cstick_ver2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "34669acd-cb52-4c41-a705-0aecdb87ca25",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "import numpy as np \n",
    "import pandas as pd\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "c9b7505d-5ee5-408e-8ca3-f6ecd38ffba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\GIRIRAJ ROY\\Downloads\\cStick.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "23a0ebd4-6d61-4ff6-b20c-8a320063f426",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>HRV</th>\n",
       "      <th>Sugar level</th>\n",
       "      <th>SpO2</th>\n",
       "      <th>Accelerometer</th>\n",
       "      <th>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.540</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.396</td>\n",
       "      <td>61.080</td>\n",
       "      <td>87.770</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.595</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.190</td>\n",
       "      <td>20.207</td>\n",
       "      <td>65.190</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.067</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.412</td>\n",
       "      <td>79.345</td>\n",
       "      <td>99.345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.090</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.266</td>\n",
       "      <td>36.180</td>\n",
       "      <td>81.545</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.430</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.480</td>\n",
       "      <td>80.000</td>\n",
       "      <td>99.990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>27.160</td>\n",
       "      <td>1.0</td>\n",
       "      <td>102.584</td>\n",
       "      <td>64.320</td>\n",
       "      <td>88.580</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57.134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>70.824</td>\n",
       "      <td>73.690</td>\n",
       "      <td>93.690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>66.356</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.816</td>\n",
       "      <td>78.460</td>\n",
       "      <td>98.460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>60.382</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.752</td>\n",
       "      <td>75.370</td>\n",
       "      <td>95.370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23.170</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.658</td>\n",
       "      <td>56.340</td>\n",
       "      <td>86.585</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Distance  Pressure      HRV  Sugar level    SpO2  Accelerometer  Decision \n",
       "0    25.540       1.0  101.396       61.080  87.770            1.0          1\n",
       "1     2.595       2.0  110.190       20.207  65.190            1.0          2\n",
       "2    68.067       0.0   87.412       79.345  99.345            0.0          0\n",
       "3    13.090       1.0   92.266       36.180  81.545            1.0          1\n",
       "4    69.430       0.0   89.480       80.000  99.990            0.0          0\n",
       "5    27.160       1.0  102.584       64.320  88.580            1.0          1\n",
       "6    57.134       0.0   70.824       73.690  93.690            0.0          0\n",
       "7    66.356       0.0   84.816       78.460  98.460            0.0          0\n",
       "8    60.382       0.0   75.752       75.370  95.370            0.0          0\n",
       "9    23.170       1.0   99.658       56.340  86.585            1.0          1"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "4b937cae-9121-4af4-a195-d05b0ea5325c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2039, 7)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "b827e472-c3ba-4a87-b34e-8f5c1cf9921a",
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2039 entries, 0 to 2038\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Distance       2039 non-null   float64\n",
      " 1   Pressure       2039 non-null   float64\n",
      " 2   HRV            2039 non-null   float64\n",
      " 3   Sugar level    2039 non-null   float64\n",
      " 4   SpO2           2039 non-null   float64\n",
      " 5   Accelerometer  2039 non-null   float64\n",
      " 6   Decision       2039 non-null   int64  \n",
      "dtypes: float64(6), int64(1)\n",
      "memory usage: 111.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "daf2a8ee-ee7c-44a0-8e0d-e9e0552b2a71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "9d115460-312c-4327-a589-ba35ce20a0e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "5faa0ed4-8983-4233-8f09-0c5bf821997c",
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   "outputs": [
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     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>HRV</th>\n",
       "      <th>Sugar level</th>\n",
       "      <th>SpO2</th>\n",
       "      <th>Accelerometer</th>\n",
       "      <th>Decision</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>2.595</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.190</td>\n",
       "      <td>20.207</td>\n",
       "      <td>65.190</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.067</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.412</td>\n",
       "      <td>79.345</td>\n",
       "      <td>99.345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>13.090</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>99.990</td>\n",
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      "text/plain": [
       "   Distance  Pressure      HRV  Sugar level    SpO2  Accelerometer  Decision \n",
       "0    25.540       1.0  101.396       61.080  87.770            1.0          1\n",
       "1     2.595       2.0  110.190       20.207  65.190            1.0          2\n",
       "2    68.067       0.0   87.412       79.345  99.345            0.0          0\n",
       "3    13.090       1.0   92.266       36.180  81.545            1.0          1\n",
       "4    69.430       0.0   89.480       80.000  99.990            0.0          0"
      ]
     },
     "execution_count": 180,
     "metadata": {},
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   "source": [
    "df1.head()"
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   "execution_count": 181,
   "id": "e8968439-dd15-4203-9785-cdac5b76fe04",
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     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['Accelerometer'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "21b467da-2a8c-48f7-b13a-c65c6c571469",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "array([1, 2, 0], dtype=int64)"
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     },
     "execution_count": 182,
     "metadata": {},
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   ],
   "source": [
    "df['Decision '].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "f5206dda-7d9b-46d1-bcf5-efc309f73c2f",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Distance</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>HRV</th>\n",
       "      <th>Sugar level</th>\n",
       "      <th>SpO2</th>\n",
       "      <th>Accelerometer</th>\n",
       "      <th>Decision</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25.540</td>\n",
       "      <td>1.0</td>\n",
       "      <td>101.396</td>\n",
       "      <td>61.080</td>\n",
       "      <td>87.770</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.595</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.190</td>\n",
       "      <td>20.207</td>\n",
       "      <td>65.190</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.067</td>\n",
       "      <td>0.0</td>\n",
       "      <td>87.412</td>\n",
       "      <td>79.345</td>\n",
       "      <td>99.345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.090</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.266</td>\n",
       "      <td>36.180</td>\n",
       "      <td>81.545</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.430</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.480</td>\n",
       "      <td>80.000</td>\n",
       "      <td>99.990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2034</th>\n",
       "      <td>5.655</td>\n",
       "      <td>2.0</td>\n",
       "      <td>116.310</td>\n",
       "      <td>162.242</td>\n",
       "      <td>71.310</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2035</th>\n",
       "      <td>9.660</td>\n",
       "      <td>2.0</td>\n",
       "      <td>124.320</td>\n",
       "      <td>177.995</td>\n",
       "      <td>79.320</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2036</th>\n",
       "      <td>15.220</td>\n",
       "      <td>1.0</td>\n",
       "      <td>93.828</td>\n",
       "      <td>40.440</td>\n",
       "      <td>82.610</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2037</th>\n",
       "      <td>9.120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>123.240</td>\n",
       "      <td>175.871</td>\n",
       "      <td>78.240</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2038</th>\n",
       "      <td>62.441</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.876</td>\n",
       "      <td>76.435</td>\n",
       "      <td>96.435</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2039 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Distance  Pressure      HRV  Sugar level    SpO2  Accelerometer  \\\n",
       "0       25.540       1.0  101.396       61.080  87.770            1.0   \n",
       "1        2.595       2.0  110.190       20.207  65.190            1.0   \n",
       "2       68.067       0.0   87.412       79.345  99.345            0.0   \n",
       "3       13.090       1.0   92.266       36.180  81.545            1.0   \n",
       "4       69.430       0.0   89.480       80.000  99.990            0.0   \n",
       "...        ...       ...      ...          ...     ...            ...   \n",
       "2034     5.655       2.0  116.310      162.242  71.310            1.0   \n",
       "2035     9.660       2.0  124.320      177.995  79.320            1.0   \n",
       "2036    15.220       1.0   93.828       40.440  82.610            1.0   \n",
       "2037     9.120       2.0  123.240      175.871  78.240            1.0   \n",
       "2038    62.441       0.0   78.876       76.435  96.435            0.0   \n",
       "\n",
       "      Decision   \n",
       "0             1  \n",
       "1             2  \n",
       "2             0  \n",
       "3             1  \n",
       "4             0  \n",
       "...         ...  \n",
       "2034          2  \n",
       "2035          2  \n",
       "2036          1  \n",
       "2037          2  \n",
       "2038          0  \n",
       "\n",
       "[2039 rows x 7 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "6957efd4-5046-4fdf-8d62-14e65bf70f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(columns=['Sugar level','Pressure','Distance'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "b1318413-4ce6-4ac0-bc02-9b21d28832a5",
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    {
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       "      <th></th>\n",
       "      <th>HRV</th>\n",
       "      <th>SpO2</th>\n",
       "      <th>Accelerometer</th>\n",
       "      <th>Decision</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>101.396</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110.190</td>\n",
       "      <td>65.190</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>87.412</td>\n",
       "      <td>99.345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>92.266</td>\n",
       "      <td>81.545</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>89.480</td>\n",
       "      <td>99.990</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>102.584</td>\n",
       "      <td>88.580</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70.824</td>\n",
       "      <td>93.690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>84.816</td>\n",
       "      <td>98.460</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>75.752</td>\n",
       "      <td>95.370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>99.658</td>\n",
       "      <td>86.585</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>79.888</td>\n",
       "      <td>96.780</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>73.464</td>\n",
       "      <td>94.590</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>100.692</td>\n",
       "      <td>87.290</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>100.252</td>\n",
       "      <td>86.990</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>110.670</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>81.428</td>\n",
       "      <td>97.305</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>102.364</td>\n",
       "      <td>88.430</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>113.730</td>\n",
       "      <td>68.730</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>84.508</td>\n",
       "      <td>98.355</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>81.120</td>\n",
       "      <td>97.200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "        HRV    SpO2  Accelerometer  Decision \n",
       "0   101.396  87.770            1.0          1\n",
       "1   110.190  65.190            1.0          2\n",
       "2    87.412  99.345            0.0          0\n",
       "3    92.266  81.545            1.0          1\n",
       "4    89.480  99.990            0.0          0\n",
       "5   102.584  88.580            1.0          1\n",
       "6    70.824  93.690            0.0          0\n",
       "7    84.816  98.460            0.0          0\n",
       "8    75.752  95.370            0.0          0\n",
       "9    99.658  86.585            1.0          1\n",
       "10   79.888  96.780            0.0          0\n",
       "11   73.464  94.590            0.0          0\n",
       "12  100.692  87.290            1.0          1\n",
       "13  100.252  86.990            1.0          1\n",
       "14  110.670  65.670            1.0          2\n",
       "15   81.428  97.305            0.0          0\n",
       "16  102.364  88.430            1.0          1\n",
       "17  113.730  68.730            1.0          2\n",
       "18   84.508  98.355            0.0          0\n",
       "19   81.120  97.200            0.0          0"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "c17d1e68-1a86-49eb-a2aa-43b464dff868",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df1.drop(columns=['Decision '])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "1392c0ec-ccc6-44b0-b31a-391950c4fe32",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
       "      <td>110.190</td>\n",
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       "      <td>87.412</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <th>2034</th>\n",
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       "      <td>71.310</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2035</th>\n",
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       "      <td>79.320</td>\n",
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       "      <th>2036</th>\n",
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       "      <td>82.610</td>\n",
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       "    <tr>\n",
       "      <th>2037</th>\n",
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       "      <td>78.240</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2038</th>\n",
       "      <td>78.876</td>\n",
       "      <td>96.435</td>\n",
       "      <td>0.0</td>\n",
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       "</table>\n",
       "<p>2039 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "          HRV    SpO2  Accelerometer\n",
       "0     101.396  87.770            1.0\n",
       "1     110.190  65.190            1.0\n",
       "2      87.412  99.345            0.0\n",
       "3      92.266  81.545            1.0\n",
       "4      89.480  99.990            0.0\n",
       "...       ...     ...            ...\n",
       "2034  116.310  71.310            1.0\n",
       "2035  124.320  79.320            1.0\n",
       "2036   93.828  82.610            1.0\n",
       "2037  123.240  78.240            1.0\n",
       "2038   78.876  96.435            0.0\n",
       "\n",
       "[2039 rows x 3 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "5915c450-ce65-4516-a589-d361dec1f566",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df1[['Decision ']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "95c7b177-7acb-446b-8118-f4982259a95d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   Decision \n",
       "0          1\n",
       "1          2\n",
       "2          0\n",
       "3          1\n",
       "4          0"
      ]
     },
     "execution_count": 189,
     "metadata": {},
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   ],
   "source": [
    "y.head()"
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  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "f7c1f58b-1930-4610-be51-1ced4a602ce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "0cd0a5bd-69db-4a3d-af76-7e93b5bfb147",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "5beb6d61-74e0-4fc6-9f7d-7977fd901e59",
   "metadata": {},
   "outputs": [
    {
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       "         HRV    SpO2  Accelerometer\n",
       "1064  66.248  92.130            0.0\n",
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      ]
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     "execution_count": 192,
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   ],
   "source": [
    "x_train.head(2)"
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  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "2dbf1137-625a-424b-b658-ee4dc4795a26",
   "metadata": {},
   "outputs": [
    {
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       "         HRV   SpO2  Accelerometer\n",
       "2011  70.296  93.51            0.0\n",
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   "execution_count": 194,
   "id": "5460f5e9-081c-4e63-9f11-f972f0d3699a",
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   "outputs": [
    {
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       "      Decision \n",
       "1064          0\n",
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   "execution_count": 195,
   "id": "7d0ae5d8-4a11-4e73-8e44-41c8371a44d2",
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    {
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Decision \n",
       "2011          0\n",
       "178           0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "552b35c9-65f1-4e2a-8831-c2f92569c6c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1631, 3)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "8888b4e6-7229-4adc-b2f7-65c9e408bb20",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "scaler.fit(x_train)\n",
    "with open('scaler_ver2.pkl', 'wb') as f:\n",
    "    pickle.dump(scaler, f)\n",
    "with open('scaler_ver2.pkl', 'rb') as f:\n",
    "    scaler = pickle.load(f)\n",
    "x_train_scaled = scaler.transform(x_train)\n",
    "\n",
    "\n",
    "x_test_scaled = scaler.transform(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "473dd9f9-e673-4324-9eb6-b130774418e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1631, 1)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "0bc4d8b0-29e1-47ed-a1a9-dfa61315c824",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('scaler_ver2.pkl', 'rb') as f:\n",
    "        scaler = pickle.load(f)\n",
    "#y_train_scaled = scaler.transform(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "6ea19884-1de1-4fc5-afb4-86013cad5194",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('scaler_ver2.pkl', 'rb') as f:\n",
    "        scaler = pickle.load(f)\n",
    "y_test_scaled = scaler.fit_transform(y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "97da2b85-38f3-4854-ac5c-efa8f59ff9e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1631, 3)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_scaled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "622d6c01-6e6e-4d80-bc5b-1d3f1765cd20",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_train_scaled[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "9a950c0c-5ecd-4fbf-b7ac-47cf2840cbad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.09615266, 0.80345086, 0.        ],\n",
       "       [0.55140043, 0.59952488, 1.        ],\n",
       "       [0.08464143, 0.79707427, 0.        ],\n",
       "       ...,\n",
       "       [0.76269621, 0.11402851, 1.        ],\n",
       "       [0.3317944 , 0.9339835 , 0.        ],\n",
       "       [0.48808864, 0.52938235, 1.        ]])"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "239f821f-0498-4934-9ecb-cdff6a5c2339",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIRIRAJ ROY\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model=Sequential()\n",
    "\n",
    "model.add(Dense(64,input_dim=3,activation='relu'))\n",
    "model.add(Dense(32,activation='relu'))\n",
    "model.add(Dense(16,activation='relu'))\n",
    "model.add(Dense(3,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "33979474-af5e-47eb-8c67-52d018a19ae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "model.compile(optimizer=Adam(learning_rate=0.001),loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "bc811987-8358-44f5-ad0a-19ec201b7829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">528</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │             \u001b[38;5;34m256\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m2,080\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)                  │             \u001b[38;5;34m528\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)                   │              \u001b[38;5;34m51\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,915</span> (11.39 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,915\u001b[0m (11.39 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,915</span> (11.39 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,915\u001b[0m (11.39 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "51bbad4b-fb13-4b09-8df3-42ec3860b778",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 997us/step - accuracy: 0.5578 - loss: 1.0039 \n",
      "Epoch 2/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 829us/step - accuracy: 0.9511 - loss: 0.5464\n",
      "Epoch 3/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 1.0000 - loss: 0.1829\n",
      "Epoch 4/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 832us/step - accuracy: 1.0000 - loss: 0.0445\n",
      "Epoch 5/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 995us/step - accuracy: 1.0000 - loss: 0.0156\n",
      "Epoch 6/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 832us/step - accuracy: 1.0000 - loss: 0.0073\n",
      "Epoch 7/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 834us/step - accuracy: 1.0000 - loss: 0.0041\n",
      "Epoch 8/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 832us/step - accuracy: 1.0000 - loss: 0.0027\n",
      "Epoch 9/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 1.0000 - loss: 0.0019\n",
      "Epoch 10/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 831us/step - accuracy: 1.0000 - loss: 0.0015\n",
      "Epoch 11/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 666us/step - accuracy: 1.0000 - loss: 0.0011   \n",
      "Epoch 12/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 661us/step - accuracy: 1.0000 - loss: 8.6268e-04\n",
      "Epoch 13/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 661us/step - accuracy: 1.0000 - loss: 7.1478e-04\n",
      "Epoch 14/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 829us/step - accuracy: 1.0000 - loss: 5.7497e-04\n",
      "Epoch 15/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 665us/step - accuracy: 1.0000 - loss: 5.0396e-04\n",
      "Epoch 16/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 828us/step - accuracy: 1.0000 - loss: 3.8528e-04\n",
      "Epoch 17/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 665us/step - accuracy: 1.0000 - loss: 3.5947e-04\n",
      "Epoch 18/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 3.1752e-04\n",
      "Epoch 19/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 663us/step - accuracy: 1.0000 - loss: 2.7819e-04\n",
      "Epoch 20/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 830us/step - accuracy: 1.0000 - loss: 2.5955e-04\n",
      "Epoch 21/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 2.1249e-04\n",
      "Epoch 22/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 665us/step - accuracy: 1.0000 - loss: 2.0882e-04\n",
      "Epoch 23/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 1.7413e-04\n",
      "Epoch 24/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 660us/step - accuracy: 1.0000 - loss: 1.5644e-04\n",
      "Epoch 25/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 663us/step - accuracy: 1.0000 - loss: 1.4611e-04\n",
      "Epoch 26/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 663us/step - accuracy: 1.0000 - loss: 1.3462e-04\n",
      "Epoch 27/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 829us/step - accuracy: 1.0000 - loss: 1.1962e-04\n",
      "Epoch 28/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 831us/step - accuracy: 1.0000 - loss: 1.1360e-04\n",
      "Epoch 29/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 830us/step - accuracy: 1.0000 - loss: 1.0235e-04\n",
      "Epoch 30/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 829us/step - accuracy: 1.0000 - loss: 9.6748e-05\n",
      "Epoch 31/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 665us/step - accuracy: 1.0000 - loss: 8.9050e-05\n",
      "Epoch 32/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 8.2645e-05\n",
      "Epoch 33/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 7.4195e-05\n",
      "Epoch 34/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 827us/step - accuracy: 1.0000 - loss: 7.0937e-05\n",
      "Epoch 35/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 6.7606e-05\n",
      "Epoch 36/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 1.0000 - loss: 6.2360e-05\n",
      "Epoch 37/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 1.0000 - loss: 5.8249e-05\n",
      "Epoch 38/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 832us/step - accuracy: 1.0000 - loss: 5.7895e-05\n",
      "Epoch 39/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 1.0000 - loss: 5.1987e-05\n",
      "Epoch 40/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 829us/step - accuracy: 1.0000 - loss: 4.8346e-05\n",
      "Epoch 41/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 830us/step - accuracy: 1.0000 - loss: 4.3436e-05\n",
      "Epoch 42/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 644us/step - accuracy: 1.0000 - loss: 4.4368e-05\n",
      "Epoch 43/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 829us/step - accuracy: 1.0000 - loss: 3.9001e-05\n",
      "Epoch 44/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 829us/step - accuracy: 1.0000 - loss: 3.6445e-05\n",
      "Epoch 45/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 663us/step - accuracy: 1.0000 - loss: 3.4746e-05\n",
      "Epoch 46/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 1.0000 - loss: 3.4281e-05\n",
      "Epoch 47/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 829us/step - accuracy: 1.0000 - loss: 3.2015e-05\n",
      "Epoch 48/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 2.9966e-05\n",
      "Epoch 49/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 621us/step - accuracy: 1.0000 - loss: 2.8721e-05\n",
      "Epoch 50/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 2.6943e-05\n",
      "Epoch 51/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 2.5709e-05\n",
      "Epoch 52/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 663us/step - accuracy: 1.0000 - loss: 2.5262e-05\n",
      "Epoch 53/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 828us/step - accuracy: 1.0000 - loss: 2.4623e-05\n",
      "Epoch 54/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 1.0000 - loss: 2.2715e-05\n",
      "Epoch 55/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 2.1498e-05\n",
      "Epoch 56/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 2.1173e-05\n",
      "Epoch 57/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 1.9144e-05\n",
      "Epoch 58/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 661us/step - accuracy: 1.0000 - loss: 1.8494e-05\n",
      "Epoch 59/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 1.0000 - loss: 1.7480e-05\n",
      "Epoch 60/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 1.6320e-05\n",
      "Epoch 61/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 1.0000 - loss: 1.6842e-05\n",
      "Epoch 62/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 663us/step - accuracy: 1.0000 - loss: 1.5471e-05\n",
      "Epoch 63/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 643us/step - accuracy: 1.0000 - loss: 1.3902e-05\n",
      "Epoch 64/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 1.4509e-05\n",
      "Epoch 65/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 1.3244e-05\n",
      "Epoch 66/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 1.3281e-05\n",
      "Epoch 67/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 1.0000 - loss: 1.2268e-05\n",
      "Epoch 68/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step - accuracy: 1.0000 - loss: 1.1734e-05\n",
      "Epoch 69/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 830us/step - accuracy: 1.0000 - loss: 1.1346e-05\n",
      "Epoch 70/70\n",
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 664us/step - accuracy: 1.0000 - loss: 1.0988e-05\n"
     ]
    }
   ],
   "source": [
    "\n",
    "history = model.fit(x_train_scaled, y_train, epochs=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "c61c8a9c-0df3-4255-84ef-d6a02f071c20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 663us/step - accuracy: 1.0000 - loss: 1.0199e-05\n",
      "Test Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_accuracy = model.evaluate(x_test_scaled, y_test)\n",
    "print(f'Test Accuracy: {test_accuracy}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "2f1c06fa-9c66-4748-9efd-6cda6f921306",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Specify the filename\n",
    "filename = 'mlp_model_ver2.pkl'\n",
    "\n",
    "# Save the model to disk\n",
    "with open(filename, 'wb') as file:\n",
    "    pickle.dump(model, file)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "11321d34-a838-4abb-8d43-53f87edfd43f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n"
     ]
    }
   ],
   "source": [
    "# Load the model from disk\n",
    "with open('mlp_model_ver2.pkl', 'rb') as file:\n",
    "    loaded_mlp_model = pickle.load(file)\n",
    "\n",
    "# Use the loaded model to make predictions\n",
    "predictions = loaded_mlp_model.predict(x_test_scaled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "9812dc86-7232-4f28-aaa1-c4f133c5d73a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.9999690e-01, 2.9393643e-06, 1.7543103e-07],\n",
       "       [9.9999881e-01, 1.1154619e-06, 6.9212042e-08],\n",
       "       [7.8996698e-07, 4.4382961e-07, 9.9999869e-01],\n",
       "       ...,\n",
       "       [6.0218053e-06, 9.9996662e-01, 2.7380771e-05],\n",
       "       [3.5760511e-06, 9.9998355e-01, 1.2867664e-05],\n",
       "       [9.6016174e-07, 1.0707649e-06, 9.9999797e-01]], dtype=float32)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "3e0bc7b0-6b82-4538-aadb-e3d053c04865",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21c255f98d0>]"
      ]
     },
     "execution_count": 213,
     "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": [
    "import matplotlib.pyplot as plt \n",
    "plt.plot(history.history['loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "af6029d5-94fd-4a0b-928e-68947003ec97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Accuracy'], loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "77a3f169-78df-4909-8657-51219e7ff4b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n"
     ]
    }
   ],
   "source": [
    "y_pred=model.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "36d2eeaa-b755-4215-9542-aa6fffabc8f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <th>591</th>\n",
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       "      <td>2</td>\n",
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       "      Decision \n",
       "1064          0\n",
       "160           1\n",
       "119           0\n",
       "591           0\n",
       "61            2"
      ]
     },
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     "metadata": {},
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    "y_train.head()"
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   "cell_type": "code",
   "execution_count": 217,
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   "outputs": [
    {
     "data": {
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       "array([[9.99996901e-01, 2.93936432e-06, 1.75431026e-07],\n",
       "       [9.99998808e-01, 1.11546194e-06, 6.92120423e-08],\n",
       "       [7.89966975e-07, 4.43829606e-07, 9.99998689e-01],\n",
       "       [5.41209101e-06, 9.99971390e-01, 2.32512593e-05],\n",
       "       [6.62486855e-06, 9.99961615e-01, 3.17606355e-05],\n",
       "       [9.99999166e-01, 8.93057859e-07, 5.79900039e-08],\n",
       "       [1.10585609e-06, 2.01416447e-06, 9.99996901e-01],\n",
       "       [1.75315529e-06, 9.84766120e-06, 9.99988317e-01],\n",
       "       [9.99997973e-01, 1.89486298e-06, 1.13133034e-07],\n",
       "       [7.70057625e-07, 3.90496012e-07, 9.99998927e-01],\n",
       "       [7.25644554e-07, 2.08737205e-07, 9.99999046e-01],\n",
       "       [9.99999166e-01, 8.75075727e-07, 5.71009728e-08],\n",
       "       [9.99993205e-01, 6.43243720e-06, 3.68567640e-07],\n",
       "       [7.27400959e-07, 2.36082471e-07, 9.99999046e-01],\n",
       "       [4.01478928e-06, 9.99980807e-01, 1.51451977e-05],\n",
       "       [9.99999285e-01, 7.47987599e-07, 5.06858235e-08],\n",
       "       [9.99997854e-01, 2.02553520e-06, 1.20982946e-07],\n",
       "       [9.99998450e-01, 1.37716188e-06, 8.28253661e-08],\n",
       "       [9.99995470e-01, 4.24632753e-06, 2.50818402e-07],\n",
       "       [2.46117861e-06, 2.71022964e-05, 9.99970436e-01]], dtype=float32)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "029bfa39-aceb-4f73-8334-690ed49ab01c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>0</td>\n",
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       "      <th>511</th>\n",
       "      <td>1</td>\n",
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       "      <th>1543</th>\n",
       "      <td>2</td>\n",
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       "      <th>487</th>\n",
       "      <td>2</td>\n",
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       "      <th>8</th>\n",
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       "      <th>403</th>\n",
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       "      <th>460</th>\n",
       "      <td>2</td>\n",
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       "      <th>98</th>\n",
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       "    <tr>\n",
       "      <th>1909</th>\n",
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       "      <td>2</td>\n",
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       "      <th>595</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1804</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1734</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1577</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Decision \n",
       "2011          0\n",
       "178           0\n",
       "454           2\n",
       "410           1\n",
       "511           1\n",
       "1943          0\n",
       "1543          2\n",
       "487           2\n",
       "8             0\n",
       "403           2\n",
       "460           2\n",
       "98            0\n",
       "1909          0\n",
       "608           2\n",
       "595           1\n",
       "1804          0\n",
       "1734          0\n",
       "1135          0\n",
       "1380          0\n",
       "1577          2"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "09420777-2872-4727-a052-d06e67eb4536",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.09615266, 0.80345086, 0.        ],\n",
       "       [0.55140043, 0.59952488, 1.        ],\n",
       "       [0.08464143, 0.79707427, 0.        ],\n",
       "       [0.12120653, 0.81732933, 0.        ],\n",
       "       [0.7156048 , 0.03750938, 1.        ],\n",
       "       [0.17266851, 0.84583646, 0.        ],\n",
       "       [0.7820868 , 0.14553638, 1.        ],\n",
       "       [0.64484457, 0.70305076, 1.        ],\n",
       "       [0.57510003, 0.62578145, 1.        ],\n",
       "       [0.51889812, 0.56351588, 1.        ]])"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_scaled[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "64baf4fb-1230-4d3c-95e1-41443e00bb47",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HRV</th>\n",
       "      <th>SpO2</th>\n",
       "      <th>Accelerometer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>66.248</td>\n",
       "      <td>92.130</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>95.830</td>\n",
       "      <td>83.975</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>65.500</td>\n",
       "      <td>91.875</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>67.876</td>\n",
       "      <td>92.685</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>106.500</td>\n",
       "      <td>61.500</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>71.220</td>\n",
       "      <td>93.825</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>110.820</td>\n",
       "      <td>65.820</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>101.902</td>\n",
       "      <td>88.115</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825</th>\n",
       "      <td>97.370</td>\n",
       "      <td>85.025</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1426</th>\n",
       "      <td>93.718</td>\n",
       "      <td>82.535</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          HRV    SpO2  Accelerometer\n",
       "1064   66.248  92.130            0.0\n",
       "160    95.830  83.975            1.0\n",
       "119    65.500  91.875            0.0\n",
       "591    67.876  92.685            0.0\n",
       "61    106.500  61.500            1.0\n",
       "1301   71.220  93.825            0.0\n",
       "1077  110.820  65.820            1.0\n",
       "562   101.902  88.115            1.0\n",
       "1825   97.370  85.025            1.0\n",
       "1426   93.718  82.535            1.0"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "a3b875cb-c3bc-409c-a763-4f1be008fa8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def convert_to_class(y_pred):\n",
    "  max_indices = np.argmax(y_pred, axis=1)\n",
    "  return max_indices\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "00d1222c-46dd-4e86-a92d-8749e930af80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 2 1 1 0 2 2 0 2 2 0 0 2 1 0 0 0 0 2 1 2 1 0 2 2 1 1 0 1 1 0 2 2 2 2 1\n",
      " 2 2 2 0 0 0 2 1 2 2 2 2 0 2 2 0 2 2 0 2 1 2 2 2 0 1 0 2 0 0 1 1 1 2 0 2 1\n",
      " 1 2 0 1 2 0 2 0 0 1 0 2 2 1 0 2 2 2 2 0 0 2 0 0 2 1 2 2 0 1 0 2 1 2 1 1 1\n",
      " 1 1 2 2 2 0 2 2 0 0 2 0 0 0 2 0 1 2 0 0 0 1 2 2 2 2 0 1 0 2 1 1 1 0 0 0 2\n",
      " 1 2 0 0 2 1 2 2 2 1 1 1 1 1 2 1 1 0 2 2 1 2 2 2 1 0 1 0 1 0 1 0 2 0 0 2 0\n",
      " 0 0 0 2 2 1 2 2 1 0 2 0 0 0 1 0 0 1 2 2 1 0 2 2 0 0 1 2 0 0 0 2 0 2 1 1 2\n",
      " 1 0 2 0 0 0 1 1 2 2 0 1 0 0 2 2 1 2 2 2 0 2 0 0 2 2 2 2 2 1 1 1 0 2 0 1 0\n",
      " 2 2 2 0 0 1 0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 1 1 0 1 1 0 0 1 2 1 2 0 1 2 0 0\n",
      " 1 0 0 2 2 1 1 1 0 0 2 1 0 1 0 0 2 1 2 1 1 1 1 1 1 1 2 1 1 1 0 1 2 0 0 1 2\n",
      " 1 1 2 2 2 2 1 1 1 0 2 0 1 1 1 2 0 2 0 1 0 2 2 1 1 0 2 1 1 2 0 0 1 2 2 1 1\n",
      " 0 2 1 1 2 0 1 2 1 0 0 2 0 1 0 0 0 1 1 2 2 0 2 1 2 2 1 1 2 0 1 2 1 1 0 1 1\n",
      " 2]\n"
     ]
    }
   ],
   "source": [
    "y_true = convert_to_class(y_pred)\n",
    "print(y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "998c2c83-c755-4b17-a8fd-14a4bfa86b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>Decision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1943</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Decision \n",
       "2011          0\n",
       "178           0\n",
       "454           2\n",
       "410           1\n",
       "511           1\n",
       "1943          0\n",
       "1543          2\n",
       "487           2\n",
       "8             0\n",
       "403           2"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "aeb64380-9f97-4ae5-8459-ec549bfb0588",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test,y_true)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "d55f6287-782d-460d-a6b8-98b0191837aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[9.9999559e-01, 4.1206226e-06, 2.4367279e-07]], dtype=float32)"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_train_scaled[[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "88c278a3-7f62-4e86-a480-71cf492e85bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 830us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[9.9999559e-01, 4.1206226e-06, 2.4367279e-07],\n",
       "       [4.1164903e-06, 9.9998021e-01, 1.5688842e-05],\n",
       "       [9.9999535e-01, 4.3974028e-06, 2.5926093e-07],\n",
       "       ...,\n",
       "       [7.7115112e-07, 3.9328367e-07, 9.9999893e-01],\n",
       "       [9.9999869e-01, 1.1633666e-06, 7.1714673e-08],\n",
       "       [5.8690207e-06, 9.9996781e-01, 2.6324100e-05]], dtype=float32)"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "780c5d2d-3eef-454f-acf1-1fc4fe927f7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m13/13\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "with open('mlp_model_ver2.pkl', 'rb') as file:\n",
    "    loaded_mlp_model = pickle.load(file)\n",
    "predictions = loaded_mlp_model.predict(x_test_scaled)\n",
    "\n",
    "def predict_fall(loaded_mlp_model, input_array):\n",
    "    with open('scaler_ver2.pkl', 'rb') as f:\n",
    "        scaler = pickle.load(f)\n",
    "    input_reshaped = np.array(input_array)\n",
    "    scaled_input=scaler.transform([input_reshaped]).flatten()\n",
    "    input_2d=np.array([scaled_input])\n",
    "    print(scaled_input)\n",
    "    output=loaded_mlp_model.predict(input_2d)\n",
    "\n",
    "    return np.argmax(output, axis=1)[0]\n",
    "    #return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "56f9e6c8-2b95-411b-9bef-a3125cf33316",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.15844875 0.83795949 0.        ]\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GIRIRAJ ROY\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_fall(loaded_mlp_model,[70.296,\t93.510,\t0.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "7255e84f-f818-4348-8749-092af05c6b8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "202a35af-d89a-47c3-84f8-bacda67ea0ab",
   "metadata": {},
   "outputs": [
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       "      <th>HRV</th>\n",
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       "      <th>454</th>\n",
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       "      <td>0.0</td>\n",
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       "      <th>8</th>\n",
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       "      <td>95.370</td>\n",
       "      <td>0.0</td>\n",
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       "          HRV    SpO2  Accelerometer\n",
       "2011   70.296  93.510            0.0\n",
       "178    82.088  97.530            0.0\n",
       "454   110.070  65.070            1.0\n",
       "410    92.618  81.785            1.0\n",
       "511    90.396  80.270            1.0\n",
       "1943   85.212  98.595            0.0\n",
       "1543  115.710  70.710            1.0\n",
       "487   120.300  75.300            1.0\n",
       "8      75.752  95.370            0.0\n",
       "403   109.530  64.530            1.0\n",
       "460   106.170  61.170            1.0\n",
       "98     85.520  98.700            0.0\n",
       "1909   61.364  90.465            0.0\n",
       "608   106.950  61.950            1.0\n",
       "595    96.138  84.185            1.0"
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    "x_test.head(15)"
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  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "841ed695-358c-4326-9500-3867ad9aeb9f",
   "metadata": {},
   "outputs": [
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