source_code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We use decision tree for imputation, LR and Random forest for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "pfk9wWslcQzH"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "K2ySjlA-cQzS",
    "outputId": "1e683d61-ab89-4207-da5b-3d9bbd3bca8d"
   },
   "outputs": [
    {
     "data": {
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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>body_temperature</th>\n",
       "      <th>limb_movement</th>\n",
       "      <th>Blood_oxygen</th>\n",
       "      <th>Sleeping_hours</th>\n",
       "      <th>Heart_rate</th>\n",
       "      <th>Stress_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91.840</td>\n",
       "      <td>16.600</td>\n",
       "      <td>89.840</td>\n",
       "      <td>1.840</td>\n",
       "      <td>74.20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.552</td>\n",
       "      <td>15.880</td>\n",
       "      <td>89.552</td>\n",
       "      <td>1.552</td>\n",
       "      <td>72.76</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>60.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90.768</td>\n",
       "      <td>13.920</td>\n",
       "      <td>88.768</td>\n",
       "      <td>0.768</td>\n",
       "      <td>68.84</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97.872</td>\n",
       "      <td>6.496</td>\n",
       "      <td>96.248</td>\n",
       "      <td>8.248</td>\n",
       "      <td>53.12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   body_temperature  limb_movement  Blood_oxygen  Sleeping_hours  Heart_rate  \\\n",
       "0            91.840         16.600        89.840           1.840       74.20   \n",
       "1            91.552         15.880        89.552           1.552       72.76   \n",
       "2            96.000         10.000        95.000           7.000       60.00   \n",
       "3            90.768         13.920        88.768           0.768       68.84   \n",
       "4            97.872          6.496        96.248           8.248       53.12   \n",
       "\n",
       "   Stress_level  \n",
       "0             3  \n",
       "1             3  \n",
       "2             1  \n",
       "3             3  \n",
       "4             0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pillow = pd.read_csv(\"SaYoPillow.csv\")\n",
    "pillow.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h4ZK5Eo7cQzT",
    "outputId": "bf2caeab-388a-4f26-b305-fab75450b612"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(630, 6)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pillow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZQ8KooKkcQzU",
    "outputId": "efa5f46d-ba72-4b8d-9c62-fde1fdd5d6da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pillow.isnull().values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f5oQZSXBcQzV",
    "outputId": "56190e0b-093c-423a-d43c-1daab8799d2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pillow.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bJYfzmEkcQzV",
    "outputId": "0382c6c9-e560-486c-f330-63b4dbf0828a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "body_temperature    float64\n",
       "limb_movement       float64\n",
       "Blood_oxygen        float64\n",
       "Sleeping_hours      float64\n",
       "Heart_rate          float64\n",
       "Stress_level          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pillow.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "XlPxZwkfcQzW",
    "outputId": "b5449c7e-673c-4876-99d7-51a613b731ba"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>body_temperature</th>\n",
       "      <th>limb_movement</th>\n",
       "      <th>Blood_oxygen</th>\n",
       "      <th>Sleeping_hours</th>\n",
       "      <th>Heart_rate</th>\n",
       "      <th>Stress_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>630.00000</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>630.000000</td>\n",
       "      <td>630.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>92.80000</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>90.900000</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.52969</td>\n",
       "      <td>4.299629</td>\n",
       "      <td>3.902483</td>\n",
       "      <td>3.054572</td>\n",
       "      <td>9.915277</td>\n",
       "      <td>1.415337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>85.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>90.50000</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>88.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>56.250000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>93.00000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>95.50000</td>\n",
       "      <td>15.750000</td>\n",
       "      <td>94.250000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.00000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       body_temperature  limb_movement  Blood_oxygen  Sleeping_hours  \\\n",
       "count         630.00000     630.000000    630.000000      630.000000   \n",
       "mean           92.80000      11.700000     90.900000        3.700000   \n",
       "std             3.52969       4.299629      3.902483        3.054572   \n",
       "min            85.00000       4.000000     82.000000        0.000000   \n",
       "25%            90.50000       8.500000     88.500000        0.500000   \n",
       "50%            93.00000      11.000000     91.000000        3.500000   \n",
       "75%            95.50000      15.750000     94.250000        6.500000   \n",
       "max            99.00000      19.000000     97.000000        9.000000   \n",
       "\n",
       "       Heart_rate  Stress_level  \n",
       "count  630.000000    630.000000  \n",
       "mean    64.500000      2.000000  \n",
       "std      9.915277      1.415337  \n",
       "min     50.000000      0.000000  \n",
       "25%     56.250000      1.000000  \n",
       "50%     62.500000      2.000000  \n",
       "75%     72.500000      3.000000  \n",
       "max     85.000000      4.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pillow.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iUXM6UICcQzX"
   },
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 583
    },
    "id": "zrXm7k71cQzp",
    "outputId": "8883c24a-9ff6-42e1-a179-433a04fcdaf1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "cols=[\"body_temperature\", \"limb_movement\",\"Blood_oxygen\",\"Sleeping_hours\", \"Heart_rate\", \"Stress_level\"]\n",
    "sns.heatmap(pillow[cols].corr(), cmap=\"icefire\", annot=True, annot_kws={'size': 15})\n",
    "plt.title(\"Correlation Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G4hhIyhScQzq"
   },
   "source": [
    "### --Stress Levels and Sleeping Hours are strongly negatively correlated. The more people sleep, the less they are likely to be stressed\n",
    "\n",
    "### --Body temperature and stress level are highly negatively correlated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "id": "qtYymoAccQzr",
    "outputId": "72390f5b-171c-4581-887a-57f16aa3d589"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z2S6lbok7IgLoSBeL6S3qtT8zs91FXce4JeUl3QU8AVwfEb+uUe3YdDjlOkkL6xmPmdmuoK6JOyL6I+IoYB5wtKTDh1VZDuwXEUcC/w+4qlY7ks6UtEzSsvXr19czZDOzZ71JmVUSEZuAG4ETh63fEhEd6f2fAkVJs2tsf1lEtEdEe1tb2yREbGb27FXPWSVtkmam95uAVwF/GFZnjiSl949O49lYr5jMzHYF9ZxVsjfwDUl5koT8/Yi4VtJZABFxKXA68A5JZaALeGP6oaaZmY1AWcuT7e3tsWzZsqkOw8xsMqjWSn9z0swsY5y4zcwyxonbzCxjnLjNzDLGidvMLGOcuM3MMsaJ28wsY5y4zcwyxonbzCxjnLjNzDLGidvMLGOcuM3MMsaJ28wsY5y4zcwyxonbzCxjnLjNzDLGidvMLGOcuM3MMsaJ28wsY5y4zcwyxonbzCxjnLjNzDLGidvMLGOcuM3MMsaJ28wsY5y4zcwyxonbzCxjnLjNzDKmUK+GJTUCNwMN6X6ujIilw+oIuAg4GdgGvC0ilk90LOVyhQfWb6Gju5+tPX00FQts7S4zq6VET3+ZYi7Pxs5eWhsKNBVzFHJiW1+FJzt7mT2tREM+x4aOXqY1FunuK9NUKrB5Wx/Tmgo0FfNs6SrTV+mntaHIho4e9mwpUcyJQj5HR0+Zzp4y05uKdPWVaSjkyUvkcyKATZ19tDYW6Cn301jMs623zLSGIv3RT15JXNObChTzOTZt62NGU5Gu3iSG/kqFrd1l9mwp0dlbZlqpSFe5n86eMi2lAk2lPE9t66WhkKdYEC2lAoc+ZzqFgl+vzbKsbokb6AEWRUSHpCJwq6TrIuL2qjonAQvS2zHAF9K/E6ZcrnDDfY+zeVsfl9y4ije0z+fiXz5Ad1+FxmKOC089nEtufICHNnax36wmPnTioWzuKnPBNSsH6yw9ZSFX/PohNnf3cdbLD+aCa5cPKfvhnQ+z6NA5Q9pdespCZjQVeN/3VwyuO3vRAr637GH+/mUHUsznWHr1yu3K3tA+n1/+4TFe3z5/SPk5ixdw+f8+xFPbegfrvvHF87n8fx+iVBDvfdUh3PdYB5+9/v4h2zQX83z1tgd544vnM62xwIMbOjlp4d5O3mYZpoio/06kZuBW4B0R8euq9V8EboyIK9Ll+4DjI2LdSG21t7fHsmXLxr3vFY88xSNPdnHulSs447gD+cqtq+nuqwyWNxZznHHcgVzyq1W885UHc9icaZx75Yrt6nzy9CO577GtNbf/5OlH8sEa23z69CN51xW/3W5f+RxcdnPtOL5y6+oR2xuIs7ruGccdCDBim2e+/ED6K/CVW1dz5suTuosP3Ysj991j3MfQzKaMaq2sa7dLUl7SXcATwPXVSTs1F3ikanlNum54O2dKWiZp2fr163cohnWbu+nsKdPdV0FiSGIDBtcn+2Gw7vA6Xb3lEbfv6q29TWdvuea+KjFyHN19FbpGiGEgzuq6EqO2WQkG61YiqffY5u6xD5yZPWvVNXFHRH9EHAXMA46WdPiwKrVeTbZ7CxARl0VEe0S0t7W17VAMe89ooqWxQGMxeagDfwc0FnNUv+morltdp6lUGHH75lLtbVpKhe3WRUBeI8fRWMzR3FC7vYE4q+sOrBupzZwYrJsT5ARzZjTWOlRmlhGTMtAZEZuAG4EThxWtAfatWp4HPDqR+16493SKeXHhqQu5ZsVazl60YEgSv/DUw7n27rUAXLNiLTnB0lMWDqmz9JSFfPnmP3LNirUsXbJ92TduW71du0tPWUgux5B1Zy9awLV3r6VtWgMXnLqwZtnZixbwjdtWb1d+zuIF/Gj5miF1B9Zds2ItB8xu4X0nHLLdNrOaS4N195rWwIK9Wlm494yJPMRmNsnqNsYtqQ3oi4hNkpqAXwD/HhHXVtV5DfAuklklxwAXR8TRo7W7o2PcMPKskj1bSvSONauktYFSQWysnlVSLLC5q2pWSXeZcn8/LQ1FNnb0MLO5RCmfzCrp7CnT0dPPtHTmSCmfI59LZ5UEbNqWzCrpLffTUMzT1VumtaFIJfrJDcwqaSxQLIw0q6SfPVuKw2aV9NNcytNczLOpu5dSPp1VUixw6BzPKjHLkJpj3PWcVbI38A1JeZKe/fcj4lpJZwFExKXAT0mS9iqS6YBvr0cghUKOw/aeWY+mzcwmXd0Sd0TcDbygxvpLq+4H8M56xWBmtivye2Yzs4xx4jYzyxgnbjOzjHHiNjPLGCduM7OMceI2M8sYJ24zs4xx4jYzyxgnbjOzjHHiNjPLGCduM7OMceI2M8sYJ24zs4xx4jYzyxgnbjOzjHHiNjPLGCduM7OMceI2M8sYJ24zs4xx4jYzyxgnbjOzjHHiNjPLGCduM7OMceI2M8sYJ24zs4xx4jYzyxgnbjOzjKlb4pa0r6RfSfq9pJWSzqlR53hJmyXdld7Oq1c8Zma7ikId2y4D74+I5ZKmAXdKuj4i7h1W75aIWFLHOMzMdil163FHxLqIWJ7e3wr8Hphbr/2Zme0uJmWMW9L+wAuAX9coPlbSCknXSVo4GfGYmWVZPYdKAJDUCvwQeE9EbBlWvBzYLyI6JJ0MXAUsqNHGmcCZAPPnz69vwGZmz3J17XFLKpIk7W9HxI+Gl0fElojoSO//FChKml2j3mUR0R4R7W1tbfUM2czsWa+es0oEfAX4fUR8doQ6c9J6SDo6jWdjvWIyM9sV1HOo5KXAW4B7JN2VrvsIMB8gIi4FTgfeIakMdAFvjIioY0xmZplXt8QdEbcCGqPO54HP1ysGM7Ndkb85aWaWMU7cZmYZ48RtZpYxI45xS3rhaBsOfCvSzMwm12gfTn5mlLIAFk1wLGZmNg4jJu6IeOVkBmJmZuMz5hi3pGZJ/yTpsnR5gSRfzc/MbIqM58PJrwG9wJ+ly2uAj9UtIjMzG9V4EvdBEfFJoA8gIroY44s1ZmZWP+NJ3L2Smkg+kETSQUBPXaMyM7MRjecr7+cDPwP2lfRtkmuQvK2OMZmZ2SjGTNwR8QtJdwIvIRkiOSciNtQ9MjMzq2nMxC3pauAK4OqI6Kx/SGZmNprxjHF/BngZcK+kH0g6XVJjneMyM7MRjGeo5CbgJkl5km9L/j3wVWB6nWMzM7MaxnU97nRWySnAG4AXAt+oZ1BmZjay8Yxxfw84hmRmySXAjRFRqXdgZmZW23h63F8D/ioi+usdjJmZjW08H07eDHzY1yoxM3t28LVKzMwyxtcqMTPLGF+rxMwsY8bz4eRSfK0SM7NnjfF8Aed6ScupulYJcFC9AzMzs9rG9QWciNgI/NfAcprI59crKDMzG9l4xrhr8YeTZmZTZGcTd0xoFGZmNm4jDpVIuobaCVrArLpFZGZmoxptjPvTO1kGgKR9gcuBOUAFuCwiLhpWR8BFwMnANuBtEbF8rLZt/CqV4OEnO9nY0Utvf4Wt3WWaS3kaiiKvHI9t6WZ2awPFnHh0czdzZzZSrgTrO3qZ2VSgqZhnU1cfDYU8nT1lZjQV2dTVxx7NRfI50dXbT0dPPzOaCoDY0t3HrJYSOUFHTz/9lQpNxQIbOnvYe3oj/RFs6OhlWkOBplI+iamrzJ4tJbb29NFYyNFYLNBT7qe3P+ju7WfPlhIVKvT0BV29/cxoLrKtt0xTMU9DIce23n629fYzvanI5q4+ZjYXacjn2NTVR3dfhT1bilQCunrLNJcKbNrWR2MxR1MpT2Mxz6ZtfUPand5YpFyp0FMO+isVmksFtnT10dpYoKevn6ZSgd5ymcZige6+Ctt6y8xsLlLI5Vi3OTme0xvzdKZxDcQwu7WB+Xu2kMt5pNGemRETd3o512eiDLw/IpZLmgbcKen6iLi3qs5JwIL0dgzwhfSvTYBKJfjlfY/z6FNddJcrfPb6++nuq9BYzHHO4gW0lPJ84abVPLWtl3MWL+C6e9ax5Mh9htR73wmH0JDP8Ymf/WFw3YdPPJTHt3Szrbefz15/P3s0l3jrsftx0Q0PDNZZespCfnjnwyw6dA4X//KBmnWGx3Duq5/LzOYC23r66eztH1L3glMX8p83ruKhjV00FnO891WHcP296zj9RfM5/5qVg/XOXrSA7y17mLNecTCX3rSK3nLw1mP347t3PMwb2udz8S+H7n+fmY189vr7h7S7R3OBjZ19NbcZaP89iw9hfUfHdsf08v99iLkzG3jrsfuzdlP3kMfwgT9/LvvNambRc5/j5G3PiCImZ7ha0k+Az0fE9VXrvkhytcEr0uX7gOMjYt1I7bS3t8eyZcvqHu+uYPX6Dq66ay0Al928mu6+py/q2FjMcebLD6S/Apf8ahWNxRyfPP1IPnjlipr1Lr5h1eC6sxcfPKTNd77yYL5y6/btV7c3Up3hMXz69CO5/4mtNeM947gDueRXq2q2P7zeV25dzRnHHQgweH+s/Q+s+/TpR3LulStG3OaM4w4kn6t9TM847kCeO2caq0Z4DGe+/EBee9RcDmxrHd+TaLu7mq/wO/vh5I7tWdofeAHw62FFc4FHqpbXpOuGb3+mpGWSlq1fv75uce5qHt/STSWgEgxJIJAsVwKkp5e7esoj1qs2vE2pdvtdveUx6wyPobOnPGK80tDlkeId2JfEkPtj7X8whjTukbaRRj6mEnSN8hgqAU9s7cbsmdihxC0pJ2mHfvlGUivwQ+A9EbFleHGNTbZ7CxARl0VEe0S0t7W17cjud2vPmd5IXpBX0tur1ljMkRMMvOFqLOZobiiMWK9arTZrbddcKoxZZ3gMLY2FEeOtfnM4WrwRQ+sP1Blr/4MxVMU9UvujxdjcMPJjyAn2muZf/rNnZszELek7kqZLagHuBe6T9IHxNC6pSJK0vx0RP6pRZQ2wb9XyPODR8bRtY9t/VgvPnzeDWS0l3nfCIUOS0TmLFzC7pcSPlq8ZXP7SzX/crt77TjiEWc2lIev2bC7RNq1hsO4P71zDOYsXDKmz9JSFfOO21Zy9aMGIdYbHcO6rn0t3X5lZLaXt6l5w6kKuvXvt4PJ7X3UI37htNeefsnBIvbMXLeDau9ey9JSk/sB+r1mxdjCW6v0f1NayXbvbevtG3Gag/f1ntdQ8pj9avoZv3Laag9patnsMH/jz53LEvBnsP6uljs+67Q7GHOOWdFdEHCXpzcCLgA8Bd0bEEWNsJ5KfOHsyIt4zQp3XAO8imVVyDHBxRBw9Wrse494xI80qKRVEIZfj8S3dzEpnlazb0j1k5sf0xgLNpVFmlUh09Y0yq6S3n/7+ZFbJxs4e5lS13dqQtD0wq2SPlhIdPX00FHI0DZtVskdLiaieVdJUZFtfjVkljUU2d/cxs6lIQ2H0WSUNxRzNpTyNhTybuvvo6kna7eorM62hSDkq9JaD8gizSvrK/TQU8+mskn5mNhco5nI86lklNrFqnizj+cp7Me05v5bkw8U+SeP5RPOlwFuAeyTdla77COlX5SPiUuCnJEl7Fcl0wLePo13bAbmc2H92K/vPHrvuC+ofzi7Px9Amw3gS9xeBPwErgJsl7QcMH6veTkTcyhhfjY+ku//OccRgZmap8Vwd8GLg4qpVD0l6Zf1CMjOz0Yznw8lz0g8nJekr6ZUBF01CbGZmVsN4pgP+bTqN79VAG8k49L/VNSozMxvReBL3wDj1ycDXImIFY4xdm5lZ/Ywncd8p6Rckifvn6XVHKmNsY2ZmdTKeWSVnAEcBqyNim6RZeNqemdmUGU+PO4DnAWenyy2Av7NrZjZFxpO4/xM4FnhTurwVuKRuEZmZ2ajGM1RyTES8UNJvASLiKUmlOsdlZmYjGE+Pu09SnvSqfZLa8IeTZmZTZjyJ+2Lgx8Bekj4O3Ar8a12jMjOzEY06VCIpBzwIfBBYTDJ/+7UR8ftJiM3MzGoYNXFHREXSZyLiWOAPkxSTmZmNYjxDJb+Q9Jfp9bXNzGyKjWdWyftI5m6XJXWTDJdEROzQT5iZmdnEGM9lXadNRiBmZjY+47ms6w3jWWdmZpNjxB63pEagGZgtaQ+eviLgdGCfSYjNzMxqGG2o5B+A95Ak6Tt5OnFvwV95NzObMiMm7oi4CLhI0rsj4v9NYkxmZjaKEce4Jb1Y0pyBpC3prZJ+IuliSXtOXohmZlZttA8nvwj0Akh6OcnPlV0ObAYuq39oZmZWy2hj3PmIeDK9/wbgsoj4IfBDSXfVPTIzM6tptB53XtJAYl8M/LKqbDxf3DEzszoYLQFfAdwkaQPQBdwCIOlgkuESMzObAqPNKvl4+kWbvYFfRESkRTng3ZMRnJmZbW+sqwPeXmPd/fULx8zMxjKeqwPuFElflfSEpN+NUH68pM2S7kpv59UrFjOzXUk9P2T8OvB5kimEI7klIpbUMQYzs11O3XrcEXEz8OSYFc3MbIfULXGP07GSVki6TtLCKY7FzCwTpnI+9nJgv4jokHQycBWwoFZFSWcCZwLMnz9/0gI0M3s2mrIed0RsiYiO9P5PgaKk2SPUvSwi2iOiva2tbVLjNDN7tpmyxC1pzsDvWEo6Oo1l41TFY2aWFXUbKpF0BXA8yQ8xrAGWAkWAiLgUOB14h6QyyTcz31j1JR8zMxtB3RJ3RLxpjPLPk0wXNDOzHTDVs0rMzGwHOXGbmWWME7eZWcY4cZuZZYwTt5lZxjhxm5lljBO3mVnGOHGbmWWME7eZWcY4cZuZZYwTt5lZxjhxm5lljBO3mVnGOHGbmWWME7eZWcY4cZuZZYwTt5lZxjhxm5lljBO3mVnGOHGbmWWME7eZWcY4cZuZZYwTt5lZxjhxm5lljBO3mVnGOHGbmWWME7eZWcY4cZuZZUyhXg1L+iqwBHgiIg6vUS7gIuBkYBvwtohYXq94zKZCuVxh5brNrNvcTWtDgUJetJYKdPSWebKzl7bWBiTo7qvQ1dvPjOYim7v6mNFUpCGf44mOHma1lNjW28+23n72bCnS119hS3eZllKBaY0FohJ09PbT2VumtVSguSFPT7nC5m19NJfytDQUKFf6IXJs7u4bbK+zt8z0xgINhTxPbO2htaHAtIY828r9bO0q09pYoKVUoKuvP3ks/UFHT5kZTck2nb19NBYKbOzspbWhQDEvKhE0FPJ095UpFQo82dlDYzFPa0OBWa0l9t2jhVxOU/ysZF/dEjfwdeDzwOUjlJ8ELEhvxwBfSP+a7RLK5QpXrVjLP131O7r7KjQWc3zkpENpKhX4558k6/ab1cRZrziYC65ZOVjn7EUL+N6yh3nHKw6muSh+/UQHF93wAHs0l3j7S/fns9ffP1j3glMX0tdf4WP/9fsR23vfCYcwZ3oDn7n+fv72zw5gVdpedfnX/udPlArabtsLTl2ICDZ29g3Z5p9ecxilfI7zrl4+uO6cxQtoKeXJ5cT0piIfvPLOIWVzZjTyp42dvOzgvZy8n6G6DZVExM3Ak6NUOQ24PBK3AzMl7V2veMwm28p1mweTNiS96g2dvYNJG2DJEXMHE+VAnYt/+QBLjpjL+desZGZLw2DCfN0L5w0m7YG6S69eyRNbe0Zt77PX38+q9Z0sOWIuG7f1DrZXXf66F86rue3Sq1fSXCput80TW3s47+qhdS+64QE2dPbyxNYeVj3RsV3Zgxs62drVz582dtb92O/qpnKMey7wSNXymnTddiSdKWmZpGXr16+flODMnql1m7sHk9eASjBk3cAwSbXuvsrg+qc6+wbLR6pbiaeXR6sjbb//6v2NtG1nT3nMx1G9n4FbrbLO3jJPbO3GnpmpTNy13itFjXVExGUR0R4R7W1tbXUOy2xi7D2jicbi0H+xvNhuXa3liOTvHi3FIeW16g4fdRitzkj7jxh525bGwrgfR04M3mqVtZQK7DWtEXtmpjJxrwH2rVqeBzw6RbGYTbiFe0/nY689fDDBNRZzzGop8S+nPb3umhVrWXrKwiF1zl60gGvvXsv5pyxkU2cP5yxeQGMxxw/vXMP7TjhkSN0LTl3IXtMaRm3vfSccwsFtLVyzYi17NpcG26su/9HyNTW3veDUhWzr6dtum7ZpDVx46tC65yxewOyWEntNa+DgvVq3KztgdgvTmvLsP6ul7sd+V6eImp3ciWlc2h+4doRZJa8B3kUyq+QY4OKIOHqsNtvb22PZsmUTHapZXezwrJKmIpu7+5jRWKShMHRWSVdvP3uks0q2dvfTXMo/Paukr59tPf3pLJJkVsmWbWWaSjlaSgXKkcwq2dLTx57NT89SaW3M01jIs35rDy3prJKuvmTWSmtDPplVUn56VklnT5lpjQUai3m29fZRKhR4qrOXlhFnlfTSWMx5VsnOq3mw6jkd8ArgeGC2pDXAUqAIEBGXAj8lSdqrSKYDvr1esZhNlUIhx5H77sGR+45d12y86pa4I+JNY5QH8M567d/MbFflb06amWWME7eZWcY4cZuZZYwTt5lZxjhxm5lljBO3mVnGOHGbmWWME7eZWcY4cZuZZYwTt5lZxjhxm5lljBO3mVnGOHGbmWWME7eZWcY4cZuZZYwTt5lZxjhxm5lljBO3mVnGOHGbmWWME7eZWcY4cZuZZYwTt5lZxjhxm5lljBO3mVnGOHGbmWWME7eZWcY4cZuZZUxdE7ekEyXdJ2mVpH+sUX68pM2S7kpv59UzHjOzXUGhXg1LygOXACcAa4A7JF0dEfcOq3pLRCypVxxmZruaeva4jwZWRcTqiOgFvgucVsf9mZntFuqZuOcCj1Qtr0nXDXespBWSrpO0sI7xmJntEuo2VAKoxroYtrwc2C8iOiSdDFwFLNiuIelM4EyA+fPnT3CYZmbZUs8e9xpg36rlecCj1RUiYktEdKT3fwoUJc0e3lBEXBYR7RHR3tbWVseQzcye/eqZuO8AFkg6QFIJeCNwdXUFSXMkKb1/dBrPxjrGZGaWeXUbKomIsqR3AT8H8sBXI2KlpLPS8kuB04F3SCoDXcAbI2L4cIqZmVVR1vJke3t7LFu2bKrDMDObDLU+K/Q3J83MssaJ28wsY5y4zcwyxonbzCxjnLjNzDLGidvMLGOcuM3MMsaJ28wsY5y4zcwyxonbzCxjnLjNzDLGidvMLGOcuM3MMsaJ28wsY5y4zcwyxonbzCxjnLjNzDLGidvMLGOcuM3MMsaJ28wsY5y4zcwyxonbzCxjnLjNzDLGidvMLGOcuM3MMsaJ28wsY5y4zcwyxonbzCxjCvVsXNKJwEVAHvhyRPzbsHKl5ScD24C3RcTyesZkZpOnXK5w72ObeXRTNzOaCpTyeZ7s7KW1qUBvXz/TGotUItjY0UtzQ4FSXhRyIoANHb1MayjQVMqzubuXaQ1FesoVOnvK7NlcoqO3j4ZinqZCnie29jCjqYgQm7v6mNFcJC/oLlfY1tNPa1OBbT1lZjQV6Y9+InJs6epjRlORnMTm7j7aWhrY1tfPk529zG4t0VjMU65U6O6rUIkKzcUinb1luvv62bOlxOauPlobCuRzYkNHL61prFu7e8nn8jQVc1QqsLmrjzkzGjlsznQKhYnpK9ctcUvKA5cAJwBrgDskXR0R91ZVOwlYkN6OAb6Q/jWzjCuXK/zk7kf56I/vYY/mEm89dj8uuuEBuvsqNBZzfPjEQylH8Kmf3ze47iMnHUpjMc95V68cXHfO4gW0NhSI6OJfr/vD4Pr3vuoQvvObh3jji+dz3T3rOOn5ew+2v9+sJv7v8QeztKqdsxct4HvLHuZdr1zA9+54iLWbenjrsfvx3Tse5p2vOIj1W3u54Jqn6y89ZSEzmgpc9dtHOOnwuTy2ZeuQ+D968mH09leGxH/O4gU0F/N89bYHOesVB3PpTat4aGMXjcUcH/+L53PaEftMSPKu51DJ0cCqiFgdEb3Ad4HThtU5Dbg8ErcDMyXtXceYzGySrFy3mY/++B66+yq87oXzBpMeQHdfhY3begeT3sC6DZ29g0l7YN1FNzzA+o4eNnT2Dln/H/99P0uOmMtFNzzA3738oCHtLzli7mDSHqh/8S8fYMkRc/nnn/yOt/7ZgYMxLTliLs0NxcGkPVD/gmtWUqnAm19yAA9u7Nwu/vUdPdvFf9END7BxWy9LjpjLBdesZMkRcwfLPvrje1i5bvOEHNt6Ju65wCNVy2vSdTtaB0lnSlomadn69esnPFAzm3jrNncPJjWJwfsDKjG+dclQRVI2fP1Au1295SHb1drf8PoD9yXo7CnXrN/ZW+apzr4djrW67eqyxzZ3j3C0dkw9E7dqrIudqENEXBYR7RHR3tbWNiHBmVl97T2jicbi0ymm+j5AXuNb11jMkRPkhmWLxmKOiORvc6lQc7uR6jeVCkPqtDTW3r6lVGDPluIOxzqwn4ihZXNmNDIR6pm41wD7Vi3PAx7diTpmlkEL957Ox//i+TQWc/zwzjWcs3jBYKJrLObYs7nEB/78uUPWzWopceGpC4esO2fxAtpaG5jdUhqy/r2vOoRr717LOYsX8KWb/zik/WtWrOWCYe2cvWgB1969ln857XAuv231YEzXrFjLtu4+lp4ytP7SUxaSy8G3bn+Q/We1bBf/7NaG7eI/Z/ECZjWXuPbutSw9ZSHX3r12sOzjf/F8Fu49Y0KOrSK26+BOTMNSAbgfWAysBe4A/ioiVlbVeQ3wLpJZJccAF0fE0aO1297eHsuWLatLzGY2sQZmlTy2qYdpTfmnZ5U0FugtPz2r5MmOPpoa8tvNKmltKNBcyrOlu5eWhiK95QqdPf3s0ZzM8Ggo5JJZJR29zGgqbDerpCet39pYoKuvzLSGIhUqRIgtXeVkG4ktw2aVzGot0VQ1qySiQtPgrJIKezYX2dLTR0spmVWysaOXloYCTcU8W3v6yOdyNBVz9FdgS1cfz5nRwPPmzNiZDyZrjUrUb1ZJRJQlvQv4Ocl0wK9GxEpJZ6XllwI/JUnaq0imA769XvGY2eQrFHIcMW8Pjpg31ZHsWurW464X97jNbDdSs8ftb06amWWME7eZWcY4cZuZZYwTt5lZxjhxm5lljBO3mVnGOHGbmWWME7eZWcY4cZuZZUzmvjkpaT3w0E5uPhvYMIHhZMnu/NjBj393fvxZfuwbIuLE4Sszl7ifCUnLIqJ9quOYCrvzYwc//t358e+Kj91DJWZmGePEbWaWMbtb4r5sqgOYQrvzYwc//t358e9yj323GuM2M9sV7G49bjOzzHPiNjPLmN0icUs6UdJ9klZJ+sepjmcySdpX0q8k/V7SSknnTHVMk01SXtJvJV071bFMNkkzJV0p6Q/pOXDsVMc0mSS9Nz3vfyfpCkkT8zPrU2yXT9yS8sAlwEnA84A3SXre1EY1qcrA+yPiMOAlwDt3s8cPcA7w+6kOYopcBPwsIg4FjmQ3Og6S5gJnA+0RcTjJb9++cWqjmhi7fOIGjgZWRcTqiOgFvgucNsUxTZqIWBcRy9P7W0n+cedObVSTR9I84DXAl6c6lskmaTrwcuArABHRGxGbpjSoyVcAmiQVgGbg0SmOZ0LsDol7LvBI1fIadqPEVU3S/sALgF9PcSiT6XPAB4HKFMcxFQ4E1gNfS4eKviypZaqDmiwRsRb4NPAwsA7YHBG/mNqoJsbukLhr/UrybjcHUlIr8EPgPRGxZarjmQySlgBPRMSdUx3LFCkALwS+EBEvADqB3eYzHkl7kLy7PgDYB2iR9NdTG9XE2B0S9xpg36rleewib5fGS1KRJGl/OyJ+NNXxTKKXAqdK+hPJENkiSd+a2pAm1RpgTUQMvMO6kiSR7y5eBTwYEesjog/4EfBnUxzThNgdEvcdwAJJB0gqkXw4cfUUxzRpJIlkjPP3EfHZqY5nMkXEhyNiXkTsT/K8/zIidoke13hExGPAI5Kem65aDNw7hSFNtoeBl0hqTv8PFrOLfDhbmOoA6i0iypLeBfyc5FPlr0bEyikOazK9FHgLcI+ku9J1H4mIn05dSDaJ3g18O+20rAbePsXxTJqI+LWkK4HlJLOrfssu8vV3f+XdzCxjdoehEjOzXYoTt5lZxjhxm5lljBO3mVnGOHGbmWWME7dNCEkh6TNVy+dKOn+C2v66pNMnoq0x9vP69Ap6vxq2fn9Jvxu27nxJ59Y7pqr9LUm/tr5C0r2S/uGZxiHpKEknT2ykNhmcuG2i9ACvkzR7qgOpll4dcrzOAP5vRLyyXvEMl178aKw6RZL5x6dExJEk15u5cQJ2fxTgxJ1BTtw2UcokyeW9wwuG95gldaR/j5d0k6TvS7pf0r9JerOk30i6R9JBVc28StItab0l6fZ5SZ+SdIeku6t6ocen1yD/DnBPjXjelLb/O0n/nq47DzgOuFTSp3bkgac919vTGH6cXiMDSTdKak/vz06/eo+kt0n6gaRrgF9I2lvSzZLuSmN62bBdTCP5stxGgIjoiYj7asRxkKSfSbozPVaHVh3/S6uPX/qFnAuBN6T7fcOOPGabWrv8NydtUl0C3C3pkzuwzZHAYcCTJN/s+3JEHK3kBx/eDbwnrbc/8ArgIOBXkg4G3kpyxbcXS2oA/kfSwNXfjgYOj4gHq3cmaR/g34EXAU+RJM7XRsSFkhYB50bEshpxHlT1zVOAOSRXngO4HHh3RNwk6UJgaVXcIzkWOCIinpT0fuDnEfHx9B1Cc3XFtM7VwEOSbgCuBa6IiOFXPLwMOCsiHpB0DPCfwKK0bH+qjh9wMHAeybWq3zVGrPYs48RtEyYitki6nOTi9V3j3OyOiFgHIOmPwEDivQeoHrL4fpqoHpC0GjgUeDVwRFVvfgawAOgFfjM8aadeDNwYEevTfX6b5JrVV40R5x8j4qiBhYHxe0kzgJkRcVNa9A3gB2O0BXB9RDyZ3r8D+Go6JHJVRNw1vHJE/J2k55NcOOlc4ATgbVXxtJJcQOkHyWU5AGioaqLW8bOMcuK2ifY5kmtDfK1qXZl0WC692E+pqqyn6n6larnC0PNz+LUZguSSve+OiJ9XF0g6nuQSprXUusxvvQw+bmD4T2YNxhcRN0t6OckPPnxT0qci4vLhjUXEPSTXnPkm8CBViTvdz6bqF5fhm4+xbBniMW6bUGkv8vskH/QN+BPJ0AQk10cu7kTTr5eUS8e9DwTuI7lw2DvSniqSDtHYPxTwa+AV6ZhzHngTcNMY24woIjYDT1WNS7+lqr0/8fTjHnFWjKT9SK4b/iWSKzm+cFh5a/piNOAo4KFhcWwBHpT0+nQbSTqyqkqt47eVZPzcMsaJ2+rhM0D17JIvkSTL3wDHMHJveDT3kSTE60jGcbtJfo7sXmC5kul6X2SMd5HpsMyHScZ5VwDLI+InOxFPtb8BPiXpbpKkemG6/tMkLyy3MfR4DHc8cJek3wJ/SfI7kdUEfFDJD17fBVzA0N72gDcDZ0haAaxk6E/01Tp+vwKe5w8ns8dXBzTbxUn6OnBtRFw51bHYxHCP28wsY9zjNjPLGPe4zcwyxonbzCxjnLjNzDLGidvMLGOcuM3MMub/A5WZ1v76WvZUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"Sleeping_hours\", y=\"Stress_level\", data=pillow, kind=\"scatter\")\n",
    "plt.xlabel(\"Number of Hours Slept\")\n",
    "plt.ylabel(\"Stress Level\")\n",
    "plt.title(\"Relationship Between Sleeping Hours and Stress\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kg5beQjIcQzr"
   },
   "source": [
    "### Here we can clearly spot the negative correlation between Stress (categorical variable) and Number of Hours Slept (continuous variable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 641
    },
    "id": "9-y-3uvncQzr",
    "outputId": "a71ad48d-e9cd-46f7-f1e8-487d094caee8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "pillow.groupby(pillow[\"Stress_level\"])[\"Sleeping_hours\"].mean().plot(kind='bar', rot=0, color='#9966cc')\n",
    "plt.title(\"Stress Levels Measured by Sleeping Hours\")\n",
    "plt.xlabel(\"Stress Levels\")\n",
    "plt.ylabel(\"Number of Hours Slept\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a4uk4MagcQzs"
   },
   "source": [
    "#### People who sleep 8 hours on average in a single day report no or very low stress levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 641
    },
    "id": "ftZ4UXcAcQzs",
    "outputId": "4d240ca6-75d3-4ef4-8b1c-437ee513ca38"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "pillow.groupby(pillow[\"Stress_level\"])[\"limb_movement\"].mean().plot(kind='bar', rot=0, color='#4b5320')\n",
    "plt.title(\"Stress Levels Measured by Limb movement\")\n",
    "plt.xlabel(\"Stress Levels\")\n",
    "plt.ylabel(\"Limb movement\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Om_l3_9EcQzs"
   },
   "source": [
    "#### It is detected that people with high limb movement have more stress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 641
    },
    "id": "J1OlUZ2wcQzt",
    "outputId": "608bf892-2c99-4466-ff06-220c3ec49d8c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "pillow.groupby(pillow[\"Stress_level\"])[\"Heart_rate\"].mean().plot(kind='bar', rot=0, color='#a52a2a')\n",
    "plt.title(\"Stress Levels Measured by heart rate\")\n",
    "plt.xlabel(\"Stress Levels\")\n",
    "plt.ylabel(\"Heart rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g-wrAB97cQzt"
   },
   "source": [
    "#### Stressed people have higher heart rate, people with low or no stress have heart rate between 50-60"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XqvBXncgcQzt"
   },
   "source": [
    "## Outliers reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 719
    },
    "id": "dxZukUincQzu",
    "outputId": "7dcf86ad-fc53-42ef-f929-208d858f1770"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ishit\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\ishit\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,5))\n",
    "plt.subplot(1,2,1)\n",
    "sns.distplot(pillow['limb_movement'])\n",
    "plt.subplot(1,2,2)\n",
    "sns.distplot(pillow['Heart_rate'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iKXNlprecQzu"
   },
   "source": [
    "### Applying IQR in limb movement "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pgdgdOcDcQzu",
    "outputId": "3a4d86d3-dbc0-4d0d-d844-354c6e5f689f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest allowed 24.59888744416733\n",
      "Lowest allowed -1.1988874441673119\n"
     ]
    }
   ],
   "source": [
    "print(\"Highest allowed\",pillow['limb_movement'].mean() + 3*pillow['limb_movement'].std())\n",
    "print(\"Lowest allowed\",pillow['limb_movement'].mean() - 3*pillow['limb_movement'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49
    },
    "id": "RJXhI-F4cQzu",
    "outputId": "ef75d314-0f52-4054-d657-58b9c2baffa3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>body_temperature</th>\n",
       "      <th>limb_movement</th>\n",
       "      <th>Blood_oxygen</th>\n",
       "      <th>Sleeping_hours</th>\n",
       "      <th>Heart_rate</th>\n",
       "      <th>Stress_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [body_temperature, limb_movement, Blood_oxygen, Sleeping_hours, Heart_rate, Stress_level]\n",
       "Index: []"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pillow[(pillow['limb_movement'] > 24.59888744416733) | (pillow['limb_movement'] < -1.1988874441673119)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cnR1iK1YcQzv"
   },
   "source": [
    "#### No outlier detected in limb movement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mMbZziUacQzv"
   },
   "source": [
    "### Detecting outlier using box plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "Q9GkkyX0cQzv",
    "outputId": "b978fb0a-41b9-44cf-81c0-59398fa83698"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ishit\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Heart_rate'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(pillow['Heart_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "iit7CIL8cQzv",
    "outputId": "5951973e-1290-4035-aea1-388122835711"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ishit\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='body_temperature'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(pillow['body_temperature'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "Qbfn7aLUcQzv",
    "outputId": "6526b19e-934c-40c1-e5ae-5b1f74f3bb84"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ishit\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Blood_oxygen'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(pillow['Blood_oxygen'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 447
    },
    "id": "DC_2iVxgcQzv",
    "outputId": "1919c844-5511-49e8-baf3-f75c5f60a76d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ishit\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sleeping_hours'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(pillow['Sleeping_hours'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tqT3HE4ZcQzw"
   },
   "source": [
    "### No outlier detected in Heart rate, body temperature, Blood oxygen, Sleeping hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "et-o9sVPcfZH"
   },
   "outputs": [],
   "source": [
    "df1 = pillow.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xc33qnw9cQzw"
   },
   "source": [
    "## Data standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "PrTJv1EqcQzw"
   },
   "outputs": [],
   "source": [
    "Y=pillow['Stress_level']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "p0iQB_INcQzw"
   },
   "outputs": [],
   "source": [
    "X=pillow.drop(['Stress_level'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "piHzHlCRcQzw",
    "outputId": "1d7d353f-436f-43f4-cb68-73116cf81dae"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>body_temperature</th>\n",
       "      <th>limb_movement</th>\n",
       "      <th>Blood_oxygen</th>\n",
       "      <th>Sleeping_hours</th>\n",
       "      <th>Heart_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91.840</td>\n",
       "      <td>16.600</td>\n",
       "      <td>89.840</td>\n",
       "      <td>1.840</td>\n",
       "      <td>74.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.552</td>\n",
       "      <td>15.880</td>\n",
       "      <td>89.552</td>\n",
       "      <td>1.552</td>\n",
       "      <td>72.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90.768</td>\n",
       "      <td>13.920</td>\n",
       "      <td>88.768</td>\n",
       "      <td>0.768</td>\n",
       "      <td>68.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97.872</td>\n",
       "      <td>6.496</td>\n",
       "      <td>96.248</td>\n",
       "      <td>8.248</td>\n",
       "      <td>53.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   body_temperature  limb_movement  Blood_oxygen  Sleeping_hours  Heart_rate\n",
       "0            91.840         16.600        89.840           1.840       74.20\n",
       "1            91.552         15.880        89.552           1.552       72.76\n",
       "2            96.000         10.000        95.000           7.000       60.00\n",
       "3            90.768         13.920        88.768           0.768       68.84\n",
       "4            97.872          6.496        96.248           8.248       53.12"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 75
    },
    "id": "1BmPHNEocQzw",
    "outputId": "a360cede-bb1a-43a5-9394-9979578d9bea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "UUspLRegcQzx"
   },
   "outputs": [],
   "source": [
    "X= scaler.transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WUg0MplkcQzx",
    "outputId": "f6475691-dccc-4c91-828f-3690e8944412"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data :  0.9698412698412698\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression(solver='liblinear',multi_class='ovr')\n",
    "lr.fit(X,Y)\n",
    "from sklearn.metrics import accuracy_score\n",
    "X_train_prediction = lr.predict(X)\n",
    "training_data_accuracy_LR= accuracy_score(Y, X_train_prediction)\n",
    "print('Accuracy score of training data : ', training_data_accuracy_LR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data :  1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
    "classifier.fit(X, Y)\n",
    "X_train_prediction = classifier.predict(X)\n",
    "training_data_accuracy_RF= accuracy_score(Y, X_train_prediction)\n",
    "print('Accuracy score of training data : ', training_data_accuracy_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4TydVoB-dceI"
   },
   "source": [
    "# Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "6ab5jOOLcQzx"
   },
   "outputs": [],
   "source": [
    "p_test=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "_Rxq0RgPcQzx"
   },
   "outputs": [],
   "source": [
    "p_test.rename(columns = {'Sleeping_hours\\t':'Sleeping_hours'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "1iQwDSF8eqRx",
    "outputId": "1bb4fdeb-cda2-4669-87ee-85b8d132ad54"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>limb_movement</th>\n",
       "      <th>Sleeping_hours</th>\n",
       "      <th>Heart_rate</th>\n",
       "      <th>Stress_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>1.840</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
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       "      <td>95</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>7.000</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>0.768</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
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       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>8.248</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   limb_movement  Sleeping_hours  Heart_rate  Stress_level\n",
       "0             16           1.840          70             3\n",
       "1             20           1.552          95             3\n",
       "2             20           7.000          65             1\n",
       "3             20           0.768          89             3\n",
       "4              7           8.248          72             0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_test = p_test.drop(['Snoring_rate','Respiration_rate','Eye_movement'],axis=1)\n",
    "p_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "e2dLOU7RdmTT",
    "outputId": "b4c1a383-1608-4d90-ca1e-4c9bba8d95c3"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>limb_movement</th>\n",
       "      <th>Sleeping_hours</th>\n",
       "      <th>Heart_rate</th>\n",
       "      <th>Stress_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>1.840</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>1.552</td>\n",
       "      <td>95</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>7.000</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>0.768</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
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       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>8.248</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   limb_movement  Sleeping_hours  Heart_rate  Stress_level\n",
       "0             16           1.840          70             3\n",
       "1             20           1.552          95             3\n",
       "2             20           7.000          65             1\n",
       "3             20           0.768          89             3\n",
       "4              7           8.248          72             0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1_test = p_test.copy()\n",
    "p1_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "BZ6_2wWYdvkx",
    "outputId": "6dc0409f-82e4-407e-e0a7-59f4f3b083d8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>body_temperature</th>\n",
       "      <th>limb_movement</th>\n",
       "      <th>Blood_oxygen</th>\n",
       "      <th>Sleeping_hours</th>\n",
       "      <th>Heart_rate</th>\n",
       "      <th>Stress_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91.840</td>\n",
       "      <td>16.600</td>\n",
       "      <td>89.840</td>\n",
       "      <td>1.840</td>\n",
       "      <td>74.20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.552</td>\n",
       "      <td>15.880</td>\n",
       "      <td>89.552</td>\n",
       "      <td>1.552</td>\n",
       "      <td>72.76</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>95.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>60.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90.768</td>\n",
       "      <td>13.920</td>\n",
       "      <td>88.768</td>\n",
       "      <td>0.768</td>\n",
       "      <td>68.84</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>97.872</td>\n",
       "      <td>6.496</td>\n",
       "      <td>96.248</td>\n",
       "      <td>8.248</td>\n",
       "      <td>53.12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   body_temperature  limb_movement  Blood_oxygen  Sleeping_hours  Heart_rate  \\\n",
       "0            91.840         16.600        89.840           1.840       74.20   \n",
       "1            91.552         15.880        89.552           1.552       72.76   \n",
       "2            96.000         10.000        95.000           7.000       60.00   \n",
       "3            90.768         13.920        88.768           0.768       68.84   \n",
       "4            97.872          6.496        96.248           8.248       53.12   \n",
       "\n",
       "   Stress_level  \n",
       "0             3  \n",
       "1             3  \n",
       "2             1  \n",
       "3             3  \n",
       "4             0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head() #reqd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "hoPCLb9uBy-k"
   },
   "outputs": [],
   "source": [
    "df2 = df1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "o325cVpxdqRl",
    "outputId": "ab7ec2f7-7afa-4caf-82b5-126aec9e79fd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>limb_movement</th>\n",
       "      <th>Sleeping_hours</th>\n",
       "      <th>Heart_rate</th>\n",
       "      <th>Stress_level</th>\n",
       "      <th>body_temperature</th>\n",
       "      <th>Blood_oxygen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.600</td>\n",
       "      <td>1.840</td>\n",
       "      <td>74.20</td>\n",
       "      <td>3</td>\n",
       "      <td>91.840</td>\n",
       "      <td>89.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.880</td>\n",
       "      <td>1.552</td>\n",
       "      <td>72.76</td>\n",
       "      <td>3</td>\n",
       "      <td>91.552</td>\n",
       "      <td>89.552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>60.00</td>\n",
       "      <td>1</td>\n",
       "      <td>96.000</td>\n",
       "      <td>95.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.920</td>\n",
       "      <td>0.768</td>\n",
       "      <td>68.84</td>\n",
       "      <td>3</td>\n",
       "      <td>90.768</td>\n",
       "      <td>88.768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.496</td>\n",
       "      <td>8.248</td>\n",
       "      <td>53.12</td>\n",
       "      <td>0</td>\n",
       "      <td>97.872</td>\n",
       "      <td>96.248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   limb_movement  Sleeping_hours  Heart_rate  Stress_level  body_temperature  \\\n",
       "0         16.600           1.840       74.20             3            91.840   \n",
       "1         15.880           1.552       72.76             3            91.552   \n",
       "2         10.000           7.000       60.00             1            96.000   \n",
       "3         13.920           0.768       68.84             3            90.768   \n",
       "4          6.496           8.248       53.12             0            97.872   \n",
       "\n",
       "   Blood_oxygen  \n",
       "0        89.840  \n",
       "1        89.552  \n",
       "2        95.000  \n",
       "3        88.768  \n",
       "4        96.248  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df1[['limb_movement','Sleeping_hours','Heart_rate','Stress_level','body_temperature','Blood_oxygen']]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nVllQqbS7TYn"
   },
   "source": [
    "## body_temperature imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fz2uwB1wdstQ",
    "outputId": "639707fa-cc22-4c73-b783-2d4ab8a49846"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.6  ,  1.84 , 74.2  ,  3.   ],\n",
       "       [15.88 ,  1.552, 72.76 ,  3.   ],\n",
       "       [10.   ,  7.   , 60.   ,  1.   ],\n",
       "       ...,\n",
       "       [17.752,  0.   , 78.76 ,  4.   ],\n",
       "       [ 9.728,  6.728, 59.32 ,  1.   ],\n",
       "       [11.392,  4.088, 63.48 ,  2.   ]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_x = df1.iloc[:,:-2].values\n",
    "df1_y = df1.iloc[:,-2].values\n",
    "df1_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "X4encXzHCsIk"
   },
   "outputs": [],
   "source": [
    "p1_test1 = np.array(p1_test)\n",
    "#p1_test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JD9SC3xlCzJb",
    "outputId": "3c490059-a61b-4d79-ed7c-a264cfbbde3c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([91.392, 91.664, 96.   , 91.2  , 98.376, 95.664, 97.272, 98.88 ,\n",
       "       96.408, 94.88 , 93.376, 95.856, 98.376, 89.72 , 91.2  , 94.784,\n",
       "       95.28 , 91.2  , 91.184, 92.384, 93.728, 93.728, 92.784, 90.368,\n",
       "       91.648, 94.784, 93.728, 94.784, 91.984, 92.208, 98.4  , 90.   ,\n",
       "       98.376, 85.08 , 92.   , 90.608, 98.376, 86.48 , 96.6  , 86.48 ,\n",
       "       92.144, 92.592, 93.408, 98.4  , 93.376, 89.72 , 92.272, 89.72 ,\n",
       "       94.88 , 86.48 ])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from sklearn.ensemble import RandomForestRegressor\n",
    "# regressor = RandomForestRegressor(n_estimators=10,random_state=0)\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor = DecisionTreeRegressor(random_state = 0)\n",
    "regressor.fit(df1_x,df1_y)\n",
    "p1_body_temp = regressor.predict(p1_test1)\n",
    "p1_body_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "DcTE8kElDGfw",
    "outputId": "ae11ed4f-7a26-4108-f5a8-7ab7fc2d0a2f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>body_temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91.392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98.376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   body_temperature\n",
       "0            91.392\n",
       "1            91.664\n",
       "2            96.000\n",
       "3            91.200\n",
       "4            98.376"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1_body_temp = pd.DataFrame(p1_body_temp, columns = ['body_temperature'])\n",
    "p1_body_temp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UNrh-sNo7Y_u"
   },
   "source": [
    "## Blood_oxygen Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "we8uTAmNDccQ",
    "outputId": "a2bee4fa-2b78-4247-b994-c1eeb886444e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.6  ,  1.84 , 74.2  ,  3.   ],\n",
       "       [15.88 ,  1.552, 72.76 ,  3.   ],\n",
       "       [10.   ,  7.   , 60.   ,  1.   ],\n",
       "       ...,\n",
       "       [17.752,  0.   , 78.76 ,  4.   ],\n",
       "       [ 9.728,  6.728, 59.32 ,  1.   ],\n",
       "       [11.392,  4.088, 63.48 ,  2.   ]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_x2 = df1.iloc[:,:-2].values\n",
    "df1_y2 = df1.iloc[:,-1].values\n",
    "df1_x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "6wNkSGvzEFRE"
   },
   "outputs": [],
   "source": [
    "#df1_y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "fcgSVuRCDmL5"
   },
   "outputs": [],
   "source": [
    "# p1_test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OxE_Jab1Dtek",
    "outputId": "3d26d860-c7fc-4857-be36-edd6c79ba812"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([89.84 , 89.68 , 95.   , 88.976, 96.552, 94.568, 96.568, 97.   ,\n",
       "       95.096, 93.32 , 90.976, 94.784, 96.552, 85.168, 89.2  , 92.936,\n",
       "       92.936, 88.976, 88.736, 90.656, 91.616, 91.6  , 90.752, 88.464,\n",
       "       89.328, 93.248, 91.84 , 92.936, 89.984, 90.112, 96.92 , 88.   ,\n",
       "       96.552, 83.056, 90.   , 88.656, 96.552, 83.68 , 95.384, 83.68 ,\n",
       "       90.112, 90.112, 91.536, 96.92 , 90.976, 85.168, 90.112, 85.168,\n",
       "       94.328, 83.824])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from sklearn.ensemble import RandomForestRegressor\n",
    "# regressor1 = RandomForestRegressor(n_estimators=10,random_state=0)\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor1 = DecisionTreeRegressor(random_state = 0)\n",
    "regressor1.fit(df1_x2,df1_y2)\n",
    "p1_blood_ox = regressor1.predict(p1_test1)\n",
    "p1_blood_ox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "YoqJMxxCENSk",
    "outputId": "746200e5-326d-4507-800b-efc0d0cc788d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Blood_oxygen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>95.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88.976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>96.552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Blood_oxygen\n",
       "0        89.840\n",
       "1        89.680\n",
       "2        95.000\n",
       "3        88.976\n",
       "4        96.552"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1_blood_ox = pd.DataFrame(p1_blood_ox, columns = ['Blood_oxygen'])\n",
    "p1_blood_ox.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "-NuOeA5qF31m",
    "outputId": "d8ed4897-c5cb-4234-e30d-076875aa9f73"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>limb_movement</th>\n",
       "      <th>Sleeping_hours</th>\n",
       "      <th>Heart_rate</th>\n",
       "      <th>Stress_level</th>\n",
       "      <th>body_temperature</th>\n",
       "      <th>Blood_oxygen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>1.840</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "      <td>91.392</td>\n",
       "      <td>89.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>1.552</td>\n",
       "      <td>95</td>\n",
       "      <td>3</td>\n",
       "      <td>91.664</td>\n",
       "      <td>89.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>7.000</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>96.000</td>\n",
       "      <td>95.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>0.768</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "      <td>91.200</td>\n",
       "      <td>88.976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>8.248</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>98.376</td>\n",
       "      <td>96.552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   limb_movement  Sleeping_hours  Heart_rate  Stress_level  body_temperature  \\\n",
       "0             16           1.840          70             3            91.392   \n",
       "1             20           1.552          95             3            91.664   \n",
       "2             20           7.000          65             1            96.000   \n",
       "3             20           0.768          89             3            91.200   \n",
       "4              7           8.248          72             0            98.376   \n",
       "\n",
       "   Blood_oxygen  \n",
       "0        89.840  \n",
       "1        89.680  \n",
       "2        95.000  \n",
       "3        88.976  \n",
       "4        96.552  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.concat([p1_test,p1_body_temp,p1_blood_ox], axis=1, join='inner')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "lAqjMT0kG0Fm",
    "outputId": "36d96d7b-e95e-458a-b4b7-a5e47f63c05a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>limb_movement</th>\n",
       "      <th>Sleeping_hours</th>\n",
       "      <th>Heart_rate</th>\n",
       "      <th>Stress_level</th>\n",
       "      <th>body_temperature</th>\n",
       "      <th>Blood_oxygen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>15</td>\n",
       "      <td>0.000</td>\n",
       "      <td>85</td>\n",
       "      <td>4</td>\n",
       "      <td>89.720</td>\n",
       "      <td>85.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>8</td>\n",
       "      <td>2.432</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>92.272</td>\n",
       "      <td>90.112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>15</td>\n",
       "      <td>0.000</td>\n",
       "      <td>89</td>\n",
       "      <td>4</td>\n",
       "      <td>89.720</td>\n",
       "      <td>85.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>4</td>\n",
       "      <td>6.344</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>94.880</td>\n",
       "      <td>94.328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>19</td>\n",
       "      <td>0.000</td>\n",
       "      <td>78</td>\n",
       "      <td>4</td>\n",
       "      <td>86.480</td>\n",
       "      <td>83.824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    limb_movement  Sleeping_hours  Heart_rate  Stress_level  body_temperature  \\\n",
       "45             15           0.000          85             4            89.720   \n",
       "46              8           2.432          67             2            92.272   \n",
       "47             15           0.000          89             4            89.720   \n",
       "48              4           6.344          70             1            94.880   \n",
       "49             19           0.000          78             4            86.480   \n",
       "\n",
       "    Blood_oxygen  \n",
       "45        85.168  \n",
       "46        90.112  \n",
       "47        85.168  \n",
       "48        94.328  \n",
       "49        83.824  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "_VsVdSkrG4lV",
    "outputId": "d6ac5089-3db6-4e33-ab4e-5f1a21c87b3a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>body_temperature</th>\n",
       "      <th>limb_movement</th>\n",
       "      <th>Blood_oxygen</th>\n",
       "      <th>Sleeping_hours</th>\n",
       "      <th>Heart_rate</th>\n",
       "      <th>Stress_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91.392</td>\n",
       "      <td>16</td>\n",
       "      <td>89.840</td>\n",
       "      <td>1.840</td>\n",
       "      <td>70</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.664</td>\n",
       "      <td>20</td>\n",
       "      <td>89.680</td>\n",
       "      <td>1.552</td>\n",
       "      <td>95</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96.000</td>\n",
       "      <td>20</td>\n",
       "      <td>95.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91.200</td>\n",
       "      <td>20</td>\n",
       "      <td>88.976</td>\n",
       "      <td>0.768</td>\n",
       "      <td>89</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98.376</td>\n",
       "      <td>7</td>\n",
       "      <td>96.552</td>\n",
       "      <td>8.248</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   body_temperature  limb_movement  Blood_oxygen  Sleeping_hours  Heart_rate  \\\n",
       "0            91.392             16        89.840           1.840          70   \n",
       "1            91.664             20        89.680           1.552          95   \n",
       "2            96.000             20        95.000           7.000          65   \n",
       "3            91.200             20        88.976           0.768          89   \n",
       "4            98.376              7        96.552           8.248          72   \n",
       "\n",
       "   Stress_level  \n",
       "0             3  \n",
       "1             3  \n",
       "2             1  \n",
       "3             3  \n",
       "4             0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = test[['body_temperature','limb_movement','Blood_oxygen','Sleeping_hours','Heart_rate','Stress_level']]\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "asdYLU3N6oNn"
   },
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "SPsvHfL7Hk-d"
   },
   "outputs": [],
   "source": [
    "Y_test = test['Stress_level']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "XWtpv7zeHk-f"
   },
   "outputs": [],
   "source": [
    "X_test = test.drop(['Stress_level'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TfQaH5hZIoGQ",
    "outputId": "516df223-be54-459f-b082-956a5ecbe316"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.2721947 ,  1.14053872, -0.27183777, -0.60940713,  0.97906569],\n",
       "       [-0.35385311,  0.97294936, -0.34569558, -0.70376694,  0.83371986],\n",
       "       [ 0.90731566, -0.39569711,  1.05144797,  1.08120619, -0.45420573],\n",
       "       ...,\n",
       "       [-1.67853398,  1.4086817 , -1.70385862, -1.21226149,  1.43932751],\n",
       "       [ 0.83019383, -0.45900864,  0.94681608,  0.99208859, -0.52284127],\n",
       "       [ 0.1678534 , -0.07169101,  0.12617376,  0.12712364, -0.1029533 ]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "H72j3EetHk-f",
    "outputId": "50c38909-49e1-4b90-c3dd-71151bdfff9b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>body_temperature</th>\n",
       "      <th>limb_movement</th>\n",
       "      <th>Blood_oxygen</th>\n",
       "      <th>Sleeping_hours</th>\n",
       "      <th>Heart_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>91.392</td>\n",
       "      <td>16</td>\n",
       "      <td>89.840</td>\n",
       "      <td>1.840</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.664</td>\n",
       "      <td>20</td>\n",
       "      <td>89.680</td>\n",
       "      <td>1.552</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96.000</td>\n",
       "      <td>20</td>\n",
       "      <td>95.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91.200</td>\n",
       "      <td>20</td>\n",
       "      <td>88.976</td>\n",
       "      <td>0.768</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98.376</td>\n",
       "      <td>7</td>\n",
       "      <td>96.552</td>\n",
       "      <td>8.248</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   body_temperature  limb_movement  Blood_oxygen  Sleeping_hours  Heart_rate\n",
       "0            91.392             16        89.840           1.840          70\n",
       "1            91.664             20        89.680           1.552          95\n",
       "2            96.000             20        95.000           7.000          65\n",
       "3            91.200             20        88.976           0.768          89\n",
       "4            98.376              7        96.552           8.248          72"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "EVPOFbnIHk-h"
   },
   "outputs": [],
   "source": [
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predicting Test results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Rt6gWSWyHk-h",
    "outputId": "a7e98eba-45b0-4e85-98d4-73c77499901e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data :  0.64\n"
     ]
    }
   ],
   "source": [
    "X_test_prediction = lr.predict(X_test)\n",
    "test_data_accuracy_LR= accuracy_score(Y_test, X_test_prediction)\n",
    "print('Accuracy score of training data : ', test_data_accuracy_LR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting test data using random Forest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=0)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
    "classifier.fit(X, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data :  0.92\n"
     ]
    }
   ],
   "source": [
    "X_test_prediction = classifier.predict(X_test)\n",
    "test_data_accuracy_RF= accuracy_score(Y_test, X_test_prediction)\n",
    "print('Accuracy score of training data : ', test_data_accuracy_RF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the GUI Interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "filename= 'trained_model.sav'\n",
    "pickle.dump(lr,open(filename,'wb'))"
   ]
  }
 ],
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