A_C_SH_SD-Model3.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": 1,
   "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,
   "metadata": {},
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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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       "     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 = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "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', 'None', 'None', 'None',\n",
       "       'None', 'None', 'None', 'Sleep Apnea', 'None', 'None', 'None',\n",
       "       'Sleep Apnea', 'None', 'None', 'None', 'None', 'None', 'None',\n",
       "       'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None',\n",
       "       'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None',\n",
       "       'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None',\n",
       "       'None', 'None', 'None', 'None', 'None', 'Sleep Apnea', 'None',\n",
       "       'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None',\n",
       "       'None', 'None', 'None', 'None'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Lintang\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.6615\n",
      "precision: 0.5484\n",
      "recall: 0.4242\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x13b559080a0>"
      ]
     },
     "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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}