.com/in/lintangnagari/'>Lintang Nagari</a>\n",
    "* Github       : <a href='https://github.com/lnt-ngr'>lnt-ngr</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# split data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# preprocess\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "\n",
    "# evaluation\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "      <th>Person ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Sleep Duration</th>\n",
       "      <th>Quality of Sleep</th>\n",
       "      <th>Physical Activity Level</th>\n",
       "      <th>Stress Level</th>\n",
       "      <th>BMI Category</th>\n",
       "      <th>Heart Rate</th>\n",
       "      <th>Daily Steps</th>\n",
       "      <th>Sleep Disorder</th>\n",
       "      <th>BloodPressure_Upper_Value</th>\n",
       "      <th>BloodPressure_Lower_Value</th>\n",
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       "      <td>Software Engineer</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "      <td>Overweight</td>\n",
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       "      <td>8.1</td>\n",
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       "     Person ID  Gender  Age         Occupation  Sleep Duration  \\\n",
       "0            1    Male   27  Software Engineer             6.1   \n",
       "1            2    Male   28             Doctor             6.2   \n",
       "2            3    Male   28             Doctor             6.2   \n",
       "3            8    Male   29             Doctor             7.8   \n",
       "4            9    Male   29             Doctor             7.8   \n",
       "..         ...     ...  ...                ...             ...   \n",
       "354        370  Female   59              Nurse             8.1   \n",
       "355        371  Female   59              Nurse             8.0   \n",
       "356        372  Female   59              Nurse             8.1   \n",
       "357        373  Female   59              Nurse             8.1   \n",
       "358        374  Female   59              Nurse             8.1   \n",
       "\n",
       "     Quality of Sleep  Physical Activity Level  Stress Level BMI Category  \\\n",
       "0                   6                       42             6   Overweight   \n",
       "1                   6                       60             8       Normal   \n",
       "2                   6                       60             8       Normal   \n",
       "3                   7                       75             6       Normal   \n",
       "4                   7                       75             6       Normal   \n",
       "..                ...                      ...           ...          ...   \n",
       "354                 9                       75             3   Overweight   \n",
       "355                 9                       75             3   Overweight   \n",
       "356                 9                       75             3   Overweight   \n",
       "357                 9                       75             3   Overweight   \n",
       "358                 9                       75             3   Overweight   \n",
       "\n",
       "     Heart Rate  Daily Steps Sleep Disorder  BloodPressure_Upper_Value  \\\n",
       "0            77         4200           None                      126.0   \n",
       "1            75        10000           None                      125.0   \n",
       "2            75        10000           None                      125.0   \n",
       "3            70         8000           None                      120.0   \n",
       "4            70         8000           None                      120.0   \n",
       "..          ...          ...            ...                        ...   \n",
       "354          68         7000    Sleep Apnea                      140.0   \n",
       "355          68         7000    Sleep Apnea                      140.0   \n",
       "356          68         7000    Sleep Apnea                      140.0   \n",
       "357          68         7000    Sleep Apnea                      140.0   \n",
       "358          68         7000    Sleep Apnea                      140.0   \n",
       "\n",
       "     BloodPressure_Lower_Value  \n",
       "0                         83.0  \n",
       "1                         80.0  \n",
       "2                         80.0  \n",
       "3                         80.0  \n",
       "4                         80.0  \n",
       "..                         ...  \n",
       "354                       95.0  \n",
       "355                       95.0  \n",
       "356                       95.0  \n",
       "357                       95.0  \n",
       "358                       95.0  \n",
       "\n",
       "[359 rows x 14 columns]"
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   "source": [
    "df = pd.read_csv('Dataset/sleep_health.csv')\n",
    "df"
   ]
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      "c:\\Users\\Lintang\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Lintang\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Lintang\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
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       "      <th>355</th>\n",
       "      <td>371</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>372</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>373</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>374</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>359 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Person ID  Gender  Age         Occupation  Sleep Duration  \\\n",
       "0            1       1   27  Software Engineer             6.1   \n",
       "1            2       1   28             Doctor             6.2   \n",
       "2            3       1   28             Doctor             6.2   \n",
       "3            8       1   29             Doctor             7.8   \n",
       "4            9       1   29             Doctor             7.8   \n",
       "..         ...     ...  ...                ...             ...   \n",
       "354        370       0   59              Nurse             8.1   \n",
       "355        371       0   59              Nurse             8.0   \n",
       "356        372       0   59              Nurse             8.1   \n",
       "357        373       0   59              Nurse             8.1   \n",
       "358        374       0   59              Nurse             8.1   \n",
       "\n",
       "     Quality of Sleep  Physical Activity Level  Stress Level BMI Category  \\\n",
       "0                   6                       42             6   Overweight   \n",
       "1                   6                       60             8       Normal   \n",
       "2                   6                       60             8       Normal   \n",
       "3                   7                       75             6       Normal   \n",
       "4                   7                       75             6       Normal   \n",
       "..                ...                      ...           ...          ...   \n",
       "354                 9                       75             3   Overweight   \n",
       "355                 9                       75             3   Overweight   \n",
       "356                 9                       75             3   Overweight   \n",
       "357                 9                       75             3   Overweight   \n",
       "358                 9                       75             3   Overweight   \n",
       "\n",
       "     Heart Rate  ...  Nurse Engineer  Accountant  Scientist  Teacher  Lawyer  \\\n",
       "0            77  ...    0.0      0.0         0.0        0.0      0.0     1.0   \n",
       "1            75  ...    0.0      0.0         0.0        0.0      0.0     0.0   \n",
       "2            75  ...    0.0      0.0         0.0        0.0      0.0     0.0   \n",
       "3            70  ...    0.0      0.0         0.0        0.0      0.0     0.0   \n",
       "4            70  ...    0.0      0.0         0.0        0.0      0.0     0.0   \n",
       "..          ...  ...    ...      ...         ...        ...      ...     ...   \n",
       "354          68  ...    0.0      0.0         1.0        0.0      0.0     0.0   \n",
       "355          68  ...    0.0      0.0         1.0        0.0      0.0     0.0   \n",
       "356          68  ...    0.0      0.0         1.0        0.0      0.0     0.0   \n",
       "357          68  ...    0.0      0.0         1.0        0.0      0.0     0.0   \n",
       "358          68  ...    0.0      0.0         1.0        0.0      0.0     0.0   \n",
       "\n",
       "     Salesperson  Underweight  Normal  Overweight  \n",
       "0            0.0          0.0     0.0         1.0  \n",
       "1            0.0          1.0     0.0         0.0  \n",
       "2            0.0          1.0     0.0         0.0  \n",
       "3            0.0          1.0     0.0         0.0  \n",
       "4            0.0          1.0     0.0         0.0  \n",
       "..           ...          ...     ...         ...  \n",
       "354          0.0          0.0     0.0         1.0  \n",
       "355          0.0          0.0     0.0         1.0  \n",
       "356          0.0          0.0     0.0         1.0  \n",
       "357          0.0          0.0     0.0         1.0  \n",
       "358          0.0          0.0     0.0         1.0  \n",
       "\n",
       "[359 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop coloumn NA\n",
    "df = df.dropna()\n",
    "\n",
    "# Handling categorical variables/columns\n",
    "df['Gender'] = LabelEncoder().fit_transform(df['Gender'])\n",
    "\n",
    "# OneHotEncoder\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "st_enc = enc.fit_transform(df[['Sleep Disorder']])\n",
    "df_enc = pd.DataFrame(st_enc)\n",
    "df['Sleep Apnea'] = df_enc[0]\n",
    "df['insomnia'] = df_enc[1]\n",
    "df['None'] = df_enc[2]\n",
    "\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "st_enc = enc.fit_transform(df[['Occupation']])\n",
    "df_enc = pd.DataFrame(st_enc)\n",
    "df['Software Engineer'] = df_enc[0]\n",
    "df['Doctor'] = df_enc[1]\n",
    "df['Manager'] = df_enc[2]\n",
    "df['Nurse'] = df_enc[3]\n",
    "df['Engineer'] = df_enc[4]\n",
    "df['Accountant'] = df_enc[5]\n",
    "df['Scientist'] = df_enc[6]\n",
    "df['Teacher'] = df_enc[7]\n",
    "df['Lawyer'] = df_enc[8]\n",
    "df['Salesperson'] = df_enc[9]\n",
    "\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "st_enc = enc.fit_transform(df[['BMI Category']])\n",
    "df_enc = pd.DataFrame(st_enc)\n",
    "df['Underweight'] = df_enc[0]\n",
    "df['Normal'] = df_enc[1]\n",
    "df['Overweight'] = df_enc[2]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Target and Predictor\n",
    "X = df.drop(['Gender', 'Occupation', 'BMI Category', 'Sleep Disorder'], axis=1)\n",
    "y = df['Sleep Disorder']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data to train and  test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.18, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the model\n",
    "model = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['None', 'None', 'None', 'None', 'None', 'Insomnia', 'None', 'None',\n",
       "       'Insomnia', 'None', 'Insomnia', 'Sleep Apnea', 'Insomnia', 'None',\n",
       "       'None', 'Sleep Apnea', 'None', 'None', 'Insomnia', 'Sleep Apnea',\n",
       "       'None', 'Insomnia', 'None', 'None', 'Sleep Apnea', 'Sleep Apnea',\n",
       "       'Sleep Apnea', 'Insomnia', 'None', 'None', 'Insomnia', 'None',\n",
       "       'None', 'None', 'None', 'Sleep Apnea', 'None', 'None', 'None',\n",
       "       'Insomnia', 'None', 'None', 'Insomnia', 'None', 'Insomnia', 'None',\n",
       "       'None', 'None', 'None', 'None', 'Insomnia', 'Sleep Apnea',\n",
       "       'Insomnia', 'None', 'None', 'Sleep Apnea', 'None', 'None', 'None',\n",
       "       'None', 'Insomnia', 'Insomnia', 'Insomnia', 'None', 'Insomnia'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predition\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.9077\n",
      "precision: 0.9045\n",
      "recall: 0.8906\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x22139ba0820>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evaluation\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print('accuracy: ' + str(round(accuracy,4)))\n",
    "\n",
    "precision = precision_score(y_test, y_pred, average='macro')\n",
    "print('precision: ' + str(round(precision,4)))\n",
    "\n",
    "recall =  recall_score(y_test, y_pred, average='macro')\n",
    "print('recall: ' + str(round(recall,4)))\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "conf_matrix\n",
    "\n",
    "ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels=model.classes_).plot()"
   ]
  }
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