diff --git a/notebooks/03_Take_Home_Exam.ipynb b/notebooks/03_Take_Home_Exam.ipynb
deleted file mode 100644
index 6f65005..0000000
--- a/notebooks/03_Take_Home_Exam.ipynb
+++ /dev/null
@@ -1,1301 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "15568cc0-be91-49e5-9a8c-1030cc298650",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Take Home Exam: Mobility Index Calculation and Profiling"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "94ccdd38",
-   "metadata": {},
-   "source": [
-    "## 1. Create \"Mobility Index\" and \"Mobility Class\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b74fd38c",
-   "metadata": {},
-   "source": [
-    "Using the the following features/indicators:<br>\n",
-    "<br>\n",
-    "    1. Total Distance Traveled<br>\n",
-    "    2. Radius of Gyration<br>\n",
-    "    3. Activity Entropy<br>\n",
-    "     \n",
-    "Create a calculated feature called **Mobility Index** (type: decimal/float) and **Mobility Class** (which is are categorized as Low, Mid, and High) for each subscriber.<br>\n",
-    "Team is free to use any methods or technique to arrive at the **OPTIMAL** Mobility Index and Mobility Class as long as it is supported by literature/s.<br>\n",
-    "<br>\n",
-    "**Deadline of the submission is September 1, 2023.** <br>\n",
-    "<br>\n",
-    "<br>\n",
-    "**Criteria for scoring**<br>\n",
-    "1. Creation of mobility class - 50 pts  <br>\n",
-    "2. Creation of mobility index - 30 pts  <br>\n",
-    "3. Efficiency of process      - 20 pts  <br>\n",
-    "    Total                     -100 pts \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "835e89f7",
-   "metadata": {},
-   "source": [
-    "## 2. Example of Mobility Index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "1cfd98fc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import random\n",
-    "import shapely\n",
-    "import pendulum\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from scipy import stats\n",
-    "pd.options.display.max_rows=200\n",
-    "import geopandas as gpd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from IPython.display import HTML, display\n",
-    "from functools import reduce\n",
-    "import pyproj\n",
-    "from functools import partial"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "238621ee",
-   "metadata": {},
-   "source": [
-    "#### Sample ABT"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "91355f76",
-   "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>Unnamed: 0</th>\n",
-       "      <th>sub_uid</th>\n",
-       "      <th>gender</th>\n",
-       "      <th>age</th>\n",
-       "      <th>name</th>\n",
-       "      <th>chi_indicator</th>\n",
-       "      <th>ewallet_user_indicator</th>\n",
-       "      <th>total_travel_distance</th>\n",
-       "      <th>radius_of_gyration</th>\n",
-       "      <th>activity_entropy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>32</td>\n",
-       "      <td>glo-sub-023</td>\n",
-       "      <td>female</td>\n",
-       "      <td>62</td>\n",
-       "      <td>Brandi Taylor</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Y</td>\n",
-       "      <td>187400.353151</td>\n",
-       "      <td>2773.800971</td>\n",
-       "      <td>1.393043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>22</td>\n",
-       "      <td>glo-sub-061</td>\n",
-       "      <td>male</td>\n",
-       "      <td>64</td>\n",
-       "      <td>David Evans</td>\n",
-       "      <td>False</td>\n",
-       "      <td>Y</td>\n",
-       "      <td>168249.882120</td>\n",
-       "      <td>1710.550891</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>17</td>\n",
-       "      <td>glo-sub-069</td>\n",
-       "      <td>male</td>\n",
-       "      <td>51</td>\n",
-       "      <td>Kevin Gibson</td>\n",
-       "      <td>True</td>\n",
-       "      <td>N</td>\n",
-       "      <td>162675.199376</td>\n",
-       "      <td>1274.739666</td>\n",
-       "      <td>1.279325</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>glo-sub-076</td>\n",
-       "      <td>male</td>\n",
-       "      <td>46</td>\n",
-       "      <td>Colin Mejia</td>\n",
-       "      <td>False</td>\n",
-       "      <td>N</td>\n",
-       "      <td>123646.921636</td>\n",
-       "      <td>1671.299423</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>98</td>\n",
-       "      <td>glo-sub-046</td>\n",
-       "      <td>male</td>\n",
-       "      <td>24</td>\n",
-       "      <td>Luis Jackson</td>\n",
-       "      <td>True</td>\n",
-       "      <td>Y</td>\n",
-       "      <td>385134.568227</td>\n",
-       "      <td>2206.908060</td>\n",
-       "      <td>1.228771</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Unnamed: 0      sub_uid  gender  age           name  chi_indicator  \\\n",
-       "32          32  glo-sub-023  female   62  Brandi Taylor           True   \n",
-       "22          22  glo-sub-061    male   64    David Evans          False   \n",
-       "17          17  glo-sub-069    male   51   Kevin Gibson           True   \n",
-       "4            4  glo-sub-076    male   46    Colin Mejia          False   \n",
-       "98          98  glo-sub-046    male   24   Luis Jackson           True   \n",
-       "\n",
-       "   ewallet_user_indicator  total_travel_distance  radius_of_gyration  \\\n",
-       "32                      Y          187400.353151         2773.800971   \n",
-       "22                      Y          168249.882120         1710.550891   \n",
-       "17                      N          162675.199376         1274.739666   \n",
-       "4                       N          123646.921636         1671.299423   \n",
-       "98                      Y          385134.568227         2206.908060   \n",
-       "\n",
-       "    activity_entropy  \n",
-       "32          1.393043  \n",
-       "22               NaN  \n",
-       "17          1.279325  \n",
-       "4                NaN  \n",
-       "98          1.228771  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "file_path_sample_data = \"C:/Users/10012425/Desktop/sds4gdsp/scoring_base.csv\"\n",
-    "ABT_mobility = pd.read_csv(file_path_sample_data)\n",
-    "ABT_mobility.sample(5)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc963d69",
-   "metadata": {},
-   "source": [
-    "### Apply min max scaling capping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c74ec061",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import MinMaxScaler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "a396858e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_capping = pd.DataFrame(ABT_mobility, columns=['total_travel_distance', 'radius_of_gyration','activity_entropy'])\n",
-    "\n",
-    "column_headers = df_capping.columns.tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "3186fb50",
-   "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>total_travel_distance</th>\n",
-       "      <th>radius_of_gyration</th>\n",
-       "      <th>activity_entropy</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.015741</td>\n",
-       "      <td>0.400992</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.076253</td>\n",
-       "      <td>0.182373</td>\n",
-       "      <td>0.504028</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.098540</td>\n",
-       "      <td>0.175793</td>\n",
-       "      <td>0.692796</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.100956</td>\n",
-       "      <td>0.024102</td>\n",
-       "      <td>0.004103</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.102464</td>\n",
-       "      <td>0.369021</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.126786</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.128279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.130826</td>\n",
-       "      <td>0.088019</td>\n",
-       "      <td>0.368835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0.137770</td>\n",
-       "      <td>0.024837</td>\n",
-       "      <td>0.115453</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0.139332</td>\n",
-       "      <td>0.223741</td>\n",
-       "      <td>0.638321</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0.141096</td>\n",
-       "      <td>0.183562</td>\n",
-       "      <td>0.408091</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.145877</td>\n",
-       "      <td>0.018146</td>\n",
-       "      <td>0.150974</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>0.164302</td>\n",
-       "      <td>0.224896</td>\n",
-       "      <td>0.669492</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>0.166832</td>\n",
-       "      <td>0.153687</td>\n",
-       "      <td>0.657595</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>0.168286</td>\n",
-       "      <td>0.053673</td>\n",
-       "      <td>0.128041</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>0.173987</td>\n",
-       "      <td>0.652952</td>\n",
-       "      <td>0.862067</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>0.193627</td>\n",
-       "      <td>0.094938</td>\n",
-       "      <td>0.560008</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>0.206985</td>\n",
-       "      <td>0.459849</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>0.227320</td>\n",
-       "      <td>0.150694</td>\n",
-       "      <td>0.693717</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>0.229192</td>\n",
-       "      <td>0.116003</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>0.231166</td>\n",
-       "      <td>0.109158</td>\n",
-       "      <td>0.392414</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>0.232562</td>\n",
-       "      <td>0.132373</td>\n",
-       "      <td>0.719609</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>0.238558</td>\n",
-       "      <td>0.151386</td>\n",
-       "      <td>0.513148</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>0.245155</td>\n",
-       "      <td>0.390631</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>0.249958</td>\n",
-       "      <td>0.694434</td>\n",
-       "      <td>0.734925</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>0.250839</td>\n",
-       "      <td>0.126739</td>\n",
-       "      <td>0.564629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>0.251877</td>\n",
-       "      <td>0.456084</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>0.262651</td>\n",
-       "      <td>0.316213</td>\n",
-       "      <td>0.641308</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>0.263391</td>\n",
-       "      <td>0.734126</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>0.271244</td>\n",
-       "      <td>0.187688</td>\n",
-       "      <td>0.644671</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>0.278703</td>\n",
-       "      <td>0.209653</td>\n",
-       "      <td>0.697244</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>0.290667</td>\n",
-       "      <td>0.420993</td>\n",
-       "      <td>0.830829</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>0.293685</td>\n",
-       "      <td>0.745656</td>\n",
-       "      <td>0.881326</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>0.306420</td>\n",
-       "      <td>0.976004</td>\n",
-       "      <td>0.846744</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>0.324686</td>\n",
-       "      <td>0.250294</td>\n",
-       "      <td>0.780299</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>0.326003</td>\n",
-       "      <td>0.416509</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>0.334206</td>\n",
-       "      <td>0.350879</td>\n",
-       "      <td>0.441645</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>0.367764</td>\n",
-       "      <td>0.224580</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>0.373848</td>\n",
-       "      <td>0.392623</td>\n",
-       "      <td>0.465124</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>0.393327</td>\n",
-       "      <td>0.585133</td>\n",
-       "      <td>0.818701</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>0.393600</td>\n",
-       "      <td>0.584121</td>\n",
-       "      <td>0.846100</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0.400479</td>\n",
-       "      <td>0.487088</td>\n",
-       "      <td>0.749415</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>0.406267</td>\n",
-       "      <td>0.665625</td>\n",
-       "      <td>0.747511</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>0.410720</td>\n",
-       "      <td>0.932120</td>\n",
-       "      <td>0.942203</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>0.422581</td>\n",
-       "      <td>0.415363</td>\n",
-       "      <td>0.388612</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>0.435018</td>\n",
-       "      <td>0.468543</td>\n",
-       "      <td>0.284707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>0.441460</td>\n",
-       "      <td>0.262436</td>\n",
-       "      <td>0.805165</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>0.443289</td>\n",
-       "      <td>0.373030</td>\n",
-       "      <td>0.488329</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>0.454203</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.921311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>0.461219</td>\n",
-       "      <td>0.954661</td>\n",
-       "      <td>0.874746</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>0.471829</td>\n",
-       "      <td>0.909112</td>\n",
-       "      <td>0.890261</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>0.476076</td>\n",
-       "      <td>0.632397</td>\n",
-       "      <td>0.780436</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>0.482461</td>\n",
-       "      <td>0.619962</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>0.486138</td>\n",
-       "      <td>0.300776</td>\n",
-       "      <td>0.892967</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>0.493235</td>\n",
-       "      <td>0.687713</td>\n",
-       "      <td>0.836162</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>0.495475</td>\n",
-       "      <td>0.502072</td>\n",
-       "      <td>0.647253</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>0.503071</td>\n",
-       "      <td>0.688536</td>\n",
-       "      <td>0.787323</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>0.505641</td>\n",
-       "      <td>0.852631</td>\n",
-       "      <td>0.950744</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>0.508837</td>\n",
-       "      <td>0.899560</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>0.518911</td>\n",
-       "      <td>0.629803</td>\n",
-       "      <td>0.561174</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>59</th>\n",
-       "      <td>0.525449</td>\n",
-       "      <td>0.415542</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>0.533844</td>\n",
-       "      <td>0.760338</td>\n",
-       "      <td>0.998669</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>61</th>\n",
-       "      <td>0.539236</td>\n",
-       "      <td>0.924828</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>0.539789</td>\n",
-       "      <td>0.722342</td>\n",
-       "      <td>0.693652</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63</th>\n",
-       "      <td>0.547888</td>\n",
-       "      <td>0.399018</td>\n",
-       "      <td>0.389120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>64</th>\n",
-       "      <td>0.550858</td>\n",
-       "      <td>0.530149</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65</th>\n",
-       "      <td>0.560427</td>\n",
-       "      <td>0.580756</td>\n",
-       "      <td>0.726224</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66</th>\n",
-       "      <td>0.573460</td>\n",
-       "      <td>0.867476</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67</th>\n",
-       "      <td>0.575976</td>\n",
-       "      <td>0.656549</td>\n",
-       "      <td>0.806102</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>68</th>\n",
-       "      <td>0.576479</td>\n",
-       "      <td>0.774121</td>\n",
-       "      <td>0.934544</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69</th>\n",
-       "      <td>0.582866</td>\n",
-       "      <td>0.577673</td>\n",
-       "      <td>0.694904</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>70</th>\n",
-       "      <td>0.584790</td>\n",
-       "      <td>0.491039</td>\n",
-       "      <td>0.128070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>71</th>\n",
-       "      <td>0.584950</td>\n",
-       "      <td>0.745514</td>\n",
-       "      <td>0.886276</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>0.586260</td>\n",
-       "      <td>0.717353</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>73</th>\n",
-       "      <td>0.614757</td>\n",
-       "      <td>0.673194</td>\n",
-       "      <td>0.603687</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>0.648191</td>\n",
-       "      <td>0.782951</td>\n",
-       "      <td>0.667259</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>0.650132</td>\n",
-       "      <td>0.620419</td>\n",
-       "      <td>0.868859</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>0.653933</td>\n",
-       "      <td>0.793290</td>\n",
-       "      <td>0.665075</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>77</th>\n",
-       "      <td>0.661956</td>\n",
-       "      <td>0.544567</td>\n",
-       "      <td>0.687681</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>0.662519</td>\n",
-       "      <td>0.740416</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>0.669378</td>\n",
-       "      <td>0.556351</td>\n",
-       "      <td>0.691892</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>80</th>\n",
-       "      <td>0.682696</td>\n",
-       "      <td>0.432683</td>\n",
-       "      <td>0.498591</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>0.683442</td>\n",
-       "      <td>0.562822</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>0.697061</td>\n",
-       "      <td>0.898199</td>\n",
-       "      <td>0.628065</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>0.701713</td>\n",
-       "      <td>0.635278</td>\n",
-       "      <td>0.682781</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>0.707848</td>\n",
-       "      <td>0.628726</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>0.708291</td>\n",
-       "      <td>0.525185</td>\n",
-       "      <td>0.548055</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>0.753964</td>\n",
-       "      <td>0.880014</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>0.754376</td>\n",
-       "      <td>0.648879</td>\n",
-       "      <td>0.705661</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>0.771381</td>\n",
-       "      <td>0.683648</td>\n",
-       "      <td>0.770484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89</th>\n",
-       "      <td>0.780210</td>\n",
-       "      <td>0.690189</td>\n",
-       "      <td>0.909890</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>90</th>\n",
-       "      <td>0.787743</td>\n",
-       "      <td>0.745578</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>0.797744</td>\n",
-       "      <td>0.564313</td>\n",
-       "      <td>0.521526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>92</th>\n",
-       "      <td>0.815482</td>\n",
-       "      <td>0.844322</td>\n",
-       "      <td>0.917914</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>0.821048</td>\n",
-       "      <td>0.578163</td>\n",
-       "      <td>0.781067</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>94</th>\n",
-       "      <td>0.832475</td>\n",
-       "      <td>0.756700</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>0.864773</td>\n",
-       "      <td>0.597244</td>\n",
-       "      <td>0.474646</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>0.867214</td>\n",
-       "      <td>0.577540</td>\n",
-       "      <td>0.861811</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>0.920367</td>\n",
-       "      <td>0.667763</td>\n",
-       "      <td>0.592899</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>0.938998</td>\n",
-       "      <td>0.663901</td>\n",
-       "      <td>0.625688</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.568807</td>\n",
-       "      <td>0.497987</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    total_travel_distance  radius_of_gyration  activity_entropy\n",
-       "0                0.000000            0.015741          0.400992\n",
-       "1                0.076253            0.182373          0.504028\n",
-       "2                0.098540            0.175793          0.692796\n",
-       "3                0.100956            0.024102          0.004103\n",
-       "4                0.102464            0.369021               NaN\n",
-       "5                0.126786            0.000000          0.128279\n",
-       "6                0.130826            0.088019          0.368835\n",
-       "7                0.137770            0.024837          0.115453\n",
-       "8                0.139332            0.223741          0.638321\n",
-       "9                0.141096            0.183562          0.408091\n",
-       "10               0.145877            0.018146          0.150974\n",
-       "11               0.164302            0.224896          0.669492\n",
-       "12               0.166832            0.153687          0.657595\n",
-       "13               0.168286            0.053673          0.128041\n",
-       "14               0.173987            0.652952          0.862067\n",
-       "15               0.193627            0.094938          0.560008\n",
-       "16               0.206985            0.459849               NaN\n",
-       "17               0.227320            0.150694          0.693717\n",
-       "18               0.229192            0.116003               NaN\n",
-       "19               0.231166            0.109158          0.392414\n",
-       "20               0.232562            0.132373          0.719609\n",
-       "21               0.238558            0.151386          0.513148\n",
-       "22               0.245155            0.390631               NaN\n",
-       "23               0.249958            0.694434          0.734925\n",
-       "24               0.250839            0.126739          0.564629\n",
-       "25               0.251877            0.456084               NaN\n",
-       "26               0.262651            0.316213          0.641308\n",
-       "27               0.263391            0.734126               NaN\n",
-       "28               0.271244            0.187688          0.644671\n",
-       "29               0.278703            0.209653          0.697244\n",
-       "30               0.290667            0.420993          0.830829\n",
-       "31               0.293685            0.745656          0.881326\n",
-       "32               0.306420            0.976004          0.846744\n",
-       "33               0.324686            0.250294          0.780299\n",
-       "34               0.326003            0.416509               NaN\n",
-       "35               0.334206            0.350879          0.441645\n",
-       "36               0.367764            0.224580               NaN\n",
-       "37               0.373848            0.392623          0.465124\n",
-       "38               0.393327            0.585133          0.818701\n",
-       "39               0.393600            0.584121          0.846100\n",
-       "40               0.400479            0.487088          0.749415\n",
-       "41               0.406267            0.665625          0.747511\n",
-       "42               0.410720            0.932120          0.942203\n",
-       "43               0.422581            0.415363          0.388612\n",
-       "44               0.435018            0.468543          0.284707\n",
-       "45               0.441460            0.262436          0.805165\n",
-       "46               0.443289            0.373030          0.488329\n",
-       "47               0.454203            1.000000          0.921311\n",
-       "48               0.461219            0.954661          0.874746\n",
-       "49               0.471829            0.909112          0.890261\n",
-       "50               0.476076            0.632397          0.780436\n",
-       "51               0.482461            0.619962               NaN\n",
-       "52               0.486138            0.300776          0.892967\n",
-       "53               0.493235            0.687713          0.836162\n",
-       "54               0.495475            0.502072          0.647253\n",
-       "55               0.503071            0.688536          0.787323\n",
-       "56               0.505641            0.852631          0.950744\n",
-       "57               0.508837            0.899560               NaN\n",
-       "58               0.518911            0.629803          0.561174\n",
-       "59               0.525449            0.415542          0.000000\n",
-       "60               0.533844            0.760338          0.998669\n",
-       "61               0.539236            0.924828               NaN\n",
-       "62               0.539789            0.722342          0.693652\n",
-       "63               0.547888            0.399018          0.389120\n",
-       "64               0.550858            0.530149               NaN\n",
-       "65               0.560427            0.580756          0.726224\n",
-       "66               0.573460            0.867476          1.000000\n",
-       "67               0.575976            0.656549          0.806102\n",
-       "68               0.576479            0.774121          0.934544\n",
-       "69               0.582866            0.577673          0.694904\n",
-       "70               0.584790            0.491039          0.128070\n",
-       "71               0.584950            0.745514          0.886276\n",
-       "72               0.586260            0.717353               NaN\n",
-       "73               0.614757            0.673194          0.603687\n",
-       "74               0.648191            0.782951          0.667259\n",
-       "75               0.650132            0.620419          0.868859\n",
-       "76               0.653933            0.793290          0.665075\n",
-       "77               0.661956            0.544567          0.687681\n",
-       "78               0.662519            0.740416               NaN\n",
-       "79               0.669378            0.556351          0.691892\n",
-       "80               0.682696            0.432683          0.498591\n",
-       "81               0.683442            0.562822               NaN\n",
-       "82               0.697061            0.898199          0.628065\n",
-       "83               0.701713            0.635278          0.682781\n",
-       "84               0.707848            0.628726               NaN\n",
-       "85               0.708291            0.525185          0.548055\n",
-       "86               0.753964            0.880014               NaN\n",
-       "87               0.754376            0.648879          0.705661\n",
-       "88               0.771381            0.683648          0.770484\n",
-       "89               0.780210            0.690189          0.909890\n",
-       "90               0.787743            0.745578               NaN\n",
-       "91               0.797744            0.564313          0.521526\n",
-       "92               0.815482            0.844322          0.917914\n",
-       "93               0.821048            0.578163          0.781067\n",
-       "94               0.832475            0.756700               NaN\n",
-       "95               0.864773            0.597244          0.474646\n",
-       "96               0.867214            0.577540          0.861811\n",
-       "97               0.920367            0.667763          0.592899\n",
-       "98               0.938998            0.663901          0.625688\n",
-       "99               1.000000            0.568807          0.497987"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a MinMaxScaler instance\n",
-    "scaler = MinMaxScaler()\n",
-    "\n",
-    "# Fit the scaler on the data and transform it\n",
-    "scaled_data = scaler.fit_transform(df_capping)\n",
-    "\n",
-    "# Convert scaled data back to a DataFrame\n",
-    "scaled_df = pd.DataFrame(scaled_data, columns=column_headers)\n",
-    "scaled_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "094e9cc1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "scaled_df = scaled_df.fillna(0)\n",
-    "\n",
-    "scaled_df[\"mobility_index\"] = (scaled_df[\"total_travel_distance\"] + scaled_df[\"radius_of_gyration\"] + scaled_df[\"activity_entropy\"]) /3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "1c0b40ab",
-   "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>total_travel_distance</th>\n",
-       "      <th>radius_of_gyration</th>\n",
-       "      <th>activity_entropy</th>\n",
-       "      <th>mobility_index</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>93</th>\n",
-       "      <td>0.821048</td>\n",
-       "      <td>0.578163</td>\n",
-       "      <td>0.781067</td>\n",
-       "      <td>0.726759</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.098540</td>\n",
-       "      <td>0.175793</td>\n",
-       "      <td>0.692796</td>\n",
-       "      <td>0.322376</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>0.771381</td>\n",
-       "      <td>0.683648</td>\n",
-       "      <td>0.770484</td>\n",
-       "      <td>0.741838</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>0.263391</td>\n",
-       "      <td>0.734126</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.332506</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>0.145877</td>\n",
-       "      <td>0.018146</td>\n",
-       "      <td>0.150974</td>\n",
-       "      <td>0.104999</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    total_travel_distance  radius_of_gyration  activity_entropy  \\\n",
-       "93               0.821048            0.578163          0.781067   \n",
-       "2                0.098540            0.175793          0.692796   \n",
-       "88               0.771381            0.683648          0.770484   \n",
-       "27               0.263391            0.734126          0.000000   \n",
-       "10               0.145877            0.018146          0.150974   \n",
-       "\n",
-       "    mobility_index  \n",
-       "93        0.726759  \n",
-       "2         0.322376  \n",
-       "88        0.741838  \n",
-       "27        0.332506  \n",
-       "10        0.104999  "
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scaled_df.sample(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "f2777ad1",
-   "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>total_travel_distance</th>\n",
-       "      <th>radius_of_gyration</th>\n",
-       "      <th>activity_entropy</th>\n",
-       "      <th>mobility_index</th>\n",
-       "      <th>Category</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>0.443289</td>\n",
-       "      <td>0.373030</td>\n",
-       "      <td>0.488329</td>\n",
-       "      <td>0.434882</td>\n",
-       "      <td>Mid</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>0.373848</td>\n",
-       "      <td>0.392623</td>\n",
-       "      <td>0.465124</td>\n",
-       "      <td>0.410532</td>\n",
-       "      <td>Mid</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>0.539789</td>\n",
-       "      <td>0.722342</td>\n",
-       "      <td>0.693652</td>\n",
-       "      <td>0.651928</td>\n",
-       "      <td>Mid</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>0.400479</td>\n",
-       "      <td>0.487088</td>\n",
-       "      <td>0.749415</td>\n",
-       "      <td>0.545661</td>\n",
-       "      <td>Mid</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>0.193627</td>\n",
-       "      <td>0.094938</td>\n",
-       "      <td>0.560008</td>\n",
-       "      <td>0.282858</td>\n",
-       "      <td>Low</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    total_travel_distance  radius_of_gyration  activity_entropy  \\\n",
-       "46               0.443289            0.373030          0.488329   \n",
-       "37               0.373848            0.392623          0.465124   \n",
-       "62               0.539789            0.722342          0.693652   \n",
-       "40               0.400479            0.487088          0.749415   \n",
-       "15               0.193627            0.094938          0.560008   \n",
-       "\n",
-       "    mobility_index Category  \n",
-       "46        0.434882      Mid  \n",
-       "37        0.410532      Mid  \n",
-       "62        0.651928      Mid  \n",
-       "40        0.545661      Mid  \n",
-       "15        0.282858      Low  "
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "low_threshold = 0.3\n",
-    "high_threshold = 0.7\n",
-    "\n",
-    "# Create a function to categorize values\n",
-    "def categorize(value):\n",
-    "    if value < low_threshold:\n",
-    "        return 'Low'\n",
-    "    elif value < high_threshold:\n",
-    "        return 'Mid'\n",
-    "    elif value > high_threshold:\n",
-    "        return 'High'\n",
-    "    else:\n",
-    "        return 'Low'\n",
-    "\n",
-    "# Apply the categorize function to the 'Value' column\n",
-    "scaled_df['Category'] = scaled_df['mobility_index'].apply(categorize)\n",
-    "\n",
-    "scaled_df.sample(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "e8536d1d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Category\n",
-       "High    21\n",
-       "Low     18\n",
-       "Mid     61\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mobility_class = pd.DataFrame(scaled_df)\n",
-    "scaled_df.groupby('Category').size()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "06681c5d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "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": "3.8.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}