{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Office Lease Rates in Manhattan\n",
    "\n",
    "Manhattan is a pretty interesting place, and like all major cities it has an abundance of office buildings. Sometimes you might find yourself in need of your own office, at which point you will either start one in your home or garage, or look for a rental.\n",
    "\n",
    "However, many office rental listings don't include the price, but many years of research and all of human history suggests that price is one of the most important factors in any purchasing decision. Fortunately, there many listings that do include the price, so we can try to construct a model that estimates the missing data.\n",
    "\n",
    "The data was obtained from a major commercial real estate site in December 2017, and will not be made available here.\n",
    "\n",
    "Contents:\n",
    "\n",
    "<ul>\n",
    "<li>Overview</li>\n",
    "<li>Cleaning and Exploration</li>\n",
    "<li>Feature Discussion</li>\n",
    "<li>Model Fitting</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the 1369 rental rates in $/sf/yr, 830 are \"Not Disclosed\", for a split of 39.4% disclosed, 60.6% not disclosed. This is not great but some of the not disclosed listings are actually in the same building as disclosed listings, so let's claim to have an even 40% of the price data.\n",
    "\n",
    "The target variable is <b>rate</b>, while the features are:\n",
    "<ul>\n",
    "<li>name: the address of the building the rental unit is located in</li>\n",
    "<li>building_sqft: the total floor space of the building</li>\n",
    "<li>property_type: the vast majority are pure office buildings, but some may be medical, industrial or retail oriented</li>\n",
    "<li>building_class: the DOMA classification from A (best) to C (worst). Only available for pure office buildings</li>\n",
    "<li>building_age: the age in years</li>\n",
    "<li>walk_score: the ease of walking near the building, based on nearby amenities</li>\n",
    "<li>transit_score: ease of access to public transportation.</li>\n",
    "<li>space_name: the name of the rental unit, usually the floor it's on</li>\n",
    "<li>space_sqft: the floor space of the rental unit</li>\n",
    "<li>service_type: the amount of utilities provided</li>\n",
    "<li>space_use: yes, they're all offices, but there are different kinds of offices</li>\n",
    "</ul>\n",
    "\n",
    "This is not a huge number of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from geopy.geocoders import GoogleV3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sys\n",
    "import re\n",
    "import json\n",
    "from time import sleep\n",
    "import unicodedata\n",
    "geolocator = GoogleV3()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease = pd.read_csv('new-york-ny-lease-office_2017-12-23.csv', index_col=0)\n",
    "addr_list=np.ndarray.tolist(ny_office_lease.name.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1369"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ny_office_lease)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Not Disclosed    830\n",
       "$65.00 /SF/Yr     30\n",
       "$55.00 /SF/Yr     24\n",
       "Name: rate, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_office_lease['rate'].value_counts().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only 450 buildings, an average of over 3 listings per building."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "450"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len (addr_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "225"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.ndarray.tolist(ny_office_lease[ny_office_lease['rate']!='Not Disclosed'].name.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the data is not ideal, but it is fairly complete with very few missing values in the features. We can certainly obtain more, by using multiple sources and websites and time periods, and compiling them all together. But let's see how information we can extract from these observations with these features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning, Setting Types, Creating Classifications"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We have the addresses of each property, which means we can obtain the geographic coordinates. With those, we can label each building with a location classification. It is reasonable to assume that location has a major effect on price.\n",
    "\n",
    "For the other variables, it is mainly a matter of extracting useful numeric information from strings, and filling in a few missing walk and transit scores that are available from walkscore.com, but for some reason not included in the listings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Name/Address - Getting Coordinates and Clustering\n",
    "\n",
    "The number and street name of an address by themselves generally do not mean much (limited exceptions may include Wall Street, and other famous streets), but the address gives us information about the geographic position of a property through its coordinates. By extracting the coordinates, we can classify by geography via clustering methods such as K Means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Getting coordinates for each address (A list of uniques)\n",
    "# The GoogleV3 engine seems to time out much more than Nominatim but it's much better at handling ambiguity (eg. Seventh = 7th)\n",
    "# Exception wrapping is used to retry the query in case of failure, and there is constant saving to make sure no\n",
    "# progress is lost.\n",
    "# The functions coords_wrapper and collect_coords should be combined. There is really no reason for them to be separate\n",
    "# and the constant reloading can be avoided, though for smallish files it's not a big deal.\n",
    "# NOTE: some listings are actually in multiple buildings with an address range such as 191-199. The Google engine returns\n",
    "# a single set of geographic coordinates for the first or last building in that range. This is fine, since they should be\n",
    "# within the same cluster anyway.\n",
    "\n",
    "def get_coord(address):\n",
    "    print ('Getting' , address)\n",
    "    geoloc = geolocator.geocode(address)\n",
    "    sleep(0.5)\n",
    "    print ('Got ', geoloc, '\\n')\n",
    "    return (geoloc.latitude, geoloc.longitude)\n",
    "            \n",
    "def coords_wrapper(addr_list):\n",
    "\n",
    "    # Start at the beginning of addr_list\n",
    "    break_point = 0\n",
    "    \n",
    "    # Open the file if it exists, otherwise create a new empty file\n",
    "    try:\n",
    "        with open('nyc_lease_coordinates.json', 'r') as infile:\n",
    "            uniques = json.load(infile)\n",
    "    except:\n",
    "        with open('nyc_lease_coordinates.json', 'w') as outfile:\n",
    "            uniques={}\n",
    "            json.dump(uniques,outfile)\n",
    "    \n",
    "    # do while there are still addresses to process\n",
    "    while(break_point < len(addr_list)):\n",
    "        # run collect_coords starting with the current breakpoint, and gets the\n",
    "        # address that it's stuck on\n",
    "        addr = addr_list[break_point]\n",
    "        try: \n",
    "            try:\n",
    "                uniques[addr] = get_coord(addr + ' Manhattan')\n",
    "                # If it works, get to the next one. Once break_point gets to the length of the list, the conditions of\n",
    "                # the loop will no longer be satisfied, and the loop ends.\n",
    "                break_point +=1\n",
    "                sleep(0.5)\n",
    "            except:\n",
    "                # If it doesn't work, don't increase the break_point and try again\n",
    "                print ('Could not get ', addr)\n",
    "                sleep(0.5)\n",
    "        except:\n",
    "            with open('nyc_lease_coordinates.json','w') as outfile:\n",
    "                json.dump(uniques,outfile)\n",
    "            sleep(0.5)\n",
    "\n",
    "    with open('nyc_lease_coordinates.json','w') as outfile:\n",
    "        json.dump(uniques,outfile)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease = pd.read_csv('new-york-ny-lease-office_2017-12-23.csv', index_col=0)\n",
    "addr_list=np.ndarray.tolist(ny_office_lease.name.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#coords_wrapper(addr_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "with open('nyc_lease_coordinates.json', 'r') as infile:\n",
    "    uniques = json.load(infile)\n",
    "\n",
    "ny_office_lease['coords'] = ny_office_lease['name'].map(uniques)\n",
    "coords=pd.DataFrame(addr_list)[0].map(uniques)\n",
    "coords = coords.values.tolist()\n",
    "lats = [i[0] for i in coords]\n",
    "longs = [i[1] for i in coords]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this list of unique coordinates, we can use K Means to cluster them around a yet to be determined number of centroids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import linalg\n",
    "from sklearn.cluster import KMeans\n",
    "from scipy.spatial.distance import cdist\n",
    "from scipy.spatial import ConvexHull\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do an elbow analysis to see the rate of decline of the sum of squared errors from a centroid as the number of centroids increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'SSE vs k')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bwixpIHA1cDywGnhEUl1ElE7anQG8GhHjJU0FrgA+AWwGLgEOz14tTYuIls/KVzpXr3fO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\nzMwsNw4yZmaWm/8PRHzCKjjLU08AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2937495b080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SSE = []\n",
    "for k in range(1,25):\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km.fit(coords)\n",
    "    labels = km.labels_\n",
    "    centroids = km.cluster_centers_\n",
    "    # Get the SSE\n",
    "    SSE.append(sum(np.min(cdist(coords, centroids, 'euclidean'),axis=1))/len(coords))\n",
    "    \n",
    "plt.plot(range(1,25),SSE, 'bo--')\n",
    "plt.xlabel('k clusters')\n",
    "plt.ylabel('SSE')\n",
    "plt.title('SSE vs k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no standout elbow, but let's try 7, as the returns diminish noticeably after that (well, sort of noticeably)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k = 7\n",
    "km = KMeans(n_clusters=k)\n",
    "km.fit(coords)\n",
    "labels = km.labels_\n",
    "centroids = km.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x293771b15f8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iM2jv3sznu7t9/viZcrr18J/97MzVQ6afAr2IyAxqacl8fsECePjhzK3sfOrt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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29376f8fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['r', 'g', 'b', 'y', 'c', 'm','C0','k']\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.title('Clustered Locations')\n",
    "for j in range(k):\n",
    "    current_coords = np.asarray(coords)[np.where(labels==j)]\n",
    "    hull = ConvexHull(current_coords)\n",
    "    j_longs = current_coords[:,1]\n",
    "    j_lats =  current_coords[:,0]\n",
    "    plt.plot(j_longs, j_lats, '%so' % colors[j], label=j)\n",
    "    for simplex in hull.simplices:\n",
    "        #plt.plot(current_coords[simplex,1],current_coords[simplex,0], '%s--'%colors[j],alpha=0.3)\n",
    "        plt.plot(current_coords[simplex,1],current_coords[simplex,0], '--', color=colors[j],alpha=0.3)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['location_cluster'] = ny_office_lease.name.map(dict(zip(addr_list,labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we knew all the prices, that would be a very useful third dimension in the clustering, and the 2 dimensional geographic representation will not necessary have contingious clusters (such as in this <a href=\"http://nbviewer.jupyter.org/github/keithqu/dsp/blob/master/gasprices/gas%20prices.ipynb\">gas prices example</a>). Since the whole point is to estimate unknown rates, we can't do that.\n",
    "\n",
    "Neighbourhood census data may also give useful information for groupings, as well as for the overall analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location_cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215.0</td>\n",
       "      <td>357844.204651</td>\n",
       "      <td>328220.972457</td>\n",
       "      <td>1333.0</td>\n",
       "      <td>102150.0</td>\n",
       "      <td>231647.0</td>\n",
       "      <td>564196.00</td>\n",
       "      <td>2038201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>123.0</td>\n",
       "      <td>811344.552846</td>\n",
       "      <td>588606.701623</td>\n",
       "      <td>10836.0</td>\n",
       "      <td>417068.0</td>\n",
       "      <td>647924.0</td>\n",
       "      <td>984000.00</td>\n",
       "      <td>3600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>65448.777778</td>\n",
       "      <td>102196.190915</td>\n",
       "      <td>2498.0</td>\n",
       "      <td>17987.5</td>\n",
       "      <td>39500.0</td>\n",
       "      <td>46865.00</td>\n",
       "      <td>345000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>278.0</td>\n",
       "      <td>175994.208633</td>\n",
       "      <td>308523.701281</td>\n",
       "      <td>4614.0</td>\n",
       "      <td>57152.0</td>\n",
       "      <td>107343.0</td>\n",
       "      <td>212000.00</td>\n",
       "      <td>2800118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.0</td>\n",
       "      <td>91648.578947</td>\n",
       "      <td>136895.518663</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>26000.0</td>\n",
       "      <td>80508.25</td>\n",
       "      <td>520000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>547.0</td>\n",
       "      <td>485963.897623</td>\n",
       "      <td>488987.146443</td>\n",
       "      <td>5504.0</td>\n",
       "      <td>133700.0</td>\n",
       "      <td>293878.0</td>\n",
       "      <td>765911.00</td>\n",
       "      <td>3143291.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>141.0</td>\n",
       "      <td>142820.546099</td>\n",
       "      <td>214999.377540</td>\n",
       "      <td>3875.0</td>\n",
       "      <td>19847.0</td>\n",
       "      <td>42000.0</td>\n",
       "      <td>189389.00</td>\n",
       "      <td>1163051.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count           mean            std      min       25%  \\\n",
       "location_cluster                                                           \n",
       "0                 215.0  357844.204651  328220.972457   1333.0  102150.0   \n",
       "1                 123.0  811344.552846  588606.701623  10836.0  417068.0   \n",
       "2                  27.0   65448.777778  102196.190915   2498.0   17987.5   \n",
       "3                 278.0  175994.208633  308523.701281   4614.0   57152.0   \n",
       "4                  38.0   91648.578947  136895.518663   3200.0   10500.0   \n",
       "5                 547.0  485963.897623  488987.146443   5504.0  133700.0   \n",
       "6                 141.0  142820.546099  214999.377540   3875.0   19847.0   \n",
       "\n",
       "                       50%        75%        max  \n",
       "location_cluster                                  \n",
       "0                 231647.0  564196.00  2038201.0  \n",
       "1                 647924.0  984000.00  3600000.0  \n",
       "2                  39500.0   46865.00   345000.0  \n",
       "3                 107343.0  212000.00  2800118.0  \n",
       "4                  26000.0   80508.25   520000.0  \n",
       "5                 293878.0  765911.00  3143291.0  \n",
       "6                  42000.0  189389.00  1163051.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_office_lease.groupby('location_cluster')['building_sqft'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# cluster 2 roughly corresponds to the area around Central Park (the empty space is noticeable), with the largest buildings\n",
    "# next up is cluster 3, which contains the World Trade Center and Wall Street."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# There are some bugs with gmplot: the proper colors only show up when zoomed in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rates\n",
    "\n",
    "Clear up the undisclosed rates to make them empty strings, and convert the string rates into floats. When there is a price range, always use the lowest (and for consistency do the same when there is an area range for each rental space)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['rate'] = ny_office_lease['rate'].replace('Not Disclosed','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get the highest rate from the range of rates if it exists, otherwise keep it at ''\n",
    "def get_float_rate (string_rate):\n",
    "    if string_rate != '':\n",
    "        float_rate = float(string_rate.strip().split()[0].replace('$',''))\n",
    "    else:\n",
    "        float_rate = ''\n",
    "    return float_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['rate'] = ny_office_lease['rate'].apply(lambda r: get_float_rate(r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Now convert the empty cells to null to make the rate column consistently float64\n",
    "ny_office_lease['rate'] = ny_office_lease['rate'].replace('',pd.NaT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['rate']=pd.to_numeric(ny_office_lease['rate'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Average rates by cluster, and descriptive')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T103gTe2sR5KkuvBMdpIkVZABL0lSBRnwkiRVkAEvSVIFGfCSJFWQAS9JUgUZ8JIkVZAB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\n8Z9c9TY6Osq3v3kDy494TNu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      "text/plain": [
       "<matplotlib.figure.Figure at 0x29379c11160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(ncols=1, figsize=(8,8))\n",
    "sns.boxplot(x='location_cluster',y='rate',data=ny_office_lease)\n",
    "plt.title('Average rates by cluster, and descriptive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the rate column appropriately convert to float, we can get an idea of the prices in each region.\n",
    "\n",
    "It seems like there are enough differences in the prices to warrant having these classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building Class\n",
    "\n",
    "BOMA defines building classes as follows:\n",
    "\n",
    ">Class A<br>\n",
    ">Most prestigious buildings competing for premier office users with rents above average for the area. Buildings have high quality standard finishes, state of the art systems, exceptional accessibility and a definite market presence.\n",
    "\n",
    ">Class B<br>\n",
    ">Buildings competing for a wide range of users with rents in the average range for the area. Building finishes are fair to good >for the area. Building finishes are fair to good for the area and systems are adequate, but the building does not compete with >Class A at the same price.\n",
    "\n",
    ">Class C<br>\n",
    ">Buildings competing for tenants requiring functional space at rents below the average for the area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location_cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>181</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>250</td>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>530</td>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>102</td>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count unique top freq\n",
       "location_cluster                      \n",
       "0                  181      3   A  139\n",
       "1                  121      3   A   75\n",
       "2                   14      2   B    8\n",
       "3                  250      3   B  144\n",
       "4                   13      2   C   12\n",
       "5                  530      3   A  269\n",
       "6                  102      3   B   60"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_office_lease.groupby('location_cluster')['building_class'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are also many buildings with no classification, but it can be see that the unclassified buildings are not pure office properties, and so assigning them our own classification 'D' might actually serve some purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease.loc[ny_office_lease['building_class'].isnull(),'building_class'] = 'D'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that on average, classes A, B and C are appropriately priced based on their rated quality, but there are huge major outliers for classes B and C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'All rates by building class')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7lT/+k2QNwrNkvejTKub/O3++RyI7nLdVL9u3BVkS8TzZcJG/0HMDdwlZo/tI/rfqTOiU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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29377187320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(ncols=2, figsize=(12,8))\n",
    "plt.subplot(1,2,1)\n",
    "sns.boxplot(x='building_class',y='rate',data=ny_office_lease)\n",
    "plt.title('Average rates by building class, and descriptive')\n",
    "plt.subplot(1,2,2)\n",
    "sns.swarmplot(x='building_class',y='rate',data=ny_office_lease)\n",
    "plt.title('All rates by building class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Walk and Transit Scores\n",
    "\n",
    "Walk and transit scores are composites of features such as distance from public transportation, number of intersections, and traffic on a scale from 0 (worst) to 100 (best)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conventiently, there are no buildings with one but not the other. They are also all in building_class D, so they are not pure office buildings. The scores actually seem to be available from walkscores.com, so we can input them manually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease.loc[671,'walk_score'] = 97\n",
    "ny_office_lease.loc[671,'transit_score'] = 100\n",
    "\n",
    "ny_office_lease.loc[678,'walk_score'] = 99\n",
    "ny_office_lease.loc[678,'transit_score'] = 100\n",
    "ny_office_lease.loc[679,'walk_score'] = 99\n",
    "ny_office_lease.loc[679,'transit_score'] = 100\n",
    "ny_office_lease.loc[680,'walk_score'] = 99\n",
    "ny_office_lease.loc[680,'transit_score'] = 100\n",
    "\n",
    "ny_office_lease.loc[973,'walk_score'] = 98\n",
    "ny_office_lease.loc[973,'transit_score'] = 100\n",
    "\n",
    "ny_office_lease.loc[1043,'walk_score'] = 99\n",
    "ny_office_lease.loc[1043,'transit_score'] = 100\n",
    "ny_office_lease.loc[1044,'walk_score'] = 99\n",
    "ny_office_lease.loc[1044,'transit_score'] = 100\n",
    "ny_office_lease.loc[1045,'walk_score'] = 99\n",
    "ny_office_lease.loc[1045,'transit_score'] = 100\n",
    "ny_office_lease.loc[1046,'walk_score'] = 99\n",
    "ny_office_lease.loc[1046,'transit_score'] = 100\n",
    "\n",
    "ny_office_lease.loc[1320,'walk_score'] = 46\n",
    "ny_office_lease.loc[1320,'transit_score'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>95.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>0.572016</td>\n",
       "      <td>97.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>97.074074</td>\n",
       "      <td>2.368478</td>\n",
       "      <td>92.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>278.0</td>\n",
       "      <td>98.967626</td>\n",
       "      <td>3.487320</td>\n",
       "      <td>46.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.0</td>\n",
       "      <td>97.315789</td>\n",
       "      <td>2.302822</td>\n",
       "      <td>92.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>547.0</td>\n",
       "      <td>99.475320</td>\n",
       "      <td>0.974413</td>\n",
       "      <td>88.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>141.0</td>\n",
       "      <td>99.836879</td>\n",
       "      <td>0.407503</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count       mean       std   min    25%    50%    75%    max\n",
       "location_cluster                                                              \n",
       "0                 215.0  99.800000  0.612944  95.0  100.0  100.0  100.0  100.0\n",
       "1                 123.0  99.796748  0.572016  97.0  100.0  100.0  100.0  100.0\n",
       "2                  27.0  97.074074  2.368478  92.0   96.0   98.0   99.0  100.0\n",
       "3                 278.0  98.967626  3.487320  46.0   99.0  100.0  100.0  100.0\n",
       "4                  38.0  97.315789  2.302822  92.0   97.0   98.0   99.0  100.0\n",
       "5                 547.0  99.475320  0.974413  88.0   99.0  100.0  100.0  100.0\n",
       "6                 141.0  99.836879  0.407503  98.0  100.0  100.0  100.0  100.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_office_lease.groupby('location_cluster')['walk_score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <td>215.0</td>\n",
       "      <td>99.944186</td>\n",
       "      <td>0.330181</td>\n",
       "      <td>98.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>123.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>96.777778</td>\n",
       "      <td>3.522819</td>\n",
       "      <td>90.0</td>\n",
       "      <td>92.5</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>278.0</td>\n",
       "      <td>99.640288</td>\n",
       "      <td>5.997601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.0</td>\n",
       "      <td>99.763158</td>\n",
       "      <td>0.998220</td>\n",
       "      <td>94.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
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       "      <th>5</th>\n",
       "      <td>547.0</td>\n",
       "      <td>99.983547</td>\n",
       "      <td>0.311124</td>\n",
       "      <td>93.0</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>141.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
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      ],
      "text/plain": [
       "                  count        mean       std    min    25%    50%    75%  \\\n",
       "location_cluster                                                            \n",
       "0                 215.0   99.944186  0.330181   98.0  100.0  100.0  100.0   \n",
       "1                 123.0  100.000000  0.000000  100.0  100.0  100.0  100.0   \n",
       "2                  27.0   96.777778  3.522819   90.0   92.5   99.0   99.0   \n",
       "3                 278.0   99.640288  5.997601    0.0  100.0  100.0  100.0   \n",
       "4                  38.0   99.763158  0.998220   94.0  100.0  100.0  100.0   \n",
       "5                 547.0   99.983547  0.311124   93.0  100.0  100.0  100.0   \n",
       "6                 141.0  100.000000  0.000000  100.0  100.0  100.0  100.0   \n",
       "\n",
       "                    max  \n",
       "location_cluster         \n",
       "0                 100.0  \n",
       "1                 100.0  \n",
       "2                  99.0  \n",
       "3                 100.0  \n",
       "4                 100.0  \n",
       "5                 100.0  \n",
       "6                 100.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_office_lease.groupby('location_cluster')['transit_score'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In general these scores in Manhattan are extremely high."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building Size\n",
    "\n",
    "The data does not contain the number of floors for each building, but in this region there should be a high correlation between total floor area and height. There is in nothing in Manhattan like the Pentagon, which has over 6.6 million square feet of floor space and only 7 above-ground floors.\n",
    "\n",
    "We can immediately see a correlation between average size and building class for classes A, B and C.\n",
    "\n",
    "If this is included as a feature for prices, we can either use arbitrary size classes, or use a combination of measuring by 1000 square feet and normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>93.0</td>\n",
       "      <td>83.246129</td>\n",
       "      <td>57.135397</td>\n",
       "      <td>40.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>62.00</td>\n",
       "      <td>86.0</td>\n",
       "      <td>330.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>229.0</td>\n",
       "      <td>72.030131</td>\n",
       "      <td>65.104618</td>\n",
       "      <td>6.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>70.0</td>\n",
       "      <td>720.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>139.0</td>\n",
       "      <td>54.680647</td>\n",
       "      <td>47.420259</td>\n",
       "      <td>21.33</td>\n",
       "      <td>44.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>584.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>78.0</td>\n",
       "      <td>69.633333</td>\n",
       "      <td>38.699070</td>\n",
       "      <td>11.96</td>\n",
       "      <td>44.55</td>\n",
       "      <td>59.03</td>\n",
       "      <td>78.0</td>\n",
       "      <td>240.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count       mean        std    min    25%    50%   75%     max\n",
       "building_class                                                                \n",
       "A                93.0  83.246129  57.135397  40.00  54.00  62.00  86.0  330.61\n",
       "B               229.0  72.030131  65.104618   6.00  50.00  60.00  70.0  720.00\n",
       "C               139.0  54.680647  47.420259  21.33  44.00  49.00  58.0  584.28\n",
       "D                78.0  69.633333  38.699070  11.96  44.55  59.03  78.0  240.00"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_office_lease.groupby('building_class')['rate'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should log linearize building size, since this effectively removes outliers and smooths the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['log_building_sqft'] = np.log(ny_office_lease['building_sqft'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>528.0</td>\n",
       "      <td>13.312452</td>\n",
       "      <td>0.709457</td>\n",
       "      <td>9.903488</td>\n",
       "      <td>12.886641</td>\n",
       "      <td>13.350614</td>\n",
       "      <td>13.696522</td>\n",
       "      <td>15.096444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>490.0</td>\n",
       "      <td>11.748973</td>\n",
       "      <td>0.972249</td>\n",
       "      <td>8.579604</td>\n",
       "      <td>11.230775</td>\n",
       "      <td>11.859030</td>\n",
       "      <td>12.495004</td>\n",
       "      <td>13.966557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>193.0</td>\n",
       "      <td>10.782073</td>\n",
       "      <td>1.058689</td>\n",
       "      <td>7.823246</td>\n",
       "      <td>9.865266</td>\n",
       "      <td>11.068153</td>\n",
       "      <td>11.617285</td>\n",
       "      <td>12.873715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>158.0</td>\n",
       "      <td>10.332363</td>\n",
       "      <td>1.238189</td>\n",
       "      <td>7.195187</td>\n",
       "      <td>9.423839</td>\n",
       "      <td>10.277442</td>\n",
       "      <td>10.995277</td>\n",
       "      <td>13.243157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count       mean       std       min        25%        50%  \\\n",
       "building_class                                                               \n",
       "A               528.0  13.312452  0.709457  9.903488  12.886641  13.350614   \n",
       "B               490.0  11.748973  0.972249  8.579604  11.230775  11.859030   \n",
       "C               193.0  10.782073  1.058689  7.823246   9.865266  11.068153   \n",
       "D               158.0  10.332363  1.238189  7.195187   9.423839  10.277442   \n",
       "\n",
       "                      75%        max  \n",
       "building_class                        \n",
       "A               13.696522  15.096444  \n",
       "B               12.495004  13.966557  \n",
       "C               11.617285  12.873715  \n",
       "D               10.995277  13.243157  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_office_lease.groupby('building_class')['log_building_sqft'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2937a0d6eb8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iSVoCjxSVpEI0761+nRD6nSoY9nipBG6hS1IhLHRJKoRTLg1T2p4nTn1oJVtpr0e30CWp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\nQljoklQIC12SCmGhS1Ih/h8PYeZ5J5CDiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29379e8a6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ny_office_lease['log_building_sqft'].hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Log building size by cluster and building class')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DvqmEHHtC0vI6xwMAqA9yNgCkrJoW6a1m9iVJNypq6bhA0h2pRAUAqFVb5uxK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29379f56c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(ncols=2,figsize=(12,8))\n",
    "plt.subplot(1,2,1)\n",
    "sns.boxplot(x='location_cluster',y='log_building_sqft',data=ny_office_lease)\n",
    "plt.title('Log building size by cluster')\n",
    "plt.subplot(1,2,2)\n",
    "sns.boxplot(x='building_class',y='log_building_sqft',data=ny_office_lease)\n",
    "plt.title('Log building size by cluster and building class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The largest building, 55 Water St, was the largest office building in the world when it was completed in 1972. The smallest, at 1005 Second Ave, is a narrow 4-storey storefront with an Italian restaurant on the ground floor, a second floor spa, and an apartment occupying the top 2 floors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rental Unit Area\n",
    "\n",
    "Like with rental rates, I'm simply going to use the highest unit area when there is a range. Almost all of them have an undisclosed price, so we're estimating anyway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['space_sqft'] = ny_office_lease['space_sqft'].apply(lambda a:int(a.strip().split()[0].replace(',','')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>building_class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>528.0</td>\n",
       "      <td>10821.517045</td>\n",
       "      <td>15985.617475</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1853.75</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>16445.25</td>\n",
       "      <td>260000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>490.0</td>\n",
       "      <td>3894.195918</td>\n",
       "      <td>4723.911066</td>\n",
       "      <td>40.0</td>\n",
       "      <td>909.50</td>\n",
       "      <td>2332.0</td>\n",
       "      <td>5082.75</td>\n",
       "      <td>30216.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>193.0</td>\n",
       "      <td>2854.839378</td>\n",
       "      <td>2682.508900</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1000.00</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>3750.00</td>\n",
       "      <td>16909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>158.0</td>\n",
       "      <td>3102.310127</td>\n",
       "      <td>3051.524720</td>\n",
       "      <td>144.0</td>\n",
       "      <td>833.25</td>\n",
       "      <td>1814.0</td>\n",
       "      <td>4300.00</td>\n",
       "      <td>14290.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count          mean           std    min      25%     50%  \\\n",
       "building_class                                                              \n",
       "A               528.0  10821.517045  15985.617475   40.0  1853.75  5879.0   \n",
       "B               490.0   3894.195918   4723.911066   40.0   909.50  2332.0   \n",
       "C               193.0   2854.839378   2682.508900   70.0  1000.00  2200.0   \n",
       "D               158.0   3102.310127   3051.524720  144.0   833.25  1814.0   \n",
       "\n",
       "                     75%       max  \n",
       "building_class                      \n",
       "A               16445.25  260000.0  \n",
       "B                5082.75   30216.0  \n",
       "C                3750.00   16909.0  \n",
       "D                4300.00   14290.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_office_lease.groupby('building_class')['space_sqft'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location_cluster</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>215.0</td>\n",
       "      <td>4928.986047</td>\n",
       "      <td>6196.903267</td>\n",
       "      <td>100.0</td>\n",
       "      <td>972.00</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>6078.50</td>\n",
       "      <td>29802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>123.0</td>\n",
       "      <td>10046.349593</td>\n",
       "      <td>14194.631795</td>\n",
       "      <td>40.0</td>\n",
       "      <td>614.00</td>\n",
       "      <td>3186.0</td>\n",
       "      <td>11103.50</td>\n",
       "      <td>57399.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4009.111111</td>\n",
       "      <td>2849.915172</td>\n",
       "      <td>195.0</td>\n",
       "      <td>1464.50</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5202.50</td>\n",
       "      <td>8750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>278.0</td>\n",
       "      <td>3768.176259</td>\n",
       "      <td>4332.201566</td>\n",
       "      <td>50.0</td>\n",
       "      <td>902.25</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>5625.00</td>\n",
       "      <td>26782.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.0</td>\n",
       "      <td>10474.342105</td>\n",
       "      <td>41694.292211</td>\n",
       "      <td>400.0</td>\n",
       "      <td>1037.50</td>\n",
       "      <td>3625.0</td>\n",
       "      <td>6034.75</td>\n",
       "      <td>260000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>547.0</td>\n",
       "      <td>7058.681901</td>\n",
       "      <td>8902.770520</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1171.00</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>8997.50</td>\n",
       "      <td>45245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>141.0</td>\n",
       "      <td>6756.808511</td>\n",
       "      <td>10383.836907</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>6400.00</td>\n",
       "      <td>51576.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count          mean           std    min      25%     50%  \\\n",
       "location_cluster                                                              \n",
       "0                 215.0   4928.986047   6196.903267  100.0   972.00  2500.0   \n",
       "1                 123.0  10046.349593  14194.631795   40.0   614.00  3186.0   \n",
       "2                  27.0   4009.111111   2849.915172  195.0  1464.50  5000.0   \n",
       "3                 278.0   3768.176259   4332.201566   50.0   902.25  2400.0   \n",
       "4                  38.0  10474.342105  41694.292211  400.0  1037.50  3625.0   \n",
       "5                 547.0   7058.681901   8902.770520   40.0  1171.00  3600.0   \n",
       "6                 141.0   6756.808511  10383.836907  100.0  1500.00  2500.0   \n",
       "\n",
       "                       75%       max  \n",
       "location_cluster                      \n",
       "0                  6078.50   29802.0  \n",
       "1                 11103.50   57399.0  \n",
       "2                  5202.50    8750.0  \n",
       "3                  5625.00   26782.0  \n",
       "4                  6034.75  260000.0  \n",
       "5                  8997.50   45245.0  \n",
       "6                  6400.00   51576.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_office_lease.groupby('location_cluster')['space_sqft'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The largest rental spaces tend to be entire floors or sections of the building. Regardless of location, class A buildings tend to have the largest rental spaces.\n",
    "\n",
    "Interestingly, some of the smallest offices are also class A. These are often executive offices. It seems like the relationship between unit area and rate is not monotonic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Rate by size of unit space')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JA9jMLCcOYDOznDiAzcxy4gA2M8uJA9jMLCd5XwtiQrru7ie44f6n2NtforOtyLvftIz3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\ntBY15Fb3V0NDQrg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XRPhR8yC768bPgeVAG/Aj4FU51+lp4Miaso8BV6TpK4CPpulzgK+T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\nsJlZThzAZmY5+f8BLYZ+2xuatfMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29379fe2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='space_sqft',y='rate',data=ny_office_lease)\n",
    "plt.title('Rate by size of unit space')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generally (very, very generally), larger units are cheaper per square footage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Space Name/Floor\n",
    "This is usually the floor number, which might be useful information. But we need to extract it from the text, which might be an ordeal since there is a lack of consistency. However, it can be seen that the first word of each string contains the proper information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['space_floor']=ny_office_lease['space_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['space_floor'] = ny_office_lease['space_floor'].apply(lambda w: w.strip().split()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease.loc[ny_office_lease['space_floor'] == 'Ground','space_floor'] = '1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Change \"Ground\" and \"Lower\" to \"1\"\n",
    "ny_office_lease.loc[ny_office_lease['space_floor'] == 'Lower','space_floor'] = '1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Now get rid of \"th\", \"rd\", \"st\", \"nd\"\n",
    "ny_office_lease['space_floor']=ny_office_lease['space_floor'].apply(lambda w:w.split(\"th\")[0])\n",
    "ny_office_lease['space_floor']=ny_office_lease['space_floor'].apply(lambda w:w.split(\"rd\")[0])\n",
    "ny_office_lease['space_floor']=ny_office_lease['space_floor'].apply(lambda w:w.split(\"nd\")[0])\n",
    "ny_office_lease['space_floor']=ny_office_lease['space_floor'].apply(lambda w:w.split(\"st\")[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>space_floor</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75.0</td>\n",
       "      <td>87.796133</td>\n",
       "      <td>61.637306</td>\n",
       "      <td>11.96</td>\n",
       "      <td>48.50</td>\n",
       "      <td>66.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>21.0</td>\n",
       "      <td>56.884286</td>\n",
       "      <td>11.167416</td>\n",
       "      <td>36.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>59.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.0</td>\n",
       "      <td>53.240000</td>\n",
       "      <td>8.371858</td>\n",
       "      <td>42.00</td>\n",
       "      <td>47.20</td>\n",
       "      <td>56.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14.0</td>\n",
       "      <td>57.285714</td>\n",
       "      <td>9.522674</td>\n",
       "      <td>44.00</td>\n",
       "      <td>50.25</td>\n",
       "      <td>55.5</td>\n",
       "      <td>65.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.0</td>\n",
       "      <td>59.500000</td>\n",
       "      <td>11.022704</td>\n",
       "      <td>45.00</td>\n",
       "      <td>51.75</td>\n",
       "      <td>61.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count       mean        std    min    25%   50%    75%    max\n",
       "space_floor                                                               \n",
       "1             75.0  87.796133  61.637306  11.96  48.50  66.0  100.0  300.0\n",
       "10            21.0  56.884286  11.167416  36.00  47.00  59.0   65.0   82.0\n",
       "11             5.0  53.240000   8.371858  42.00  47.20  56.0   59.0   62.0\n",
       "12            14.0  57.285714   9.522674  44.00  50.25  55.5   65.0   75.0\n",
       "13             6.0  59.500000  11.022704  45.00  51.75  61.0   65.0   75.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_office_lease.groupby('space_floor')['rate'].describe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How can these be classified? It's going to be somewhat arbitrary:\n",
    ">\"base\": basement\n",
    "\n",
    ">\"lower\": Meazzaine, Concourse, Floors 1-5\n",
    "\n",
    ">\"mid\": 5-14\n",
    "\n",
    ">\"high\": 15-24\n",
    "\n",
    ">\"upper\": 25+, Pent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def floor_class (floor):\n",
    "    if floor == 'Basement':\n",
    "        return 'base'\n",
    "    elif floor == 'Concourse':\n",
    "        return 'lower'\n",
    "    elif floor == 'Mezzanine':\n",
    "        return 'lower'\n",
    "    elif floor == 'Pen':\n",
    "        return 'upper'\n",
    "    else:\n",
    "        floor = int(floor)\n",
    "        if floor < 5:\n",
    "            return 'lower'\n",
    "        elif floor < 15:\n",
    "            return 'mid'\n",
    "        elif floor < 25:\n",
    "            return 'high'\n",
    "        else:\n",
    "            return 'upper'\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['floor_class'] = ny_office_lease['space_floor'].apply(lambda fl:floor_class(fl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Rental rate by floor of rental unit')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jnZZtK8u6iIgzImJWRMxatGhRHUOXpMHBgw0lqTG9PDMfi4hdgZkRce8m6kYP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\niwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2937a46b6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(ncols=2,figsize=(12,8))\n",
    "plt.subplot(1,2,1)\n",
    "sns.boxplot(x='floor_class',y='log_building_sqft',data=ny_office_lease)\n",
    "plt.title('Log building size by floor of rental unit')\n",
    "plt.subplot(1,2,2)\n",
    "sns.swarmplot(x='floor_class',y='rate',data=ny_office_lease)\n",
    "plt.title('Rental rate by floor of rental unit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Service Type\n",
    "Utilities, etc.\n",
    "\n",
    "\"Negotiable\" and \"to be determined\" might be combinable. On the other hand, it might be determined to not be negotiable, so we can only do some limited groupings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# these are fine\n",
    "ny_office_lease.loc[ny_office_lease['service_type'] == 'Modified Gross','service_type'] = 'mod_gross'\n",
    "ny_office_lease.loc[ny_office_lease['service_type'] == 'Plus Electric','service_type'] = 'elec'\n",
    "\n",
    "# put these together\n",
    "ny_office_lease.loc[ny_office_lease['service_type'] == 'Tenant Electric','service_type'] = 'elec'\n",
    "ny_office_lease.loc[ny_office_lease['service_type'] == 'Plus Electric And Cleaning','service_type'] = 'elec'\n",
    "ny_office_lease.loc[ny_office_lease['service_type'] == 'Plus All Utilities','service_type'] = 'elec'\n",
    "\n",
    "# Put net, triple net in net\n",
    "ny_office_lease.loc[ny_office_lease['service_type'] == 'Net','service_type'] = 'net'\n",
    "ny_office_lease.loc[ny_office_lease['service_type'] == 'Triple Net','service_type'] = 'net'\n",
    "\n",
    "# Put tbd, neg in tbd\n",
    "ny_office_lease.loc[ny_office_lease['service_type'] == 'To Be Determined','service_type'] = 'tbd'\n",
    "ny_office_lease.loc[ny_office_lease['service_type'] == 'Negotiable','service_type'] = 'tbd'\n",
    "\n",
    "ny_office_lease.loc[ny_office_lease['service_type'] == 'Full Service Gross','service_type'] = 'fsg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>service_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>elec</th>\n",
       "      <td>165.0</td>\n",
       "      <td>62.591576</td>\n",
       "      <td>46.017399</td>\n",
       "      <td>21.33</td>\n",
       "      <td>48.00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>65.0000</td>\n",
       "      <td>584.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fsg</th>\n",
       "      <td>76.0</td>\n",
       "      <td>98.268289</td>\n",
       "      <td>111.225982</td>\n",
       "      <td>6.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>63.0</td>\n",
       "      <td>91.4775</td>\n",
       "      <td>720.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mod_gross</th>\n",
       "      <td>246.0</td>\n",
       "      <td>64.455610</td>\n",
       "      <td>36.406544</td>\n",
       "      <td>11.96</td>\n",
       "      <td>47.05</td>\n",
       "      <td>56.0</td>\n",
       "      <td>70.0000</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>net</th>\n",
       "      <td>43.0</td>\n",
       "      <td>65.988605</td>\n",
       "      <td>34.812040</td>\n",
       "      <td>25.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>56.0</td>\n",
       "      <td>80.0000</td>\n",
       "      <td>186.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tbd</th>\n",
       "      <td>9.0</td>\n",
       "      <td>86.578889</td>\n",
       "      <td>37.058616</td>\n",
       "      <td>42.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>70.0</td>\n",
       "      <td>117.0000</td>\n",
       "      <td>150.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count       mean         std    min    25%   50%       75%  \\\n",
       "service_type                                                               \n",
       "elec          165.0  62.591576   46.017399  21.33  48.00  58.0   65.0000   \n",
       "fsg            76.0  98.268289  111.225982   6.00  50.00  63.0   91.4775   \n",
       "mod_gross     246.0  64.455610   36.406544  11.96  47.05  56.0   70.0000   \n",
       "net            43.0  65.988605   34.812040  25.00  45.00  56.0   80.0000   \n",
       "tbd             9.0  86.578889   37.058616  42.00  60.00  70.0  117.0000   \n",
       "\n",
       "                 max  \n",
       "service_type          \n",
       "elec          584.28  \n",
       "fsg           720.00  \n",
       "mod_gross     300.00  \n",
       "net           186.00  \n",
       "tbd           150.00  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_office_lease.groupby('service_type')['rate'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Space Use\n",
    ">\"Executive\": Office(Executive Suite), Office/Medical(Executive Suite)\n",
    "\n",
    ">\"Office\": keep\n",
    "\n",
    ">\"Retail\": keep\n",
    "\n",
    ">\"Medical\": keep\n",
    "\n",
    ">\"Mixed\": Office/Medical, Office/Retail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease.loc[ny_office_lease['space_use'] == 'Office(Executive Suite)','space_use'] = 'executive'\n",
    "ny_office_lease.loc[ny_office_lease['space_use'] == 'Office/Medical(Executive Suite)','space_use'] = 'executive'\n",
    "ny_office_lease.loc[ny_office_lease['space_use'] == 'Office/Medical','space_use'] = 'mixed'\n",
    "ny_office_lease.loc[ny_office_lease['space_use'] == 'Office/Retail','space_use'] = 'mixed'\n",
    "ny_office_lease.loc[ny_office_lease['space_use'] == 'Medical','space_use'] = 'mixed'\n",
    "ny_office_lease.loc[ny_office_lease['space_use'] == 'Office','space_use'] = 'office'\n",
    "ny_office_lease.loc[ny_office_lease['space_use'] == 'Retail','space_use'] = 'retail'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['office', 'mixed', 'executive', 'retail'], dtype=object)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ny_office_lease['space_use'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Rate by type by building class')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2937a3dfb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='building_class', y='rate', hue='space_use', data=ny_office_lease)\n",
    "plt.title('Rate by type by building class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retail tends to be the most expensive in all classes but B, but we saw that class B had some major executive office outliers above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building age\n",
    "This seems to be all over the place. There are many old historical buildings that are well maintained and clearly not falling apart. This doesn't seem to be too big of a deal for Manhattan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Within each location cluster, there doesn't seem to be much of a relationship between building age and rental rate either."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,\"Doesn't seem to be anything there\")"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hk432me2PP8PuCNY+8IgHRYxAT7fo5523as/J2A2frX5kr9fKnUja2dru9m82\nvajWNq1Wnu/3Ihsdk7fYBTzQoAVd/PHSAy22vptV7zlb/Tzm9Ysmyre5ZkyIFn3xan61dOro3239\niO1sbXfLuYoyaeUPWiqHoAqGXXKgngcuPI0HLzyt4QFhNOp982r189hoOKg1p6cTfTMXfupUd0uj\nZNhtB4JW+Ktz+7WyTfMT2JP7tCfB563jVvepsegOq/eZa/XzmNfP17mVYab2vJ7uuulmjbo33KVk\n7daL+1RlV5K1ZkJ03XSzRi0Xt4qt3Xpxn5pov4TtFLfozbrQWF8JtFs026LvhXXphI626CW9VdI9\nkjZKWjoWr2FWZu044d0LJ82bPTncC+vSzdqe6CX1AV8CTgVeCbxb0ivb/TpmZdaObphe7MqpJf+l\nbuW/allz2t51I+kE4FMRcUqaXwYQEZ+r9Rh33ZhZPb14onk8dLLrZiawpTC/NZWZmY1Imb6ddMJY\nDEqtNqh9r68NkpYASwDmzPGbZ2a1dcslwnvVWLTotwKzC/OzgIcqK0XEJRExEBED06ZNG4MwzMwM\nxibR/xiYJ+koSfsCZwIrx+B1zMysCW3vuomIXZL+G3AT0Ad8LSJ+0e7XMTOz5ozJhSMiYhVQ+1+B\nzcxs3PgSCGZmJedEb2ZWck70ZmYl1xUXNZM0BGwa5dMcDjzchnDGg2Ntv16JE3on1l6JE3on1nbH\neWRENByf3hWJvh0kDTbzU+Bu4Fjbr1fihN6JtVfihN6JtVNxuuvGzKzknOjNzEquTIn+kk4H0ALH\n2n69Eif0Tqy9Eif0TqwdibM0ffRmZlZdmVr0ZmZWhRO9mVnJlSLRd+t/1EqaLek2SXdL+oWkD6Xy\nQyXdImlDuj+k07HmJPVJ+qmk76b5oyStTbF+M12RtOMkTZV0naRfpe17QjduV0kfSe/9eknfkLR/\nt2xTSV+TtEPS+kJZ1W2ozEXpM3aXpOM7HOf/TO/9XZK+I2lqYdmyFOc9kk4ZrzhrxVpY9jFJIenw\nND9u27TnE32X/0ftLuAvIuIVwALg3BTbUmB1RMwDVqf5bvEh4O7C/OeB5SnWR4FzOhLV3r4IfC8i\nXg4cSxZzV21XSTOBPwcGIuIYsqu5nkn3bNPLgLdWlNXahqcC89JtCXDxOMUI1eO8BTgmIl4D3Ass\nA0ifrzOBV6XH/GPKEePlMvaOFUmzgZOA4r+bj982jYievgEnADcV5pcByzodV41Yb0hv9j3AEans\nCOCeTseWYplF9uF+C/Bdsn8LexiYVG1bdzDOg4AHSIMJCuVdtV15/m81DyW7Uux3gVO6aZsC/cD6\nRtsQ+Cfg3dXqdSLOimX/CbhwCjEOAAACrUlEQVQqTQ/7/JNdLv2ETm7TVHYdWYPkQeDw8d6mPd+i\np0f+o1ZSP/BaYC0wIyK2AaT76Z2LbJgvAH8FPJfmDwMei4hdab5btu1LgCHg66mb6auSDqTLtmtE\n/Br4W7JW3DZgJ7CO7tymuVrbsJs/Z38C/Gua7ro4Jb0D+HVE3FmxaNxiLUOib+o/ajtJ0hTgW8CH\nI+LxTsdTjaS3ATsiYl2xuErVbti2k4DjgYsj4rXAk3RX9xcAqX/7dOAo4MXAgWRf1yt1wzZtpCv3\nBUnnkXWRXpUXVanWsTglvQA4D/hktcVVysYk1jIk+qb+o7ZTJE0mS/JXRcS3U/F2SUek5UcAOzoV\nX8EbgXdIehC4hqz75gvAVEn5H9R0y7bdCmyNiLVp/jqyxN9t2/UPgQciYigingW+Dfw7unOb5mpt\nw677nElaDLwNeE+kvg+6L865ZAf6O9NnaxbwE0kvYhxjLUOi79r/qJUk4FLg7oj4+8KilcDiNL2Y\nrO++oyJiWUTMioh+sm14a0S8B7gNeGeq1i2x/gbYIullqehE4Jd033bdDCyQ9IK0L+Rxdt02Lai1\nDVcCZ6eRIguAnXkXTydIeivwceAdEfFUYdFK4ExJ+0k6iuxE5486ESNARPw8IqZHRH/6bG0Fjk/7\n8Pht0/E8STGGJz8Wkp15vw84r9PxFOL692Rfxe4CfpZuC8n6vlcDG9L9oZ2OtSLuNwPfTdMvIfug\nbAT+Gdiv0/GluI4DBtO2vR44pBu3K3AB8CtgPXAFsF+3bFPgG2TnDp4lS0Dn1NqGZN0MX0qfsZ+T\njSTqZJwbyfq388/Vlwv1z0tx3gOc2ultWrH8QZ4/GTtu29SXQDAzK7kydN2YmVkdTvRmZiXnRG9m\nVnJO9GZmJedEb2ZWck70ZmYl50RvZlZy/x8qR2UXJr8gfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29374334908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ny_office_lease['building_age'], ny_office_lease['rate'], 'o', markersize=2)\n",
    "plt.title(\"Doesn't seem to be anything there\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Transformations\n",
    "\n",
    "\n",
    "### Rate/sqft/year -> Monthly Rent\n",
    "\n",
    "With some significant outliers, overall the rate does not seem to depend too much on unit size, and so on average lease rates increase linearly with square footage. Getting the monthly rate is useful for visualization, because the relationship between unit size and price is generally pretty linear. However, we will not use it in the model or there will significant leakage, and anyway it does not really affect our results based on a mean absolute log deviation metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(27.0568,0.5,'Rate')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Gd2lqGpV8WL8xQsn1KFbGp1O9Vozwrxu62V4KHzP7GeLolq20GB+IgB/wxJbB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\nX8+GOdTmd0WZAFTQoSiKohyXpJR4HR14hcKoNIyvXt+CrolBB+76EuK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955Z3bMmDHNbeO///3vC999992wpmlyyJAhyfPOOy/m8/nkihUrvEcddVQ5gN/vt2fNmrVhX4kO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\nSZIkqVH/C+B3afsAq9JOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2937b77c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ny_office_lease['log_monthly_rate'] = np.log(ny_office_lease['rate'] * ny_office_lease['space_sqft'] / 12)\n",
    "ny_office_lease['log_space_sqft'] = np.log(ny_office_lease['space_sqft'])\n",
    "\n",
    "# let's sneak this in here too\n",
    "ny_office_lease['log_rate'] = np.log(ny_office_lease['rate'])\n",
    "\n",
    "sns.lmplot(x='log_space_sqft',y='log_monthly_rate',hue='space_use',data=ny_office_lease, size=10,aspect=1)\n",
    "plt.title('Rate vs unit size by unit type')\n",
    "plt.xlabel('Unit Size')\n",
    "plt.ylabel('Rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building size relative to cluster mean\n",
    "\n",
    "The size of a building compared to its neighbours could have an effect on price. If building height data or highest floor were known, we could also find the relative height of a building. However, there should be high correlation between total building floor space and height within a cluster (e.g. in a cluster with mostly skyscrapers, it's unlikely there will be a building like the Pentagon, with only 5 above ground storeys but millions of square feet in floors). Size/height past a certain point could have prestige attached to it: a 55 storey building might be more prestigious than a 50 storey one, but 10 and 15 aren't too different.\n",
    "\n",
    "The area variables have already been log transformed, so this variable is just log(building_sqft) - log(cluster mean)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cluster_mean_sqft = dict(zip(range(k),ny_office_lease.groupby('location_cluster')['building_sqft'].describe()['mean']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['cluster_mean_sqft'] = np.log(ny_office_lease['location_cluster'].map(cluster_mean_sqft))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['building_rel_size'] = ny_office_lease['log_building_sqft'] - ny_office_lease['cluster_mean_sqft']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The information given by this new measure is similar, but changes some of the orderings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2937b87eac8>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pJNGI78+ebUM4BFW7fGkuhA15XsOx0QkEowZ0IaBpAgAgpMRSpDL9poHG6cxx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\nZF1LbEoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2937b7cd198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2)=plt.subplots(ncols=2,figsize=(12,8))\n",
    "sns.regplot(x='building_rel_size',y='log_rate',data=ny_office_lease, ax=ax1)\n",
    "\n",
    "sns.regplot(x='log_building_sqft',y='log_rate',data=ny_office_lease, ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fit is still not great, but gives us different information than just the size of the building (the orderings are different)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building size relative to same class\n",
    "My class A building is bigger/better than your class A building! Or is it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "building_class_mean_sqft = dict(ny_office_lease.groupby('building_class')['building_sqft'].describe()['mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['building_class_mean_sqft'] = np.log(ny_office_lease['building_class'].map(building_class_mean_sqft))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['building_rel_to_class'] = ny_office_lease['log_building_sqft'] - ny_office_lease['building_class_mean_sqft']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2937bce8cf8>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EXYeIG6SqPP/WyKx55iIiF+TglwEDbihYqXBDwfWDX270zGQJUGqH1F2j6xpX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\nVeD/Aw+Iema+z5TBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2937b896780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2)=plt.subplots(ncols=2,figsize=(12,8))\n",
    "sns.regplot(x='building_rel_to_class',y='log_monthly_rate',data=ny_office_lease, ax=ax1)\n",
    "\n",
    "sns.regplot(x='building_rel_to_class',y='rate',data=ny_office_lease, ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This does not seem to be a particularly informative measure, so we are not going to include it in the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Unit size relative to others of the same type\n",
    "\n",
    "Is it not reasonable to assume that, for example, a 200 square foot executive office in midtown will probably cost more than a 100 square foot executive office next door?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "space_use_mean_sqft = dict(ny_office_lease.groupby('space_use')['space_sqft'].describe()['mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['space_use_mean_sqft'] = np.log(ny_office_lease['space_use'].map(space_use_mean_sqft))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease['space_rel_size'] = ny_office_lease['log_space_sqft'] - ny_office_lease['space_use_mean_sqft']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2937bdb3a20>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0VeZy8wObsYTlEAOVvoyk+8ZgCl5AQ2vumKXJrbXcvWkn3396HwCrl87iM5ee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\nAxTShBBCCCGE1MD/Bxbk/4EdVO3IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2937bd46400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2)=plt.subplots(ncols=2,figsize=(12,8))\n",
    "sns.regplot(x='space_rel_size',y='log_monthly_rate',data=ny_office_lease, ax=ax1)\n",
    "\n",
    "sns.regplot(x='space_rel_size',y='rate',data=ny_office_lease, ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of course, the general trends are still the same: as floor space increases, monthly rate increases, and $/sqft/year decreases slightly. But it is hoped that this can give us information about size differences specific to a type.\n",
    "\n",
    "We will include this measure and the relative building size to cluster mean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dummify(df, col):\n",
    "    dummies=pd.get_dummies(df[col], prefix=col, drop_first=False)\n",
    "    df = pd.concat((df,dummies),axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease = dummify(ny_office_lease,'building_class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease = dummify(ny_office_lease,'location_cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease = dummify(ny_office_lease,'floor_class')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease = dummify(ny_office_lease,'service_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ny_office_lease = dummify(ny_office_lease,'space_use')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We decide not to use building or the walk/transit scores. Age may be more useful in some clusters than in others. The walk/transit scores tend to all be very high, with averages close to 100, making them relatively less informative."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "columns2 = ['name', 'log_rate', 'log_building_sqft', \n",
    "       'log_space_sqft','building_class_A','building_class_B', 'building_class_C',\n",
    "       'building_class_D','space_use_executive',\n",
    "       'space_use_mixed', 'space_use_office',\n",
    "       'space_use_retail',\n",
    "        'location_cluster_0',\n",
    "       'location_cluster_1', 'location_cluster_2', 'location_cluster_3',\n",
    "       'location_cluster_4', 'location_cluster_5', 'location_cluster_6',\n",
    "            'building_rel_size', 'space_rel_size',\n",
    "                   'service_type_elec', 'service_type_fsg', 'service_type_mod_gross',\n",
    "       'service_type_net', 'service_type_tbd'\n",
    "           ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data=ny_office_lease[columns2].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting monthly rental rates\n",
    "\n",
    "Our loss function will be mean absolute deviation of log monthly rent: $MAD = mean(\\left | rent - predictedrent \\right |)$, which gives us an approximation of the percent deviation.\n",
    "\n",
    "This is a much more informative measure than something like $R^{2}$ for price prediction, and gives us an approximation of the average percentage deviation from the true price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "holdout = data[data.isnull()['log_rate']==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = data[data.isnull()['log_rate']==False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X=train.drop(['name','log_rate'],axis=1)\n",
    "y=train['log_rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split, cross_val_predict\n",
    "from sklearn.metrics import accuracy_score, r2_score, mean_squared_error\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint, uniform\n",
    "import xgboost as xgb\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor, BaggingRegressor\n",
    "from pandas.plotting import scatter_matrix\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def mald (val, target):\n",
    "    return np.mean(abs(np.log(target)-np.log(val)))\n",
    "\n",
    "def mad (val,target):\n",
    "    return np.mean(abs(target-val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grad_test = GradientBoostingRegressor(learning_rate=0.05,n_estimators=350,loss='lad')\n",
    "hyper_params = {'n_estimators': randint(400, 800),\n",
    "                'max_depth': randint(1,10),\n",
    "                'subsample': uniform(0,1),\n",
    "                'min_samples_split': randint(2,5),\n",
    "                'min_samples_leaf': randint(1,5)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  17 tasks      | elapsed:    7.1s\n",
      "[Parallel(n_jobs=4)]: Done  90 tasks      | elapsed:   40.7s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:   42.6s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'max_depth': 3,\n",
       "  'min_samples_leaf': 4,\n",
       "  'min_samples_split': 4,\n",
       "  'n_estimators': 772,\n",
       "  'subsample': 0.79214170481007606},\n",
       " 0.34784996955787872)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search = RandomizedSearchCV(grad_test, param_distributions=hyper_params ,n_jobs=4, verbose=4, cv=10)\n",
    "search.fit(X_train,y_train)\n",
    "search.best_params_, search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  17 tasks      | elapsed:   12.7s\n",
      "[Parallel(n_jobs=4)]: Done  90 tasks      | elapsed:   37.4s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:   39.4s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'max_delta_step': 9,\n",
       "  'max_depth': 8,\n",
       "  'min_child_weight': 4,\n",
       "  'n_estimators': 1871,\n",
       "  'subsample': 0.47517782019874788},\n",
       " 0.34784996955787872)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg = xgb.XGBRegressor(n_estimators=1000, max_depth=2, learning_rate=0.01, n_jobs=4, eval_metric='mae')\n",
    "hyper_params1 = {'n_estimators': randint(1000, 2000),\n",
    "                'max_depth': randint(1,12),\n",
    "               'subsample': uniform(0,1),\n",
    "               'min_child_weight': randint(1,10),\n",
    "               'max_delta_step': randint(1,10)}\n",
    "\n",
    "search1 = RandomizedSearchCV(xg, param_distributions=hyper_params1 ,n_jobs=4, verbose=4, cv=10)\n",
    "search1.fit(X_train,y_train)\n",
    "search1.best_params_, search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LINEAR REGRESSION\n",
      "Training MAD:  0.2418320139890109\n",
      "Test MAD:  0.27474306943340693\n",
      "GRADIENT BOOSTED\n",
      "Training MAD:  0.11080319517455038\n",
      "Test MAD:  0.20708264188231537\n",
      "XGBOOST\n",
      "Training MAD:  0.04704005075546215\n",
      "Test MAD:  0.21513588320397473\n"
     ]
    }
   ],
   "source": [
    "# We can just use good old fashioned OLS as an example. Variants like ridge and lasso don't perform much differently here.\n",
    "lm=LinearRegression()\n",
    "lm.fit(X_train,y_train)\n",
    "lm_est = lm.predict(X_test)\n",
    "\n",
    "print('LINEAR REGRESSION')\n",
    "print('Training MAD: ',mad(y_train,lm.predict(X_train)))\n",
    "print('Test MAD: ', mad(y_test,lm_est))\n",
    "\n",
    "est = search.best_estimator_.predict(X_test)\n",
    "print('GRADIENT BOOSTED')\n",
    "print('Training MAD: ',mad(y_train,search.best_estimator_.predict(X_train)))\n",
    "print('Test MAD: ', mad(y_test,est))\n",
    "\n",
    "est1 = search1.best_estimator_.predict(X_test)\n",
    "print('XGBOOST')\n",
    "print('Training MAD: ',mad(y_train,search1.best_estimator_.predict(X_train)))\n",
    "print('Test MAD: ', mad(y_test,est1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importance under both gradient boosting methods\n"
     ]
    },
    {
     "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>feature</th>\n",
       "      <th>gb</th>\n",
       "      <th>xgboost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>log_building_sqft</td>\n",
       "      <td>0.183381</td>\n",
       "      <td>0.201601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>log_space_sqft</td>\n",
       "      <td>0.235754</td>\n",
       "      <td>0.252305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>building_class_A</td>\n",
       "      <td>0.008107</td>\n",
       "      <td>0.004774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>building_class_B</td>\n",
       "      <td>0.010445</td>\n",
       "      <td>0.018384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>building_class_C</td>\n",
       "      <td>0.011188</td>\n",
       "      <td>0.014144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>building_class_D</td>\n",
       "      <td>0.007741</td>\n",
       "      <td>0.017583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>space_use_executive</td>\n",
       "      <td>0.001348</td>\n",
       "      <td>0.002194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>space_use_mixed</td>\n",
       "      <td>0.009603</td>\n",
       "      <td>0.013491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>space_use_office</td>\n",
       "      <td>0.009531</td>\n",
       "      <td>0.021646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>space_use_retail</td>\n",
       "      <td>0.005681</td>\n",
       "      <td>0.012424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>location_cluster_0</td>\n",
       "      <td>0.015787</td>\n",
       "      <td>0.006938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>location_cluster_1</td>\n",
       "      <td>0.022209</td>\n",
       "      <td>0.010704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>location_cluster_2</td>\n",
       "      <td>0.025761</td>\n",
       "      <td>0.008510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>location_cluster_3</td>\n",
       "      <td>0.003907</td>\n",
       "      <td>0.015004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>location_cluster_4</td>\n",
       "      <td>0.007882</td>\n",
       "      <td>0.011297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>location_cluster_5</td>\n",
       "      <td>0.008822</td>\n",
       "      <td>0.015063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>location_cluster_6</td>\n",
       "      <td>0.014751</td>\n",
       "      <td>0.009281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>building_rel_size</td>\n",
       "      <td>0.157884</td>\n",
       "      <td>0.191964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>space_rel_size</td>\n",
       "      <td>0.230346</td>\n",
       "      <td>0.119881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>service_type_elec</td>\n",
       "      <td>0.010595</td>\n",
       "      <td>0.012394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>service_type_fsg</td>\n",
       "      <td>0.007911</td>\n",
       "      <td>0.018443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>service_type_mod_gross</td>\n",
       "      <td>0.007527</td>\n",
       "      <td>0.016694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>service_type_net</td>\n",
       "      <td>0.003839</td>\n",
       "      <td>0.005278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>service_type_tbd</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   feature        gb   xgboost\n",
       "0        log_building_sqft  0.183381  0.201601\n",
       "1           log_space_sqft  0.235754  0.252305\n",
       "2         building_class_A  0.008107  0.004774\n",
       "3         building_class_B  0.010445  0.018384\n",
       "4         building_class_C  0.011188  0.014144\n",
       "5         building_class_D  0.007741  0.017583\n",
       "6      space_use_executive  0.001348  0.002194\n",
       "7          space_use_mixed  0.009603  0.013491\n",
       "8         space_use_office  0.009531  0.021646\n",
       "9         space_use_retail  0.005681  0.012424\n",
       "10      location_cluster_0  0.015787  0.006938\n",
       "11      location_cluster_1  0.022209  0.010704\n",
       "12      location_cluster_2  0.025761  0.008510\n",
       "13      location_cluster_3  0.003907  0.015004\n",
       "14      location_cluster_4  0.007882  0.011297\n",
       "15      location_cluster_5  0.008822  0.015063\n",
       "16      location_cluster_6  0.014751  0.009281\n",
       "17       building_rel_size  0.157884  0.191964\n",
       "18          space_rel_size  0.230346  0.119881\n",
       "19       service_type_elec  0.010595  0.012394\n",
       "20        service_type_fsg  0.007911  0.018443\n",
       "21  service_type_mod_gross  0.007527  0.016694\n",
       "22        service_type_net  0.003839  0.005278\n",
       "23        service_type_tbd  0.000000  0.000000"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Feature importance under both gradient boosting methods')\n",
    "imp_df = pd.DataFrame(list(zip(X.columns,search.best_estimator_.feature_importances_,search1.best_estimator_.feature_importances_)))\n",
    "imp_df.columns=['feature','gb','xgboost']\n",
    "imp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble\n",
    "\n",
    "Let's try a weighted average of the two gradient boosted results. This can smooth out the errors in the individual models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ENSEMBLE\n",
      "Test MAD:  0.20738144371569303\n"
     ]
    }
   ],
   "source": [
    "ensemble_est = 0.5 * est + 0.5 * est1\n",
    "print('ENSEMBLE')\n",
    "print('Test MAD: ', mad(y_test,ensemble_est))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ensemble MAD seems to have a lower variance compared to the individual methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bootstrapping\n",
    "\n",
    "On an open-ended PhD econometrics exam question, a friend answered simply, \"I will bootstrap.\" And maybe so should we. We have been speculating about the sample size being small, so let's see what happens if we manufacture a larger training set by drawing with replacement from our real sample. We need to take care not to contaminate the training set with observations from the test set, or the MAD is going to be ineligibly good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  17 tasks      | elapsed: 17.0min\n",
      "[Parallel(n_jobs=4)]: Done  90 tasks      | elapsed: 71.6min\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed: 74.6min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'max_depth': 9,\n",
       "  'min_samples_leaf': 2,\n",
       "  'min_samples_split': 3,\n",
       "  'n_estimators': 647,\n",
       "  'subsample': 0.2990008276272238},\n",
       " 0.98745846063131515)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_bs, X_test_bs, y_train_bs, y_test_bs = train_test_split(X,y,test_size=0.3)\n",
    "train_bs = pd.concat((pd.Series(y_train_bs),X_train_bs),axis=1).sample(n=25000,replace=True)\n",
    "X_train_bs = train_bs.drop(['log_rate'],axis=1)\n",
    "y_train_bs = train_bs['log_rate']\n",
    "\n",
    "search = RandomizedSearchCV(grad_test, param_distributions=hyper_params ,n_jobs=4, verbose=4, cv=10)\n",
    "search.fit(X_train_bs,y_train_bs)\n",
    "search.best_params_, search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BOOTSTRAPPED GRADIENT BOOSTED\n",
      "Training MAD:  0.010200217959639886\n",
      "Test MAD:  0.2100096286359437\n"
     ]
    }
   ],
   "source": [
    "est = search.best_estimator_.predict(X_test_bs)\n",
    "print('BOOTSTRAPPED GRADIENT BOOSTED')\n",
    "print('Training MAD: ',mad(y_train_bs,search.best_estimator_.predict(X_train_bs)))\n",
    "print('Test MAD: ', mad(y_test_bs,est))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.18965469,  4.04311682,  5.22643646, ...,  4.1589247 ,\n",
       "        4.38224071,  3.91185968])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "Ultimately, our mean average deviation tends to be in the 0.15-0.20 range, occassionally spiking to 0.25 depending on the validation split. With only 539 observations there is a fair amount of variability in our mean absolute deviation results, which are highly dependent on how the training and validation sets are split. Aside from obtaining more data from different sources or continuously adding new observations from the same source (which may add trend issues to the analysis), or researching more features, there are likely still ways to extract more information from the existing data.\n",
    "\n",
    "The relative building size metric, for example, ended up being a feature of great significance, and its inclusion single handedly reduced MAD from the 0.2-0.3 range to the 0.15-0.25 range. The type and class comparison metrics did not significantly reduce MAD, but the inclusion of the type comparison may have reduced some of the variations in the score due to sample splitting issues.\n",
    "\n",
    "There are also variations that would be impossible or extremely difficult to explain with our current data. For example, there are 3 50 square foot executive office suites at 1214-1216 Broadway, on the 2nd, 3rd and 4th floors. All other features are identical. The third office is listed at 352. But the first two are 455.69, which would be a 0.27 absolute log difference if we accurately predict one of the prices. Is the 4th floor that bad? Or is it because of the specific building in the 1214-1216 range? Or there is something specific about the third office for which we need some sort of up-close, possibly even subjective rating. Maybe it's next to a loud air vent, or the view isn't as good (although we're comparing the fourth floor with second and third). Or it gives people a bad feeling when they walk in. We just don't know, and this is something that can not be fixed by adding more observations.\n",
    "\n",
    "It would be interesting to see how this model fares with a larger dataset, especially if we could increase the unique addresses from 450 (of which only 225 unique addresses had at least one disclosed rate). A larger set would both help alleviate the validation issues and at the same time give us more information with which to predict test prices. Of course, more 'up close' features would also be helpful."
   ]
  }
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