Parameter_Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cRaPVSGMtOan"
   },
   "source": [
    "### BY: Rohan Thakur ,102103762 , 3CO27"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B_2gz_a3pZa0"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wX6OVMOXtOap"
   },
   "source": [
    "### 1. Reading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "hOxdHOSbpbeo"
   },
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('dataset/Room_Occupancy_Estimation.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8uKVOZRJtOap",
    "outputId": "8dcfca52-157f-4c15-d97c-dab05ac2bf2e"
   },
   "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>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>S1_Temp</th>\n",
       "      <th>S2_Temp</th>\n",
       "      <th>S3_Temp</th>\n",
       "      <th>S4_Temp</th>\n",
       "      <th>S1_Light</th>\n",
       "      <th>S2_Light</th>\n",
       "      <th>S3_Light</th>\n",
       "      <th>S4_Light</th>\n",
       "      <th>S1_Sound</th>\n",
       "      <th>S2_Sound</th>\n",
       "      <th>S3_Sound</th>\n",
       "      <th>S4_Sound</th>\n",
       "      <th>S5_CO2</th>\n",
       "      <th>S5_CO2_Slope</th>\n",
       "      <th>S6_PIR</th>\n",
       "      <th>S7_PIR</th>\n",
       "      <th>Room_Occupancy_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017/12/22</td>\n",
       "      <td>10:49:41</td>\n",
       "      <td>24.94</td>\n",
       "      <td>24.75</td>\n",
       "      <td>24.56</td>\n",
       "      <td>25.38</td>\n",
       "      <td>121</td>\n",
       "      <td>34</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>390</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017/12/22</td>\n",
       "      <td>10:50:12</td>\n",
       "      <td>24.94</td>\n",
       "      <td>24.75</td>\n",
       "      <td>24.56</td>\n",
       "      <td>25.44</td>\n",
       "      <td>121</td>\n",
       "      <td>33</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>390</td>\n",
       "      <td>0.646154</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017/12/22</td>\n",
       "      <td>10:50:42</td>\n",
       "      <td>25.00</td>\n",
       "      <td>24.75</td>\n",
       "      <td>24.50</td>\n",
       "      <td>25.44</td>\n",
       "      <td>121</td>\n",
       "      <td>34</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.06</td>\n",
       "      <td>390</td>\n",
       "      <td>0.519231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017/12/22</td>\n",
       "      <td>10:51:13</td>\n",
       "      <td>25.00</td>\n",
       "      <td>24.75</td>\n",
       "      <td>24.56</td>\n",
       "      <td>25.44</td>\n",
       "      <td>121</td>\n",
       "      <td>34</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>390</td>\n",
       "      <td>0.388462</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017/12/22</td>\n",
       "      <td>10:51:44</td>\n",
       "      <td>25.00</td>\n",
       "      <td>24.75</td>\n",
       "      <td>24.56</td>\n",
       "      <td>25.44</td>\n",
       "      <td>121</td>\n",
       "      <td>34</td>\n",
       "      <td>54</td>\n",
       "      <td>40</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>390</td>\n",
       "      <td>0.253846</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time  S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  \\\n",
       "0  2017/12/22  10:49:41    24.94    24.75    24.56    25.38       121   \n",
       "1  2017/12/22  10:50:12    24.94    24.75    24.56    25.44       121   \n",
       "2  2017/12/22  10:50:42    25.00    24.75    24.50    25.44       121   \n",
       "3  2017/12/22  10:51:13    25.00    24.75    24.56    25.44       121   \n",
       "4  2017/12/22  10:51:44    25.00    24.75    24.56    25.44       121   \n",
       "\n",
       "   S2_Light  S3_Light  S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  \\\n",
       "0        34        53        40      0.08      0.19      0.06      0.06   \n",
       "1        33        53        40      0.93      0.05      0.06      0.06   \n",
       "2        34        53        40      0.43      0.11      0.08      0.06   \n",
       "3        34        53        40      0.41      0.10      0.10      0.09   \n",
       "4        34        54        40      0.18      0.06      0.06      0.06   \n",
       "\n",
       "   S5_CO2  S5_CO2_Slope  S6_PIR  S7_PIR  Room_Occupancy_Count  \n",
       "0     390      0.769231       0       0                     1  \n",
       "1     390      0.646154       0       0                     1  \n",
       "2     390      0.519231       0       0                     1  \n",
       "3     390      0.388462       0       0                     1  \n",
       "4     390      0.253846       0       0                     1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZEfCiD6Ar93s",
    "outputId": "ba795580-b98f-4c0b-d4c4-ac92cde476b2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10129, 19)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W4eBOX7pUVbl"
   },
   "source": [
    "### 2. Data Cleaning and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rs9oyhLFtsB_"
   },
   "outputs": [],
   "source": [
    "df2 = df1.drop(['Date','Time'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "id": "daWE15YjtWDF",
    "outputId": "988ba87b-ae11-45ae-e645-8eafd40125be"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Room_Occupancy_Count', ylabel='count'>"
      ]
     },
     "execution_count": 6,
     "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": [
    "sns.countplot(x = 'Room_Occupancy_Count', data=df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DJrr7u6Xwbm-"
   },
   "outputs": [],
   "source": [
    "X = df2.iloc[:,0:-1]\n",
    "y = df2['Room_Occupancy_Count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UhGIawKStOaq",
    "outputId": "c2225112-8285-4ddb-fb4b-088bd621402c"
   },
   "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>S1_Temp</th>\n",
       "      <th>S2_Temp</th>\n",
       "      <th>S3_Temp</th>\n",
       "      <th>S4_Temp</th>\n",
       "      <th>S1_Light</th>\n",
       "      <th>S2_Light</th>\n",
       "      <th>S3_Light</th>\n",
       "      <th>S4_Light</th>\n",
       "      <th>S1_Sound</th>\n",
       "      <th>S2_Sound</th>\n",
       "      <th>S3_Sound</th>\n",
       "      <th>S4_Sound</th>\n",
       "      <th>S5_CO2</th>\n",
       "      <th>S5_CO2_Slope</th>\n",
       "      <th>S6_PIR</th>\n",
       "      <th>S7_PIR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.94</td>\n",
       "      <td>24.75</td>\n",
       "      <td>24.56</td>\n",
       "      <td>25.38</td>\n",
       "      <td>121</td>\n",
       "      <td>34</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>390</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.94</td>\n",
       "      <td>24.75</td>\n",
       "      <td>24.56</td>\n",
       "      <td>25.44</td>\n",
       "      <td>121</td>\n",
       "      <td>33</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>390</td>\n",
       "      <td>0.646154</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25.00</td>\n",
       "      <td>24.75</td>\n",
       "      <td>24.50</td>\n",
       "      <td>25.44</td>\n",
       "      <td>121</td>\n",
       "      <td>34</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.06</td>\n",
       "      <td>390</td>\n",
       "      <td>0.519231</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.00</td>\n",
       "      <td>24.75</td>\n",
       "      <td>24.56</td>\n",
       "      <td>25.44</td>\n",
       "      <td>121</td>\n",
       "      <td>34</td>\n",
       "      <td>53</td>\n",
       "      <td>40</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>390</td>\n",
       "      <td>0.388462</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.00</td>\n",
       "      <td>24.75</td>\n",
       "      <td>24.56</td>\n",
       "      <td>25.44</td>\n",
       "      <td>121</td>\n",
       "      <td>34</td>\n",
       "      <td>54</td>\n",
       "      <td>40</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.06</td>\n",
       "      <td>390</td>\n",
       "      <td>0.253846</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  S4_Light  \\\n",
       "0    24.94    24.75    24.56    25.38       121        34        53        40   \n",
       "1    24.94    24.75    24.56    25.44       121        33        53        40   \n",
       "2    25.00    24.75    24.50    25.44       121        34        53        40   \n",
       "3    25.00    24.75    24.56    25.44       121        34        53        40   \n",
       "4    25.00    24.75    24.56    25.44       121        34        54        40   \n",
       "\n",
       "   S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  S6_PIR  \\\n",
       "0      0.08      0.19      0.06      0.06     390      0.769231       0   \n",
       "1      0.93      0.05      0.06      0.06     390      0.646154       0   \n",
       "2      0.43      0.11      0.08      0.06     390      0.519231       0   \n",
       "3      0.41      0.10      0.10      0.09     390      0.388462       0   \n",
       "4      0.18      0.06      0.06      0.06     390      0.253846       0   \n",
       "\n",
       "   S7_PIR  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qF7IVOaltOar"
   },
   "source": [
    "### 3. Normalizing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RfJLhgietOar"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import learning_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "C-KMtpckIYKr"
   },
   "outputs": [],
   "source": [
    "ss = StandardScaler()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PAKSBY_gtOar",
    "outputId": "9a7056b1-6a01-48b1-e3e8-c2c805bacd76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.46303347, -1.35777517, -1.16233429, ...,  0.66446859,\n",
       "        -0.31474885, -0.29402867],\n",
       "       [-1.46303347, -1.35777517, -1.16233429, ...,  0.558817  ,\n",
       "        -0.31474885, -0.29402867],\n",
       "       [-1.29225542, -1.35777517, -1.30276354, ...,  0.4498638 ,\n",
       "        -0.31474885, -0.29402867],\n",
       "       ...,\n",
       "       [-0.92223631, -0.82903247, -0.85807092, ...,  0.00414617,\n",
       "        -0.31474885, -0.29402867],\n",
       "       [-0.92223631, -0.82903247, -0.85807092, ...,  0.00414617,\n",
       "        -0.31474885, -0.29402867],\n",
       "       [-0.92223631, -0.82903247, -0.85807092, ...,  0.00414617,\n",
       "        -0.31474885, -0.29402867]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s2VfCpWjUafO"
   },
   "source": [
    "### 4. Creating 10 samples with 70-30 ratio of Training and Testing Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "H7MnfxqttiXl"
   },
   "outputs": [],
   "source": [
    "samples = []\n",
    "for i in range(10):\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=i)\n",
    "    samples.append((X_train, X_test, y_train, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gPp8uFHkMC2D",
    "outputId": "95a1d839-385f-4e19-dac7-c3e2dae0a873"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(      S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  8983    25.25    25.31    24.94    25.44         0         0         0   \n",
       "  1142    25.69    25.75    25.38    25.94         0         0         0   \n",
       "  3680    26.25    26.19    26.00    26.38       114        19       180   \n",
       "  9889    25.06    25.06    24.56    25.25         0         0         1   \n",
       "  8334    25.94    26.19    25.88    25.94       146       231       170   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  9225    25.19    25.25    24.75    25.38         0         0         0   \n",
       "  4859    25.19    25.19    24.63    25.56         0         0         0   \n",
       "  3264    25.88    26.81    25.50    26.25       120        26        48   \n",
       "  9845    25.06    25.06    24.56    25.19         0         0         0   \n",
       "  2732    25.19    25.13    24.81    25.69        12        14        58   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  8983         0      0.07      0.04      0.06      0.09     345      0.000000   \n",
       "  1142         0      0.06      0.06      0.07      0.07     675     -2.153846   \n",
       "  3680         7      0.08      0.05      0.10      0.07    1040     -0.446154   \n",
       "  9889         0      0.08      0.04      0.05      0.09     345      0.000000   \n",
       "  8334        19      0.48      0.60      0.71      0.17     835      1.419231   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  9225         0      0.07      0.05      0.06      0.08     345      0.000000   \n",
       "  4859         0      0.07      0.06      0.06      0.06     360      0.000000   \n",
       "  3264        35      0.22      0.08      0.07      0.06     420     -1.623077   \n",
       "  9845         0      0.08      0.05      0.05      0.08     345      0.000000   \n",
       "  2732        39      0.08      0.05      0.06      0.06     360      0.069231   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  8983       0       0  \n",
       "  1142       0       0  \n",
       "  3680       0       0  \n",
       "  9889       0       0  \n",
       "  8334       1       0  \n",
       "  ...      ...     ...  \n",
       "  9225       0       0  \n",
       "  4859       0       0  \n",
       "  3264       0       0  \n",
       "  9845       0       0  \n",
       "  2732       0       0  \n",
       "  \n",
       "  [7090 rows x 16 columns],\n",
       "        S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  8549    25.56    25.63    25.25    25.69         0         0         0   \n",
       "  6003    25.44    25.44    25.38    26.19        12        15        62   \n",
       "  3449    26.19    25.88    25.69    26.31       117        23       189   \n",
       "  1666    25.31    25.31    24.81    25.69         0         0         0   \n",
       "  8833    25.38    25.38    25.00    25.50         0         0         0   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  4347    25.44    25.44    24.94    25.69         0         0         0   \n",
       "  785     26.31    26.31    25.94    26.38       148       234       178   \n",
       "  7709    25.19    25.19    24.69    25.50         0         0         0   \n",
       "  8645    25.50    25.50    25.13    25.63         0         0         0   \n",
       "  8193    25.81    26.13    25.69    25.88         5         7        24   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  8549         0      0.07      0.06      0.06      0.09     535     -2.530769   \n",
       "  6003        45      0.08      0.05      0.06      0.09     355      0.000000   \n",
       "  3449        12      0.58      0.41      1.56      0.50     680      2.757692   \n",
       "  1666         0      0.07      0.05      0.07      0.06     370      0.000000   \n",
       "  8833         0      0.08      0.05      0.05      0.08     360     -0.253846   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  4347         0      0.06      0.04      0.06      0.06     365     -0.165385   \n",
       "  785         10      0.61      0.19      0.16      0.11     970      4.173077   \n",
       "  7709         0      0.07      0.05      0.06      0.09     355      0.000000   \n",
       "  8645         0      0.08      0.05      0.06      0.09     390     -1.707692   \n",
       "  8193        22      2.56      0.31      0.45      0.20     575      3.800000   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  8549       0       0  \n",
       "  6003       0       0  \n",
       "  3449       0       1  \n",
       "  1666       0       0  \n",
       "  8833       0       0  \n",
       "  ...      ...     ...  \n",
       "  4347       0       0  \n",
       "  785        1       0  \n",
       "  7709       0       0  \n",
       "  8645       0       0  \n",
       "  8193       0       1  \n",
       "  \n",
       "  [3039 rows x 16 columns],\n",
       "  8983    0\n",
       "  1142    0\n",
       "  3680    2\n",
       "  9889    0\n",
       "  8334    2\n",
       "         ..\n",
       "  9225    0\n",
       "  4859    0\n",
       "  3264    1\n",
       "  9845    0\n",
       "  2732    0\n",
       "  Name: Room_Occupancy_Count, Length: 7090, dtype: int64,\n",
       "  8549    0\n",
       "  6003    0\n",
       "  3449    2\n",
       "  1666    0\n",
       "  8833    0\n",
       "         ..\n",
       "  4347    0\n",
       "  785     3\n",
       "  7709    0\n",
       "  8645    0\n",
       "  8193    3\n",
       "  Name: Room_Occupancy_Count, Length: 3039, dtype: int64),\n",
       " (      S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  8153    25.69    26.38    25.56    25.75         9        10        32   \n",
       "  3732    26.25    26.13    25.94    26.25       114        19       180   \n",
       "  9865    25.13    25.06    24.56    25.19         0         0         0   \n",
       "  3335    26.06    26.81    25.56    26.25       118        22        33   \n",
       "  8242    25.81    26.06    25.75    25.88         3         4        22   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  2895    25.56    25.31    25.13    25.88       124        38        71   \n",
       "  7813    25.13    25.19    24.63    25.38         0         0         1   \n",
       "  905     26.31    27.94    26.06    26.44       150       239       180   \n",
       "  5192    25.06    25.13    24.50    25.50         5         5        28   \n",
       "  235     25.94    28.00    25.69    26.25       163       252       221   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  8153        29      0.26      0.88      0.26      0.15     455      2.569231   \n",
       "  3732         7      2.42      0.59      2.02      0.70    1025      0.626923   \n",
       "  9865         0      0.08      0.04      0.05      0.08     345      0.000000   \n",
       "  3335        23      0.08      0.06      0.06      0.06     475      0.769231   \n",
       "  8242        14      0.33      0.31      0.13      0.09     730      2.676923   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  2895        51      0.70      0.18      0.21      0.14     370      0.253846   \n",
       "  7813         0      0.08      0.04      0.06      0.10     355      0.023077   \n",
       "  905         11      1.17      0.44      2.01      0.57    1220      1.365385   \n",
       "  5192        19      0.07      0.05      0.06      0.07     360      0.000000   \n",
       "  235         70      1.74      0.65      2.00      0.43     855      6.488462   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  8153       1       1  \n",
       "  3732       0       0  \n",
       "  9865       0       0  \n",
       "  3335       0       0  \n",
       "  8242       1       1  \n",
       "  ...      ...     ...  \n",
       "  2895       1       0  \n",
       "  7813       0       0  \n",
       "  905        1       1  \n",
       "  5192       0       0  \n",
       "  235        1       1  \n",
       "  \n",
       "  [7090 rows x 16 columns],\n",
       "        S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  8164    25.69    26.13    25.56    25.81         7         7        29   \n",
       "  9015    25.25    25.31    24.88    25.38         0         0         0   \n",
       "  2053    25.19    25.13    24.56    25.50         0         0         0   \n",
       "  5757    25.31    25.31    25.19    25.94        17        20        74   \n",
       "  3986    25.69    25.63    25.25    25.88         0         0         0   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  8543    25.63    25.63    25.31    25.69         0         0         0   \n",
       "  9828    25.13    25.13    24.56    25.19         0         0         0   \n",
       "  7079    25.38    25.38    24.94    25.81         0         0         0   \n",
       "  4203    25.44    25.44    25.00    25.56         0         0         0   \n",
       "  1224    25.63    25.63    25.19    25.81         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  8164        26      0.29      2.86      0.21      0.15     475      3.057692   \n",
       "  9015         0      0.07      0.05      0.05      0.09     345      0.000000   \n",
       "  2053         0      0.09      0.05      0.05      0.06     360      0.000000   \n",
       "  5757        53      0.08      0.06      0.06      0.10     355      0.000000   \n",
       "  3986         0      0.07      0.04      0.05      0.07     540     -2.207692   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  8543         0      0.08      0.04      0.05      0.09     550     -2.042308   \n",
       "  9828         0      0.07      0.05      0.06      0.07     345      0.000000   \n",
       "  7079         0      0.08      0.05      0.06      0.10     355      0.000000   \n",
       "  4203         0      0.06      0.05      0.06      0.06     375      0.000000   \n",
       "  1224         0      0.07      0.04      0.06      0.06     515     -2.011538   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  8164       1       1  \n",
       "  9015       0       0  \n",
       "  2053       0       0  \n",
       "  5757       0       0  \n",
       "  3986       0       0  \n",
       "  ...      ...     ...  \n",
       "  8543       0       0  \n",
       "  9828       0       0  \n",
       "  7079       0       0  \n",
       "  4203       0       0  \n",
       "  1224       0       0  \n",
       "  \n",
       "  [3039 rows x 16 columns],\n",
       "  8153    3\n",
       "  3732    2\n",
       "  9865    0\n",
       "  3335    1\n",
       "  8242    3\n",
       "         ..\n",
       "  2895    1\n",
       "  7813    0\n",
       "  905     3\n",
       "  5192    0\n",
       "  235     3\n",
       "  Name: Room_Occupancy_Count, Length: 7090, dtype: int64,\n",
       "  8164    3\n",
       "  9015    0\n",
       "  2053    0\n",
       "  5757    0\n",
       "  3986    0\n",
       "         ..\n",
       "  8543    0\n",
       "  9828    0\n",
       "  7079    0\n",
       "  4203    0\n",
       "  1224    0\n",
       "  Name: Room_Occupancy_Count, Length: 3039, dtype: int64),\n",
       " (      S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  6713    25.38    25.44    25.06    25.88         0         0         0   \n",
       "  3245    25.75    26.81    25.50    26.19         9        10        49   \n",
       "  2576    25.06    25.06    24.63    25.44         7         8        39   \n",
       "  4131    25.50    25.50    25.06    25.63         0         0         0   \n",
       "  2425    25.06    25.06    24.50    25.38         5         5        28   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  1099    25.81    25.88    25.44    26.00         0         0         0   \n",
       "  2514    25.00    25.06    24.56    25.19         6         6        33   \n",
       "  6637    25.44    25.44    25.06    26.00         0         0         0   \n",
       "  2575    25.06    25.06    24.63    25.44         7         8        40   \n",
       "  7336    25.31    25.31    24.88    25.69         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  6713         0      0.08      0.05      0.06      0.10     355     -0.015385   \n",
       "  3245        35      0.07      0.07      0.06      0.08     445     -1.026923   \n",
       "  2576        26      0.08      0.05      0.06      0.07     355     -0.046154   \n",
       "  4131         0      0.06      0.05      0.06      0.06     385     -0.215385   \n",
       "  2425        19      0.07      0.05      0.06      0.05     360      0.080769   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  1099         0      0.07      0.04      0.06      0.07     765     -2.030769   \n",
       "  2514        21      0.07      0.05      0.06      0.06     360      0.000000   \n",
       "  6637         0      0.07      0.05      0.06      0.09     355      0.073077   \n",
       "  2575        26      0.08      0.05      0.06      0.06     360      0.000000   \n",
       "  7336         0      0.08      0.06      0.06      0.10     355      0.000000   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  6713       0       0  \n",
       "  3245       0       0  \n",
       "  2576       0       0  \n",
       "  4131       0       0  \n",
       "  2425       0       0  \n",
       "  ...      ...     ...  \n",
       "  1099       0       0  \n",
       "  2514       0       0  \n",
       "  6637       0       0  \n",
       "  2575       0       0  \n",
       "  7336       0       0  \n",
       "  \n",
       "  [7090 rows x 16 columns],\n",
       "        S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  7868    25.06    25.06    24.56    25.19         1         1         7   \n",
       "  8859    25.31    25.38    25.00    25.44         0         0         0   \n",
       "  2763    25.19    25.19    24.88    25.81        13        14        60   \n",
       "  8911    25.31    25.31    24.94    25.50         0         0         0   \n",
       "  709     26.19    25.81    25.81    26.31       114        24       176   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  6701    25.38    25.44    25.06    25.88         0         0         0   \n",
       "  6879    25.38    25.44    25.06    25.94         0         0         0   \n",
       "  7509    25.25    25.31    24.81    25.69         0         0         0   \n",
       "  958     26.31    27.06    26.06    26.31         0         0         0   \n",
       "  5706    25.25    25.31    25.13    25.88        17        20        73   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  7868         5      0.08      0.05      0.06      0.10     350      0.000000   \n",
       "  8859         0      0.06      0.05      0.05      0.08     360      0.000000   \n",
       "  2763        42      0.08      0.05      0.06      0.06     360     -0.042308   \n",
       "  8911         0      0.06      0.05      0.05      0.09     350     -0.176923   \n",
       "  709         10      0.07      0.06      0.09      0.06     785      1.534615   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  6701         0      0.08      0.05      0.06      0.09     355      0.000000   \n",
       "  6879         0      0.08      0.05      0.06      0.10     355      0.000000   \n",
       "  7509         0      0.07      0.05      0.06      0.09     355      0.000000   \n",
       "  958          0      0.07      0.05      2.81      0.06    1240      1.903846   \n",
       "  5706        53      0.07      0.05      0.06      0.11     355      0.000000   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  7868       0       0  \n",
       "  8859       0       0  \n",
       "  2763       0       0  \n",
       "  8911       0       0  \n",
       "  709        0       0  \n",
       "  ...      ...     ...  \n",
       "  6701       0       0  \n",
       "  6879       0       0  \n",
       "  7509       0       0  \n",
       "  958        0       0  \n",
       "  5706       0       0  \n",
       "  \n",
       "  [3039 rows x 16 columns],\n",
       "  6713    0\n",
       "  3245    0\n",
       "  2576    0\n",
       "  4131    0\n",
       "  2425    0\n",
       "         ..\n",
       "  1099    0\n",
       "  2514    0\n",
       "  6637    0\n",
       "  2575    0\n",
       "  7336    0\n",
       "  Name: Room_Occupancy_Count, Length: 7090, dtype: int64,\n",
       "  7868    0\n",
       "  8859    0\n",
       "  2763    0\n",
       "  8911    0\n",
       "  709     2\n",
       "         ..\n",
       "  6701    0\n",
       "  6879    0\n",
       "  7509    0\n",
       "  958     0\n",
       "  5706    0\n",
       "  Name: Room_Occupancy_Count, Length: 3039, dtype: int64),\n",
       " (      S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  4684    25.25    25.31    24.75    25.63         0         0         0   \n",
       "  861     26.31    26.63    26.00    26.38       150       236       181   \n",
       "  5327    25.06    25.06    24.56    25.44         7         7        37   \n",
       "  5117    25.06    25.13    24.50    25.44         2         2        13   \n",
       "  93      25.44    25.56    24.88    25.75       155       236        68   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  6400    25.44    25.44    25.19    26.13         0         0         0   \n",
       "  9160    25.19    25.31    24.81    25.38         0         0         0   \n",
       "  9859    25.13    25.06    24.56    25.19         0         0         0   \n",
       "  1688    25.31    25.31    24.81    25.75         0         0         0   \n",
       "  5994    25.38    25.44    25.44    26.19        14        17        66   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  4684         0      0.06      0.05      0.06      0.06     360      0.000000   \n",
       "  861         10      0.71      0.09      0.23      0.07    1155      3.065385   \n",
       "  5327        24      0.08      0.05      0.06      0.10     355     -0.046154   \n",
       "  5117         8      0.07      0.05      0.07      0.06     360      0.046154   \n",
       "  93          53      1.02      0.51      0.17      0.17     480      0.819231   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  6400         0      0.09      0.05      0.06      0.11     355      0.192308   \n",
       "  9160         0      0.07      0.04      0.06      0.08     345      0.000000   \n",
       "  9859         0      0.08      0.04      0.06      0.08     345      0.000000   \n",
       "  1688         0      0.07      0.05      0.06      0.06     365     -0.146154   \n",
       "  5994        48      0.08      0.06      0.06      0.10     355      0.000000   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  4684       0       0  \n",
       "  861        0       1  \n",
       "  5327       0       0  \n",
       "  5117       0       0  \n",
       "  93         1       1  \n",
       "  ...      ...     ...  \n",
       "  6400       0       0  \n",
       "  9160       0       0  \n",
       "  9859       0       0  \n",
       "  1688       0       0  \n",
       "  5994       0       0  \n",
       "  \n",
       "  [7090 rows x 16 columns],\n",
       "        S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  1897    25.25    25.25    24.69    25.50         0         0         0   \n",
       "  213     25.81    29.00    25.38    26.06       160       243       221   \n",
       "  9864    25.06    25.06    24.56    25.19         0         0         0   \n",
       "  8962    25.31    25.31    24.94    25.38         0         0         0   \n",
       "  577     26.13    25.75    25.56    26.31       117        27        45   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  8079    25.06    25.06    24.69    25.25         6         7        34   \n",
       "  1733    25.25    25.31    24.81    25.63         0         0         0   \n",
       "  4227    25.44    25.44    25.00    25.56         0         0         0   \n",
       "  5512    25.13    25.13    24.81    25.75        13        14        57   \n",
       "  8522    25.63    25.63    25.31    25.69         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  1897         0      0.08      0.05      0.06      0.05     360     -0.196154   \n",
       "  213         66      0.57      0.63      0.58      0.23     700      2.353846   \n",
       "  9864         0      0.07      0.04      0.05      0.08     345      0.000000   \n",
       "  8962         0      0.07      0.04      0.05      0.09     345     -0.265385   \n",
       "  577         26      0.16      0.05      0.15      0.07     605      0.788462   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  8079        22      0.07      0.05      0.07      0.10     350      0.000000   \n",
       "  1733         0      0.08      0.06      0.06      0.06     365     -0.050000   \n",
       "  4227         0      0.07      0.05      0.07      0.07     370     -0.153846   \n",
       "  5512        40      0.08      0.04      0.06      0.10     355      0.000000   \n",
       "  8522         0      0.08      0.05      0.05      0.10     595     -1.500000   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  1897       0       0  \n",
       "  213        1       0  \n",
       "  9864       0       0  \n",
       "  8962       0       0  \n",
       "  577        0       0  \n",
       "  ...      ...     ...  \n",
       "  8079       0       0  \n",
       "  1733       0       0  \n",
       "  4227       0       0  \n",
       "  5512       0       0  \n",
       "  8522       0       0  \n",
       "  \n",
       "  [3039 rows x 16 columns],\n",
       "  4684    0\n",
       "  861     3\n",
       "  5327    0\n",
       "  5117    0\n",
       "  93      2\n",
       "         ..\n",
       "  6400    0\n",
       "  9160    0\n",
       "  9859    0\n",
       "  1688    0\n",
       "  5994    0\n",
       "  Name: Room_Occupancy_Count, Length: 7090, dtype: int64,\n",
       "  1897    0\n",
       "  213     3\n",
       "  9864    0\n",
       "  8962    0\n",
       "  577     1\n",
       "         ..\n",
       "  8079    0\n",
       "  1733    0\n",
       "  4227    0\n",
       "  5512    0\n",
       "  8522    0\n",
       "  Name: Room_Occupancy_Count, Length: 3039, dtype: int64),\n",
       " (      S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  789     26.31    26.44    26.00    26.38       148       234       178   \n",
       "  8726    25.50    25.44    25.06    25.56         0         0         0   \n",
       "  1960    25.19    25.19    24.69    25.56         0         0         0   \n",
       "  833     26.25    26.63    26.00    26.44       150       236       180   \n",
       "  8970    25.31    25.31    24.94    25.38         0         0         0   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  456     25.81    25.94    25.75    26.31        14        16        80   \n",
       "  6017    25.44    25.44    25.38    26.19        12        14        59   \n",
       "  709     26.19    25.81    25.81    26.31       114        24       176   \n",
       "  8366    25.94    26.19    25.75    25.94       142       228         5   \n",
       "  1146    25.75    25.75    25.38    25.94         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  789         10      0.24      0.14      0.48      0.15     975      4.473077   \n",
       "  8726         0      0.07      0.05      0.07      0.08     380     -0.111538   \n",
       "  1960         0      0.08      0.05      0.06      0.06     360      0.000000   \n",
       "  833         10      0.82      0.79      1.54      0.42    1080      2.326923   \n",
       "  8970         0      0.08      0.05      0.05      0.09     345     -0.126923   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  456         49      0.08      0.05      0.08      0.07     675     -1.796154   \n",
       "  6017        44      0.10      0.11      0.11      0.10     355      0.000000   \n",
       "  709         10      0.07      0.06      0.09      0.06     785      1.534615   \n",
       "  8366         7      0.59      0.94      0.19      0.14     835     -0.461538   \n",
       "  1146         0      0.07      0.05      0.06      0.06     665     -2.400000   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  789        1       0  \n",
       "  8726       0       0  \n",
       "  1960       0       0  \n",
       "  833        1       1  \n",
       "  8970       0       0  \n",
       "  ...      ...     ...  \n",
       "  456        0       0  \n",
       "  6017       0       0  \n",
       "  709        0       0  \n",
       "  8366       1       1  \n",
       "  1146       0       0  \n",
       "  \n",
       "  [7090 rows x 16 columns],\n",
       "        S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  3536    26.19    26.19    25.81    26.31       149       234       185   \n",
       "  8878    25.38    25.31    25.00    25.44         0         0         0   \n",
       "  4628    25.31    25.31    24.75    25.63         0         0         0   \n",
       "  2123    25.13    25.19    24.56    25.31         0         0         0   \n",
       "  2774    25.19    25.19    24.88    25.88        13        14        59   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  8465    25.69    25.81    25.38    25.81         0         0         0   \n",
       "  4462    25.38    25.38    24.88    25.69         0         0         0   \n",
       "  9237    25.19    25.25    24.81    25.31         0         0         0   \n",
       "  5808    25.31    25.31    25.25    25.94        16        20        72   \n",
       "  1264    25.56    25.63    25.19    25.88         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  3536        11      0.37      0.34      0.08      0.09     825      1.876923   \n",
       "  8878         0      0.07      0.04      0.05      0.09     355     -0.253846   \n",
       "  4628         0      0.06      0.04      0.07      0.07     360     -0.030769   \n",
       "  2123         0      0.08      0.05      0.06      0.06     360      0.000000   \n",
       "  2774        42      0.08      0.05      0.06      0.06     360      0.057692   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  8465         0      0.08      0.06      0.05      0.09     690     -1.938462   \n",
       "  4462         0      0.06      0.04      0.06      0.06     365      0.000000   \n",
       "  9237         0      0.07      0.04      0.06      0.09     345      0.000000   \n",
       "  5808        51      0.08      0.05      0.06      0.10     355      0.000000   \n",
       "  1264         0      0.07      0.05      0.06      0.06     465     -1.026923   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  3536       0       1  \n",
       "  8878       0       0  \n",
       "  4628       0       0  \n",
       "  2123       0       0  \n",
       "  2774       0       0  \n",
       "  ...      ...     ...  \n",
       "  8465       0       0  \n",
       "  4462       0       0  \n",
       "  9237       0       0  \n",
       "  5808       0       0  \n",
       "  1264       0       0  \n",
       "  \n",
       "  [3039 rows x 16 columns],\n",
       "  789     3\n",
       "  8726    0\n",
       "  1960    0\n",
       "  833     3\n",
       "  8970    0\n",
       "         ..\n",
       "  456     0\n",
       "  6017    0\n",
       "  709     2\n",
       "  8366    2\n",
       "  1146    0\n",
       "  Name: Room_Occupancy_Count, Length: 7090, dtype: int64,\n",
       "  3536    3\n",
       "  8878    0\n",
       "  4628    0\n",
       "  2123    0\n",
       "  2774    0\n",
       "         ..\n",
       "  8465    0\n",
       "  4462    0\n",
       "  9237    0\n",
       "  5808    0\n",
       "  1264    0\n",
       "  Name: Room_Occupancy_Count, Length: 3039, dtype: int64),\n",
       " (      S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  705     26.19    25.75    25.81    26.31       114        24       176   \n",
       "  9179    25.19    25.25    24.81    25.31         0         0         0   \n",
       "  7144    25.38    25.38    24.94    25.81         0         0         0   \n",
       "  2942    25.69    25.38    25.19    26.00       124        39        74   \n",
       "  6861    25.38    25.38    25.00    25.94         0         0         0   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  3046    26.00    26.00    25.38    26.25       161       251        81   \n",
       "  9917    25.06    25.06    24.50    25.19         0         0         4   \n",
       "  4079    25.50    25.56    25.13    25.69         0         0         0   \n",
       "  2254    25.06    25.06    24.44    25.31         0         0         1   \n",
       "  2915    25.69    25.38    25.13    25.94       123        38        69   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  705         10      0.58      0.26      0.25      0.15     770      1.326923   \n",
       "  9179         0      0.07      0.05      0.05      0.09     345      0.000000   \n",
       "  7144         0      0.07      0.05      0.06      0.09     355      0.000000   \n",
       "  2942        54      0.65      0.17      0.28      0.14     380      0.219231   \n",
       "  6861         0      0.08      0.05      0.08      0.11     355      0.000000   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  3046        60      0.42      0.99      0.22      0.13     525      3.538462   \n",
       "  9917         3      0.08      0.04      0.05      0.08     345      0.000000   \n",
       "  4079         0      0.07      0.05      0.06      0.07     390     -1.653846   \n",
       "  2254         1      0.08      0.05      0.05      0.06     355      0.030769   \n",
       "  2915        49      0.59      0.21      0.17      0.12     375      0.296154   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  705        1       1  \n",
       "  9179       0       0  \n",
       "  7144       0       0  \n",
       "  2942       1       0  \n",
       "  6861       0       0  \n",
       "  ...      ...     ...  \n",
       "  3046       0       0  \n",
       "  9917       0       0  \n",
       "  4079       0       0  \n",
       "  2254       0       0  \n",
       "  2915       0       0  \n",
       "  \n",
       "  [7090 rows x 16 columns],\n",
       "        S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  3097    26.13    27.13    25.50    26.38       160       248        77   \n",
       "  6044    25.44    25.44    25.44    26.13        10        12        50   \n",
       "  5268    25.13    25.13    24.63    25.31         6         7        35   \n",
       "  1789    25.25    25.25    24.81    25.69         0         0         0   \n",
       "  9793    25.13    25.06    24.56    25.19         0         0         0   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  9998    25.06    25.06    24.56    25.06         4         4        18   \n",
       "  5860    25.31    25.38    25.25    26.00        20        24        85   \n",
       "  806     26.31    26.31    26.00    26.38       148       234       178   \n",
       "  7122    25.38    25.38    24.94    25.88         0         0         0   \n",
       "  4645    25.31    25.31    24.75    25.63         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  3097        57      0.53      0.97      0.28      0.21     620      0.946154   \n",
       "  6044        36      0.08      0.05      0.06      0.10     355      0.000000   \n",
       "  5268        22      0.06      0.05      0.06      0.07     360     -0.019231   \n",
       "  1789         0      0.07      0.05      0.06      0.06     365      0.000000   \n",
       "  9793         0      0.08      0.04      0.05      0.08     345      0.000000   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  9998        14      0.08      0.05      0.05      0.09     345      0.000000   \n",
       "  5860        62      0.08      0.05      0.06      0.10     355      0.000000   \n",
       "  806         10      0.46      0.21      0.59      0.24    1025      2.484615   \n",
       "  7122         0      0.07      0.05      0.06      0.10     355      0.000000   \n",
       "  4645         0      0.07      0.05      0.06      0.06     360      0.000000   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  3097       1       0  \n",
       "  6044       0       0  \n",
       "  5268       0       0  \n",
       "  1789       0       0  \n",
       "  9793       0       0  \n",
       "  ...      ...     ...  \n",
       "  9998       0       0  \n",
       "  5860       0       0  \n",
       "  806        0       1  \n",
       "  7122       0       0  \n",
       "  4645       0       0  \n",
       "  \n",
       "  [3039 rows x 16 columns],\n",
       "  705     2\n",
       "  9179    0\n",
       "  7144    0\n",
       "  2942    1\n",
       "  6861    0\n",
       "         ..\n",
       "  3046    2\n",
       "  9917    0\n",
       "  4079    0\n",
       "  2254    0\n",
       "  2915    1\n",
       "  Name: Room_Occupancy_Count, Length: 7090, dtype: int64,\n",
       "  3097    2\n",
       "  6044    0\n",
       "  5268    0\n",
       "  1789    0\n",
       "  9793    0\n",
       "         ..\n",
       "  9998    0\n",
       "  5860    0\n",
       "  806     3\n",
       "  7122    0\n",
       "  4645    0\n",
       "  Name: Room_Occupancy_Count, Length: 3039, dtype: int64),\n",
       " (      S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  3833    25.94    25.88    25.56    26.06         0         0         0   \n",
       "  1712    25.31    25.31    24.81    25.69         0         0         0   \n",
       "  5567    25.19    25.19    24.94    25.81        12        14        54   \n",
       "  4081    25.50    25.56    25.13    25.75         0         0         0   \n",
       "  797     26.31    26.38    26.00    26.31       148       234       178   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  9040    25.25    25.31    24.94    25.38         0         0         0   \n",
       "  8527    25.63    25.63    25.31    25.69         0         0         0   \n",
       "  4714    25.25    25.25    24.69    25.63         0         0         0   \n",
       "  8419    25.81    25.94    25.56    25.88         0         0         0   \n",
       "  2761    25.19    25.19    24.88    25.81        13        14        59   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  3833         0      0.07      0.04      0.06      0.06     860     -2.700000   \n",
       "  1712         0      0.07      0.05      0.07      0.06     365      0.034615   \n",
       "  5567        40      0.07      0.05      0.06      0.11     355      0.000000   \n",
       "  4081         0      0.07      0.05      0.05      0.06     390     -1.603846   \n",
       "  797         10      2.23      0.45      1.55      0.47     995      3.892308   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  9040         0      0.07      0.05      0.06      0.10     345      0.000000   \n",
       "  8527         0      0.07      0.05      0.05      0.09     590     -1.719231   \n",
       "  4714         0      0.07      0.05      0.06      0.06     360      0.000000   \n",
       "  8419         0      0.07      0.05      0.05      0.08     780     -2.026923   \n",
       "  2761        42      0.08      0.05      0.07      0.06     355     -0.026923   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  3833       0       0  \n",
       "  1712       0       0  \n",
       "  5567       0       0  \n",
       "  4081       0       0  \n",
       "  797        1       1  \n",
       "  ...      ...     ...  \n",
       "  9040       0       0  \n",
       "  8527       0       0  \n",
       "  4714       0       0  \n",
       "  8419       0       0  \n",
       "  2761       0       0  \n",
       "  \n",
       "  [7090 rows x 16 columns],\n",
       "        S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  8008    25.06    25.06    24.63    25.19         6         6        31   \n",
       "  4207    25.44    25.44    25.00    25.50         0         0         0   \n",
       "  4632    25.31    25.31    24.75    25.63         0         0         0   \n",
       "  4381    25.38    25.38    24.88    25.69         0         0         0   \n",
       "  9164    25.25    25.25    24.81    25.31         0         0         0   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  2513    25.00    25.06    24.56    25.25         6         6        33   \n",
       "  6877    25.38    25.38    25.06    25.88         0         0         0   \n",
       "  5372    25.00    25.06    24.69    25.50         8         9        42   \n",
       "  2999    25.94    25.81    25.38    26.19       160       249        80   \n",
       "  7535    25.25    25.25    24.81    25.63         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  8008        21      0.08      0.05      0.06      0.10     350      0.000000   \n",
       "  4207         0      0.07      0.04      0.06      0.06     375      0.000000   \n",
       "  4632         0      0.06      0.05      0.06      0.06     360     -0.046154   \n",
       "  4381         0      0.06      0.05      0.06      0.06     365     -0.119231   \n",
       "  9164         0      0.07      0.04      0.06      0.08     345      0.000000   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  2513        21      0.08      0.05      0.06      0.06     360      0.000000   \n",
       "  6877         0      0.08      0.06      0.07      0.10     355      0.000000   \n",
       "  5372        29      0.08      0.05      0.07      0.10     355      0.000000   \n",
       "  2999        59      0.57      0.94      0.17      0.21     465      2.557692   \n",
       "  7535         0      0.09      0.04      0.06      0.09     355      0.000000   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  8008       0       0  \n",
       "  4207       0       0  \n",
       "  4632       0       0  \n",
       "  4381       0       0  \n",
       "  9164       0       0  \n",
       "  ...      ...     ...  \n",
       "  2513       0       0  \n",
       "  6877       0       0  \n",
       "  5372       0       0  \n",
       "  2999       1       0  \n",
       "  7535       0       0  \n",
       "  \n",
       "  [3039 rows x 16 columns],\n",
       "  3833    0\n",
       "  1712    0\n",
       "  5567    0\n",
       "  4081    0\n",
       "  797     3\n",
       "         ..\n",
       "  9040    0\n",
       "  8527    0\n",
       "  4714    0\n",
       "  8419    0\n",
       "  2761    0\n",
       "  Name: Room_Occupancy_Count, Length: 7090, dtype: int64,\n",
       "  8008    0\n",
       "  4207    0\n",
       "  4632    0\n",
       "  4381    0\n",
       "  9164    0\n",
       "         ..\n",
       "  2513    0\n",
       "  6877    0\n",
       "  5372    0\n",
       "  2999    2\n",
       "  7535    0\n",
       "  Name: Room_Occupancy_Count, Length: 3039, dtype: int64),\n",
       " (      S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  9113    25.25    25.31    24.81    25.38         0         0         0   \n",
       "  4511    25.38    25.38    24.88    25.75         0         0         0   \n",
       "  6656    25.44    25.44    25.06    25.94         0         0         0   \n",
       "  8164    25.69    26.13    25.56    25.81         7         7        29   \n",
       "  3460    26.19    25.88    25.69    26.31       118        23       188   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  919     26.38    27.94    26.06    26.44       150       239       181   \n",
       "  4307    25.44    25.44    24.94    25.69         0         0         0   \n",
       "  5699    25.25    25.25    25.13    25.94        17        20        74   \n",
       "  537     26.06    25.75    25.56    26.31       119        30        56   \n",
       "  9412    25.19    25.19    24.75    25.25         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  9113         0      0.06      0.04      0.05      0.08     345      0.000000   \n",
       "  4511         0      0.07      0.05      0.06      0.06     365      0.000000   \n",
       "  6656         0      0.08      0.05      0.06      0.09     355     -0.107692   \n",
       "  8164        26      0.29      2.86      0.21      0.15     475      3.057692   \n",
       "  3460        11      0.60      0.13      1.93      0.69     710      2.392308   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  919         11      3.10      0.30      0.44      0.18    1200     -0.407692   \n",
       "  4307         0      0.07      0.04      0.06      0.06     365     -0.046154   \n",
       "  5699        53      0.08      0.05      0.06      0.09     355      0.000000   \n",
       "  537         35      0.08      0.06      0.08      0.06     595      1.315385   \n",
       "  9412         0      0.07      0.04      0.06      0.08     345      0.000000   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  9113       0       0  \n",
       "  4511       0       0  \n",
       "  6656       0       0  \n",
       "  8164       1       1  \n",
       "  3460       1       1  \n",
       "  ...      ...     ...  \n",
       "  919        0       0  \n",
       "  4307       0       0  \n",
       "  5699       0       0  \n",
       "  537        0       0  \n",
       "  9412       0       0  \n",
       "  \n",
       "  [7090 rows x 16 columns],\n",
       "        S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  2914    25.63    25.38    25.13    25.94       123        37        67   \n",
       "  1592    25.38    25.38    24.94    25.75         0         0         0   \n",
       "  3577    26.25    26.31    25.88    26.38       149       234       185   \n",
       "  6016    25.44    25.50    25.44    26.13        12        14        60   \n",
       "  4106    25.56    25.56    25.06    25.63         0         0         0   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  4053    25.56    25.56    25.19    25.75         0         0         0   \n",
       "  3106    26.06    27.19    25.50    26.38        15        15        71   \n",
       "  26      25.13    24.88    24.69    25.50       123        35        59   \n",
       "  300     26.06    27.06    26.00    26.25       165       255       275   \n",
       "  1704    25.31    25.31    24.81    25.69         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  2914        49      0.62      0.22      0.20      0.13     375      0.288462   \n",
       "  1592         0      0.08      0.05      0.06      0.05     370     -0.138462   \n",
       "  3577        10      0.08      0.18      0.06      0.07     950      2.569231   \n",
       "  6016        44      0.08      0.05      0.06      0.10     355      0.000000   \n",
       "  4106         0      0.07      0.05      0.06      0.06     385     -0.273077   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  4053         0      0.06      0.05      0.06      0.06     425     -1.403846   \n",
       "  3106        51      0.07      0.06      0.06      0.06     660      2.207692   \n",
       "  26          44      0.21      0.19      0.17      0.07     405      0.480769   \n",
       "  300         70      0.42      0.33      1.04      0.09     910      1.323077   \n",
       "  1704         0      0.07      0.05      0.06      0.06     365     -0.073077   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  2914       0       0  \n",
       "  1592       0       0  \n",
       "  3577       0       0  \n",
       "  6016       0       0  \n",
       "  4106       0       0  \n",
       "  ...      ...     ...  \n",
       "  4053       0       0  \n",
       "  3106       0       0  \n",
       "  26         1       0  \n",
       "  300        1       1  \n",
       "  1704       0       0  \n",
       "  \n",
       "  [3039 rows x 16 columns],\n",
       "  9113    0\n",
       "  4511    0\n",
       "  6656    0\n",
       "  8164    3\n",
       "  3460    2\n",
       "         ..\n",
       "  919     3\n",
       "  4307    0\n",
       "  5699    0\n",
       "  537     1\n",
       "  9412    0\n",
       "  Name: Room_Occupancy_Count, Length: 7090, dtype: int64,\n",
       "  2914    1\n",
       "  1592    0\n",
       "  3577    3\n",
       "  6016    0\n",
       "  4106    0\n",
       "         ..\n",
       "  4053    0\n",
       "  3106    0\n",
       "  26      1\n",
       "  300     3\n",
       "  1704    0\n",
       "  Name: Room_Occupancy_Count, Length: 3039, dtype: int64),\n",
       " (      S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  9490    25.19    25.19    24.75    25.31         0         0         0   \n",
       "  6903    25.38    25.44    25.00    25.94         0         0         0   \n",
       "  7783    25.19    25.19    24.63    25.38         0         0         0   \n",
       "  4791    25.25    25.25    24.69    25.56         0         0         0   \n",
       "  5957    25.38    25.44    25.38    26.13        15        20        73   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  6995    25.38    25.38    25.00    25.81         0         0         0   \n",
       "  2181    25.06    25.13    24.50    25.19         0         0         0   \n",
       "  2033    25.13    25.13    24.56    25.50         0         0         0   \n",
       "  9556    25.13    25.19    24.63    25.25         0         0         0   \n",
       "  4547    25.31    25.38    24.81    25.69         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  9490         0      0.08      0.05      0.06      0.08     345      0.000000   \n",
       "  6903         0      0.07      0.06      0.06      0.09     355      0.000000   \n",
       "  7783         0      0.07      0.06      0.05      0.11     355     -0.050000   \n",
       "  4791         0      0.07      0.05      0.06      0.06     360      0.000000   \n",
       "  5957        53      0.08      0.05      0.06      0.10     355      0.000000   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  6995         0      0.07      0.05      0.06      0.09     355      0.000000   \n",
       "  2181         0      0.08      0.05      0.06      0.05     355     -0.103846   \n",
       "  2033         0      0.08      0.05      0.07      0.06     360      0.000000   \n",
       "  9556         0      0.08      0.05      0.05      0.08     345      0.000000   \n",
       "  4547         0      0.06      0.05      0.06      0.07     360     -0.042308   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  9490       0       0  \n",
       "  6903       0       0  \n",
       "  7783       0       0  \n",
       "  4791       0       0  \n",
       "  5957       0       0  \n",
       "  ...      ...     ...  \n",
       "  6995       0       0  \n",
       "  2181       0       0  \n",
       "  2033       0       0  \n",
       "  9556       0       0  \n",
       "  4547       0       0  \n",
       "  \n",
       "  [7090 rows x 16 columns],\n",
       "        S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  5370    25.00    25.06    24.69    25.50         8         9        42   \n",
       "  2507    25.06    25.06    24.56    25.25         6         6        33   \n",
       "  8071    25.06    25.13    24.63    25.31         6         6        33   \n",
       "  9637    25.13    25.13    24.63    25.25         0         0         0   \n",
       "  676     26.19    25.75    25.75    26.31       116        24       180   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  7004    25.44    25.38    25.00    25.88         0         0         0   \n",
       "  5832    25.38    25.38    25.25    26.00        18        21        76   \n",
       "  726     26.19    25.81    25.88    26.25       116        24       177   \n",
       "  3108    26.06    27.13    25.50    26.38        15        15        71   \n",
       "  9753    25.13    25.13    24.56    25.19         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  5370        29      0.07      0.05      0.06      0.10     355      0.000000   \n",
       "  2507        21      0.08      0.05      0.06      0.06     360      0.000000   \n",
       "  8071        21      0.08      0.06      0.07      0.10     350      0.000000   \n",
       "  9637         0      0.08      0.05      0.05      0.08     345      0.000000   \n",
       "  676         13      0.22      0.16      0.99      0.22     730      1.553846   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  7004         0      0.07      0.06      0.06      0.09     355      0.000000   \n",
       "  5832        55      0.07      0.05      0.05      0.10     355      0.000000   \n",
       "  726          9      0.61      0.11      0.13      0.10     810      1.761538   \n",
       "  3108        51      0.07      0.06      0.06      0.06     665      2.592308   \n",
       "  9753         0      0.07      0.05      0.05      0.09     345      0.000000   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  5370       0       0  \n",
       "  2507       0       0  \n",
       "  8071       0       0  \n",
       "  9637       0       0  \n",
       "  676        0       1  \n",
       "  ...      ...     ...  \n",
       "  7004       0       0  \n",
       "  5832       0       0  \n",
       "  726        0       1  \n",
       "  3108       0       0  \n",
       "  9753       0       0  \n",
       "  \n",
       "  [3039 rows x 16 columns],\n",
       "  9490    0\n",
       "  6903    0\n",
       "  7783    0\n",
       "  4791    0\n",
       "  5957    0\n",
       "         ..\n",
       "  6995    0\n",
       "  2181    0\n",
       "  2033    0\n",
       "  9556    0\n",
       "  4547    0\n",
       "  Name: Room_Occupancy_Count, Length: 7090, dtype: int64,\n",
       "  5370    0\n",
       "  2507    0\n",
       "  8071    0\n",
       "  9637    0\n",
       "  676     2\n",
       "         ..\n",
       "  7004    0\n",
       "  5832    0\n",
       "  726     2\n",
       "  3108    0\n",
       "  9753    0\n",
       "  Name: Room_Occupancy_Count, Length: 3039, dtype: int64),\n",
       " (      S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  9749    25.13    25.13    24.56    25.25         0         0         0   \n",
       "  5266    25.13    25.13    24.63    25.38         6         7        35   \n",
       "  4412    25.38    25.44    24.94    25.75         0         0         0   \n",
       "  7054    25.38    25.38    25.00    25.88         0         0         0   \n",
       "  7545    25.25    25.31    24.81    25.63         0         0         0   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  6200    25.44    25.44    25.31    26.19         2         2        12   \n",
       "  501     25.94    25.88    25.63    26.31       120        33        69   \n",
       "  6782    25.38    25.44    25.06    25.94         0         0         0   \n",
       "  4444    25.38    25.38    24.94    25.75         0         0         0   \n",
       "  8574    25.56    25.56    25.25    25.63         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  9749         0      0.07      0.05      0.05      0.08     345      0.000000   \n",
       "  5266        22      0.07      0.05      0.06      0.08     360     -0.026923   \n",
       "  4412         0      0.07      0.05      0.06      0.06     365      0.000000   \n",
       "  7054         0      0.08      0.05      0.07      0.10     355      0.000000   \n",
       "  7545         0      0.08      0.05      0.06      0.10     355      0.000000   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  6200         9      0.08      0.05      0.06      0.10     350     -0.019231   \n",
       "  501         43      0.72      0.63      0.26      0.29     570     -1.276923   \n",
       "  6782         0      0.08      0.05      0.06      0.09     355     -0.015385   \n",
       "  4444         0      0.07      0.05      0.06      0.06     365      0.000000   \n",
       "  8574         0      0.08      0.05      0.05      0.09     490     -1.811538   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  9749       0       0  \n",
       "  5266       0       0  \n",
       "  4412       0       0  \n",
       "  7054       0       0  \n",
       "  7545       0       0  \n",
       "  ...      ...     ...  \n",
       "  6200       0       0  \n",
       "  501        0       0  \n",
       "  6782       0       0  \n",
       "  4444       0       0  \n",
       "  8574       0       0  \n",
       "  \n",
       "  [7090 rows x 16 columns],\n",
       "        S1_Temp  S2_Temp  S3_Temp  S4_Temp  S1_Light  S2_Light  S3_Light  \\\n",
       "  6793    25.38    25.44    25.06    25.88         0         0         0   \n",
       "  5485    25.13    25.13    24.75    25.69        11        13        53   \n",
       "  6475    25.44    25.44    25.19    26.00         0         0         0   \n",
       "  8959    25.31    25.31    24.94    25.44         0         0         0   \n",
       "  5098    25.06    25.13    24.50    25.38         1         1        10   \n",
       "  ...       ...      ...      ...      ...       ...       ...       ...   \n",
       "  4414    25.38    25.44    24.88    25.69         0         0         0   \n",
       "  8492    25.69    25.75    25.38    25.69         0         0         0   \n",
       "  1575    25.31    25.38    24.94    25.81         0         0         0   \n",
       "  752     26.19    25.94    25.88    26.31       150       238       179   \n",
       "  5017    25.13    25.13    24.56    25.44         0         0         0   \n",
       "  \n",
       "        S4_Light  S1_Sound  S2_Sound  S3_Sound  S4_Sound  S5_CO2  S5_CO2_Slope  \\\n",
       "  6793         0      0.08      0.05      0.06      0.09     355      0.019231   \n",
       "  5485        37      0.08      0.05      0.06      0.11     355      0.000000   \n",
       "  6475         0      0.09      0.05      0.06      0.12     355      0.126923   \n",
       "  8959         0      0.07      0.04      0.06      0.09     345     -0.253846   \n",
       "  5098         6      0.07      0.05      0.06      0.07     360      0.057692   \n",
       "  ...        ...       ...       ...       ...       ...     ...           ...   \n",
       "  4414         0      0.06      0.06      0.08      0.06     365      0.000000   \n",
       "  8492         0      0.09      0.07      0.09      0.09     630     -1.476923   \n",
       "  1575         0      0.07      0.05      0.06      0.06     370     -0.192308   \n",
       "  752         11      0.52      0.81      1.95      0.43     860      1.153846   \n",
       "  5017         0      0.07      0.05      0.07      0.06     355     -0.126923   \n",
       "  \n",
       "        S6_PIR  S7_PIR  \n",
       "  6793       0       0  \n",
       "  5485       0       0  \n",
       "  6475       0       0  \n",
       "  8959       0       0  \n",
       "  5098       0       0  \n",
       "  ...      ...     ...  \n",
       "  4414       0       0  \n",
       "  8492       0       0  \n",
       "  1575       0       0  \n",
       "  752        0       1  \n",
       "  5017       0       0  \n",
       "  \n",
       "  [3039 rows x 16 columns],\n",
       "  9749    0\n",
       "  5266    0\n",
       "  4412    0\n",
       "  7054    0\n",
       "  7545    0\n",
       "         ..\n",
       "  6200    0\n",
       "  501     1\n",
       "  6782    0\n",
       "  4444    0\n",
       "  8574    0\n",
       "  Name: Room_Occupancy_Count, Length: 7090, dtype: int64,\n",
       "  6793    0\n",
       "  5485    0\n",
       "  6475    0\n",
       "  8959    0\n",
       "  5098    0\n",
       "         ..\n",
       "  4414    0\n",
       "  8492    0\n",
       "  1575    0\n",
       "  752     3\n",
       "  5017    0\n",
       "  Name: Room_Occupancy_Count, Length: 3039, dtype: int64)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xbpZpTYpUuTl"
   },
   "source": [
    "### 5. Defining Fitness Function and calling it for every sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "UEJ2yRAeMOb2"
   },
   "outputs": [],
   "source": [
    "kernels = ['linear', 'poly', 'rbf', 'sigmoid']\n",
    "result = pd.DataFrame(columns=['Sample', 'Best Accuracy', 'Best Kernel', 'Best Nu', 'Best Epsilon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3XHWEfctVUGH"
   },
   "outputs": [],
   "source": [
    "def fitnessFunction(kernel, C, gamma):\n",
    "  svm = SVC(kernel=kernel, C=C, gamma=gamma, max_iter=1000)\n",
    "  svm.fit(X_train, y_train)\n",
    "  y_pred = svm.predict(X_test)\n",
    "  return accuracy_score(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fjgJ1WVBWsxx",
    "outputId": "fc80765b-2070-405b-f8e9-e2824626322c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Accuracy =  0.92 Best Kernel =  poly Best Nu =  7.38 Best Epsilon =  6.43\n",
      "Best Accuracy =  0.95 Best Kernel =  linear Best Nu =  9.9 Best Epsilon =  5.3\n",
      "Best Accuracy =  0.94 Best Kernel =  linear Best Nu =  0.03 Best Epsilon =  2.32\n",
      "Best Accuracy =  0.83 Best Kernel =  linear Best Nu =  4.33 Best Epsilon =  2.87\n",
      "Best Accuracy =  0.95 Best Kernel =  poly Best Nu =  2.4 Best Epsilon =  7.34\n",
      "Best Accuracy =  0.86 Best Kernel =  poly Best Nu =  7.44 Best Epsilon =  6.68\n",
      "Best Accuracy =  0.92 Best Kernel =  linear Best Nu =  1.21 Best Epsilon =  2.54\n",
      "Best Accuracy =  0.97 Best Kernel =  linear Best Nu =  4.53 Best Epsilon =  7.02\n",
      "Best Accuracy =  0.93 Best Kernel =  poly Best Nu =  9.22 Best Epsilon =  9.97\n",
      "Best Accuracy =  0.92 Best Kernel =  poly Best Nu =  9.33 Best Epsilon =  1.74\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(samples)):\n",
    "  best_accuracy = 0\n",
    "  best_C = 0\n",
    "  best_gamma = 0\n",
    "  for kernel in kernels:\n",
    "    X_train, X_test, y_train, y_test = samples[i]\n",
    "    C = np.random.uniform(0, 10)\n",
    "    gamma = np.random.uniform(0, 10)\n",
    "    score = fitnessFunction(kernel, C, gamma)\n",
    "    if score>best_accuracy:\n",
    "      best_accuracy = round(score, 2)\n",
    "      best_C = round(C, 2)\n",
    "      best_gamma = round(gamma, 2)\n",
    "      best_kernel = kernel\n",
    "  print('Best Accuracy = ', best_accuracy, 'Best Kernel = ', best_kernel, 'Best Nu = ', best_C, 'Best Epsilon = ', best_gamma)\n",
    "  result.loc[i] = [i+1, best_accuracy, best_kernel, best_C, best_gamma]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JybxcLU4U6Pf"
   },
   "source": [
    "### 6. Tabulating the Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 362
    },
    "id": "MUpSkOBfcFbV",
    "outputId": "909ddf74-6a3d-4f12-cef7-7081454b859f"
   },
   "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>Sample</th>\n",
       "      <th>Best Accuracy</th>\n",
       "      <th>Best Kernel</th>\n",
       "      <th>Best Nu</th>\n",
       "      <th>Best Epsilon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.92</td>\n",
       "      <td>poly</td>\n",
       "      <td>7.38</td>\n",
       "      <td>6.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.95</td>\n",
       "      <td>linear</td>\n",
       "      <td>9.90</td>\n",
       "      <td>5.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.94</td>\n",
       "      <td>linear</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.83</td>\n",
       "      <td>linear</td>\n",
       "      <td>4.33</td>\n",
       "      <td>2.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.95</td>\n",
       "      <td>poly</td>\n",
       "      <td>2.40</td>\n",
       "      <td>7.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.86</td>\n",
       "      <td>poly</td>\n",
       "      <td>7.44</td>\n",
       "      <td>6.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.92</td>\n",
       "      <td>linear</td>\n",
       "      <td>1.21</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.97</td>\n",
       "      <td>linear</td>\n",
       "      <td>4.53</td>\n",
       "      <td>7.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.93</td>\n",
       "      <td>poly</td>\n",
       "      <td>9.22</td>\n",
       "      <td>9.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.92</td>\n",
       "      <td>poly</td>\n",
       "      <td>9.33</td>\n",
       "      <td>1.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sample  Best Accuracy Best Kernel  Best Nu  Best Epsilon\n",
       "0       1           0.92        poly     7.38          6.43\n",
       "1       2           0.95      linear     9.90          5.30\n",
       "2       3           0.94      linear     0.03          2.32\n",
       "3       4           0.83      linear     4.33          2.87\n",
       "4       5           0.95        poly     2.40          7.34\n",
       "5       6           0.86        poly     7.44          6.68\n",
       "6       7           0.92      linear     1.21          2.54\n",
       "7       8           0.97      linear     4.53          7.02\n",
       "8       9           0.93        poly     9.22          9.97\n",
       "9      10           0.92        poly     9.33          1.74"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "roEQAPDjU9ux"
   },
   "source": [
    "### 7. Plotting of the Convergence Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HN0vIrNjMHRL"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = samples[result['Best Accuracy'].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DjxAGZsPNCK3"
   },
   "outputs": [],
   "source": [
    "train_sizes, train_scores, test_scores = learning_curve(SVC(kernel=result['Best Kernel'].iloc[result['Best Accuracy'].idxmax()],\n",
    "                                                        C=result['Best Nu'].iloc[result['Best Accuracy'].idxmax()],\n",
    "                                                        gamma=result['Best Epsilon'].iloc[result['Best Accuracy'].idxmax()],\n",
    "                                                        max_iter = 1000), X_train, y_train, cv=10, scoring='accuracy', n_jobs=-1,\n",
    "                                                        train_sizes = np.linspace(0.01, 1.0, 50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AdFu3OLlP9KM",
    "outputId": "d8881f3f-7a6c-4179-fd5b-d80435f0fe7b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  63,  192,  321,  450,  579,  708,  837,  966, 1095, 1224, 1353,\n",
       "       1481, 1610, 1739, 1868, 1997, 2126, 2255, 2384, 2513, 2642, 2771,\n",
       "       2900, 3029, 3157, 3286, 3415, 3544, 3673, 3802, 3931, 4060, 4189,\n",
       "       4318, 4447, 4576, 4705, 4833, 4962, 5091, 5220, 5349, 5478, 5607,\n",
       "       5736, 5865, 5994, 6123, 6252, 6381])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jcTidkqyNRDn"
   },
   "outputs": [],
   "source": [
    "train_mean = np.mean(train_scores, axis=1)\n",
    "test_mean = np.mean(test_scores, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "id": "_2jLVvf4PEV4",
    "outputId": "d8677443-d3ad-437e-a50b-9155d94a5b29"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(train_sizes, train_mean, label='Training Score')\n",
    "plt.plot(train_sizes, test_mean, label='Cross-Validation Score')\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Convergence Graph')\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}