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Edited the take home exam.
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June Elle Tanedo committed Aug 29, 2023
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"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 index - 30 pts <br>\n",
"2. Soundness of mobility class - 50 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": 2,
"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": 3,
"id": "91355f76",
"metadata": {},
"outputs": [
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" <th>radius_of_gyration</th>\n",
" <th>activity_entropy</th>\n",
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" <td>25</td>\n",
" <td>Kelly Medina</td>\n",
" <td>True</td>\n",
" <td>Y</td>\n",
" <td>227598.186011</td>\n",
" <td>1852.066940</td>\n",
" <td>0.975380</td>\n",
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" <td>glo-sub-087</td>\n",
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" <td>39</td>\n",
" <td>Ryan Morris</td>\n",
" <td>False</td>\n",
" <td>N</td>\n",
" <td>223710.515056</td>\n",
" <td>1755.474028</td>\n",
" <td>1.052595</td>\n",
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" Unnamed: 0 sub_uid gender age name chi_indicator \\\n",
"78 78 glo-sub-030 male 36 Gerald Harrison False \n",
"0 0 glo-sub-059 female 19 Leslie Mccall False \n",
"5 5 glo-sub-016 female 38 Sabrina Mclaughlin True \n",
"47 47 glo-sub-081 female 48 Jessica Wood True \n",
"44 44 glo-sub-054 male 25 Kelly Medina True \n",
"43 43 glo-sub-087 male 39 Ryan Morris False \n",
"\n",
" ewallet_user_indicator total_travel_distance radius_of_gyration \\\n",
"78 N 298711.609702 2345.886941 \n",
"0 N 91618.337212 1029.615314 \n",
"5 Y 131249.837053 1001.024076 \n",
"47 Y 233595.159933 2817.385939 \n",
"44 Y 227598.186011 1852.066940 \n",
"43 N 223710.515056 1755.474028 \n",
"\n",
" activity_entropy \n",
"78 NaN \n",
"0 1.061794 \n",
"5 0.859135 \n",
"47 1.448455 \n",
"44 0.975380 \n",
"43 1.052595 "
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}
],
"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(6)"
]
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