Data (The Battle of Neighborhoods).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data necessary to answer the problem statement includes the **locations and the types of venues** around the Toronto city area. \n",
    "\n",
    "First, the **locations of all existing laundromats or laundry services in the Toronto city region** will be extracted to survey the competition. Ideally, the new laundromat would be set up at a certain distance away from other existing laundromats. \n",
    "Secondly, the **locations of travel accommodations** such as hotels, hostels and motels in the region will be obtained. This is to estimate the amount of visitors that would frequent the laundromat, based on the assumption that the clientele of the laundromat would be mainly made up of travellers. \n",
    "\n",
    "A suitable location for the new laundromat would be at the centre of a cluster of hotels and hostels so as to maximise the business potential. A *clustering algorithm* would be performed on the locations of laundromats and hotels separately to find the concentration of both types of the establishments. \n",
    "\n",
    "Finally, the ideal location for setting up a new laundromat can be inferred based on the clustering result. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}