Assignment_3_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Statements\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clustering\n",
    "\n",
    "The file `data/activity.csv` contains data from wearable devices (Fitbit-like)  measured from 62 individuals recorded during a 5 minutes time interval. These inidividuals were engaged in a small subset of activities (i.e., sleeping, running, or walking), but we are not provided any information about these activities not about the individuals who were engaged in the same activities (ex. running)\n",
    "\n",
    "In this exercise, you will use `k-means` clustering algorithm to cluster the time series data. The objective is to identify the number of activities represented in this dataset, and the clusters of individuals that were involved in the same activities.  \n",
    "\n",
    "Important: the dataset contains data from two cohorts. Unfortunately, for the first cohort of individuals, the activity was measured each second, while for the second cohort, the activity was measured every 2 seconds. Such inconsistency is very common in real data.\n",
    "\n",
    "Read the data into a variable called `activities` and display the 15 first lines and the last 15 lines. Observe that for the individuals who were sampled every two seconds, half of the data contain missing values (NaNs).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data into a variable\n",
    "activities = pd.read_csv('data/activity.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 15 lines of Data\n"
     ]
    },
    {
     "data": {
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       "      <th>60</th>\n",
       "      <th>61</th>\n",
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       "      <td>12.000000</td>\n",
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       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
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       "      <th>1</th>\n",
       "      <td>12.071557</td>\n",
       "      <td>12.065385</td>\n",
       "      <td>11.724136</td>\n",
       "      <td>12.101567</td>\n",
       "      <td>12.139391</td>\n",
       "      <td>12.360870</td>\n",
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       "      <td>11.818063</td>\n",
       "      <td>11.690854</td>\n",
       "      <td>11.873143</td>\n",
       "      <td>...</td>\n",
       "      <td>18.262320</td>\n",
       "      <td>18.154305</td>\n",
       "      <td>17.523602</td>\n",
       "      <td>17.805053</td>\n",
       "      <td>18.073211</td>\n",
       "      <td>17.999266</td>\n",
       "      <td>18.149337</td>\n",
       "      <td>18.167567</td>\n",
       "      <td>18.040851</td>\n",
       "      <td>18.252954</td>\n",
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       "      <th>2</th>\n",
       "      <td>12.183714</td>\n",
       "      <td>12.021565</td>\n",
       "      <td>11.577950</td>\n",
       "      <td>12.209226</td>\n",
       "      <td>12.072627</td>\n",
       "      <td>12.322689</td>\n",
       "      <td>12.307760</td>\n",
       "      <td>11.630762</td>\n",
       "      <td>12.008822</td>\n",
       "      <td>11.825949</td>\n",
       "      <td>...</td>\n",
       "      <td>18.093134</td>\n",
       "      <td>18.152622</td>\n",
       "      <td>17.474589</td>\n",
       "      <td>17.908550</td>\n",
       "      <td>17.753720</td>\n",
       "      <td>18.203611</td>\n",
       "      <td>18.016420</td>\n",
       "      <td>18.433005</td>\n",
       "      <td>17.903249</td>\n",
       "      <td>18.188720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.400325</td>\n",
       "      <td>12.187446</td>\n",
       "      <td>11.571325</td>\n",
       "      <td>12.423728</td>\n",
       "      <td>12.307252</td>\n",
       "      <td>12.466640</td>\n",
       "      <td>12.241498</td>\n",
       "      <td>11.497206</td>\n",
       "      <td>12.123636</td>\n",
       "      <td>11.974786</td>\n",
       "      <td>...</td>\n",
       "      <td>17.946807</td>\n",
       "      <td>18.093075</td>\n",
       "      <td>17.254170</td>\n",
       "      <td>17.780044</td>\n",
       "      <td>17.738934</td>\n",
       "      <td>18.271126</td>\n",
       "      <td>17.698719</td>\n",
       "      <td>18.367698</td>\n",
       "      <td>17.842029</td>\n",
       "      <td>17.992113</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12.611085</td>\n",
       "      <td>11.745219</td>\n",
       "      <td>11.930236</td>\n",
       "      <td>12.350737</td>\n",
       "      <td>12.381180</td>\n",
       "      <td>12.207986</td>\n",
       "      <td>12.119051</td>\n",
       "      <td>11.555645</td>\n",
       "      <td>12.403555</td>\n",
       "      <td>12.059095</td>\n",
       "      <td>...</td>\n",
       "      <td>17.985591</td>\n",
       "      <td>18.238300</td>\n",
       "      <td>17.282918</td>\n",
       "      <td>18.003957</td>\n",
       "      <td>17.562703</td>\n",
       "      <td>18.190507</td>\n",
       "      <td>17.800618</td>\n",
       "      <td>18.317763</td>\n",
       "      <td>17.725902</td>\n",
       "      <td>17.779425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12.335551</td>\n",
       "      <td>11.792342</td>\n",
       "      <td>11.826714</td>\n",
       "      <td>12.182895</td>\n",
       "      <td>12.359720</td>\n",
       "      <td>12.016699</td>\n",
       "      <td>11.902020</td>\n",
       "      <td>11.518179</td>\n",
       "      <td>12.135129</td>\n",
       "      <td>12.112459</td>\n",
       "      <td>...</td>\n",
       "      <td>17.911432</td>\n",
       "      <td>18.616591</td>\n",
       "      <td>17.199353</td>\n",
       "      <td>17.908310</td>\n",
       "      <td>17.618975</td>\n",
       "      <td>18.208032</td>\n",
       "      <td>18.069070</td>\n",
       "      <td>18.285425</td>\n",
       "      <td>17.906178</td>\n",
       "      <td>17.926519</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12.147986</td>\n",
       "      <td>11.946515</td>\n",
       "      <td>11.871472</td>\n",
       "      <td>11.973934</td>\n",
       "      <td>12.449263</td>\n",
       "      <td>12.111180</td>\n",
       "      <td>11.736938</td>\n",
       "      <td>11.070533</td>\n",
       "      <td>11.861965</td>\n",
       "      <td>12.044634</td>\n",
       "      <td>...</td>\n",
       "      <td>18.357063</td>\n",
       "      <td>18.676709</td>\n",
       "      <td>17.317736</td>\n",
       "      <td>17.966484</td>\n",
       "      <td>17.607053</td>\n",
       "      <td>18.302451</td>\n",
       "      <td>17.936622</td>\n",
       "      <td>18.340404</td>\n",
       "      <td>17.887137</td>\n",
       "      <td>17.932443</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12.250993</td>\n",
       "      <td>11.650798</td>\n",
       "      <td>11.868187</td>\n",
       "      <td>11.580662</td>\n",
       "      <td>12.135088</td>\n",
       "      <td>12.408003</td>\n",
       "      <td>12.326757</td>\n",
       "      <td>10.646393</td>\n",
       "      <td>11.832171</td>\n",
       "      <td>12.118647</td>\n",
       "      <td>...</td>\n",
       "      <td>18.324557</td>\n",
       "      <td>18.573497</td>\n",
       "      <td>17.313905</td>\n",
       "      <td>17.836376</td>\n",
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       "      <td>18.330296</td>\n",
       "      <td>17.775959</td>\n",
       "      <td>18.224190</td>\n",
       "      <td>17.627595</td>\n",
       "      <td>18.059186</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.353750</td>\n",
       "      <td>11.879549</td>\n",
       "      <td>12.105866</td>\n",
       "      <td>11.991904</td>\n",
       "      <td>11.909735</td>\n",
       "      <td>12.479126</td>\n",
       "      <td>12.575693</td>\n",
       "      <td>10.525020</td>\n",
       "      <td>11.932728</td>\n",
       "      <td>12.093900</td>\n",
       "      <td>...</td>\n",
       "      <td>18.391601</td>\n",
       "      <td>18.535546</td>\n",
       "      <td>16.934705</td>\n",
       "      <td>18.023821</td>\n",
       "      <td>18.179743</td>\n",
       "      <td>18.360433</td>\n",
       "      <td>17.670979</td>\n",
       "      <td>17.645353</td>\n",
       "      <td>17.482907</td>\n",
       "      <td>18.181811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12.456760</td>\n",
       "      <td>11.947248</td>\n",
       "      <td>12.611252</td>\n",
       "      <td>11.771262</td>\n",
       "      <td>11.670950</td>\n",
       "      <td>12.416514</td>\n",
       "      <td>12.305478</td>\n",
       "      <td>10.616557</td>\n",
       "      <td>12.292000</td>\n",
       "      <td>12.185461</td>\n",
       "      <td>...</td>\n",
       "      <td>18.580355</td>\n",
       "      <td>19.039488</td>\n",
       "      <td>16.741287</td>\n",
       "      <td>17.892671</td>\n",
       "      <td>18.236645</td>\n",
       "      <td>18.206623</td>\n",
       "      <td>17.785301</td>\n",
       "      <td>17.883907</td>\n",
       "      <td>16.843054</td>\n",
       "      <td>17.777841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13.227306</td>\n",
       "      <td>11.864191</td>\n",
       "      <td>12.505078</td>\n",
       "      <td>11.727011</td>\n",
       "      <td>11.699538</td>\n",
       "      <td>12.416372</td>\n",
       "      <td>12.040987</td>\n",
       "      <td>10.067056</td>\n",
       "      <td>12.433216</td>\n",
       "      <td>12.086075</td>\n",
       "      <td>...</td>\n",
       "      <td>18.765270</td>\n",
       "      <td>19.066469</td>\n",
       "      <td>16.742444</td>\n",
       "      <td>18.229354</td>\n",
       "      <td>18.183257</td>\n",
       "      <td>18.120028</td>\n",
       "      <td>18.031004</td>\n",
       "      <td>18.242845</td>\n",
       "      <td>16.744156</td>\n",
       "      <td>17.549824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>13.341484</td>\n",
       "      <td>11.990747</td>\n",
       "      <td>12.407191</td>\n",
       "      <td>11.671649</td>\n",
       "      <td>12.046041</td>\n",
       "      <td>12.166291</td>\n",
       "      <td>12.137360</td>\n",
       "      <td>9.736876</td>\n",
       "      <td>12.384679</td>\n",
       "      <td>12.049561</td>\n",
       "      <td>...</td>\n",
       "      <td>18.811928</td>\n",
       "      <td>18.947586</td>\n",
       "      <td>16.760466</td>\n",
       "      <td>18.030154</td>\n",
       "      <td>18.518461</td>\n",
       "      <td>18.571720</td>\n",
       "      <td>17.783083</td>\n",
       "      <td>18.120365</td>\n",
       "      <td>16.751685</td>\n",
       "      <td>17.345699</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.568597</td>\n",
       "      <td>12.444886</td>\n",
       "      <td>12.616023</td>\n",
       "      <td>11.733130</td>\n",
       "      <td>12.492301</td>\n",
       "      <td>12.287194</td>\n",
       "      <td>12.246813</td>\n",
       "      <td>10.244777</td>\n",
       "      <td>12.179406</td>\n",
       "      <td>11.851569</td>\n",
       "      <td>...</td>\n",
       "      <td>18.779702</td>\n",
       "      <td>18.718573</td>\n",
       "      <td>16.479331</td>\n",
       "      <td>17.761029</td>\n",
       "      <td>18.512470</td>\n",
       "      <td>18.817413</td>\n",
       "      <td>17.483427</td>\n",
       "      <td>17.907034</td>\n",
       "      <td>16.724458</td>\n",
       "      <td>17.182486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13.759398</td>\n",
       "      <td>12.481259</td>\n",
       "      <td>12.752401</td>\n",
       "      <td>11.896277</td>\n",
       "      <td>12.619911</td>\n",
       "      <td>12.463661</td>\n",
       "      <td>12.356590</td>\n",
       "      <td>10.167773</td>\n",
       "      <td>12.425406</td>\n",
       "      <td>11.844568</td>\n",
       "      <td>...</td>\n",
       "      <td>19.184508</td>\n",
       "      <td>18.353393</td>\n",
       "      <td>16.172039</td>\n",
       "      <td>17.951339</td>\n",
       "      <td>18.578805</td>\n",
       "      <td>18.522645</td>\n",
       "      <td>17.951426</td>\n",
       "      <td>17.879614</td>\n",
       "      <td>17.120488</td>\n",
       "      <td>17.098415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13.889676</td>\n",
       "      <td>12.530903</td>\n",
       "      <td>13.121743</td>\n",
       "      <td>12.068372</td>\n",
       "      <td>12.720080</td>\n",
       "      <td>12.373242</td>\n",
       "      <td>12.305598</td>\n",
       "      <td>10.244371</td>\n",
       "      <td>12.232465</td>\n",
       "      <td>11.682368</td>\n",
       "      <td>...</td>\n",
       "      <td>19.164556</td>\n",
       "      <td>18.426109</td>\n",
       "      <td>16.183288</td>\n",
       "      <td>17.833010</td>\n",
       "      <td>18.587255</td>\n",
       "      <td>18.049251</td>\n",
       "      <td>17.915961</td>\n",
       "      <td>17.842113</td>\n",
       "      <td>17.235470</td>\n",
       "      <td>17.298245</td>\n",
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       "            0          1          2          3          4          5  \\\n",
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       "1   12.071557  12.065385  11.724136  12.101567  12.139391  12.360870   \n",
       "2   12.183714  12.021565  11.577950  12.209226  12.072627  12.322689   \n",
       "3   12.400325  12.187446  11.571325  12.423728  12.307252  12.466640   \n",
       "4   12.611085  11.745219  11.930236  12.350737  12.381180  12.207986   \n",
       "5   12.335551  11.792342  11.826714  12.182895  12.359720  12.016699   \n",
       "6   12.147986  11.946515  11.871472  11.973934  12.449263  12.111180   \n",
       "7   12.250993  11.650798  11.868187  11.580662  12.135088  12.408003   \n",
       "8   12.353750  11.879549  12.105866  11.991904  11.909735  12.479126   \n",
       "9   12.456760  11.947248  12.611252  11.771262  11.670950  12.416514   \n",
       "10  13.227306  11.864191  12.505078  11.727011  11.699538  12.416372   \n",
       "11  13.341484  11.990747  12.407191  11.671649  12.046041  12.166291   \n",
       "12  13.568597  12.444886  12.616023  11.733130  12.492301  12.287194   \n",
       "13  13.759398  12.481259  12.752401  11.896277  12.619911  12.463661   \n",
       "14  13.889676  12.530903  13.121743  12.068372  12.720080  12.373242   \n",
       "\n",
       "            6          7          8          9  ...         52         53  \\\n",
       "0   12.000000  12.000000  12.000000  12.000000  ...  18.000000  18.000000   \n",
       "1   12.114123  11.818063  11.690854  11.873143  ...  18.262320  18.154305   \n",
       "2   12.307760  11.630762  12.008822  11.825949  ...  18.093134  18.152622   \n",
       "3   12.241498  11.497206  12.123636  11.974786  ...  17.946807  18.093075   \n",
       "4   12.119051  11.555645  12.403555  12.059095  ...  17.985591  18.238300   \n",
       "5   11.902020  11.518179  12.135129  12.112459  ...  17.911432  18.616591   \n",
       "6   11.736938  11.070533  11.861965  12.044634  ...  18.357063  18.676709   \n",
       "7   12.326757  10.646393  11.832171  12.118647  ...  18.324557  18.573497   \n",
       "8   12.575693  10.525020  11.932728  12.093900  ...  18.391601  18.535546   \n",
       "9   12.305478  10.616557  12.292000  12.185461  ...  18.580355  19.039488   \n",
       "10  12.040987  10.067056  12.433216  12.086075  ...  18.765270  19.066469   \n",
       "11  12.137360   9.736876  12.384679  12.049561  ...  18.811928  18.947586   \n",
       "12  12.246813  10.244777  12.179406  11.851569  ...  18.779702  18.718573   \n",
       "13  12.356590  10.167773  12.425406  11.844568  ...  19.184508  18.353393   \n",
       "14  12.305598  10.244371  12.232465  11.682368  ...  19.164556  18.426109   \n",
       "\n",
       "           54         55         56         57         58         59  \\\n",
       "0   18.000000  18.000000  18.000000  18.000000  18.000000  18.000000   \n",
       "1   17.523602  17.805053  18.073211  17.999266  18.149337  18.167567   \n",
       "2   17.474589  17.908550  17.753720  18.203611  18.016420  18.433005   \n",
       "3   17.254170  17.780044  17.738934  18.271126  17.698719  18.367698   \n",
       "4   17.282918  18.003957  17.562703  18.190507  17.800618  18.317763   \n",
       "5   17.199353  17.908310  17.618975  18.208032  18.069070  18.285425   \n",
       "6   17.317736  17.966484  17.607053  18.302451  17.936622  18.340404   \n",
       "7   17.313905  17.836376  17.996417  18.330296  17.775959  18.224190   \n",
       "8   16.934705  18.023821  18.179743  18.360433  17.670979  17.645353   \n",
       "9   16.741287  17.892671  18.236645  18.206623  17.785301  17.883907   \n",
       "10  16.742444  18.229354  18.183257  18.120028  18.031004  18.242845   \n",
       "11  16.760466  18.030154  18.518461  18.571720  17.783083  18.120365   \n",
       "12  16.479331  17.761029  18.512470  18.817413  17.483427  17.907034   \n",
       "13  16.172039  17.951339  18.578805  18.522645  17.951426  17.879614   \n",
       "14  16.183288  17.833010  18.587255  18.049251  17.915961  17.842113   \n",
       "\n",
       "           60         61  \n",
       "0   18.000000  18.000000  \n",
       "1   18.040851  18.252954  \n",
       "2   17.903249  18.188720  \n",
       "3   17.842029  17.992113  \n",
       "4   17.725902  17.779425  \n",
       "5   17.906178  17.926519  \n",
       "6   17.887137  17.932443  \n",
       "7   17.627595  18.059186  \n",
       "8   17.482907  18.181811  \n",
       "9   16.843054  17.777841  \n",
       "10  16.744156  17.549824  \n",
       "11  16.751685  17.345699  \n",
       "12  16.724458  17.182486  \n",
       "13  17.120488  17.098415  \n",
       "14  17.235470  17.298245  \n",
       "\n",
       "[15 rows x 62 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the first 15 lines of data\n",
    "print('First 15 lines of Data')\n",
    "activities.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last 15 lines of Data\n"
     ]
    },
    {
     "data": {
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       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>14.122604</td>\n",
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       "      <td>10.208223</td>\n",
       "      <td>13.795504</td>\n",
       "      <td>9.424423</td>\n",
       "      <td>...</td>\n",
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       "      <td>15.885948</td>\n",
       "      <td>18.389838</td>\n",
       "      <td>18.712782</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>14.362847</td>\n",
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       "      <td>15.241938</td>\n",
       "      <td>9.508848</td>\n",
       "      <td>13.804891</td>\n",
       "      <td>9.595310</td>\n",
       "      <td>...</td>\n",
       "      <td>19.643568</td>\n",
       "      <td>15.909133</td>\n",
       "      <td>18.615129</td>\n",
       "      <td>18.498901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>14.281232</td>\n",
       "      <td>13.957188</td>\n",
       "      <td>15.750667</td>\n",
       "      <td>14.763080</td>\n",
       "      <td>11.134495</td>\n",
       "      <td>13.457107</td>\n",
       "      <td>15.340680</td>\n",
       "      <td>11.160020</td>\n",
       "      <td>14.184445</td>\n",
       "      <td>10.258473</td>\n",
       "      <td>...</td>\n",
       "      <td>19.347595</td>\n",
       "      <td>15.859156</td>\n",
       "      <td>18.643241</td>\n",
       "      <td>18.445079</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>13.873607</td>\n",
       "      <td>14.014362</td>\n",
       "      <td>14.874583</td>\n",
       "      <td>15.273142</td>\n",
       "      <td>11.172436</td>\n",
       "      <td>13.205805</td>\n",
       "      <td>14.846096</td>\n",
       "      <td>9.547966</td>\n",
       "      <td>13.994866</td>\n",
       "      <td>9.988850</td>\n",
       "      <td>...</td>\n",
       "      <td>19.403287</td>\n",
       "      <td>16.185251</td>\n",
       "      <td>18.711837</td>\n",
       "      <td>18.244215</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>13.671990</td>\n",
       "      <td>14.081253</td>\n",
       "      <td>15.278115</td>\n",
       "      <td>14.781807</td>\n",
       "      <td>11.040039</td>\n",
       "      <td>13.368447</td>\n",
       "      <td>14.556660</td>\n",
       "      <td>10.128988</td>\n",
       "      <td>13.828001</td>\n",
       "      <td>10.102379</td>\n",
       "      <td>...</td>\n",
       "      <td>19.845535</td>\n",
       "      <td>15.678376</td>\n",
       "      <td>18.942684</td>\n",
       "      <td>18.583586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>13.297832</td>\n",
       "      <td>14.212962</td>\n",
       "      <td>16.082501</td>\n",
       "      <td>14.955819</td>\n",
       "      <td>11.125217</td>\n",
       "      <td>13.312654</td>\n",
       "      <td>15.614490</td>\n",
       "      <td>10.237584</td>\n",
       "      <td>13.980807</td>\n",
       "      <td>9.192057</td>\n",
       "      <td>...</td>\n",
       "      <td>19.768138</td>\n",
       "      <td>15.695432</td>\n",
       "      <td>18.918036</td>\n",
       "      <td>18.981066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>13.227529</td>\n",
       "      <td>14.615003</td>\n",
       "      <td>15.463920</td>\n",
       "      <td>14.870602</td>\n",
       "      <td>11.129046</td>\n",
       "      <td>13.256702</td>\n",
       "      <td>15.229193</td>\n",
       "      <td>9.705364</td>\n",
       "      <td>13.672247</td>\n",
       "      <td>10.466217</td>\n",
       "      <td>...</td>\n",
       "      <td>20.081987</td>\n",
       "      <td>15.204785</td>\n",
       "      <td>19.330079</td>\n",
       "      <td>19.111129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>13.231212</td>\n",
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       "      <td>15.280460</td>\n",
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       "      <td>13.414776</td>\n",
       "      <td>14.707691</td>\n",
       "      <td>10.417376</td>\n",
       "      <td>13.545696</td>\n",
       "      <td>10.350350</td>\n",
       "      <td>...</td>\n",
       "      <td>19.931529</td>\n",
       "      <td>16.469298</td>\n",
       "      <td>19.240516</td>\n",
       "      <td>19.282820</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0          1          2          3          4          5  \\\n",
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       "286  13.545855  13.735032  14.661142  14.845983  11.535718  13.873083   \n",
       "287  13.624746  13.624588  15.068268  15.655154  11.250927  13.795657   \n",
       "288  13.540549  13.751174  15.092340  14.718916  11.197596  13.786575   \n",
       "289  13.598504  13.791759  14.514193  14.158281  11.111747  13.791653   \n",
       "290  14.013584  13.488610  14.671553  15.482426  11.229458  13.407718   \n",
       "291  14.187809  13.798111  15.268326  14.382826  10.909833  13.404951   \n",
       "292  14.122604  14.157287  15.359477  14.986797  11.002268  13.267005   \n",
       "293  14.362847  14.034729  14.621602  14.595198  11.407129  13.168380   \n",
       "294  14.281232  13.957188  15.750667  14.763080  11.134495  13.457107   \n",
       "295  13.873607  14.014362  14.874583  15.273142  11.172436  13.205805   \n",
       "296  13.671990  14.081253  15.278115  14.781807  11.040039  13.368447   \n",
       "297  13.297832  14.212962  16.082501  14.955819  11.125217  13.312654   \n",
       "298  13.227529  14.615003  15.463920  14.870602  11.129046  13.256702   \n",
       "299  13.231212  14.579613  15.134296  15.280460  11.000749  13.414776   \n",
       "\n",
       "             6          7          8          9  ...         52         53  \\\n",
       "285  14.451349   9.531037  14.231535   9.741154  ...  19.859492  16.290565   \n",
       "286  14.581369   9.487124  14.034374  11.084968  ...  19.939409  16.023299   \n",
       "287  14.730439  10.530685  13.977543   9.074079  ...  19.705306  15.432278   \n",
       "288  15.416667  10.180831  14.216496   9.660678  ...  19.911658  15.364228   \n",
       "289  15.110271   9.999843  14.395425   9.609422  ...  20.013794  16.973349   \n",
       "290  14.743402  10.224737  14.120853  10.799269  ...  19.691936  16.171265   \n",
       "291  14.884738   9.492620  14.191149  10.431112  ...  19.684388  17.081737   \n",
       "292  15.097054  10.208223  13.795504   9.424423  ...  19.761579  15.885948   \n",
       "293  15.241938   9.508848  13.804891   9.595310  ...  19.643568  15.909133   \n",
       "294  15.340680  11.160020  14.184445  10.258473  ...  19.347595  15.859156   \n",
       "295  14.846096   9.547966  13.994866   9.988850  ...  19.403287  16.185251   \n",
       "296  14.556660  10.128988  13.828001  10.102379  ...  19.845535  15.678376   \n",
       "297  15.614490  10.237584  13.980807   9.192057  ...  19.768138  15.695432   \n",
       "298  15.229193   9.705364  13.672247  10.466217  ...  20.081987  15.204785   \n",
       "299  14.707691  10.417376  13.545696  10.350350  ...  19.931529  16.469298   \n",
       "\n",
       "            54         55  56  57  58  59  60  61  \n",
       "285  19.060086  18.135866 NaN NaN NaN NaN NaN NaN  \n",
       "286  18.850009  18.065544 NaN NaN NaN NaN NaN NaN  \n",
       "287  18.693957  18.113996 NaN NaN NaN NaN NaN NaN  \n",
       "288  18.393472  18.130604 NaN NaN NaN NaN NaN NaN  \n",
       "289  18.380923  18.215027 NaN NaN NaN NaN NaN NaN  \n",
       "290  18.500946  18.394696 NaN NaN NaN NaN NaN NaN  \n",
       "291  18.496662  18.655685 NaN NaN NaN NaN NaN NaN  \n",
       "292  18.389838  18.712782 NaN NaN NaN NaN NaN NaN  \n",
       "293  18.615129  18.498901 NaN NaN NaN NaN NaN NaN  \n",
       "294  18.643241  18.445079 NaN NaN NaN NaN NaN NaN  \n",
       "295  18.711837  18.244215 NaN NaN NaN NaN NaN NaN  \n",
       "296  18.942684  18.583586 NaN NaN NaN NaN NaN NaN  \n",
       "297  18.918036  18.981066 NaN NaN NaN NaN NaN NaN  \n",
       "298  19.330079  19.111129 NaN NaN NaN NaN NaN NaN  \n",
       "299  19.240516  19.282820 NaN NaN NaN NaN NaN NaN  \n",
       "\n",
       "[15 rows x 62 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the last 15 lines of data\n",
    "print('Last 15 lines of Data')\n",
    "activities.tail(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to resample the data and get rid of the missing values. Imagine we have the following dataset ( `a=[7, 9, 8, 11, 10 , 6], b=[7, 8, 10, nan, nan, nan]`) , where `a` was sampled every second and `b` was sampled every two seconds. Although `a` and `b`  look different, comparing `a` and `b` using the correct time scale shows that they are identical.\n",
    "\n",
    "<img src=\"images/a_b.png\" width=600/>\n",
    "<center><b>a</b> and <b>b</b> match when compared on t=1,3,5</center>\n",
    "\n",
    "\n",
    "Here, the missing values are added to `b` to make it the same length as `a`.\n",
    "\n",
    "A simple approach to align `a` and `b` is to drop values at every even position in `a` and remove missing values from `b`.  \n",
    "\n",
    "\n",
    "Dropping values at even positions in `a `will yield `a = [1,3,5]`. Removing missing values will result in `b` becoming `[1,3,5]`. The values are now aligned and can be compared.\n",
    "\n",
    "\n",
    "Implement the strategy above to align all your data. Use any approach from Pandas to allows you to accomplish this, even if it's something we haven't covered in class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>17.759483</td>\n",
       "      <td>17.769792</td>\n",
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       "      <td>18.093134</td>\n",
       "      <td>18.152622</td>\n",
       "      <td>17.474589</td>\n",
       "      <td>17.908550</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>298</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>17.416177</td>\n",
       "      <td>21.327357</td>\n",
       "      <td>19.933064</td>\n",
       "      <td>21.547285</td>\n",
       "      <td>19.229356</td>\n",
       "      <td>21.549217</td>\n",
       "      <td>20.081987</td>\n",
       "      <td>15.204785</td>\n",
       "      <td>19.330079</td>\n",
       "      <td>19.111129</td>\n",
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       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>300 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0          1          2          3          4          5   \\\n",
       "0    12.000000  12.000000  12.000000  12.000000  12.000000  12.000000   \n",
       "1    11.979131  11.398474  12.064637  11.909273  12.003037  12.057355   \n",
       "2    12.057793  11.512709  12.321400  12.254518  11.808075  12.027914   \n",
       "3    11.910181  11.561441  12.580398  12.077052  11.937193  12.140882   \n",
       "4    11.946432  11.343399  12.689242  12.231411  11.941069  12.468042   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "295        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "296        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "297        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "298        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "299        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "            6          7         8         9   ...         52         53  \\\n",
       "0    12.000000  12.000000  0.000000  0.000000  ...  18.000000  18.000000   \n",
       "1    12.078527  12.205379  0.101753  0.470533  ...        NaN        NaN   \n",
       "2    12.169244  12.325294 -0.884755  0.608083  ...  17.763611  18.501380   \n",
       "3    11.852799  12.649052 -0.606370 -0.000757  ...        NaN        NaN   \n",
       "4    11.862845  12.732217  0.021132  0.241356  ...  17.009796  18.667504   \n",
       "..         ...        ...       ...       ...  ...        ...        ...   \n",
       "295        NaN        NaN       NaN       NaN  ...        NaN        NaN   \n",
       "296        NaN        NaN       NaN       NaN  ...  17.334849  21.395502   \n",
       "297        NaN        NaN       NaN       NaN  ...        NaN        NaN   \n",
       "298        NaN        NaN       NaN       NaN  ...  17.416177  21.327357   \n",
       "299        NaN        NaN       NaN       NaN  ...        NaN        NaN   \n",
       "\n",
       "            54         55         56         57         58         59  \\\n",
       "0    18.000000  18.000000  18.000000  18.000000  18.000000  18.000000   \n",
       "1          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "2    18.277977  17.759483  17.769792  18.433222  18.093134  18.152622   \n",
       "3          NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "4    17.986875  17.549259  18.191688  18.619982  17.985591  18.238300   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
       "295        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "296  19.974861  21.330876  19.280167  21.963835  19.845535  15.678376   \n",
       "297        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "298  19.933064  21.547285  19.229356  21.549217  20.081987  15.204785   \n",
       "299        NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "\n",
       "            60         61  \n",
       "0    18.000000  18.000000  \n",
       "1          NaN        NaN  \n",
       "2    17.474589  17.908550  \n",
       "3          NaN        NaN  \n",
       "4    17.282918  18.003957  \n",
       "..         ...        ...  \n",
       "295        NaN        NaN  \n",
       "296  18.942684  18.583586  \n",
       "297        NaN        NaN  \n",
       "298  19.330079  19.111129  \n",
       "299        NaN        NaN  \n",
       "\n",
       "[300 rows x 62 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop values at specific regions\n",
    "a = activities.iloc[:, np.r_[10:18, 34:40, 56:62]]\n",
    "b = activities.iloc[:, np.r_[0:10, 18:34, 40:56]]\n",
    "# Remove the missing values\n",
    "val_a = b.iloc[::2, :]\n",
    "val_b = val_a.dropna()\n",
    "dataset = a.reset_index(drop=True)\n",
    "final_df = pd.concat([dataset, val_b], axis=1, ignore_index=True)\n",
    "# Print the final data frame to check whether the values agree. \n",
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `NaN` values here can be ignored, since the data that we are concerned with should only go up to 140. The following values can be ignored in the graph. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the Silhoutte index as function of k\n",
    "final_df.plot(legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Compute the Silhoutte index with other values of $k$ (ex. $k \\in [3, 5, ..., 10]$ ) to find the most optimal number of cluster for partitioning the data. You can use the Silhouette index implemented in `Scikit-Learn`\n",
    "\n",
    "Plot you Silhoutte index as a value of $k$. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Use the `k-means` algorithm as implemented in the `scikit-learn`  to cluster activities data into the $k$ groups they belong to.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "External Reference:\n",
    "https://scikit-learn.org/stable/modules/clustering.html#k-means\n",
    "26_clustering.ipynb notebook provided by Professor Mahdi to outline K-Means Steps\n",
    "'''\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "from scipy.spatial import distance\n",
    "\n",
    "# Calculate the range of the clusters\n",
    "cluster_range = [2,3,4,5,6]\n",
    "\n",
    "# Initialize clusters by k points; one per cluster by iterating through the clusters range\n",
    "for i in cluster_range:\n",
    "    k_means = KMeans(n_clusters=i).fit(final_df)\n",
    "    # Assign the labels\n",
    "    # Assign the values to the silhouette score\n",
    "    # Append these to the values\n",
    "\n",
    "# Assign points to clusters\n",
    "\n",
    "# Once all points are assigned, recompute the centroids of the k clusters\n",
    "\n",
    "# Print the values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Plot the time series using $k$ different colors, each representing one of the $k$ clusters. Your plot should look like the following althought the number of clusters (lines with different clusters) may be different.\n",
    "\n",
    " * For simplicity, this sample plot contains only a small subset of the data. You graph should contain all the data. \n",
    " \n",
    "<img src=\"images/activities.png\" alt=\"drawing\" style=\"width:800px;\"/>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Assign colors to each cluster\n",
    "cluster_colors = ['b', 'g', 'p']\n",
    "# Plot the graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Compute the average movement value for each cluster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the average by dividing the total sum of the score of the K-Means by the length of the score\n",
    "average_movement = sum(k_means_score)/len(k_means_scre)\n",
    "# Print the average movement value for each cluster\n",
    "print('The average movement value for the cluster is: ', average_movement)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Briefly describe how you could have used Gaussian Mixture models to cluster your data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the ways that we can use the Gaussian Mixture model to cluster data is to determine which data point can be assigned to the cluster. This will allow us to calculate the probability of the data point belonging to a specific cluster. To cluster this data, one of the ways that we could do this is dependent on the K-Means algorithm. It is possible to split the data up into two separate clusters, with one cluster being the group that belonged to 1 second, and the other cluster being the group that belonged to the 2 second interval. This data can then be sorted by removing the even time intervals and bringing the data together in order to remove data that is otherwise non-overlapping. This would provide a dataset that can be used to cluster the data by using the `gmm.predict(X)` where `X` is the data to be predicted. This will provide the Gaussian mixture model that accounts for the variance in the data. "
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