RJS-FinalCapstone_Intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SOLVING A BUSINESS OPTIMAL LOCATION PROBLEM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Problem Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, the problem attempted to solve will be to find the best possible location or the most optimal, for a mexican restaurant in the city of Madrid, Spain. To achieve this task, an analytical approach will be used, based on advanced machine learning techniques and data analysis, concretely clustering and perhaps some data visualization techniques.\n",
    "\n",
    "During the process of analysis, several data transformations will be performed, in order the find the best possible data format for the machine learning model to ingest. Once the data is set up and prepared, a modeling process will be carried out, and this statistical analysis will provide the best possible places to locate the mexican restaurant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data Presentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data that will be used to develop this project is based on two sites:\n",
    "\n",
    "1. __The Foursquare Api:__ This data will be accesed via Python, and used to obtain the most common venues per neighborhood in the city of Madrid. This way, it is possible to have a taste of how the city's venues are distributed, what are the most common places for leisure, and in general, it will provide an idea of what people's likes are.\n",
    "\n",
    "2. __The Madrid City Hall's Web Portal:__ This site provides several data sources of great utility to solve this problem. The files are provided in Excel format, and they are built over a statstical exploitation and use basis. The data contains updated information about the inmigrant population per country and per nationality. This data will be analyzed in such a way that one could determine the best location of r anew venue/restaurant/other based on people's nationalities. For the sake of simplicity, it will be assumed for this exercise that people's likes varies according to their nationality, and that people from one specific country will be more attracted to place that matches the environment and culture of their own countries, rather than the ones from foreign countries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's see what the data looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 1</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <th>Unnamed: 19</th>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <th>Unnamed: 21</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "      <th>Unnamed: 23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Rumanía</td>\n",
       "      <td>45036.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>754.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>753.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2241.0</td>\n",
       "      <td>4784.0</td>\n",
       "      <td>1286.0</td>\n",
       "      <td>2888.0</td>\n",
       "      <td>1466.0</td>\n",
       "      <td>3646.0</td>\n",
       "      <td>3384.0</td>\n",
       "      <td>2606.0</td>\n",
       "      <td>2929.0</td>\n",
       "      <td>661.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>China</td>\n",
       "      <td>37276.0</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>564.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9207.0</td>\n",
       "      <td>3602.0</td>\n",
       "      <td>564.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>23953.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>619.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>3290.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>2471.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>498.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>23359.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>913.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>1564.0</td>\n",
       "      <td>933.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>794.0</td>\n",
       "      <td>...</td>\n",
       "      <td>875.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>1434.0</td>\n",
       "      <td>909.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>1486.0</td>\n",
       "      <td>314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>22618.0</td>\n",
       "      <td>998.0</td>\n",
       "      <td>717.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>803.0</td>\n",
       "      <td>551.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>659.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1752.0</td>\n",
       "      <td>1733.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>1792.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>1282.0</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                  Unnamed: 1  Unnamed: 2  Unnamed: 3  Unnamed: 4  \\\n",
       "0         NaN                     Rumanía     45036.0       815.0       754.0   \n",
       "1         NaN  China                          37276.0      1508.0      1356.0   \n",
       "2         NaN  Ecuador                        23953.0       647.0       741.0   \n",
       "3         NaN  Venezuela                      23359.0      1563.0       913.0   \n",
       "4         NaN  Colombia                       22618.0       998.0       717.0   \n",
       "\n",
       "   Unnamed: 5  Unnamed: 6  Unnamed: 7  Unnamed: 8  Unnamed: 9  ...  \\\n",
       "0       480.0       753.0       680.0      1468.0       597.0  ...   \n",
       "1       564.0       755.0       652.0      1988.0       816.0  ...   \n",
       "2       265.0       619.0       380.0      1395.0       453.0  ...   \n",
       "3       638.0      1564.0       933.0      1310.0       794.0  ...   \n",
       "4       483.0       803.0       551.0       822.0       659.0  ...   \n",
       "\n",
       "   Unnamed: 14  Unnamed: 15  Unnamed: 16  Unnamed: 17  Unnamed: 18  \\\n",
       "0       2241.0       4784.0       1286.0       2888.0       1466.0   \n",
       "1       9207.0       3602.0        564.0       1960.0       1104.0   \n",
       "2       1806.0       3290.0        491.0       2471.0        401.0   \n",
       "3        875.0       1829.0        480.0       1858.0       1434.0   \n",
       "4       1752.0       1733.0        482.0       1792.0        910.0   \n",
       "\n",
       "   Unnamed: 19  Unnamed: 20  Unnamed: 21  Unnamed: 22  Unnamed: 23  \n",
       "0       3646.0       3384.0       2606.0       2929.0        661.0  \n",
       "1       1236.0        685.0        472.0        972.0        190.0  \n",
       "2       2017.0        498.0        439.0       1015.0        138.0  \n",
       "3        909.0        762.0        321.0       1486.0        314.0  \n",
       "4       1618.0        740.0        384.0       1282.0        258.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_excel('C4210618.xls', sheet_name = 'C4210618', skiprows = 12)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Country of Procedence</th>\n",
       "      <th>Total Ciudad de Madrid</th>\n",
       "      <th>Centro</th>\n",
       "      <th>Arganzuela</th>\n",
       "      <th>Retiro</th>\n",
       "      <th>Salamanca</th>\n",
       "      <th>Chamartin</th>\n",
       "      <th>Tetuán</th>\n",
       "      <th>Chamberí</th>\n",
       "      <th>Fuencarral-El Pardo</th>\n",
       "      <th>...</th>\n",
       "      <th>Usera</th>\n",
       "      <th>Puente de Vallecas</th>\n",
       "      <th>Moratalaz</th>\n",
       "      <th>Ciudad Lineal</th>\n",
       "      <th>Hortaleza</th>\n",
       "      <th>Villaverde</th>\n",
       "      <th>Villa de Vallecas</th>\n",
       "      <th>Vicálvaro</th>\n",
       "      <th>San Blas-Canillejas</th>\n",
       "      <th>Barajas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rumanía</td>\n",
       "      <td>45036.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>754.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>753.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2241.0</td>\n",
       "      <td>4784.0</td>\n",
       "      <td>1286.0</td>\n",
       "      <td>2888.0</td>\n",
       "      <td>1466.0</td>\n",
       "      <td>3646.0</td>\n",
       "      <td>3384.0</td>\n",
       "      <td>2606.0</td>\n",
       "      <td>2929.0</td>\n",
       "      <td>661.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>37276.0</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>1356.0</td>\n",
       "      <td>564.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>1733.0</td>\n",
       "      <td>...</td>\n",
       "      <td>9207.0</td>\n",
       "      <td>3602.0</td>\n",
       "      <td>564.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1104.0</td>\n",
       "      <td>1236.0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>972.0</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ecuador</td>\n",
       "      <td>23953.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>619.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>1395.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1806.0</td>\n",
       "      <td>3290.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>2471.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>498.0</td>\n",
       "      <td>439.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>138.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>23359.0</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>913.0</td>\n",
       "      <td>638.0</td>\n",
       "      <td>1564.0</td>\n",
       "      <td>933.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>794.0</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>...</td>\n",
       "      <td>875.0</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>1858.0</td>\n",
       "      <td>1434.0</td>\n",
       "      <td>909.0</td>\n",
       "      <td>762.0</td>\n",
       "      <td>321.0</td>\n",
       "      <td>1486.0</td>\n",
       "      <td>314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>22618.0</td>\n",
       "      <td>998.0</td>\n",
       "      <td>717.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>803.0</td>\n",
       "      <td>551.0</td>\n",
       "      <td>822.0</td>\n",
       "      <td>659.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1752.0</td>\n",
       "      <td>1733.0</td>\n",
       "      <td>482.0</td>\n",
       "      <td>1792.0</td>\n",
       "      <td>910.0</td>\n",
       "      <td>1618.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>1282.0</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Marruecos</td>\n",
       "      <td>21909.0</td>\n",
       "      <td>1101.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1393.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>930.0</td>\n",
       "      <td>...</td>\n",
       "      <td>942.0</td>\n",
       "      <td>3437.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>426.0</td>\n",
       "      <td>3372.0</td>\n",
       "      <td>1655.0</td>\n",
       "      <td>802.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Italia</td>\n",
       "      <td>20308.0</td>\n",
       "      <td>3030.0</td>\n",
       "      <td>1219.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>1817.0</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>1194.0</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>1195.0</td>\n",
       "      <td>...</td>\n",
       "      <td>412.0</td>\n",
       "      <td>704.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1109.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>786.0</td>\n",
       "      <td>337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Perú</td>\n",
       "      <td>18829.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>521.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>612.0</td>\n",
       "      <td>419.0</td>\n",
       "      <td>965.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>805.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1131.0</td>\n",
       "      <td>2079.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>1726.0</td>\n",
       "      <td>603.0</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>564.0</td>\n",
       "      <td>338.0</td>\n",
       "      <td>810.0</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>18682.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>521.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>3311.0</td>\n",
       "      <td>584.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>...</td>\n",
       "      <td>727.0</td>\n",
       "      <td>1354.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1619.0</td>\n",
       "      <td>583.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>581.0</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>República Dominicana</td>\n",
       "      <td>17511.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>654.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>344.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2272.0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1202.0</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>1581.0</td>\n",
       "      <td>359.0</td>\n",
       "      <td>1881.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>889.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Honduras</td>\n",
       "      <td>15981.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>863.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1115.0</td>\n",
       "      <td>2483.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>1062.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1040.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>14930.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>401.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2827.0</td>\n",
       "      <td>1573.0</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>454.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Filipinas</td>\n",
       "      <td>12628.0</td>\n",
       "      <td>1344.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>661.0</td>\n",
       "      <td>4473.0</td>\n",
       "      <td>771.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>...</td>\n",
       "      <td>225.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>9860.0</td>\n",
       "      <td>769.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>695.0</td>\n",
       "      <td>534.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>509.0</td>\n",
       "      <td>693.0</td>\n",
       "      <td>...</td>\n",
       "      <td>283.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>612.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Francia</td>\n",
       "      <td>9561.0</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>968.0</td>\n",
       "      <td>554.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>699.0</td>\n",
       "      <td>366.0</td>\n",
       "      <td>...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>599.0</td>\n",
       "      <td>1802.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Ucrania</td>\n",
       "      <td>9453.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>214.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>312.0</td>\n",
       "      <td>...</td>\n",
       "      <td>428.0</td>\n",
       "      <td>912.0</td>\n",
       "      <td>239.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>1649.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>390.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Brasil</td>\n",
       "      <td>9324.0</td>\n",
       "      <td>677.0</td>\n",
       "      <td>309.0</td>\n",
       "      <td>244.0</td>\n",
       "      <td>431.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>361.0</td>\n",
       "      <td>...</td>\n",
       "      <td>410.0</td>\n",
       "      <td>604.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>421.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>417.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>7842.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>...</td>\n",
       "      <td>395.0</td>\n",
       "      <td>760.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>780.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>516.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Estados Unidos de América</td>\n",
       "      <td>6791.0</td>\n",
       "      <td>1637.0</td>\n",
       "      <td>406.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>749.0</td>\n",
       "      <td>389.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>657.0</td>\n",
       "      <td>297.0</td>\n",
       "      <td>...</td>\n",
       "      <td>86.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Reino Unido</td>\n",
       "      <td>5915.0</td>\n",
       "      <td>1274.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>466.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>313.0</td>\n",
       "      <td>...</td>\n",
       "      <td>81.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>5886.0</td>\n",
       "      <td>2742.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>...</td>\n",
       "      <td>175.0</td>\n",
       "      <td>649.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>489.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>5725.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>257.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>...</td>\n",
       "      <td>301.0</td>\n",
       "      <td>513.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>483.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Polonia</td>\n",
       "      <td>5487.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>...</td>\n",
       "      <td>298.0</td>\n",
       "      <td>424.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>5061.0</td>\n",
       "      <td>588.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>271.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>...</td>\n",
       "      <td>230.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Alemania</td>\n",
       "      <td>4707.0</td>\n",
       "      <td>592.0</td>\n",
       "      <td>199.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>442.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>607.0</td>\n",
       "      <td>...</td>\n",
       "      <td>60.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country of Procedence  Total Ciudad de Madrid  Centro  Arganzuela  \\\n",
       "0                      Rumanía                 45036.0   815.0       754.0   \n",
       "1   China                                      37276.0  1508.0      1356.0   \n",
       "2   Ecuador                                    23953.0   647.0       741.0   \n",
       "3   Venezuela                                  23359.0  1563.0       913.0   \n",
       "4   Colombia                                   22618.0   998.0       717.0   \n",
       "5   Marruecos                                  21909.0  1101.0       390.0   \n",
       "6   Italia                                     20308.0  3030.0      1219.0   \n",
       "7                         Perú                 18829.0   563.0       521.0   \n",
       "8   Paraguay                                   18682.0   364.0       474.0   \n",
       "9   República Dominicana                       17511.0   365.0       654.0   \n",
       "10  Honduras                                   15981.0   149.0       228.0   \n",
       "11  Bolivia                                    14930.0   284.0       407.0   \n",
       "12  Filipinas                                  12628.0  1344.0       640.0   \n",
       "13  Portugal                                    9860.0   769.0       372.0   \n",
       "14  Francia                                     9561.0  1608.0       455.0   \n",
       "15  Ucrania                                     9453.0   152.0       214.0   \n",
       "16  Brasil                                      9324.0   677.0       309.0   \n",
       "17  Bulgaria                                    7842.0   262.0       137.0   \n",
       "18   Estados Unidos de América                  6791.0  1637.0       406.0   \n",
       "19  Reino Unido                                 5915.0  1274.0       324.0   \n",
       "20  Bangladesh                                  5886.0  2742.0       381.0   \n",
       "21  Cuba                                        5725.0   435.0       243.0   \n",
       "22  Polonia                                     5487.0   246.0       173.0   \n",
       "23  Argentina                                   5061.0   588.0       262.0   \n",
       "24  Alemania                                    4707.0   592.0       199.0   \n",
       "\n",
       "    Retiro  Salamanca  Chamartin  Tetuán  Chamberí  Fuencarral-El Pardo  ...  \\\n",
       "0    480.0      753.0      680.0  1468.0     597.0               1830.0  ...   \n",
       "1    564.0      755.0      652.0  1988.0     816.0               1733.0  ...   \n",
       "2    265.0      619.0      380.0  1395.0     453.0                632.0  ...   \n",
       "3    638.0     1564.0      933.0  1310.0     794.0               1428.0  ...   \n",
       "4    483.0      803.0      551.0   822.0     659.0                999.0  ...   \n",
       "5    184.0      322.0      280.0  1393.0     320.0                930.0  ...   \n",
       "6    840.0     1817.0     1060.0  1194.0    1640.0               1195.0  ...   \n",
       "7    253.0      612.0      419.0   965.0     567.0                805.0  ...   \n",
       "8    237.0      521.0      657.0  3311.0     584.0               1024.0  ...   \n",
       "9    204.0      344.0      322.0  2272.0     443.0                589.0  ...   \n",
       "10   232.0      332.0      337.0   755.0     317.0                863.0  ...   \n",
       "11   182.0      342.0      315.0   576.0     280.0                401.0  ...   \n",
       "12   142.0      578.0      661.0  4473.0     771.0                442.0  ...   \n",
       "13   262.0      695.0      534.0   590.0     509.0                693.0  ...   \n",
       "14   370.0      968.0      554.0   387.0     699.0                366.0  ...   \n",
       "15   133.0      220.0      176.0   221.0     149.0                312.0  ...   \n",
       "16   244.0      431.0      280.0   567.0     322.0                361.0  ...   \n",
       "17   115.0      113.0      123.0   245.0      74.0                316.0  ...   \n",
       "18   385.0      749.0      389.0   300.0     657.0                297.0  ...   \n",
       "19   256.0      550.0      466.0   329.0     501.0                313.0  ...   \n",
       "20    33.0       32.0       21.0   210.0      48.0                 27.0  ...   \n",
       "21   111.0      169.0      161.0   257.0     175.0                305.0  ...   \n",
       "22   117.0      138.0      130.0   220.0     151.0                184.0  ...   \n",
       "23   186.0      323.0      208.0   271.0     291.0                261.0  ...   \n",
       "24   175.0      494.0      442.0   245.0     367.0                607.0  ...   \n",
       "\n",
       "     Usera  Puente de Vallecas  Moratalaz  Ciudad Lineal  Hortaleza  \\\n",
       "0   2241.0              4784.0     1286.0         2888.0     1466.0   \n",
       "1   9207.0              3602.0      564.0         1960.0     1104.0   \n",
       "2   1806.0              3290.0      491.0         2471.0      401.0   \n",
       "3    875.0              1829.0      480.0         1858.0     1434.0   \n",
       "4   1752.0              1733.0      482.0         1792.0      910.0   \n",
       "5    942.0              3437.0      258.0         1011.0      426.0   \n",
       "6    412.0               704.0      310.0         1258.0     1109.0   \n",
       "7   1131.0              2079.0      668.0         1726.0      603.0   \n",
       "8    727.0              1354.0      360.0         1619.0      583.0   \n",
       "9   1202.0              1989.0      223.0         1581.0      359.0   \n",
       "10  1115.0              2483.0      281.0         1062.0      349.0   \n",
       "11  2827.0              1573.0      227.0         1086.0      479.0   \n",
       "12   225.0               418.0       76.0          590.0      300.0   \n",
       "13   283.0               597.0      173.0          612.0      727.0   \n",
       "14    66.0               138.0       85.0          599.0     1802.0   \n",
       "15   428.0               912.0      239.0          348.0      282.0   \n",
       "16   410.0               604.0      201.0          421.0      388.0   \n",
       "17   395.0               760.0      285.0          780.0      283.0   \n",
       "18    86.0               139.0      232.0          252.0      206.0   \n",
       "19    81.0               142.0       59.0          285.0      319.0   \n",
       "20   175.0               649.0       23.0          179.0       27.0   \n",
       "21   301.0               513.0      118.0          483.0      248.0   \n",
       "22   298.0               424.0       88.0          266.0      194.0   \n",
       "23   230.0               319.0       74.0          317.0      270.0   \n",
       "24    60.0                93.0       39.0          240.0      327.0   \n",
       "\n",
       "    Villaverde  Villa de Vallecas  Vicálvaro  San Blas-Canillejas  Barajas  \n",
       "0       3646.0             3384.0     2606.0               2929.0    661.0  \n",
       "1       1236.0              685.0      472.0                972.0    190.0  \n",
       "2       2017.0              498.0      439.0               1015.0    138.0  \n",
       "3        909.0              762.0      321.0               1486.0    314.0  \n",
       "4       1618.0              740.0      384.0               1282.0    258.0  \n",
       "5       3372.0             1655.0      802.0                649.0    333.0  \n",
       "6        330.0              427.0      189.0                786.0    337.0  \n",
       "7       1280.0              564.0      338.0                810.0    106.0  \n",
       "8        870.0              217.0      199.0                581.0    151.0  \n",
       "9       1881.0              296.0      151.0                889.0    103.0  \n",
       "10      1040.0              387.0      165.0                598.0     62.0  \n",
       "11       737.0              222.0      159.0                454.0     71.0  \n",
       "12       140.0               25.0       24.0                152.0     30.0  \n",
       "13       302.0              262.0      168.0                565.0    191.0  \n",
       "14        68.0               59.0       52.0                357.0    197.0  \n",
       "15      1649.0              256.0      152.0                390.0     56.0  \n",
       "16       417.0              223.0       81.0                308.0     91.0  \n",
       "17       516.0              261.0      269.0                662.0     92.0  \n",
       "18        54.0               51.0       33.0                101.0     57.0  \n",
       "19        61.0               49.0       27.0                120.0     74.0  \n",
       "20       489.0               57.0       32.0                 78.0      2.0  \n",
       "21       362.0              175.0       88.0                325.0     78.0  \n",
       "22       506.0              164.0      273.0                213.0     62.0  \n",
       "23       162.0              119.0       50.0                157.0     75.0  \n",
       "24        39.0               46.0       25.0                130.0     89.0  \n",
       "\n",
       "[25 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns = ['Unnamed: 0'], inplace = True)\n",
    "data.columns = ['Country of Procedence', 'Total Ciudad de Madrid', 'Centro', 'Arganzuela', 'Retiro', 'Salamanca', 'Chamartin',\n",
    "                'Tetuán', 'Chamberí', 'Fuencarral-El Pardo', 'Moncloa-Aravaca', 'Latina', 'Carabanchel',\n",
    "                'Usera', 'Puente de Vallecas', 'Moratalaz', 'Ciudad Lineal', 'Hortaleza', 'Villaverde',\n",
    "                'Villa de Vallecas', 'Vicálvaro', 'San Blas-Canillejas', 'Barajas']\n",
    "data.to_csv('Madrid Neighborhoods.csv')\n",
    "data.head(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the look of part of the actual data that will be used to tackle this optimal business location problem. In combination with the Foursquare API data, it should be enough to cary out a good analytical approach to solve this problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Target Audience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target audience of this project could be any business owner that is planning to open a new business local, restaurant, real state agency, shops, etc... Since this approach could be aplicable not only to mexican food restaurant but to other kind of businesses, anybody who is considering to place a new business local or even relocate it, could beneficiate of this project's approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "conda-env-python-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}