Intel Lab - KMeans.ipynb

{
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sdate</th>\n",
       "      <th>stime</th>\n",
       "      <th>epoch</th>\n",
       "      <th>moteid</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>light</th>\n",
       "      <th>voltage</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-03-31</td>\n",
       "      <td>03:38:15.757551</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>122.1530</td>\n",
       "      <td>-3.91901</td>\n",
       "      <td>11.04</td>\n",
       "      <td>2.03397</td>\n",
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       "      <td>2004-02-28</td>\n",
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      "text/plain": [
       "              sdate            stime  epoch  moteid      temp  humidity  \\\n",
       "0        2004-03-31  03:38:15.757551      2     1.0  122.1530  -3.91901   \n",
       "1        2004-02-28   00:59:16.02785      3     1.0   19.9884  37.09330   \n",
       "2        2004-02-28   01:03:16.33393     11     1.0   19.3024  38.46290   \n",
       "3        2004-02-28  01:06:16.013453     17     1.0   19.1652  38.80390   \n",
       "4        2004-02-28  01:06:46.778088     18     1.0   19.1750  38.83790   \n",
       "...             ...              ...    ...     ...       ...       ...   \n",
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       "2313678  2004-04-02  01:17:53.670524  61275     NaN       NaN       NaN   \n",
       "2313679  2004-04-02  01:20:52.807972  61276     NaN       NaN       NaN   \n",
       "2313680  2004-04-02  01:26:53.950342  61278     NaN       NaN       NaN   \n",
       "2313681  2004-04-02  01:35:53.897412  61280     NaN       NaN       NaN   \n",
       "\n",
       "         light  voltage  \n",
       "0        11.04  2.03397  \n",
       "1        45.08  2.69964  \n",
       "2        45.08  2.68742  \n",
       "3        45.08  2.68742  \n",
       "4        45.08  2.69964  \n",
       "...        ...      ...  \n",
       "2313677    NaN      NaN  \n",
       "2313678    NaN      NaN  \n",
       "2313679    NaN      NaN  \n",
       "2313680    NaN      NaN  \n",
       "2313681    NaN      NaN  \n",
       "\n",
       "[2313682 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel = pd.read_csv('inteldata.csv')\n",
    "intel = intel.drop(['Unnamed: 0'], axis=1)\n",
    "intel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>humidity</th>\n",
       "      <th>light</th>\n",
       "      <th>temp</th>\n",
       "      <th>voltage</th>\n",
       "      <th>moteid</th>\n",
       "      <th>sdate</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>2004-04-02</td>\n",
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      ],
      "text/plain": [
       "         humidity  light      temp  voltage moteid       sdate\n",
       "0        -3.91901  11.04  122.1530  2.03397      1  2004-03-31\n",
       "1        37.09330  45.08   19.9884  2.69964      1  2004-02-28\n",
       "2        38.46290  45.08   19.3024  2.68742      1  2004-02-28\n",
       "3        38.80390  45.08   19.1652  2.68742      1  2004-02-28\n",
       "4        38.83790  45.08   19.1750  2.69964      1  2004-02-28\n",
       "...           ...    ...       ...      ...    ...         ...\n",
       "2313677       NaN    NaN       NaN      NaN    NaN  2004-04-02\n",
       "2313678       NaN    NaN       NaN      NaN    NaN  2004-04-02\n",
       "2313679       NaN    NaN       NaN      NaN    NaN  2004-04-02\n",
       "2313680       NaN    NaN       NaN      NaN    NaN  2004-04-02\n",
       "2313681       NaN    NaN       NaN      NaN    NaN  2004-04-02\n",
       "\n",
       "[2313682 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intel['moteid'] = intel['moteid'].astype(object)\n",
    "motesummarydtd = intel[{'sdate','moteid','temp','humidity','light','voltage'}]\n",
    "motesummarydtd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(intel.corr(),annot=True,cmap='coolwarm',linewidth=5)\n",
    "fig=plt.gcf() #method to make heatmap\n",
    "fig.set_size_inches(8,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>sdate</th>\n",
       "      <th>moteid</th>\n",
       "      <th>meantemp</th>\n",
       "      <th>medhumidity</th>\n",
       "      <th>meanlight</th>\n",
       "      <th>meanvoltage</th>\n",
       "      <th>maxvoltage</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-02-28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.005487</td>\n",
       "      <td>35.888716</td>\n",
       "      <td>175.619408</td>\n",
       "      <td>2.710136</td>\n",
       "      <td>2.710136</td>\n",
       "      <td>1317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-02-28</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.653947</td>\n",
       "      <td>36.251354</td>\n",
       "      <td>213.939634</td>\n",
       "      <td>2.678726</td>\n",
       "      <td>2.678726</td>\n",
       "      <td>1149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-02-28</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20.703610</td>\n",
       "      <td>36.603421</td>\n",
       "      <td>156.593533</td>\n",
       "      <td>2.696685</td>\n",
       "      <td>2.696685</td>\n",
       "      <td>1602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-02-28</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.897784</td>\n",
       "      <td>37.089357</td>\n",
       "      <td>190.258510</td>\n",
       "      <td>2.672143</td>\n",
       "      <td>2.672143</td>\n",
       "      <td>1906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-02-28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.669458</td>\n",
       "      <td>2.669458</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>2004-04-03</td>\n",
       "      <td>34.0</td>\n",
       "      <td>-38.400000</td>\n",
       "      <td>33.304767</td>\n",
       "      <td>1847.360000</td>\n",
       "      <td>1.877793</td>\n",
       "      <td>1.877793</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>2004-04-03</td>\n",
       "      <td>58.0</td>\n",
       "      <td>25.014983</td>\n",
       "      <td>32.020783</td>\n",
       "      <td>27.462000</td>\n",
       "      <td>2.703037</td>\n",
       "      <td>2.703037</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>2004-04-04</td>\n",
       "      <td>58.0</td>\n",
       "      <td>22.098667</td>\n",
       "      <td>35.386300</td>\n",
       "      <td>32.251111</td>\n",
       "      <td>2.593648</td>\n",
       "      <td>2.593648</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>2004-04-05</td>\n",
       "      <td>56.0</td>\n",
       "      <td>23.797191</td>\n",
       "      <td>34.915146</td>\n",
       "      <td>0.886231</td>\n",
       "      <td>2.816561</td>\n",
       "      <td>2.816561</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1659</th>\n",
       "      <td>2004-04-05</td>\n",
       "      <td>58.0</td>\n",
       "      <td>21.185633</td>\n",
       "      <td>37.237533</td>\n",
       "      <td>95.680000</td>\n",
       "      <td>2.527593</td>\n",
       "      <td>2.527593</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1660 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           sdate  moteid   meantemp  medhumidity    meanlight  meanvoltage  \\\n",
       "0     2004-02-28     1.0  21.005487    35.888716   175.619408     2.710136   \n",
       "1     2004-02-28     2.0  20.653947    36.251354   213.939634     2.678726   \n",
       "2     2004-02-28     3.0  20.703610    36.603421   156.593533     2.696685   \n",
       "3     2004-02-28     4.0  20.897784    37.089357   190.258510     2.672143   \n",
       "4     2004-02-28     5.0   0.000000     0.000000     0.000000     2.669458   \n",
       "...          ...     ...        ...          ...          ...          ...   \n",
       "1655  2004-04-03    34.0 -38.400000    33.304767  1847.360000     1.877793   \n",
       "1656  2004-04-03    58.0  25.014983    32.020783    27.462000     2.703037   \n",
       "1657  2004-04-04    58.0  22.098667    35.386300    32.251111     2.593648   \n",
       "1658  2004-04-05    56.0  23.797191    34.915146     0.886231     2.816561   \n",
       "1659  2004-04-05    58.0  21.185633    37.237533    95.680000     2.527593   \n",
       "\n",
       "      maxvoltage  count  \n",
       "0       2.710136   1317  \n",
       "1       2.678726   1149  \n",
       "2       2.696685   1602  \n",
       "3       2.672143   1906  \n",
       "4       2.669458     10  \n",
       "...          ...    ...  \n",
       "1655    1.877793      3  \n",
       "1656    2.703037    120  \n",
       "1657    2.593648      9  \n",
       "1658    2.816561     46  \n",
       "1659    2.527593      6  \n",
       "\n",
       "[1660 rows x 8 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = motesummarydtd.groupby(['sdate','moteid']).agg(meantemp =  ('temp', 'mean'),\n",
    "                           medhumidity = ('humidity', 'mean'),\n",
    "                           meanlight =  ('light', 'mean'),\n",
    "                           meanvoltage = ('voltage', 'mean'),\n",
    "                           maxvoltage = ('voltage', 'mean'),\n",
    "                           count=   ('moteid', 'count')).reset_index()\n",
    "final.fillna(0, inplace=True)\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "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>meantemp</th>\n",
       "      <th>meanvoltage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.005487</td>\n",
       "      <td>2.710136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.653947</td>\n",
       "      <td>2.678726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.703610</td>\n",
       "      <td>2.696685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.897784</td>\n",
       "      <td>2.672143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.669458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>-38.400000</td>\n",
       "      <td>1.877793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>25.014983</td>\n",
       "      <td>2.703037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>22.098667</td>\n",
       "      <td>2.593648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>23.797191</td>\n",
       "      <td>2.816561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1659</th>\n",
       "      <td>21.185633</td>\n",
       "      <td>2.527593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1660 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       meantemp  meanvoltage\n",
       "0     21.005487     2.710136\n",
       "1     20.653947     2.678726\n",
       "2     20.703610     2.696685\n",
       "3     20.897784     2.672143\n",
       "4      0.000000     2.669458\n",
       "...         ...          ...\n",
       "1655 -38.400000     1.877793\n",
       "1656  25.014983     2.703037\n",
       "1657  22.098667     2.593648\n",
       "1658  23.797191     2.816561\n",
       "1659  21.185633     2.527593\n",
       "\n",
       "[1660 rows x 2 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = final.drop(['sdate','moteid','medhumidity','meanlight','maxvoltage','count'], axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d7c70ff1c0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(final['meantemp'],final['meanvoltage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cs = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 1660, n_init = 10, random_state = 0)\n",
    "    kmeans.fit(X)\n",
    "    cs.append(kmeans.inertia_)\n",
    "plt.plot(range(1, 11), cs)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('CS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X[['meantemp','meanvoltage']] = scaler.fit_transform(X[['meantemp','meanvoltage']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 2, 2, 2])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3,  n_init=900, max_iter=1660, random_state=0) \n",
    "kmeans.fit(X)\n",
    "predict=kmeans.predict(X)\n",
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9673911367843375"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_avg = metrics.silhouette_score(X, kmeans.labels_)\n",
    "silhouette_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[['meantemp','meanvoltage']] = scaler.inverse_transform(X[['meantemp','meanvoltage']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "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>meantemp</th>\n",
       "      <th>meanvoltage</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.005487</td>\n",
       "      <td>2.710136</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.653947</td>\n",
       "      <td>2.678726</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.703610</td>\n",
       "      <td>2.696685</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.897784</td>\n",
       "      <td>2.672143</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.669458</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655</th>\n",
       "      <td>-38.400000</td>\n",
       "      <td>1.877793</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>25.014983</td>\n",
       "      <td>2.703037</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>22.098667</td>\n",
       "      <td>2.593648</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>23.797191</td>\n",
       "      <td>2.816561</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1659</th>\n",
       "      <td>21.185633</td>\n",
       "      <td>2.527593</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1660 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       meantemp  meanvoltage  cluster\n",
       "0     21.005487     2.710136        2\n",
       "1     20.653947     2.678726        2\n",
       "2     20.703610     2.696685        2\n",
       "3     20.897784     2.672143        2\n",
       "4      0.000000     2.669458        2\n",
       "...         ...          ...      ...\n",
       "1655 -38.400000     1.877793        0\n",
       "1656  25.014983     2.703037        2\n",
       "1657  22.098667     2.593648        2\n",
       "1658  23.797191     2.816561        2\n",
       "1659  21.185633     2.527593        2\n",
       "\n",
       "[1660 rows x 3 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['cluster'] = pd.Series(predict, index=X.index)\n",
    "X.to_csv(r'C:\\Users\\prash\\Desktop\\Data Science Projects\\Intel Clustering of Sensors\\Intel-Final-Data.csv', index = False)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "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>meantemp</th>\n",
       "      <th>meanvoltage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.499758</td>\n",
       "      <td>2.409259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121.161907</td>\n",
       "      <td>2.170784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.920682</td>\n",
       "      <td>2.628328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           meantemp  meanvoltage\n",
       "cluster                         \n",
       "0         23.499758     2.409259\n",
       "1        121.161907     2.170784\n",
       "2         21.920682     2.628328"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.groupby('cluster').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.374902418350946"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d7c71bc250>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X['meantemp'],X['meanvoltage'],c=X['cluster'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}