Project - Fuzzy Affordability Assessment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5c85983a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('D:\\Birkbeck\\MSc Data Science\\Project\\Data sets\\Selected datasets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "882c91f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import skfuzzy as fuzz\n",
    "from skfuzzy import control as ctrl\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.width', None)\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "pd.set_option('display.precision',2)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b03d7ff3",
   "metadata": {},
   "source": [
    "### Review data to define universe of discourse & fuzzy sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ef386094",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>g_belt_area</th>\n",
       "      <th>total_area</th>\n",
       "      <th>rpi</th>\n",
       "      <th>int_rate_max</th>\n",
       "      <th>int_rate_min</th>\n",
       "      <th>median_income</th>\n",
       "      <th>all_new_sls_price</th>\n",
       "      <th>det_new_sls_price</th>\n",
       "      <th>semi_new_sls_price</th>\n",
       "      <th>terr_new_sls_price</th>\n",
       "      <th>flat_new_sls_price</th>\n",
       "      <th>all_exist_sls_price</th>\n",
       "      <th>det_exist_sls_price</th>\n",
       "      <th>semi_exist_sls_price</th>\n",
       "      <th>terr_exist_sls_price</th>\n",
       "      <th>flat_exist_sls_price</th>\n",
       "      <th>all_sls_price</th>\n",
       "      <th>det_sls_price</th>\n",
       "      <th>semi_sls_price</th>\n",
       "      <th>terr_sls_price</th>\n",
       "      <th>flat_sls_price</th>\n",
       "      <th>all_new_sls_num</th>\n",
       "      <th>det_new_sls_num</th>\n",
       "      <th>semi_new_sls_num</th>\n",
       "      <th>terr_new_sls_num</th>\n",
       "      <th>flat_new_sls_num</th>\n",
       "      <th>all_exist_sls_num</th>\n",
       "      <th>det_exist_sls_num</th>\n",
       "      <th>semi_exist_sls_num</th>\n",
       "      <th>terr_exist_sls_num</th>\n",
       "      <th>flat_exist_sls_num</th>\n",
       "      <th>all_sls_num</th>\n",
       "      <th>det_sls_num</th>\n",
       "      <th>semi_sls_num</th>\n",
       "      <th>terr_sls_num</th>\n",
       "      <th>flat_sls_num</th>\n",
       "      <th>population</th>\n",
       "      <th>num_hh_1dp</th>\n",
       "      <th>num_hh_&gt;2dp</th>\n",
       "      <th>num_hh_2dp</th>\n",
       "      <th>num_hh_1f</th>\n",
       "      <th>num_hh_1m</th>\n",
       "      <th>num_hh_oth</th>\n",
       "      <th>num_hh_tot</th>\n",
       "      <th>gdp</th>\n",
       "      <th>dwell_stock</th>\n",
       "      <th>net_additions</th>\n",
       "      <th>num_wait_lst</th>\n",
       "      <th>aff_own</th>\n",
       "      <th>aff_rent</th>\n",
       "      <th>aff_int_rent</th>\n",
       "      <th>aff_lon_rent</th>\n",
       "      <th>aff_sh_own</th>\n",
       "      <th>aff_soc_rent</th>\n",
       "      <th>aff_unkwn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4.29e+03</td>\n",
       "      <td>4.29e+03</td>\n",
       "      <td>4.29e+03</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4.29e+03</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4.29e+03</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4.29e+03</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4284.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2011.03</td>\n",
       "      <td>5709.36</td>\n",
       "      <td>43132.51</td>\n",
       "      <td>233.70</td>\n",
       "      <td>2.14</td>\n",
       "      <td>1.83</td>\n",
       "      <td>24968.45</td>\n",
       "      <td>217573.78</td>\n",
       "      <td>3.31e+05</td>\n",
       "      <td>2.26e+05</td>\n",
       "      <td>2.14e+05</td>\n",
       "      <td>162201.85</td>\n",
       "      <td>187981.56</td>\n",
       "      <td>3.05e+05</td>\n",
       "      <td>192223.70</td>\n",
       "      <td>157351.88</td>\n",
       "      <td>122380.31</td>\n",
       "      <td>191158.34</td>\n",
       "      <td>3.07e+05</td>\n",
       "      <td>194206.28</td>\n",
       "      <td>160439.85</td>\n",
       "      <td>129280.13</td>\n",
       "      <td>313.55</td>\n",
       "      <td>91.90</td>\n",
       "      <td>57.75</td>\n",
       "      <td>59.48</td>\n",
       "      <td>104.42</td>\n",
       "      <td>2630.87</td>\n",
       "      <td>615.59</td>\n",
       "      <td>814.22</td>\n",
       "      <td>839.39</td>\n",
       "      <td>361.68</td>\n",
       "      <td>2944.42</td>\n",
       "      <td>707.49</td>\n",
       "      <td>871.96</td>\n",
       "      <td>898.87</td>\n",
       "      <td>466.10</td>\n",
       "      <td>1.72e+05</td>\n",
       "      <td>9156.78</td>\n",
       "      <td>3396.67</td>\n",
       "      <td>7857.47</td>\n",
       "      <td>11699.65</td>\n",
       "      <td>10042.85</td>\n",
       "      <td>29351.46</td>\n",
       "      <td>71504.88</td>\n",
       "      <td>4279.06</td>\n",
       "      <td>5704.80</td>\n",
       "      <td>591.81</td>\n",
       "      <td>4521.35</td>\n",
       "      <td>34.77</td>\n",
       "      <td>35.58</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0.14</td>\n",
       "      <td>12.57</td>\n",
       "      <td>58.98</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.62</td>\n",
       "      <td>8518.71</td>\n",
       "      <td>54234.04</td>\n",
       "      <td>37.88</td>\n",
       "      <td>2.07</td>\n",
       "      <td>1.83</td>\n",
       "      <td>4291.20</td>\n",
       "      <td>83803.91</td>\n",
       "      <td>1.81e+05</td>\n",
       "      <td>1.12e+05</td>\n",
       "      <td>1.03e+05</td>\n",
       "      <td>72077.80</td>\n",
       "      <td>80956.47</td>\n",
       "      <td>1.44e+05</td>\n",
       "      <td>87528.54</td>\n",
       "      <td>76222.84</td>\n",
       "      <td>48324.70</td>\n",
       "      <td>80739.50</td>\n",
       "      <td>1.42e+05</td>\n",
       "      <td>88094.47</td>\n",
       "      <td>77183.72</td>\n",
       "      <td>49291.66</td>\n",
       "      <td>269.15</td>\n",
       "      <td>98.32</td>\n",
       "      <td>55.05</td>\n",
       "      <td>51.24</td>\n",
       "      <td>170.13</td>\n",
       "      <td>1778.41</td>\n",
       "      <td>389.84</td>\n",
       "      <td>615.68</td>\n",
       "      <td>769.62</td>\n",
       "      <td>428.24</td>\n",
       "      <td>1973.49</td>\n",
       "      <td>447.96</td>\n",
       "      <td>642.52</td>\n",
       "      <td>795.49</td>\n",
       "      <td>540.94</td>\n",
       "      <td>1.20e+05</td>\n",
       "      <td>6611.81</td>\n",
       "      <td>3196.08</td>\n",
       "      <td>5088.49</td>\n",
       "      <td>8186.85</td>\n",
       "      <td>8295.14</td>\n",
       "      <td>18335.66</td>\n",
       "      <td>48744.65</td>\n",
       "      <td>3450.00</td>\n",
       "      <td>9383.19</td>\n",
       "      <td>499.80</td>\n",
       "      <td>6113.84</td>\n",
       "      <td>51.36</td>\n",
       "      <td>72.51</td>\n",
       "      <td>16.08</td>\n",
       "      <td>3.91</td>\n",
       "      <td>34.92</td>\n",
       "      <td>74.22</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2002.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2140.00</td>\n",
       "      <td>176.20</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.10</td>\n",
       "      <td>14953.00</td>\n",
       "      <td>61450.00</td>\n",
       "      <td>8.71e+04</td>\n",
       "      <td>3.50e+04</td>\n",
       "      <td>3.80e+04</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>33000.00</td>\n",
       "      <td>8.30e+04</td>\n",
       "      <td>48000.00</td>\n",
       "      <td>25000.00</td>\n",
       "      <td>19725.00</td>\n",
       "      <td>36000.00</td>\n",
       "      <td>8.64e+04</td>\n",
       "      <td>49725.00</td>\n",
       "      <td>25000.00</td>\n",
       "      <td>32000.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>368.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>89.00</td>\n",
       "      <td>73.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>425.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>96.00</td>\n",
       "      <td>96.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>3.53e+04</td>\n",
       "      <td>1522.46</td>\n",
       "      <td>569.29</td>\n",
       "      <td>1607.37</td>\n",
       "      <td>2106.25</td>\n",
       "      <td>1407.10</td>\n",
       "      <td>6255.76</td>\n",
       "      <td>13726.77</td>\n",
       "      <td>608.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-2285.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2006.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10320.00</td>\n",
       "      <td>198.10</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>21926.50</td>\n",
       "      <td>156997.50</td>\n",
       "      <td>2.29e+05</td>\n",
       "      <td>1.57e+05</td>\n",
       "      <td>1.49e+05</td>\n",
       "      <td>113247.50</td>\n",
       "      <td>130000.00</td>\n",
       "      <td>2.11e+05</td>\n",
       "      <td>130250.00</td>\n",
       "      <td>102425.00</td>\n",
       "      <td>89000.00</td>\n",
       "      <td>132850.00</td>\n",
       "      <td>2.15e+05</td>\n",
       "      <td>132999.00</td>\n",
       "      <td>105200.00</td>\n",
       "      <td>95000.00</td>\n",
       "      <td>147.00</td>\n",
       "      <td>28.00</td>\n",
       "      <td>21.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>24.00</td>\n",
       "      <td>1559.00</td>\n",
       "      <td>364.00</td>\n",
       "      <td>452.00</td>\n",
       "      <td>398.00</td>\n",
       "      <td>111.00</td>\n",
       "      <td>1763.00</td>\n",
       "      <td>423.00</td>\n",
       "      <td>489.00</td>\n",
       "      <td>438.00</td>\n",
       "      <td>154.50</td>\n",
       "      <td>1.04e+05</td>\n",
       "      <td>5245.21</td>\n",
       "      <td>1777.92</td>\n",
       "      <td>4786.19</td>\n",
       "      <td>6989.56</td>\n",
       "      <td>5381.06</td>\n",
       "      <td>18663.41</td>\n",
       "      <td>43634.21</td>\n",
       "      <td>2221.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>292.50</td>\n",
       "      <td>1613.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2011.00</td>\n",
       "      <td>1340.00</td>\n",
       "      <td>27900.00</td>\n",
       "      <td>235.20</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>24652.00</td>\n",
       "      <td>194995.00</td>\n",
       "      <td>2.85e+05</td>\n",
       "      <td>1.96e+05</td>\n",
       "      <td>1.85e+05</td>\n",
       "      <td>143995.00</td>\n",
       "      <td>171500.00</td>\n",
       "      <td>2.65e+05</td>\n",
       "      <td>170000.00</td>\n",
       "      <td>141000.00</td>\n",
       "      <td>114650.00</td>\n",
       "      <td>174995.00</td>\n",
       "      <td>2.66e+05</td>\n",
       "      <td>170000.00</td>\n",
       "      <td>145000.00</td>\n",
       "      <td>120000.00</td>\n",
       "      <td>240.00</td>\n",
       "      <td>59.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>45.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>2147.00</td>\n",
       "      <td>540.00</td>\n",
       "      <td>641.00</td>\n",
       "      <td>599.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>2422.00</td>\n",
       "      <td>618.00</td>\n",
       "      <td>697.00</td>\n",
       "      <td>656.00</td>\n",
       "      <td>313.00</td>\n",
       "      <td>1.34e+05</td>\n",
       "      <td>6782.33</td>\n",
       "      <td>2435.07</td>\n",
       "      <td>6137.41</td>\n",
       "      <td>8956.19</td>\n",
       "      <td>7225.82</td>\n",
       "      <td>23537.03</td>\n",
       "      <td>55338.32</td>\n",
       "      <td>3318.00</td>\n",
       "      <td>3042.00</td>\n",
       "      <td>465.00</td>\n",
       "      <td>2699.00</td>\n",
       "      <td>19.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.00</td>\n",
       "      <td>8140.00</td>\n",
       "      <td>58060.00</td>\n",
       "      <td>263.10</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4.00</td>\n",
       "      <td>27543.00</td>\n",
       "      <td>254950.00</td>\n",
       "      <td>3.80e+05</td>\n",
       "      <td>2.65e+05</td>\n",
       "      <td>2.50e+05</td>\n",
       "      <td>194950.00</td>\n",
       "      <td>230000.00</td>\n",
       "      <td>3.60e+05</td>\n",
       "      <td>230000.00</td>\n",
       "      <td>191912.50</td>\n",
       "      <td>145000.00</td>\n",
       "      <td>232000.00</td>\n",
       "      <td>3.60e+05</td>\n",
       "      <td>232250.00</td>\n",
       "      <td>195000.00</td>\n",
       "      <td>151849.00</td>\n",
       "      <td>390.00</td>\n",
       "      <td>120.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>79.00</td>\n",
       "      <td>122.00</td>\n",
       "      <td>3125.00</td>\n",
       "      <td>769.50</td>\n",
       "      <td>981.00</td>\n",
       "      <td>977.50</td>\n",
       "      <td>446.50</td>\n",
       "      <td>3455.00</td>\n",
       "      <td>882.00</td>\n",
       "      <td>1042.50</td>\n",
       "      <td>1053.00</td>\n",
       "      <td>569.00</td>\n",
       "      <td>2.02e+05</td>\n",
       "      <td>11367.72</td>\n",
       "      <td>3917.71</td>\n",
       "      <td>9248.68</td>\n",
       "      <td>14053.81</td>\n",
       "      <td>11737.21</td>\n",
       "      <td>34138.56</td>\n",
       "      <td>84742.97</td>\n",
       "      <td>5128.00</td>\n",
       "      <td>6878.50</td>\n",
       "      <td>711.00</td>\n",
       "      <td>5133.75</td>\n",
       "      <td>47.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>81.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2021.00</td>\n",
       "      <td>43570.00</td>\n",
       "      <td>503220.00</td>\n",
       "      <td>305.00</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.25</td>\n",
       "      <td>43486.00</td>\n",
       "      <td>697373.00</td>\n",
       "      <td>3.59e+06</td>\n",
       "      <td>1.52e+06</td>\n",
       "      <td>1.08e+06</td>\n",
       "      <td>775000.00</td>\n",
       "      <td>600000.00</td>\n",
       "      <td>1.75e+06</td>\n",
       "      <td>750000.00</td>\n",
       "      <td>625000.00</td>\n",
       "      <td>380000.00</td>\n",
       "      <td>598000.00</td>\n",
       "      <td>1.66e+06</td>\n",
       "      <td>750000.00</td>\n",
       "      <td>630000.00</td>\n",
       "      <td>415000.00</td>\n",
       "      <td>4060.00</td>\n",
       "      <td>1002.00</td>\n",
       "      <td>466.00</td>\n",
       "      <td>428.00</td>\n",
       "      <td>3293.00</td>\n",
       "      <td>19138.00</td>\n",
       "      <td>4703.00</td>\n",
       "      <td>7082.00</td>\n",
       "      <td>7926.00</td>\n",
       "      <td>5714.00</td>\n",
       "      <td>20865.00</td>\n",
       "      <td>4868.00</td>\n",
       "      <td>7270.00</td>\n",
       "      <td>8192.00</td>\n",
       "      <td>6437.00</td>\n",
       "      <td>1.14e+06</td>\n",
       "      <td>59755.08</td>\n",
       "      <td>37925.42</td>\n",
       "      <td>45220.48</td>\n",
       "      <td>68638.39</td>\n",
       "      <td>71761.14</td>\n",
       "      <td>147990.07</td>\n",
       "      <td>426334.34</td>\n",
       "      <td>31358.00</td>\n",
       "      <td>82305.00</td>\n",
       "      <td>5472.00</td>\n",
       "      <td>97818.00</td>\n",
       "      <td>635.00</td>\n",
       "      <td>1052.00</td>\n",
       "      <td>598.00</td>\n",
       "      <td>177.00</td>\n",
       "      <td>386.00</td>\n",
       "      <td>801.00</td>\n",
       "      <td>43.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          year  g_belt_area  total_area      rpi  int_rate_max  int_rate_min  \\\n",
       "count  4291.00      4291.00     4291.00  4291.00       4291.00       4291.00   \n",
       "mean   2011.03      5709.36    43132.51   233.70          2.14          1.83   \n",
       "std       5.62      8518.71    54234.04    37.88          2.07          1.83   \n",
       "min    2002.00         0.00     2140.00   176.20          0.25          0.10   \n",
       "25%    2006.00         0.00    10320.00   198.10          0.50          0.50   \n",
       "50%    2011.00      1340.00    27900.00   235.20          0.75          0.50   \n",
       "75%    2016.00      8140.00    58060.00   263.10          4.50          4.00   \n",
       "max    2021.00     43570.00   503220.00   305.00          5.75          5.25   \n",
       "\n",
       "       median_income  all_new_sls_price  det_new_sls_price  \\\n",
       "count        4291.00            4291.00           4.29e+03   \n",
       "mean        24968.45          217573.78           3.31e+05   \n",
       "std          4291.20           83803.91           1.81e+05   \n",
       "min         14953.00           61450.00           8.71e+04   \n",
       "25%         21926.50          156997.50           2.29e+05   \n",
       "50%         24652.00          194995.00           2.85e+05   \n",
       "75%         27543.00          254950.00           3.80e+05   \n",
       "max         43486.00          697373.00           3.59e+06   \n",
       "\n",
       "       semi_new_sls_price  terr_new_sls_price  flat_new_sls_price  \\\n",
       "count            4.29e+03            4.29e+03             4291.00   \n",
       "mean             2.26e+05            2.14e+05           162201.85   \n",
       "std              1.12e+05            1.03e+05            72077.80   \n",
       "min              3.50e+04            3.80e+04            10000.00   \n",
       "25%              1.57e+05            1.49e+05           113247.50   \n",
       "50%              1.96e+05            1.85e+05           143995.00   \n",
       "75%              2.65e+05            2.50e+05           194950.00   \n",
       "max              1.52e+06            1.08e+06           775000.00   \n",
       "\n",
       "       all_exist_sls_price  det_exist_sls_price  semi_exist_sls_price  \\\n",
       "count              4291.00             4.29e+03               4291.00   \n",
       "mean             187981.56             3.05e+05             192223.70   \n",
       "std               80956.47             1.44e+05              87528.54   \n",
       "min               33000.00             8.30e+04              48000.00   \n",
       "25%              130000.00             2.11e+05             130250.00   \n",
       "50%              171500.00             2.65e+05             170000.00   \n",
       "75%              230000.00             3.60e+05             230000.00   \n",
       "max              600000.00             1.75e+06             750000.00   \n",
       "\n",
       "       terr_exist_sls_price  flat_exist_sls_price  all_sls_price  \\\n",
       "count               4291.00               4291.00        4291.00   \n",
       "mean              157351.88             122380.31      191158.34   \n",
       "std                76222.84              48324.70       80739.50   \n",
       "min                25000.00              19725.00       36000.00   \n",
       "25%               102425.00              89000.00      132850.00   \n",
       "50%               141000.00             114650.00      174995.00   \n",
       "75%               191912.50             145000.00      232000.00   \n",
       "max               625000.00             380000.00      598000.00   \n",
       "\n",
       "       det_sls_price  semi_sls_price  terr_sls_price  flat_sls_price  \\\n",
       "count       4.29e+03         4291.00         4291.00         4291.00   \n",
       "mean        3.07e+05       194206.28       160439.85       129280.13   \n",
       "std         1.42e+05        88094.47        77183.72        49291.66   \n",
       "min         8.64e+04        49725.00        25000.00        32000.00   \n",
       "25%         2.15e+05       132999.00       105200.00        95000.00   \n",
       "50%         2.66e+05       170000.00       145000.00       120000.00   \n",
       "75%         3.60e+05       232250.00       195000.00       151849.00   \n",
       "max         1.66e+06       750000.00       630000.00       415000.00   \n",
       "\n",
       "       all_new_sls_num  det_new_sls_num  semi_new_sls_num  terr_new_sls_num  \\\n",
       "count          4291.00          4291.00           4291.00           4291.00   \n",
       "mean            313.55            91.90             57.75             59.48   \n",
       "std             269.15            98.32             55.05             51.24   \n",
       "min              28.00             5.00              5.00              5.00   \n",
       "25%             147.00            28.00             21.00             24.00   \n",
       "50%             240.00            59.00             40.00             45.00   \n",
       "75%             390.00           120.00             75.00             79.00   \n",
       "max            4060.00          1002.00            466.00            428.00   \n",
       "\n",
       "       flat_new_sls_num  all_exist_sls_num  det_exist_sls_num  \\\n",
       "count           4291.00            4291.00            4291.00   \n",
       "mean             104.42            2630.87             615.59   \n",
       "std              170.13            1778.41             389.84   \n",
       "min                5.00             368.00              14.00   \n",
       "25%               24.00            1559.00             364.00   \n",
       "50%               55.00            2147.00             540.00   \n",
       "75%              122.00            3125.00             769.50   \n",
       "max             3293.00           19138.00            4703.00   \n",
       "\n",
       "       semi_exist_sls_num  terr_exist_sls_num  flat_exist_sls_num  \\\n",
       "count             4291.00             4291.00             4291.00   \n",
       "mean               814.22              839.39              361.68   \n",
       "std                615.68              769.62              428.24   \n",
       "min                 89.00               73.00                5.00   \n",
       "25%                452.00              398.00              111.00   \n",
       "50%                641.00              599.00              245.00   \n",
       "75%                981.00              977.50              446.50   \n",
       "max               7082.00             7926.00             5714.00   \n",
       "\n",
       "       all_sls_num  det_sls_num  semi_sls_num  terr_sls_num  flat_sls_num  \\\n",
       "count      4291.00      4291.00       4291.00       4291.00       4291.00   \n",
       "mean       2944.42       707.49        871.96        898.87        466.10   \n",
       "std        1973.49       447.96        642.52        795.49        540.94   \n",
       "min         425.00        23.00         96.00         96.00         14.00   \n",
       "25%        1763.00       423.00        489.00        438.00        154.50   \n",
       "50%        2422.00       618.00        697.00        656.00        313.00   \n",
       "75%        3455.00       882.00       1042.50       1053.00        569.00   \n",
       "max       20865.00      4868.00       7270.00       8192.00       6437.00   \n",
       "\n",
       "       population  num_hh_1dp  num_hh_>2dp  num_hh_2dp  num_hh_1f  num_hh_1m  \\\n",
       "count    4.29e+03     4291.00      4291.00     4291.00    4291.00    4291.00   \n",
       "mean     1.72e+05     9156.78      3396.67     7857.47   11699.65   10042.85   \n",
       "std      1.20e+05     6611.81      3196.08     5088.49    8186.85    8295.14   \n",
       "min      3.53e+04     1522.46       569.29     1607.37    2106.25    1407.10   \n",
       "25%      1.04e+05     5245.21      1777.92     4786.19    6989.56    5381.06   \n",
       "50%      1.34e+05     6782.33      2435.07     6137.41    8956.19    7225.82   \n",
       "75%      2.02e+05    11367.72      3917.71     9248.68   14053.81   11737.21   \n",
       "max      1.14e+06    59755.08     37925.42    45220.48   68638.39   71761.14   \n",
       "\n",
       "       num_hh_oth  num_hh_tot       gdp  dwell_stock  net_additions  \\\n",
       "count     4291.00     4291.00   4291.00      4291.00        4291.00   \n",
       "mean     29351.46    71504.88   4279.06      5704.80         591.81   \n",
       "std      18335.66    48744.65   3450.00      9383.19         499.80   \n",
       "min       6255.76    13726.77    608.00         0.00       -2285.00   \n",
       "25%      18663.41    43634.21   2221.50         0.00         292.50   \n",
       "50%      23537.03    55338.32   3318.00      3042.00         465.00   \n",
       "75%      34138.56    84742.97   5128.00      6878.50         711.00   \n",
       "max     147990.07   426334.34  31358.00     82305.00        5472.00   \n",
       "\n",
       "       num_wait_lst  aff_own  aff_rent  aff_int_rent  aff_lon_rent  \\\n",
       "count       4284.00  4291.00   4291.00       4291.00       4291.00   \n",
       "mean        4521.35    34.77     35.58          3.67          0.14   \n",
       "std         6113.84    51.36     72.51         16.08          3.91   \n",
       "min            0.00     0.00      0.00          0.00          0.00   \n",
       "25%         1613.00     2.00      0.00          0.00          0.00   \n",
       "50%         2699.00    19.00      0.00          0.00          0.00   \n",
       "75%         5133.75    47.00     41.00          0.00          0.00   \n",
       "max        97818.00   635.00   1052.00        598.00        177.00   \n",
       "\n",
       "       aff_sh_own  aff_soc_rent  aff_unkwn  \n",
       "count     4291.00       4291.00    4291.00  \n",
       "mean        12.57         58.98       0.01  \n",
       "std         34.92         74.22       0.66  \n",
       "min          0.00          0.00       0.00  \n",
       "25%          0.00          8.00       0.00  \n",
       "50%          0.00         37.00       0.00  \n",
       "75%          4.00         81.00       0.00  \n",
       "max        386.00        801.00      43.00  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('la_file.csv')\n",
    "data = data.drop(columns ='Unnamed: 0')\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3705d718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>median_income</th>\n",
       "      <th>all_sls_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4291.00</td>\n",
       "      <td>4291.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>24968.45</td>\n",
       "      <td>191158.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4291.20</td>\n",
       "      <td>80739.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>14953.00</td>\n",
       "      <td>36000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21926.50</td>\n",
       "      <td>132850.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24652.00</td>\n",
       "      <td>174995.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27543.00</td>\n",
       "      <td>232000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>43486.00</td>\n",
       "      <td>598000.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       median_income  all_sls_price\n",
       "count        4291.00        4291.00\n",
       "mean        24968.45      191158.34\n",
       "std          4291.20       80739.50\n",
       "min         14953.00       36000.00\n",
       "25%         21926.50      132850.00\n",
       "50%         24652.00      174995.00\n",
       "75%         27543.00      232000.00\n",
       "max         43486.00      598000.00"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['median_income', 'all_sls_price']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "645a3244",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "174995.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.all_sls_price.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e6aae45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24652.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.median_income.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d379fe59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>year</th>\n",
       "      <th>all_sls_price</th>\n",
       "      <th>sls_75%</th>\n",
       "      <th>sls_25%</th>\n",
       "      <th>sls_min</th>\n",
       "      <th>sls_max</th>\n",
       "      <th>net_additions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>110.00</td>\n",
       "      <td>142.50</td>\n",
       "      <td>78.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>245.0</td>\n",
       "      <td>96048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>130.00</td>\n",
       "      <td>165.00</td>\n",
       "      <td>90.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>250.0</td>\n",
       "      <td>113361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>147.50</td>\n",
       "      <td>180.34</td>\n",
       "      <td>118.25</td>\n",
       "      <td>51.00</td>\n",
       "      <td>288.0</td>\n",
       "      <td>117216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>155.00</td>\n",
       "      <td>190.00</td>\n",
       "      <td>122.75</td>\n",
       "      <td>46.38</td>\n",
       "      <td>295.0</td>\n",
       "      <td>134571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>161.25</td>\n",
       "      <td>195.00</td>\n",
       "      <td>128.50</td>\n",
       "      <td>63.00</td>\n",
       "      <td>320.0</td>\n",
       "      <td>147035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2007</td>\n",
       "      <td>175.00</td>\n",
       "      <td>211.54</td>\n",
       "      <td>136.00</td>\n",
       "      <td>76.00</td>\n",
       "      <td>350.0</td>\n",
       "      <td>155691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008</td>\n",
       "      <td>171.00</td>\n",
       "      <td>210.00</td>\n",
       "      <td>133.50</td>\n",
       "      <td>93.75</td>\n",
       "      <td>372.5</td>\n",
       "      <td>150878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009</td>\n",
       "      <td>151.62</td>\n",
       "      <td>185.00</td>\n",
       "      <td>123.50</td>\n",
       "      <td>83.00</td>\n",
       "      <td>350.0</td>\n",
       "      <td>116236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>166.75</td>\n",
       "      <td>204.38</td>\n",
       "      <td>132.00</td>\n",
       "      <td>89.00</td>\n",
       "      <td>395.0</td>\n",
       "      <td>99959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>170.00</td>\n",
       "      <td>208.75</td>\n",
       "      <td>128.62</td>\n",
       "      <td>85.00</td>\n",
       "      <td>385.0</td>\n",
       "      <td>96309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>173.22</td>\n",
       "      <td>220.00</td>\n",
       "      <td>130.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>405.0</td>\n",
       "      <td>96688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>175.00</td>\n",
       "      <td>225.00</td>\n",
       "      <td>135.00</td>\n",
       "      <td>74.00</td>\n",
       "      <td>460.0</td>\n",
       "      <td>92187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>189.49</td>\n",
       "      <td>240.00</td>\n",
       "      <td>141.50</td>\n",
       "      <td>91.00</td>\n",
       "      <td>450.0</td>\n",
       "      <td>104278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>192.00</td>\n",
       "      <td>246.12</td>\n",
       "      <td>147.80</td>\n",
       "      <td>77.50</td>\n",
       "      <td>495.0</td>\n",
       "      <td>130730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>214.62</td>\n",
       "      <td>275.00</td>\n",
       "      <td>153.00</td>\n",
       "      <td>105.00</td>\n",
       "      <td>491.5</td>\n",
       "      <td>138401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>228.86</td>\n",
       "      <td>294.98</td>\n",
       "      <td>160.69</td>\n",
       "      <td>110.00</td>\n",
       "      <td>511.5</td>\n",
       "      <td>149831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>246.00</td>\n",
       "      <td>313.50</td>\n",
       "      <td>169.59</td>\n",
       "      <td>100.10</td>\n",
       "      <td>537.5</td>\n",
       "      <td>160870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "      <td>248.72</td>\n",
       "      <td>311.19</td>\n",
       "      <td>178.00</td>\n",
       "      <td>92.75</td>\n",
       "      <td>577.5</td>\n",
       "      <td>166197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020</td>\n",
       "      <td>254.50</td>\n",
       "      <td>312.03</td>\n",
       "      <td>185.56</td>\n",
       "      <td>115.00</td>\n",
       "      <td>598.0</td>\n",
       "      <td>157511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021</td>\n",
       "      <td>295.00</td>\n",
       "      <td>349.00</td>\n",
       "      <td>220.25</td>\n",
       "      <td>125.00</td>\n",
       "      <td>585.0</td>\n",
       "      <td>115469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  all_sls_price  sls_75%  sls_25%  sls_min  sls_max  net_additions\n",
       "0   2002         110.00   142.50    78.00    36.00    245.0          96048\n",
       "1   2003         130.00   165.00    90.00    39.00    250.0         113361\n",
       "2   2004         147.50   180.34   118.25    51.00    288.0         117216\n",
       "3   2005         155.00   190.00   122.75    46.38    295.0         134571\n",
       "4   2006         161.25   195.00   128.50    63.00    320.0         147035\n",
       "5   2007         175.00   211.54   136.00    76.00    350.0         155691\n",
       "6   2008         171.00   210.00   133.50    93.75    372.5         150878\n",
       "7   2009         151.62   185.00   123.50    83.00    350.0         116236\n",
       "8   2010         166.75   204.38   132.00    89.00    395.0          99959\n",
       "9   2011         170.00   208.75   128.62    85.00    385.0          96309\n",
       "10  2012         173.22   220.00   130.00    75.00    405.0          96688\n",
       "11  2013         175.00   225.00   135.00    74.00    460.0          92187\n",
       "12  2014         189.49   240.00   141.50    91.00    450.0         104278\n",
       "13  2015         192.00   246.12   147.80    77.50    495.0         130730\n",
       "14  2016         214.62   275.00   153.00   105.00    491.5         138401\n",
       "15  2017         228.86   294.98   160.69   110.00    511.5         149831\n",
       "16  2018         246.00   313.50   169.59   100.10    537.5         160870\n",
       "17  2019         248.72   311.19   178.00    92.75    577.5         166197\n",
       "18  2020         254.50   312.03   185.56   115.00    598.0         157511\n",
       "19  2021         295.00   349.00   220.25   125.00    585.0         115469"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#review sales by year\n",
    "sls_yr = pd.DataFrame(data.groupby('year').all_sls_price.median()/1000)\n",
    "sls_yr['sls_75%'] = data.groupby('year').all_sls_price.quantile(0.75)/1000\n",
    "sls_yr['sls_25%'] = data.groupby('year').all_sls_price.quantile(0.25)/1000\n",
    "sls_yr['sls_min'] = data.groupby('year').all_sls_price.min()/1000\n",
    "sls_yr['sls_max'] = data.groupby('year').all_sls_price.max()/1000\n",
    "sls_yr['net_additions'] = data.groupby('year').net_additions.sum()\n",
    "sls_yr = sls_yr.reset_index()\n",
    "sls_yr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60adf242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>year</th>\n",
       "      <th>median_income</th>\n",
       "      <th>inc_75%</th>\n",
       "      <th>inc_25%</th>\n",
       "      <th>inc_min</th>\n",
       "      <th>inc_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>19.37</td>\n",
       "      <td>21.71</td>\n",
       "      <td>18.04</td>\n",
       "      <td>14.95</td>\n",
       "      <td>30.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>20.11</td>\n",
       "      <td>22.43</td>\n",
       "      <td>18.52</td>\n",
       "      <td>15.23</td>\n",
       "      <td>30.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>20.77</td>\n",
       "      <td>22.88</td>\n",
       "      <td>19.26</td>\n",
       "      <td>15.90</td>\n",
       "      <td>31.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>21.62</td>\n",
       "      <td>23.66</td>\n",
       "      <td>19.93</td>\n",
       "      <td>15.17</td>\n",
       "      <td>32.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>22.01</td>\n",
       "      <td>24.00</td>\n",
       "      <td>20.51</td>\n",
       "      <td>14.96</td>\n",
       "      <td>34.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2007</td>\n",
       "      <td>22.66</td>\n",
       "      <td>25.02</td>\n",
       "      <td>20.94</td>\n",
       "      <td>16.23</td>\n",
       "      <td>35.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008</td>\n",
       "      <td>23.60</td>\n",
       "      <td>25.84</td>\n",
       "      <td>21.64</td>\n",
       "      <td>16.43</td>\n",
       "      <td>36.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009</td>\n",
       "      <td>24.11</td>\n",
       "      <td>26.13</td>\n",
       "      <td>22.47</td>\n",
       "      <td>16.28</td>\n",
       "      <td>37.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>24.01</td>\n",
       "      <td>26.41</td>\n",
       "      <td>22.67</td>\n",
       "      <td>17.06</td>\n",
       "      <td>36.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>24.58</td>\n",
       "      <td>26.51</td>\n",
       "      <td>22.57</td>\n",
       "      <td>16.93</td>\n",
       "      <td>40.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>24.93</td>\n",
       "      <td>26.97</td>\n",
       "      <td>23.07</td>\n",
       "      <td>17.80</td>\n",
       "      <td>38.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>25.43</td>\n",
       "      <td>27.65</td>\n",
       "      <td>23.66</td>\n",
       "      <td>18.09</td>\n",
       "      <td>36.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>25.45</td>\n",
       "      <td>27.97</td>\n",
       "      <td>23.70</td>\n",
       "      <td>19.87</td>\n",
       "      <td>37.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>25.83</td>\n",
       "      <td>27.99</td>\n",
       "      <td>23.85</td>\n",
       "      <td>19.44</td>\n",
       "      <td>36.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>26.53</td>\n",
       "      <td>28.76</td>\n",
       "      <td>24.77</td>\n",
       "      <td>19.61</td>\n",
       "      <td>43.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>26.86</td>\n",
       "      <td>29.41</td>\n",
       "      <td>24.99</td>\n",
       "      <td>20.22</td>\n",
       "      <td>37.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>27.67</td>\n",
       "      <td>30.31</td>\n",
       "      <td>25.75</td>\n",
       "      <td>21.83</td>\n",
       "      <td>38.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "      <td>28.65</td>\n",
       "      <td>30.80</td>\n",
       "      <td>26.50</td>\n",
       "      <td>23.10</td>\n",
       "      <td>39.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020</td>\n",
       "      <td>29.37</td>\n",
       "      <td>32.07</td>\n",
       "      <td>27.47</td>\n",
       "      <td>22.88</td>\n",
       "      <td>42.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021</td>\n",
       "      <td>29.86</td>\n",
       "      <td>31.93</td>\n",
       "      <td>27.88</td>\n",
       "      <td>24.25</td>\n",
       "      <td>42.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  median_income  inc_75%  inc_25%  inc_min  inc_max\n",
       "0   2002          19.37    21.71    18.04    14.95    30.74\n",
       "1   2003          20.11    22.43    18.52    15.23    30.10\n",
       "2   2004          20.77    22.88    19.26    15.90    31.76\n",
       "3   2005          21.62    23.66    19.93    15.17    32.22\n",
       "4   2006          22.01    24.00    20.51    14.96    34.10\n",
       "5   2007          22.66    25.02    20.94    16.23    35.58\n",
       "6   2008          23.60    25.84    21.64    16.43    36.59\n",
       "7   2009          24.11    26.13    22.47    16.28    37.68\n",
       "8   2010          24.01    26.41    22.67    17.06    36.28\n",
       "9   2011          24.58    26.51    22.57    16.93    40.12\n",
       "10  2012          24.93    26.97    23.07    17.80    38.51\n",
       "11  2013          25.43    27.65    23.66    18.09    36.90\n",
       "12  2014          25.45    27.97    23.70    19.87    37.87\n",
       "13  2015          25.83    27.99    23.85    19.44    36.67\n",
       "14  2016          26.53    28.76    24.77    19.61    43.49\n",
       "15  2017          26.86    29.41    24.99    20.22    37.90\n",
       "16  2018          27.67    30.31    25.75    21.83    38.13\n",
       "17  2019          28.65    30.80    26.50    23.10    39.14\n",
       "18  2020          29.37    32.07    27.47    22.88    42.03\n",
       "19  2021          29.86    31.93    27.88    24.25    42.44"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Review income by year\n",
    "income_yr =pd.DataFrame(data.groupby('year').median_income.median()/1000)\n",
    "income_yr['inc_75%'] = data.groupby('year').median_income.quantile(0.75)/1000\n",
    "income_yr['inc_25%'] = data.groupby('year').median_income.quantile(0.25)/1000\n",
    "income_yr['inc_min'] = data.groupby('year').median_income.min()/1000\n",
    "income_yr['inc_max'] = data.groupby('year').median_income.max()/1000\n",
    "income_yr = income_yr.reset_index()\n",
    "income_yr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "028fb8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>year</th>\n",
       "      <th>all_sls_price</th>\n",
       "      <th>sls_75%</th>\n",
       "      <th>sls_25%</th>\n",
       "      <th>sls_min</th>\n",
       "      <th>sls_max</th>\n",
       "      <th>net_additions</th>\n",
       "      <th>median_income</th>\n",
       "      <th>inc_75%</th>\n",
       "      <th>inc_25%</th>\n",
       "      <th>inc_min</th>\n",
       "      <th>inc_max</th>\n",
       "      <th>l_income_calc</th>\n",
       "      <th>h_income_calc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>110.00</td>\n",
       "      <td>142.50</td>\n",
       "      <td>78.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>245.0</td>\n",
       "      <td>96048</td>\n",
       "      <td>19.37</td>\n",
       "      <td>21.71</td>\n",
       "      <td>18.04</td>\n",
       "      <td>14.95</td>\n",
       "      <td>30.74</td>\n",
       "      <td>12.11</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2003</td>\n",
       "      <td>130.00</td>\n",
       "      <td>165.00</td>\n",
       "      <td>90.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>250.0</td>\n",
       "      <td>113361</td>\n",
       "      <td>20.11</td>\n",
       "      <td>22.43</td>\n",
       "      <td>18.52</td>\n",
       "      <td>15.23</td>\n",
       "      <td>30.10</td>\n",
       "      <td>12.57</td>\n",
       "      <td>32.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2004</td>\n",
       "      <td>147.50</td>\n",
       "      <td>180.34</td>\n",
       "      <td>118.25</td>\n",
       "      <td>51.00</td>\n",
       "      <td>288.0</td>\n",
       "      <td>117216</td>\n",
       "      <td>20.77</td>\n",
       "      <td>22.88</td>\n",
       "      <td>19.26</td>\n",
       "      <td>15.90</td>\n",
       "      <td>31.76</td>\n",
       "      <td>12.98</td>\n",
       "      <td>33.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2005</td>\n",
       "      <td>155.00</td>\n",
       "      <td>190.00</td>\n",
       "      <td>122.75</td>\n",
       "      <td>46.38</td>\n",
       "      <td>295.0</td>\n",
       "      <td>134571</td>\n",
       "      <td>21.62</td>\n",
       "      <td>23.66</td>\n",
       "      <td>19.93</td>\n",
       "      <td>15.17</td>\n",
       "      <td>32.22</td>\n",
       "      <td>13.51</td>\n",
       "      <td>34.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2006</td>\n",
       "      <td>161.25</td>\n",
       "      <td>195.00</td>\n",
       "      <td>128.50</td>\n",
       "      <td>63.00</td>\n",
       "      <td>320.0</td>\n",
       "      <td>147035</td>\n",
       "      <td>22.01</td>\n",
       "      <td>24.00</td>\n",
       "      <td>20.51</td>\n",
       "      <td>14.96</td>\n",
       "      <td>34.10</td>\n",
       "      <td>13.76</td>\n",
       "      <td>35.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>175.00</td>\n",
       "      <td>211.54</td>\n",
       "      <td>136.00</td>\n",
       "      <td>76.00</td>\n",
       "      <td>350.0</td>\n",
       "      <td>155691</td>\n",
       "      <td>22.66</td>\n",
       "      <td>25.02</td>\n",
       "      <td>20.94</td>\n",
       "      <td>16.23</td>\n",
       "      <td>35.58</td>\n",
       "      <td>14.16</td>\n",
       "      <td>36.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>171.00</td>\n",
       "      <td>210.00</td>\n",
       "      <td>133.50</td>\n",
       "      <td>93.75</td>\n",
       "      <td>372.5</td>\n",
       "      <td>150878</td>\n",
       "      <td>23.60</td>\n",
       "      <td>25.84</td>\n",
       "      <td>21.64</td>\n",
       "      <td>16.43</td>\n",
       "      <td>36.59</td>\n",
       "      <td>14.75</td>\n",
       "      <td>37.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>2009</td>\n",
       "      <td>151.62</td>\n",
       "      <td>185.00</td>\n",
       "      <td>123.50</td>\n",
       "      <td>83.00</td>\n",
       "      <td>350.0</td>\n",
       "      <td>116236</td>\n",
       "      <td>24.11</td>\n",
       "      <td>26.13</td>\n",
       "      <td>22.47</td>\n",
       "      <td>16.28</td>\n",
       "      <td>37.68</td>\n",
       "      <td>15.07</td>\n",
       "      <td>38.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2010</td>\n",
       "      <td>166.75</td>\n",
       "      <td>204.38</td>\n",
       "      <td>132.00</td>\n",
       "      <td>89.00</td>\n",
       "      <td>395.0</td>\n",
       "      <td>99959</td>\n",
       "      <td>24.01</td>\n",
       "      <td>26.41</td>\n",
       "      <td>22.67</td>\n",
       "      <td>17.06</td>\n",
       "      <td>36.28</td>\n",
       "      <td>15.01</td>\n",
       "      <td>38.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>2011</td>\n",
       "      <td>170.00</td>\n",
       "      <td>208.75</td>\n",
       "      <td>128.62</td>\n",
       "      <td>85.00</td>\n",
       "      <td>385.0</td>\n",
       "      <td>96309</td>\n",
       "      <td>24.58</td>\n",
       "      <td>26.51</td>\n",
       "      <td>22.57</td>\n",
       "      <td>16.93</td>\n",
       "      <td>40.12</td>\n",
       "      <td>15.36</td>\n",
       "      <td>39.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2012</td>\n",
       "      <td>173.22</td>\n",
       "      <td>220.00</td>\n",
       "      <td>130.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>405.0</td>\n",
       "      <td>96688</td>\n",
       "      <td>24.93</td>\n",
       "      <td>26.97</td>\n",
       "      <td>23.07</td>\n",
       "      <td>17.80</td>\n",
       "      <td>38.51</td>\n",
       "      <td>15.58</td>\n",
       "      <td>39.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>2013</td>\n",
       "      <td>175.00</td>\n",
       "      <td>225.00</td>\n",
       "      <td>135.00</td>\n",
       "      <td>74.00</td>\n",
       "      <td>460.0</td>\n",
       "      <td>92187</td>\n",
       "      <td>25.43</td>\n",
       "      <td>27.65</td>\n",
       "      <td>23.66</td>\n",
       "      <td>18.09</td>\n",
       "      <td>36.90</td>\n",
       "      <td>15.89</td>\n",
       "      <td>40.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2014</td>\n",
       "      <td>189.49</td>\n",
       "      <td>240.00</td>\n",
       "      <td>141.50</td>\n",
       "      <td>91.00</td>\n",
       "      <td>450.0</td>\n",
       "      <td>104278</td>\n",
       "      <td>25.45</td>\n",
       "      <td>27.97</td>\n",
       "      <td>23.70</td>\n",
       "      <td>19.87</td>\n",
       "      <td>37.87</td>\n",
       "      <td>15.90</td>\n",
       "      <td>40.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>2015</td>\n",
       "      <td>192.00</td>\n",
       "      <td>246.12</td>\n",
       "      <td>147.80</td>\n",
       "      <td>77.50</td>\n",
       "      <td>495.0</td>\n",
       "      <td>130730</td>\n",
       "      <td>25.83</td>\n",
       "      <td>27.99</td>\n",
       "      <td>23.85</td>\n",
       "      <td>19.44</td>\n",
       "      <td>36.67</td>\n",
       "      <td>16.14</td>\n",
       "      <td>41.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>2016</td>\n",
       "      <td>214.62</td>\n",
       "      <td>275.00</td>\n",
       "      <td>153.00</td>\n",
       "      <td>105.00</td>\n",
       "      <td>491.5</td>\n",
       "      <td>138401</td>\n",
       "      <td>26.53</td>\n",
       "      <td>28.76</td>\n",
       "      <td>24.77</td>\n",
       "      <td>19.61</td>\n",
       "      <td>43.49</td>\n",
       "      <td>16.58</td>\n",
       "      <td>42.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>2017</td>\n",
       "      <td>228.86</td>\n",
       "      <td>294.98</td>\n",
       "      <td>160.69</td>\n",
       "      <td>110.00</td>\n",
       "      <td>511.5</td>\n",
       "      <td>149831</td>\n",
       "      <td>26.86</td>\n",
       "      <td>29.41</td>\n",
       "      <td>24.99</td>\n",
       "      <td>20.22</td>\n",
       "      <td>37.90</td>\n",
       "      <td>16.79</td>\n",
       "      <td>42.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>2018</td>\n",
       "      <td>246.00</td>\n",
       "      <td>313.50</td>\n",
       "      <td>169.59</td>\n",
       "      <td>100.10</td>\n",
       "      <td>537.5</td>\n",
       "      <td>160870</td>\n",
       "      <td>27.67</td>\n",
       "      <td>30.31</td>\n",
       "      <td>25.75</td>\n",
       "      <td>21.83</td>\n",
       "      <td>38.13</td>\n",
       "      <td>17.29</td>\n",
       "      <td>44.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>2019</td>\n",
       "      <td>248.72</td>\n",
       "      <td>311.19</td>\n",
       "      <td>178.00</td>\n",
       "      <td>92.75</td>\n",
       "      <td>577.5</td>\n",
       "      <td>166197</td>\n",
       "      <td>28.65</td>\n",
       "      <td>30.80</td>\n",
       "      <td>26.50</td>\n",
       "      <td>23.10</td>\n",
       "      <td>39.14</td>\n",
       "      <td>17.91</td>\n",
       "      <td>45.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>2020</td>\n",
       "      <td>254.50</td>\n",
       "      <td>312.03</td>\n",
       "      <td>185.56</td>\n",
       "      <td>115.00</td>\n",
       "      <td>598.0</td>\n",
       "      <td>157511</td>\n",
       "      <td>29.37</td>\n",
       "      <td>32.07</td>\n",
       "      <td>27.47</td>\n",
       "      <td>22.88</td>\n",
       "      <td>42.03</td>\n",
       "      <td>18.36</td>\n",
       "      <td>47.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>2021</td>\n",
       "      <td>295.00</td>\n",
       "      <td>349.00</td>\n",
       "      <td>220.25</td>\n",
       "      <td>125.00</td>\n",
       "      <td>585.0</td>\n",
       "      <td>115469</td>\n",
       "      <td>29.86</td>\n",
       "      <td>31.93</td>\n",
       "      <td>27.88</td>\n",
       "      <td>24.25</td>\n",
       "      <td>42.44</td>\n",
       "      <td>18.66</td>\n",
       "      <td>47.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  year  all_sls_price  sls_75%  sls_25%  sls_min  sls_max  \\\n",
       "0       0  2002         110.00   142.50    78.00    36.00    245.0   \n",
       "1       1  2003         130.00   165.00    90.00    39.00    250.0   \n",
       "2       2  2004         147.50   180.34   118.25    51.00    288.0   \n",
       "3       3  2005         155.00   190.00   122.75    46.38    295.0   \n",
       "4       4  2006         161.25   195.00   128.50    63.00    320.0   \n",
       "5       5  2007         175.00   211.54   136.00    76.00    350.0   \n",
       "6       6  2008         171.00   210.00   133.50    93.75    372.5   \n",
       "7       7  2009         151.62   185.00   123.50    83.00    350.0   \n",
       "8       8  2010         166.75   204.38   132.00    89.00    395.0   \n",
       "9       9  2011         170.00   208.75   128.62    85.00    385.0   \n",
       "10     10  2012         173.22   220.00   130.00    75.00    405.0   \n",
       "11     11  2013         175.00   225.00   135.00    74.00    460.0   \n",
       "12     12  2014         189.49   240.00   141.50    91.00    450.0   \n",
       "13     13  2015         192.00   246.12   147.80    77.50    495.0   \n",
       "14     14  2016         214.62   275.00   153.00   105.00    491.5   \n",
       "15     15  2017         228.86   294.98   160.69   110.00    511.5   \n",
       "16     16  2018         246.00   313.50   169.59   100.10    537.5   \n",
       "17     17  2019         248.72   311.19   178.00    92.75    577.5   \n",
       "18     18  2020         254.50   312.03   185.56   115.00    598.0   \n",
       "19     19  2021         295.00   349.00   220.25   125.00    585.0   \n",
       "\n",
       "    net_additions  median_income  inc_75%  inc_25%  inc_min  inc_max  \\\n",
       "0           96048          19.37    21.71    18.04    14.95    30.74   \n",
       "1          113361          20.11    22.43    18.52    15.23    30.10   \n",
       "2          117216          20.77    22.88    19.26    15.90    31.76   \n",
       "3          134571          21.62    23.66    19.93    15.17    32.22   \n",
       "4          147035          22.01    24.00    20.51    14.96    34.10   \n",
       "5          155691          22.66    25.02    20.94    16.23    35.58   \n",
       "6          150878          23.60    25.84    21.64    16.43    36.59   \n",
       "7          116236          24.11    26.13    22.47    16.28    37.68   \n",
       "8           99959          24.01    26.41    22.67    17.06    36.28   \n",
       "9           96309          24.58    26.51    22.57    16.93    40.12   \n",
       "10          96688          24.93    26.97    23.07    17.80    38.51   \n",
       "11          92187          25.43    27.65    23.66    18.09    36.90   \n",
       "12         104278          25.45    27.97    23.70    19.87    37.87   \n",
       "13         130730          25.83    27.99    23.85    19.44    36.67   \n",
       "14         138401          26.53    28.76    24.77    19.61    43.49   \n",
       "15         149831          26.86    29.41    24.99    20.22    37.90   \n",
       "16         160870          27.67    30.31    25.75    21.83    38.13   \n",
       "17         166197          28.65    30.80    26.50    23.10    39.14   \n",
       "18         157511          29.37    32.07    27.47    22.88    42.03   \n",
       "19         115469          29.86    31.93    27.88    24.25    42.44   \n",
       "\n",
       "    l_income_calc  h_income_calc  \n",
       "0           12.11          31.00  \n",
       "1           12.57          32.17  \n",
       "2           12.98          33.23  \n",
       "3           13.51          34.59  \n",
       "4           13.76          35.22  \n",
       "5           14.16          36.25  \n",
       "6           14.75          37.76  \n",
       "7           15.07          38.57  \n",
       "8           15.01          38.42  \n",
       "9           15.36          39.33  \n",
       "10          15.58          39.89  \n",
       "11          15.89          40.69  \n",
       "12          15.90          40.72  \n",
       "13          16.14          41.32  \n",
       "14          16.58          42.45  \n",
       "15          16.79          42.97  \n",
       "16          17.29          44.27  \n",
       "17          17.91          45.84  \n",
       "18          18.36          47.00  \n",
       "19          18.66          47.78  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sls_inc_data = sls_yr.merge(income_yr, on = 'year')\n",
    "# based on parameter that low income is 60% of median, also applied as high income as +60%. \n",
    "sls_inc_data['l_income_calc'] = sls_inc_data['median_income']/1.6\n",
    "sls_inc_data['h_income_calc'] = sls_inc_data['median_income']*1.6\n",
    "sls_inc_data = sls_inc_data.reset_index()\n",
    "sls_inc_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "45c203bf",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>year</th>\n",
       "      <th>all_sls_price</th>\n",
       "      <th>sls_75%</th>\n",
       "      <th>sls_25%</th>\n",
       "      <th>sls_min</th>\n",
       "      <th>sls_max</th>\n",
       "      <th>net_additions</th>\n",
       "      <th>median_income</th>\n",
       "      <th>inc_75%</th>\n",
       "      <th>inc_25%</th>\n",
       "      <th>inc_min</th>\n",
       "      <th>inc_max</th>\n",
       "      <th>l_income_calc</th>\n",
       "      <th>h_income_calc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.50</td>\n",
       "      <td>2011.50</td>\n",
       "      <td>187.78</td>\n",
       "      <td>233.97</td>\n",
       "      <td>140.63</td>\n",
       "      <td>81.37</td>\n",
       "      <td>418.07</td>\n",
       "      <td>126973.30</td>\n",
       "      <td>24.67</td>\n",
       "      <td>26.92</td>\n",
       "      <td>22.91</td>\n",
       "      <td>18.31</td>\n",
       "      <td>36.91</td>\n",
       "      <td>15.42</td>\n",
       "      <td>39.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.92</td>\n",
       "      <td>5.92</td>\n",
       "      <td>46.54</td>\n",
       "      <td>57.09</td>\n",
       "      <td>31.94</td>\n",
       "      <td>24.80</td>\n",
       "      <td>111.39</td>\n",
       "      <td>25068.85</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.07</td>\n",
       "      <td>2.84</td>\n",
       "      <td>2.94</td>\n",
       "      <td>3.74</td>\n",
       "      <td>1.88</td>\n",
       "      <td>4.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2002.00</td>\n",
       "      <td>110.00</td>\n",
       "      <td>142.50</td>\n",
       "      <td>78.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>245.00</td>\n",
       "      <td>92187.00</td>\n",
       "      <td>19.37</td>\n",
       "      <td>21.71</td>\n",
       "      <td>18.04</td>\n",
       "      <td>14.95</td>\n",
       "      <td>30.10</td>\n",
       "      <td>12.11</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.75</td>\n",
       "      <td>2006.75</td>\n",
       "      <td>159.68</td>\n",
       "      <td>193.75</td>\n",
       "      <td>127.25</td>\n",
       "      <td>71.25</td>\n",
       "      <td>342.50</td>\n",
       "      <td>103198.25</td>\n",
       "      <td>22.50</td>\n",
       "      <td>24.76</td>\n",
       "      <td>20.83</td>\n",
       "      <td>16.15</td>\n",
       "      <td>35.21</td>\n",
       "      <td>14.06</td>\n",
       "      <td>36.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.50</td>\n",
       "      <td>2011.50</td>\n",
       "      <td>174.11</td>\n",
       "      <td>215.77</td>\n",
       "      <td>134.25</td>\n",
       "      <td>84.00</td>\n",
       "      <td>400.00</td>\n",
       "      <td>123973.00</td>\n",
       "      <td>24.76</td>\n",
       "      <td>26.74</td>\n",
       "      <td>22.87</td>\n",
       "      <td>17.43</td>\n",
       "      <td>37.29</td>\n",
       "      <td>15.47</td>\n",
       "      <td>39.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>14.25</td>\n",
       "      <td>2016.25</td>\n",
       "      <td>218.18</td>\n",
       "      <td>280.00</td>\n",
       "      <td>154.92</td>\n",
       "      <td>95.34</td>\n",
       "      <td>499.12</td>\n",
       "      <td>150092.75</td>\n",
       "      <td>26.61</td>\n",
       "      <td>28.92</td>\n",
       "      <td>24.83</td>\n",
       "      <td>19.96</td>\n",
       "      <td>38.66</td>\n",
       "      <td>16.63</td>\n",
       "      <td>42.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19.00</td>\n",
       "      <td>2021.00</td>\n",
       "      <td>295.00</td>\n",
       "      <td>349.00</td>\n",
       "      <td>220.25</td>\n",
       "      <td>125.00</td>\n",
       "      <td>598.00</td>\n",
       "      <td>166197.00</td>\n",
       "      <td>29.86</td>\n",
       "      <td>32.07</td>\n",
       "      <td>27.88</td>\n",
       "      <td>24.25</td>\n",
       "      <td>43.49</td>\n",
       "      <td>18.66</td>\n",
       "      <td>47.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index     year  all_sls_price  sls_75%  sls_25%  sls_min  sls_max  \\\n",
       "count  20.00    20.00          20.00    20.00    20.00    20.00    20.00   \n",
       "mean    9.50  2011.50         187.78   233.97   140.63    81.37   418.07   \n",
       "std     5.92     5.92          46.54    57.09    31.94    24.80   111.39   \n",
       "min     0.00  2002.00         110.00   142.50    78.00    36.00   245.00   \n",
       "25%     4.75  2006.75         159.68   193.75   127.25    71.25   342.50   \n",
       "50%     9.50  2011.50         174.11   215.77   134.25    84.00   400.00   \n",
       "75%    14.25  2016.25         218.18   280.00   154.92    95.34   499.12   \n",
       "max    19.00  2021.00         295.00   349.00   220.25   125.00   598.00   \n",
       "\n",
       "       net_additions  median_income  inc_75%  inc_25%  inc_min  inc_max  \\\n",
       "count          20.00          20.00    20.00    20.00    20.00    20.00   \n",
       "mean       126973.30          24.67    26.92    22.91    18.31    36.91   \n",
       "std         25068.85           3.00     3.07     2.84     2.94     3.74   \n",
       "min         92187.00          19.37    21.71    18.04    14.95    30.10   \n",
       "25%        103198.25          22.50    24.76    20.83    16.15    35.21   \n",
       "50%        123973.00          24.76    26.74    22.87    17.43    37.29   \n",
       "75%        150092.75          26.61    28.92    24.83    19.96    38.66   \n",
       "max        166197.00          29.86    32.07    27.88    24.25    43.49   \n",
       "\n",
       "       l_income_calc  h_income_calc  \n",
       "count          20.00          20.00  \n",
       "mean           15.42          39.47  \n",
       "std             1.88           4.80  \n",
       "min            12.11          31.00  \n",
       "25%            14.06          36.00  \n",
       "50%            15.47          39.61  \n",
       "75%            16.63          42.58  \n",
       "max            18.66          47.78  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sls_inc_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12edb360",
   "metadata": {},
   "source": [
    "## Fuzzy System -  Affordability Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b1bb873a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Antecedent/Consequent for universe variables & membership functions\n",
    "income = ctrl.Antecedent(np.arange(0, 60, 1), 'income')\n",
    "price = ctrl.Antecedent(np.arange(0, 600, 10), 'price')\n",
    "afford = ctrl.Consequent(np.arange(1, 90, 1), 'affordability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc194e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom membership functions \n",
    "income['low'] = fuzz.trapmf(income.universe,[0,0,15,25])\n",
    "income['medium'] = fuzz.trimf(income.universe,[15.5,25,40])\n",
    "income['high'] = fuzz.trapmf(income.universe,[30,40,60,60])\n",
    "\n",
    "price['low'] = fuzz.trapmf(price.universe,[0,0,75,175])\n",
    "price['medium'] = fuzz.trimf(price.universe,[75,200,325])\n",
    "price['high'] = fuzz.trapmf(price.universe,[250,400,600,600])\n",
    "\n",
    "afford['low'] = fuzz.trapmf(afford.universe, [0, 0, 20, 35])\n",
    "afford['medium'] = fuzz.trimf(afford.universe, [15, 30, 45])\n",
    "afford['high'] = fuzz.trapmf(afford.universe, [25, 40, 90,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "858549d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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O71BSlyeCKyde6XQY/Qrlh7YdApd/jo+Pv6jTWH9rtvX8rBVLR1vJzhpBsF8rev5tXQdUA+OBucD/E5GRl+xI5G4R2SAiG3pecqnwERHKIqVOYOf8gWDm3gE7X4G2lvAdM4y6TTfrG9drfWCQZsyYwb59+y5cJTz3XHRNRLQzEdQBkwKeT8T7m3+gvwFeNl41wH7gkvVXjTGrjDHzjTHzx4wZY1vAqn9l0yIlEVRArk3DRoMZOQ6mlMP2l/rfNgrtbtrN6OTRZI/IdjqUqJSSksLjjz/OsmXLKC8vJzs7m1GjomcJbztvDa0HpolIHnAYuBW4vcc2B4FPAGtEJBu4DNhnY0xqiErzM/nO/25ztn2lvz7wf34V3uOWrIC//F/vV5fxNHhYlKOziXuTm5vL9u3bLzz/h3/4h0u2ufrqq9m9ezfGGO69917mz58PcKEJvV/gfiKFbf+TjTGdwH3AW8Au4HljzA4RuUdE7vFt9m9AqYhsA/4MfMsYEwG/bqreZKUmMSE9ha2HHRxXX18No3PDUx8IVHAtnGmExsj7jzxUlQ2VLB6vt4WG4r//+7+ZO3cuhYWFnDp1iq985StOhxQybUyjBuzfXttJekoi939imjMBrPkJtByFT/97+I/97g/h3Em4/sfhP7ZN2rvaWfrsUt6++W1GDrukROc4bUwzcNqYRtmuvCCLtU4uSx2u+QPBzLsDtr0A7a3OHN8GW45tYeqoqRGZBPyi7RdWJw3m70oTgRqwhXkZbK07xbn2rvAf3F8fmFIa/mODd8nrCfNh56vOHN8GkX5bKDExkZaWFk0GITDG0NLSQmJi4oA+pytLqQEbkZRA4fiRrK9t4orpYR7FVb8ZMnLDXx8IVLICPngM5t7mXAwWqmyo5P559zsdRq8yMjJoamrizJkzTocSFRITE8nIGNj/D00EalD86w6FPRGEY32h/kxfBq//PRz7EMZc5mwsQ3Sm/Qw1J2uYN3ae06H0Kj4+Hh02bi+9NaQGpXyaQ3UCJ+sDfvGJMPd22PSUs3FYYEPjBmaPmU1SfFL/GyvX0kSgBmXOxHRqj7dy8mwY21d2dcCh9TB5SfiO2Zviu2DLs9DZ5nQkQ+Jp9OhsYqWJQA3OsIQ4FuSO5oNwtq+MhPqAX8ZUyJ4Ju19zOpIhqazXZaeVJgI1BGFfljoS6gOB/D2No9Sx1mMcO3eMyzMudzoU5TBNBGrQwp8IIqA+EGjGDXBkJ5zY63Qkg+Jp9LAgZwHxcfFOh6IcpolADdpl2WmcOd9J3ckwTK6KpPqAX0ISzLkVNv/W6UgGRW8LKT9NBGrQ4uKE0oIwNbWPpPpAoOK7oPoZb6KKIsYYPI0eFo3TheaUJgI1RGX5meEZRrr//ciqD/iNucxbOP7oTacjGZADpw/QbbrJHZnrdCgqAmgiUEPirROcsH/6f6TVBwJFYdHY0+AdNhrpbSlVeGgiUEMyKWM4w4fF89ERGzt3dbZDXYTVBwLNXA6HN0LzQacjCVllg9YH1Mc0Eaghs330UP1myMiLvPqAX2IKzL4ZNj/tdCQh6eruoqqxSusD6gJNBGrIygoy7U0EkTZ/IJjiFd5E0BVZTcmD2d20m6yULMYOH+t0KCpCaCJQQ1aan0VVbRMdXd32HKC2IvITQc4sSBsHNe84HUm/Khsq9WpAXUQTgRqyjBHDmDR6OFvrmq3fub8+MCVC6wOBSlbApiedjqJfWh9QPWkiUJYon+YdPWS5+s3e4Zkpo63ft9UKb4ID6+B0g9OR9Kqtq42tx7ayIGeB06GoCKKJQFmiND+TCjvqBLXvR+6w0Z6SUqHw81AduUXj6qPVFKQXkDYszelQVATRRKAssTAvgx2HT9HabnGxNBrqA4GKV3j7FHTbVC8ZIk+DziZWl9JEoCwxfFgChRNGUbW/ybqddrZD3YboqA/4jZ8Hyemw712nIwlK6wMqGE0EyjLlBVms22thnaB+U/TUB/xEIrZofLr9NHub9zJn7BynQ1ERRhOBskxZQSYVeyysE0TD/IFgZt8Me9+DlqNOR3KR9Y3rmTNmjralVJfQRKAsUzQxnUMnW2myqn1lJK8v1JfkUXD5Dd5VSSOIp8HD4vF6W0hdShOBskxifBwLczNYZ8VqpNFYHwhUstJbNLZ7Mb4B0IlkqjeaCJSl/KuRDln9JsjMj676QKCJCyB+mPeqJgIcOXuEpvNNzBg9w+lQVATSRKAsZdkCdNFaH/CLsKKxp9HDwpyF2pZSBaWJQFlqenYqre1dHGoaYvvKaK0PBCq6BT76E7RaOKR2kPz9B5QKRhOBspSIDH01Un99IFL7D4RqeAZMvw62POtoGMYYKuu1PqB6N6BEICIjRUTnpqs+lRVksXYo8wku1AfSLYvJMf7bQw4Wjfef3k98XDyT0yY7FoOKbCElAhGZLyLbgK3AdhHZIiIl9oamolVZQRbrao7T3T3IH37RXh8INKUMujvhUJVjIfivBrQtpepNqFcETwBfNcbkGmOmAPcC/2NfWCqaTUhPYWRKIh8eOTO4HbihPuAnAsV3OVo01vqA6k+oieCMMWaN/4kxpgIY5P9yFQtK8wdZJ3BLfSDQnNth12twrjnsh+7s7mT9kfVaH1B9CjURVInIL0XkKhG5UkQeB94TkWIRKe7tQyKyTEQ+FJEaEfl2L9tcJSLVIrJDRP4ymJNQkad8sMNID290T33AL3UM5F8N214I+6F3ndhF9vBsslKywn5sFT0SQtxuru/rv/Z4vRQwwDU9PyAi8cBjwCeBOmC9iKw2xuwM2CYdeBxYZow5KCLaRNUlluRn8s0Xt9Le2c2whAGMSYi2ZadDVbIC3v4uLPiy93ZRmOhqoyoUISUCY8zVg9j3QqDGGLMPQESeBZYDOwO2uR142Rhz0HecyFqlSw1a+vBh5GWNYEtdMwtyM0L/YO0aWPxV+wJzSt5VcP60t+PahF4voi3nafBw58w7w3Y8FZ36TAQicocx5mkR+Uaw940xP+nj4xOAQwHP64CeNyqnA4ki8h6QBvzMGPNUv1GrqFDqW4005ETQ2ea9NTTZhb/BxsVB8Z2w8TdhSwTnO8+z9fhWSrJ1gJ/qW3/X7CN8X9N6efQl2PVvz/GECUAJ8BngOuAhEZl+yY5E7haRDSKy4dixY/0cVkUKb3+CAdQJDm+CzAJ31QcCzb0Ddr4CbS1hOdzmo5u5bPRlpA5LDcvxVPTq84rAGPNL39fvDWLfdcCkgOcTgfog2xw3xpwFzorI+8Ac4KMecawCVgHMnz8/cpZzVH2aPyWDnfWnOdvWyYikEO5CumnYaDAjx8GUctj+krdmYDNdbVSFKtQJZWNE5J9FZJWIPOF/9POx9cA0EckTkWHArcDqHtu8CiwVkQQRGY731tGugZ6Eikwpw+KZPXEA7SvdNJGsN2FciE7nD6hQhTqc41VgFPAO8HrAo1fGmE7gPuAtvD/cnzfG7BCRe0TkHt82u4A38c5YrgJ+ZYzZPpgTUZGpvCCLilCGkbq5PhCo4Fo40wiN9n6bn2o7Re3pWuaM0baUqn+hDh8dboz51kB3box5A3ijx2u/6PH8x8CPB7pvFR1KC7L455e39b+h2+sDfnHxMO8O71XB9fZ9269vXM/csXNJjE+07RjKPUK9InhNRK63NRLlSkUTRnG4+RzHW9r63rC2AvJcflvIb96d3sll7UNcqrsPlQ2VLM5x+dWVskyfiUBEzojIaeBreJPBORE5HfC6Un1KiI9jUV4m6/pbjTQW6gN+6ZNgwnzY+apth/A0eLRQrELWZyIwxqQZY0b6vsYZY1ICno8MV5AqupUXZLJ2Tx91glipDwSysWjceLaR5rZmLsu4zJb9K/cJddRQmYiM8P35DhH5iYjo4uYqJGW+grHpbU3+wxshaxokjwpvYE6avgya9sGxDy3fdWVDJQtzFhIn2ndKhSbU75T/AlpFZA7wj8AB4Le2RaVcpWBsKh1d3RzsrX2l2+cPBBOfCHNvh03WT6T3NHhYPD6Grq7UkIWaCDqN99e55XiXgfgZ/c8sVgrwt6/MYm1NL3WCWKoPBCq+y9vGsrOfQvoAGGO0UKwGLOR+BCLyT8AdwOu+lUV1XJoKWa/9CTrbvENHY6k+4JcxFbILYdcfLNvlvlP7SIpPYmLaRMv2qdwv1ERwC9AGfMkY04h3QTkd+69CVuZbd+iS9pWxWB8IZHHR2L+shLalVAPRbyLw/fb/tDHmJ/4uZcaYg7pKqBqI8ekpjB4+jJ0NPUYdu7X/QKhm3ABHdsKJvZbsTvsPqMHoNxEYY7rwFopj9Fc2ZZWyYKuRxmp9wC8hCebcCpuHPvais7uTjY0bWZiz0ILAVCwJ9dbQeWCbiPxaRB71P+wMTLlPWUEmFYEF41iuDwQqvguqn4GujiHtZseJHYxLHUdmSqZFgalYEWoieB14CHgf2BjwUCpkS6ZmsenASdo6u7wv1G2ArOmQHONzE8dcBhn58OEfh7QbnU2sBivUVpVPikgKMNkYY/0MGBUTRg1PZOqYEWw+2MziqZmxOX+gN/6i8czPDXoXlQ2VrCxcaV1MKmaEOrP4s0A13iWjEZG5ItKzt4BS/SrNz2KdfxhprNcHAs1c7h1B1XxwUB8/13mO7ce3Mz97vsWBqVgQ6q2hh/E2o28GMMZUA3m2RKRc7UJ/go7zWh8IlJgCs2+GzU8P6uObj2zm8ozLGZ443OLAVCwYyMziUz1e05aRasDm545md+MZWvd7vPfGY70+EKhkJWz6LXR1DvijlY3allINXqiJYLuI3A7Ei8g0Efk5sM7GuJRLJSfGM3dSOg1b3omd/gOhyi6EkeOh5p0Bf7SyXhOBGrxQE8H9QCHe2cW/B04DX7cpJuVyZQVZxB2M8YlkvRnETOPm880cPHOQoqwim4JSbhdSIjDGtBpjvgN8ArjaGPMdY8x5e0NTblWem0r2mV0wSX+DvUThTXBgHZxuCPkjVY1VFI8t1raUatBCHTW0QES24W0yv01EtohIib2hKbeaZfawlwkc7RjmdCiRJykVCm+E6tCLxjp/QA1VqLeGfg181RiTa4zJBe4F/se2qJSrxR9cy+FRJXzQX/vKWFWywtunoLs7pM11fSE1VCEvQ+1fcA7AGFMBnLEnJOV6tRUkTF1KRV/tK2PZ+HmQnA773u130/qWelo6Wpg2epr9cSnX6q95fbGIFANVIvJLEblKRK4UkceB98ISoXIX3/yBvJJrWdtX+8pYF2LR2NPgYVHOIm1LqYakvyUm/qPH838N+LP+D1YDd3gDjJ3B1Ak5dJkd1J5oJS9rhNNRRZ7ZN8Ofvw8tRyF1bK+b+fsPKDUUfSYCY8zV4QpExQjf+kIft688rokgmORRMOOz3lVJy78edBNjDJ4GDw8UPxDe2JTrhDpqKF1EHhCRn+gy1GpIAhrRlOVnBW9fqbz8ReNebp/VNNeQkpDChNQJYQ5MuU2oNxbfAHKBbegy1GqwOs5D/eYL6wuVFWTxwb4Tl7avVF4TF0D8MG/yDMLT4GHxeB0tpIYupGWogWRjzDdsjUS53+ENMGYGJKUBkDMqmcwR3vaVsyZoA7xLiPjWH3oy6HIclQ2V3JB/Q/jjUq4T6hXBb0Xkb0VknIhk+B+2RqbcZ/+aS/oPXFiNVAVX9Nfw0Z+gtemilzu6O9h0ZBOLcrRQrIYu1ETQDvwY+ICPbwttsCso5VJBGtWXFmidoE/DM2D6dbDl2Yte3nF8BxPSJjA6ebRDgSk3CTURfAMo8M0szvM9ptoZmHKZC/WBi3+DXTw18+L2lepS/jkFAUVjnU2srBRqItgBtNoZiHK5uvUw9vIL9QG/USmJFGSnselAszNxRYMpZdDdCYeqLryk8weUlUItFncB1SLyLt6lqAEwxugAZhWaPvoTlxdksrbmOEvyM8McVJQQgeIVsPE3MHkRrR2t7Dqxi+KxxU5Hplwi1CuCV4Af4G1Go8NH1cAFqQ/4leVnsXav1gn6NPd22P06nGtm09FNXJ6pbSmVdUK6IjDGPCkiKcBkY8yHoe5cRJYBPwPigV8ZY/69l+0WAJXALcaYF0Pdv4oSvdQH/IqnjOajxjOcPt/ByGRdUz+oEVmQfzVsewFP3GmtDyhLhTqz+LNANfCm7/lcEVndz2figceATwMzgdtEZGYv2/1f4K0BRa6iRy/1Ab/kxHjmTR6NZ19T0PeVT8kK2PgklfVaKFbWCvXW0MPAQqAZwBhTDeT185mFQI0xZp8xph14FlgeZLv7gZeAoyHGoqJNH/UBvzIdRtq/vKs42X6Kw2cOUJhV6HQ0ykVCTQSdxphTPV7rb12ACcChgOd1vtcuEJEJwI3AL0KMQ0Wj2jX99icu8xWMVR/i4vBMu5JiGU5inN5CU9YJNRFsF5HbgXgRmSYiP8dbOO6LBHmtZ/L4KfAtY0yfg8hF5G4R2SAiG44dOxZiyCoidJyD+upe6wN+heNHcayljaOntRV2XzwjUll8vA7aWpwORblIqIngfqAQ79DR3wOnga/385k6YFLA84lAfY9t5gPPikgt8FfA4yLy+Z47MsasMsbMN8bMHzNmTIghq4jQT33ALz5OWJyXqaOH+lF5fAuLMotg+0tOh6JcJKREYIxpNcZ8xxizwPcD+TvGmP5+dVsPTBORPBEZBtwKXFRg9s1Q9vdBfhFvX+RXBn4aKmKFUB/wK5uWxdoa7WPcm7ozdZzrPEfB/LtD6l6mVKj6HD7a38ggY8zn+nivU0TuwzsaKB54whizQ0Tu8b2vdYFYUFsBS0NbuLYsP5PH363BGINIsDuLsc3T4GHRuEXItE/C69+Axm2QM9vpsJQL9DePYAnegu/vAQ/B7/v3yhjzBt5eBoGvBU0AxpiVA9m3igL++sCk0IY65mWNQID9x88ydUyqraFFI0+DhyXjl0BcPMy7EzY+CZ95xOmwlAv0d2soB/hnYBbeiWGfBI4bY/5ijPmL3cGpKFe3HrJnQlJoP9RFRFcj7UW36cbT6Pl4/sC8O2D7i9CuS4CpoeszERhjuowxbxpjVgCLgRrgPRG5PyzRqehWW+FdMG0Aygu0ThDMnpN7SE1MZVzqOO8L6ZNgwnzY+aqzgSlX6LdYLCJJInIT8DRwL/Ao8LLdgSkXqK0I2lmrL6X5mXyw7wRd2r7yIkGXnfYvT63UEPWZCETkSbzzBYqB7/lGDf2bMeZwWKJT0WuA9QG/sSOTyR6ZxI76nvMXY5u/UHyR6cugaR8cC3n5L6WC6u+K4E5gOvA1YJ2InPY9zojIafvDU1HrUNWA6gOBSvO1fWWgjq4ONh3dxMKchRe/EZ/oXZV001POBKZco78aQZwxJs33GBnwSDPGjAxXkCoKDWD+QE/lBVms0zrBBduOb2Ny2mTSk9MvfbP4Ltjye+hsu/Q9pUIU6sxipQZmCIlg0dQMNh88yfkObV8JvvrA+F5usWVMhexZsOsP4Q1KuYomAmW99lZo2DLg+oBfWnIil+WksenASYsDi06eBg+Lc/r4u9SisRoiTQTKenXrIbtwUPUBv7ICrRMA3raUTbuYlz2v941m3ABHdsKJveELTLmKJgJlvSHcFvIrK8hi7V6tE2w4soFZWbNISUjpfaOEJJhzK2z+bfgCU66iiUBZz4JEMG9yOnuPtnDqXIdFQUWnyoZKFuX0vYQ34C0aVz8DXbH996UGRxOBstaF+kAIP7z6kJQQT/GU0VTui+2rAk+Dp/dCcaAxl0FGPnz4R/uDUq6jiUBZq65qyPUBv7L82O5aduLcCRpaGijMDLEtpRaN1SBpIlDWsuC2kF+s9zGuaqyiJKeEhLj+Fgn2mbkcDm+E5oP2BqZcRxOBspaFiWDmuJE0nW2n8VRstq8Mur5QXxJTYPbNsPlp+4JSrqSJQFnHovqAX1ycUJofm1cFxhgq6weYCABKVsKm30JXpy1xKXfSRKCsU1flneVqQX3Ar7QgNusEdWfqaO9uZ+qoqQP7YHYhjBwPNe/YE5hyJU0EyjqDWHa6P2X5WazdexxjYmtZ6spG79XAoFp2atFYDZAmAmUdC+sDflMyh5MQF8feYy2W7jfSVdZXXrrsdKgKb4ID6+B0vbVBKdfSRKCs0d4KDVstqw/4iQhlBZkx1bWs23SzvnH9wOsDfkmpUHgjbP6dtYEp19JEoKxxyAM5s2DYCMt3HWvrDn108iNGJY0iZ0TO4HdSsgI2PwXd3dYFplxLE4Gyhg23hfxK87Pw7DtBZ1ds/FAb0m0hv/HzIDkd9r1rSUzK3TQRKGvYmAjGpCUxPj2FbYdjo32lv1A8ZFo0ViHSRKCGrv0sNFpfHwhUmp/FuhhYjbSjq4Pqo9UsyFkw9J3Nvhn2vQctR4e+L+VqmgjU0B2qgpzZttQH/MqnZVKxx/11gi3HtpA7MpdRSaOGvrPkUTDjs95VSZXqgyYCNXS1FZBr7fyBnhbmZbK1rplz7e5uX1nZYEF9IFDJCm9z+xibh6EGRhOBGjob6wN+qUkJzBg3kg0Hmmw9jtM8DR5r6gN+ExdA/DDvv5FSvdBEoIam/Sw0boNJC20/lHc1UvfWCVraW/jw5IfMG9tHW8qBEvGuP7TxN9btU7mOJgI1NGGoD/i5vT/BxiMbKcoqIjkh2dodF/017HkbWt19NaUGTxOBGpow3Bbymzd5NPuPn6W5tT0sxws3y+sDfsMzYPp1sOVZ6/etXEETgRqaMCaCYQlxlEwZzQcuHUY64P4DA+GfU6BFYxWEJgI1eGGsD/iVF3hXI3Wb4+eOc6T1CDMzZ9pzgCll0N3pvZWnVA+aCNTgHfKErT7gV+rSBeg8DR4WZC8gPi7engOIQPEKLRqroDQRqMEL420hv8tzRnLqXAeHm8+F9bh28zR47KkPBJp7O+x+Hc4123scFXVsTQQiskxEPhSRGhH5dpD3vyAiW32PdSIyx854lMVsaETTn7g4YYnLRg8ZY7z1gfE21Qf8RmRB/tWw7QV7j6Oijm2JQETigceATwMzgdtEpOcN0P3AlcaYIuDfgFV2xaMs1n4WGrfDxPDVB/zK8rNY56JEcOjMIbq6u8gbmWf/wUpWwkYtGquL2XlFsBCoMcbsM8a0A88CywM3MMasM8ac9D2tBCbaGI+y0iEPjCuCYcPDfmhvwfiEa9pX+q8GBtWWcqDyroS201C/yf5jqahhZyKYABwKeF7ne603XwL+aGM8ykoO1Af8JmcOJykhjo+OuKN9pW3zB4KJi4Piu7xXBUr52JkIgv16E/RXOBG5Gm8i+FYv798tIhtEZMOxY8csDFENmoOJAHxXBS64PdRtuqlqrGJRTpgSAcC8O2DnK9DmjkSqhs7ORFAHTAp4PhG4pJu2iBQBvwKWG2OCjgs0xqwyxsw3xswfM2aMLcGqAXCwPuBXWpDFOhfMJ9jdtJuM5AyyR2SH76BpOTClHLa/FL5jqohmZyJYD0wTkTwRGQbcCqwO3EBEJgMvA3caYz6yMRZlJQfrA36l+Zl49jVFfftKW2cT90UXolMBbEsExphO4D7gLWAX8LwxZoeI3CMi9/g2+y6QCTwuItUissGueJSFHL4tBJCVmsSE0SlsqYvu9pVhmT8QTMEnvJ3LGreF/9gq4tg6j8AY84YxZroxJt8Y8wPfa78wxvzC9+cvG2NGG2Pm+h7z7YxHWSQCEgFEf52gvavduraUAxUX760VaNFYoTOL1UBFQH3AryzKE8GWY1vIT89n5LCRzgQw7w7Y/iK0tzpzfBUxNBGogTnkgXFzHK0P+C3My2Db4VO0tnc6HcqgOFYf8EufBBPme0cQqZimiUANTITcFgIYkZRA4fiRrK892f/GESis8wd6U7JCbw8pTQRqgPavgdwyp6O4oKwgOpebONN+hpqTNcwdO9fZQKYvg5P74ehuZ+NQjtJEoELX1gJHdkREfcCvrCCLiihMBBsaN1A0poik+CRnA4lPhLlfgE1PORuHcpQmAhW6CKoP+M2dlM7BE600nY2u9pWeRo+z9YFAxXfC1meh47zTkSiHaCJQoYug+oBfYnwc83Ojr31lZb3DheJAGVMhexbsfs3pSJRDNBGo0EVgIgDfMNIoWm7iWOsxjp8/zoyMGU6H8rES7V4WyzQRqND46wNh7E8cqmibT1DZUGlvW8rBmHEDHN0FJ/Y6HYlygCYCFZpDHhg/FxJTnI7kEpdlp3G2rZNDTdExMcrTEEH1Ab+EJJhzK2zSoaSxSBOBCk2E3hYCf/vK6FiN1N+W0vH5A8EUr4Dq30NndBXe1dBpIlChieBEAFBekMnamsgvGB84fQARYcrIKU6Hcqkx0yGzAD7S/lCxRhOB6t+F+QMOLI4WolLfFUGkt6+sbKhkUc6i8LSlHAydaRyTNBGo/h2qjNj6gN+kjOGMSErgwyNnnA6lT54GD4vHR1h9INDM5VC/GU4ecDoSFUaaCFT/Ivy2kF9pfhYVeyK3TtDV3RX+tpQDlZgCs2+GzU87HYkKI00Eqn9RkgjKC7JYF8ETy3Y37Wbs8LGMGR7h7VZLVngTQVd0ruqqBk4TgepbWwsc2RnR9QG/JfmZVO1voiNC21d+0PBBZI4W6im7EEaOh5q3nY5EhYkmAtW3Q5Uwfl5E1wf8MkYMY0rmcLYcanY6lKAicv5Ab7RoHFM0Eai+RcltIb9IXY20rauNrce2Mj87SrqxFt4EBz+A0/VOR6LCQBOB6lsUJoJ1ETifoPpoNQWjC0gdlup0KKFJSoXCG2Hz75yORIWBJgLVuyiqD/gtyB3N9vpTnG2LrEKn420pB6NkBWx+Crojs+airKOJQPXuQn0g2elIQjZ8WAKzJ4yiqrbJ6VAuElX1Ab/x8yA5Hfa963QkymaaCFTvouy2kF+kta883X6avc17mTNmjtOhDFzJCl2ILgZoIlC9278mahNBRQTVCdY3rmfu2LkMix/mdCgDN/tm2PcetBx1OhJlI00EKri2M9716aOoPuA3Z+Io6k62cqKlzelQgAjrRjZQyaNgxmeh+hmnI1E20kSggjvogQnFUVUf8EuIj2NRXkbEzDL2NHqiYyJZb0pWeJvbR/iCfmrwNBGo4Ha/BnlXOh3FoJVGSH+CI2ePcPL8ychqSzlQExdA/DBvzUi5kiYCdalTdbDzFZj/N05HMmjl0yJjYpmn0cOCnAXESRT/VxOBkpXa09jFovi7U9mm4j+h+C4YkeV0JIM2bWwq5zu6OXjC2faVUV0fCFT017DnbWiNrGG5yhqaCNTFTtXB9peg9AGnIxkSEaEsP5O1Dt4eMsZE5/yBYIZnwPTrYMuzTkeibKCJQF1szU+8vWuj+GrAr7Qgi7UO3h7af2o/8XHxTEqb5FgMlvLPKdCisetoIlAfO1UHO16G0vudjsQSZb7+BN3dzvzg8i8rEbFtKQdqShl0d8Ihj9ORKItpIlAfc9HVAMCE9BRGpSSyu9GZ9pWVDZXRPWy0JxHv94cuT+06mgiU14WrgeiuDfRUVpDpyDDSzu5ONhzZ4K5EADD3dtj9OpxrdjoSZSFbE4GILBORD0WkRkS+HeR9EZFHfe9vFZFiO+NRfVjzH94hgiMynY7EUmX5zgwj3XliJ9nDs8lKccfV1QUjsiD/atj2gtORKAvZlghEJB54DPg0MBO4TURm9tjs08A03+Nu4L/sikf1ofkQ7PhfWOKO2kCgJfmZbKw9SXtneJdSds1ooWBKVnpvD2nR2DXsvCJYCNQYY/YZY9qBZ4HlPbZZDjxlvCqBdBEZZ2NMKpiKn7jyagAgffgwcrNGUB3m9pVR2X8gVHlXQttpqN/kdCTKIgk27nsCcCjgeR3Q84ZpsG0mAA297fTgid3c99QSq2JU4L3fm7cU/nyf05HYoiOrha++00ZKYnzYjnmaXXQ1rOAp1oftmOF0PZ9gyW9WcjRhvNOhqBDN/dZbvb5nZyIINmau57VkKNsgInfjvXUE0PbYisrtQ4wt0mUBYb6xvTu8h3PkHMMqaytXuvb8nvB+yYLtrj1HH/d8n35b3jTGLAv2lp2JoA4InEkzEejZCTuUbTDGrAJWAYjIBmNMlHQAHxw9x+jn9vMDPUc3sbNGsB6YJiJ5IjIMuBVY3WOb1cBdvtFDi4FTxphebwsppZSynm1XBMaYThG5D3gLiAeeMMbsEJF7fO//AngDuB6oAVqB6F3uUimlopSdt4YwxryB94d94Gu/CPizAe4d4G5XWRBapNNzjH5uPz/Qc3QNMToWWCmlYpouMaGUUjEuqhJBf0tWRCMReUJEjorI9oDXMkTkbRHZ4/s62skYh0JEJonIuyKyS0R2iMjXfK+76RyTRaRKRLb4zvF7vtddc47gXS1ARDaLyGu+5247v1oR2SYi1SKywfeaq86xN1GTCEJcsiIa/QboObb328CfjTHTgD/7nkerTuDvjTGXA4uBe33/bm46xzbgGmPMHGAusMw3Cs5N5wjwNWBXwHO3nR/A1caYuQFDRt14jpeImkRAaEtWRB1jzPtAz/5/ywH/Wr9PAp8PZ0xWMsY0GGM2+f58Bu8Pkgm46xyNMabF9zTR9zC46BxFZCLwGeBXAS+75vz6EAvnGFWJoLflKNwo2z+fwvd1rMPxWEJEcoF5gAeXnaPvtkk1cBR42xjjtnP8KfCPQODqfW46P/Am7z+JyEbfagbgvnMMytbhoxYLaTkKFZlEJBV4Cfi6Mea0a7p2+RhjuoC5IpIO/K+IzHI4JMuIyA3AUWPMRhG5yuFw7FRmjKkXkbHA2yIS9nVXnBJNVwQhLUfhEkf8q7D6vh51OJ4hEZFEvEngd8aYl30vu+oc/YwxzcB7eOs+bjnHMuBzIlKL95bsNSLyNO45PwCMMfW+r0eB/8V7O9pV59ibaEoEoSxZ4RargRW+P68AXnUwliER76/+vwZ2GWN+EvCWm85xjO9KABFJAa7Fu4qfK87RGPNPxpiJxphcvP/v/j9jzB245PwARGSEiKT5/wx8CtiOi86xL1E1oUxErsd7r9K/ZMUPnI1o6ETk98BVeFc5PAL8K/AK8DwwGTgI3GyM6VlQjgoiUg6sAbbx8f3lf8ZbJ3DLORbhLSTG4/3l6nljzPdFJBOXnKOf79bQPxhjbnDT+YnIVLxXAeC9Zf6MMeYHbjrHvkRVIlBKKWW9aLo1pJRSygaaCJRSKsZpIlBKqRiniUAppWKcJgKllIpxmgiUAkRkndMxKOUUHT6qlFIxTq8IlAJEpMX39SoReU9EXhSR3SLyO9/saERkgYis8/UdqBKRNF8vgv/xrWO/WUSu9m27UkReEZE/iMh+EblPRL7h26ZSRDJ82+WLyJu+hc7WiMgM5/4WVKyKpkXnlAqXeUAh3rWs1gJlIlIFPAfcYoxZLyIjgXN41+jHGDPb90P8TyIy3befWb59JQM1wLeMMfNE5D+Bu/DOkl8F3GOM2SMii4DHgWvCdJ5KAZoIlAqmyhhTB+BbWjoXOAU0GGPWAxhjTvveLwd+7nttt4gcAPyJ4F1fD4YzInIK+IPv9W1AkW9F1lLghYDVWJPsPTWlLqWJQKlLtQX8uQvv/xMh+LLnfa2nHbif7oDn3b59xgHNxpi5g45UKQtojUCp0OwGxovIAgBffSABeB/4gu+16XgXJ/swlB36rir2i8jNvs+LiMyxI3il+qKJQKkQ+Nqj3gL8XES2AG/jvff/OBAvItvw1hBWGmPaet/TJb4AfMm3zx24oP2qij46fFQppWKcXhEopVSM00SglFIxThOBUkrFOE0ESikV4zQRKKVUjNNEoJRSMU4TgVJKxThNBEopFeP+f5KE9UtlXeNOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "income.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7adbebf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76794c7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "afford.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ade98dce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#set the rules\n",
    "rule1 = ctrl.Rule(income['low'] & price['low']| \n",
    "                  income['medium'] & price['low']|\n",
    "                  income['high'] & price['low']|\n",
    "                  income['high'] & price['medium'], afford['high'])\n",
    "                  \n",
    "rule2 = ctrl.Rule(income['low'] & price['medium']|\n",
    "                  income['low'] & price['high']| \n",
    "                  income['medium']  & price['high'], afford['low'])\n",
    "\n",
    "rule3 = ctrl.Rule(income['medium'] & price['medium']| \n",
    "                  income['high'] & price['high'], afford['medium'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "34a31981",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crontol system\n",
    "afford_ctrl = ctrl.ControlSystem([rule1, rule2, rule3])\n",
    "affordability = ctrl.ControlSystemSimulation(afford_ctrl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "376b7019",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test using antecedent labels, low income and high price ,should be an unaffordable score/ low affordability score\n",
    "affordability.input['income'] = 17.92\n",
    "affordability.input['price'] = 349\n",
    "\n",
    "affordability.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b2812ca7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Low income, high price, affordability score:  16\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Low income, high price, affordability score: ',round(affordability.output['affordability']))\n",
    "afford.view(sim=affordability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "77277f7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# test high income, high price, should be medium affordability score\n",
    "inp_dict = {'income': 50, 'price': 350}\n",
    "affordability.inputs(inp_dict)\n",
    "\n",
    "affordability.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bc94fde2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High income, high price, affordability score:  30\n"
     ]
    },
    {
     "data": {
      "image/png": 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dC3isKSvOZUf1eQKBKDvQGmPV8Z1KBIXroK4CeqOf3qrjA8oN48eP57nnnuPOO++kvLycyZMnM2mSAzupOcTJ0tAeoERECoHTwP3AJwYcUwd8AHhHRCYD84EaB2NS/UydNJ7sjFSON15m8fQovmmbjlt1/KxZzgSWngO5c+H0Xpi9xvbL6q/U093Xzdysuc7EpcasOXPmcPTo0av3/+Iv/uK6Y2677TZOnDiBMYaHH36Y0lJrHUtoE/qQ/ufxCseuCIwxvcCXgdeBd4FfGWOOichDIvJQ8LC/BdaIyBHgTeAbxpgYt8ZUQykfTnnIybJQyDDaUoc2odG208oN//zP/8yyZctYtGgRly5d4gtf+ILbIdnm6J7FxphXgVcHPPZ8v6/PAB9yMgY1tLLiPH5ecYov3BrFp+iazbDsk47FBFiJZvM/wG1/ZfslFWcqWDtjrXMxKTWERx99lEcffdTtMIZFVxaPcauKctl/6iJd/j57L+jtgVM7rTq+k2athrNHoeuyrcMDJsDus7t1fCBBjbadFN00nH8rTQRj3KTxKZRMnsD+OpttqU/vter36TnOBpYyHmbcDKe22zr8RMsJstKymJIxxdm4VNylpKTQ1tamycAGYwxtbW2kpEQ3fdrR0pAaHULjBGvm2ujdH4/xgZDQNNL5d0U8dFfjLr0aSFA5OTm0tLRw5coVt0MZFVJSUsjJie6DmiYCRVlxHt997QRf/7CNg2u2wPrr2kY5o2g9/OZhW4dWNFbw3+f/d2fjUa7w+XzotHFnaWlIsXx2FlVNbVzqjND/pOsynDtq7SQWD1OXwZVGuHJ2yMO6+7o52HSQW6bEqB22UmOMJgJFWrKPm2ZlUVFzYegDT+2A6cut+n08JPlgTnnEttSHmg4xN2suE1OvW5SulLJBE4ECbK4niOf4QIiNdhO6mlipkdFEoABrnCBiW2pXEsFt1vsOMWNEE4FSI6OJQAGwcOpELrb3cKa1M/wBV85a9fqpy+IaF7lzQZLg/B/CPn255zLVrdXcWHBjfONSKoFoIlAAJCUJa4YqD9Vsser1Sb74BiYy5Kb2exr3cGP+jaT50sI+r5SKTBOBuqq8OI8d1YMMGJ/cAnNvi29AIaFdy8KoaKxg9TRtO63USGgiUFeVB8cJrlvBaUxwfMCtRHAr1L4Dfddv5qHjA0qNnCYCddXMnHTGp/iobGq79okLVVadPqfIncAyC2DiDGg8eM3DZ9vP0trdyvyc+e7EpVSC0ESgrlFWnMu2ygHjBDWbofBWq17vlqJboebtax6qaKxgxZQVJIl+Gys1EvoTpK4RdvtKN6aNDhRmwLiisYJV07QspNRIaSJQ11gzN4/dJ1vw9wWsB/p6rfp80a3uBjZ7DZzeDz0dgNVlURvNKRUbmgjUNXIyUpmVm87hhlbrgcZDVn0+s8DVuEibAFOXQt1OAKpbq0nzpTFzwswIL1RKRaKJQF2nvDiPbZXBaaQ1b7tfFgrp125CZwspFTuaCNR1rhkn8ML4QMjARKDjA0rFhCYCdZ1b5uRw7Mwl2tsuw5kDVn3eC6bfDBdr8V85x75z+1g5ZaXbESmVEDQRqOuMT/WxZMYkKvdshClLIC3T7ZAsvhSYtZqjx37BjAkzyB6X7XZESiUETQQqrPLiPDpOvOmdslBI0XoqTr2p4wNKxZAmAhVWWXEeBed3ejMRXKnVRKBUDOmexSqsJdm9dPY20nzhAvldb8T25H8Y/vk6ert412e4KSW6zbmVUoPTRKDCSq7bRk3aAmpPXuKeWd2xPfnl08N+6d4rNSwmjfT6XVBwQwyDUmrs0tKQCq/qTbqzStjelOp2JNeoaK9j1fgpULnR7VCUShiaCFR41W8zZVYx286lDrVLZNxVtNWxKnep1fYiEHA7HKUSgiYCdb2Wk+BvZ2ZBPgaobYvzrmSDOO9v56z/CguziiE1E84dcTskpRKCJgJ1vZNbIG8ekiSsKehhm0fKQ7va6yjNmEGyJFl7GVdvdjskpRKCJgJ1vcqNVzehKS/oYXtTissBWSra61iVMcu6k1MMVTpOoFQsRJUIRGSiiExwKhjlAYEA1G6DvHkAlBX4qWhOpc/lcQJjjJUIMmdbD+QWw+m90BvjGU1KjUG2EoGIlIrIEeAwcFREDonIzc6Gplxx7iikZsB4q31DwfgABeMCHLvo7kzjup5WAsZQmBpsK5GaDhOnQ/1uV+NSKhHYvSJ4EfiSMWaOMWY28DDwU+fCUq6p2Wx92u6nzAPjBNbVwCyk/3aZOUVQ/fbgL1JK2WI3EVwxxrwTumOM2QZccSYk5arKN6yB2H6scQKXE0HbqffHB0JyS3ScQKkYsJsIdovIj0RkvYjcKiLPAZtFZLmILB/sRSJyp4i8JyJVIvLYIMesF5GDInJMRLaEO0bFSW83nN533RXBynw/B1uS6epzJ6w+E2B3R/31iSB7DlyohM5WN8JSKmHYLfwuC/75NwMeXwMY4PaBLxARH/As8EGgAdgjIhuMMcf7HZMFPAfcaYypExGX90Mc4+p3w8RpkJJ+zcOZKYYFk3rZez6F8sn+uIf1blcT+cmZ5KcMaIftS4Gcudbg9g0fjXtcSiUKW4nAGHPbMM69AqgyxtQAiMgvgXuA4/2O+QTwijGmLvg+TcN4HxUr1W9fnTY6UFmBn21Nqa4kgoq2uuuvBkJyiqD6TU0ESo3AkIlARD5ljPm5iPx5uOeNMd8f4uXTgfp+9xuAgVtKzQNSRGQzMAH4gTHmXyJGrZxRtRHmlId9qrygh789nAm0xzcmoKL9FJ/MGaQCmVcCR1+Ob0BKJZhIYwQZwT8nDHIbioR5bOBs9GTgZuAjwIeBb4nIvOtOJPKgiOwVkb3Nzc0R3lYNS9clq96eXRj26WW5fmqv+GjtCfff6mBYAT9HOs9yS8aM8AdMnA4dLXBp+B1NlRrrhrwiMMb8KPjnt4dx7gZgZr/7M4AzYY45b4xpB9pFZCtwI/CHAXG8ALwAUFpa6qEWaAmkdps1SOwLv4o4NQlK8/zsbErlrhnxW8R1oOMM89LyyfSlhT9AkiB/ntUWY9kn4haXUonE7oKyfBH5KxF5QUReDN0ivGwPUCIihSKSCtwPbBhwzG+BtSKSLCLpWKWjd6P9S6gYqHpz0KuBEGs9QXzbTYTWDwwpu0jbUis1Ananj/4WmARsAv6z321Qxphe4MvA61i/3H9ljDkmIg+JyEPBY94FXsNasbwb+LEx5uhw/iJqhKrfutpWYjDlk+O/nmDIgeKQvOAVgZf6ZSs1itidPppujPlGtCc3xrwKvDrgsecH3H8SeDLac6sYunQaOi5YU0eHMH9iH23+JOrbk5iZ4fxeAK29nZzqucjS8VOHPjAjD5J80HxCdy1TahjsXhH8TkTudjQS5Z5g22lk6G8HEVhT0MOOOF0V7G6v56b0aaQk2dgPIXee1R5DKRW1IX/yReSKiFwGvoqVDDpF5HK/x1Ui6Nd2OpLyOPYdstpOz7Z3cG6R1R5DKRW1IROBMWaCMWZi8M8kY8z4fvcnxitI5SBj3r8isKFssnVFEIhDOb6i/VTkgeKQ3HlQvwv64r/gTanRzu6soTIRyQh+/SkR+b6I2PwJVZ7W/B4kJVt1dhumpweYmBLgvUvObl95uucS7X1+StLsxUVaJmQUwOn9jsalVCKyO0bwT0CHiNwI/A/gFPCvjkWl4qdms7U6NwplcehGuqu9jpWZM0mSKBaw5WpbaqWGw24i6DXGGKxeQT8wxvyAyCuL1WhQ+YbVuC0K5ZP9jo8TVLTVsdru+ECIbl+p1LDY3o9ARP4S+BTwn8HOot7YyFYNX58f6iusvv5RWJXfw97zKfQ4NIM0YIx1RRBp/cBAOUXWDmvdbc4EplSCspsI7gO6gc8ZY85iNZTTuf+j3en9kJFv1dejkJVqKJrQx4ELznwWqOw+T6YvjWmpUc5HSE6zVkef2uFIXEolqoiJIPjp/+fGmO+HdikzxtRpl9AEUP121GWhkLKCHradc6Y8FHY3MrtyiqB6U2wDUirBRUwExpg+rIHiSXGIR8VT1cbrdiOzy8ntK3cOpywUklditctQStlmtzTUBRwRkZ+IyDOhm5OBKYd1t0HTMdsLyQa6Oc/Pe5d9XPHHti21P9DHwY4zrMyYGfngcCbNhMtnoE33OFLKLru9hiI2mVOjTN1Oq56ePEh75wjG+WBZTi+7mlO4Y1pPzMI61NnInNRsJiWPH94JknzBJnRbYckfxSwupRKZ3a0qfyYi44FZxpj3HI5JxUN15LbTkZQF203EMhFEtZp4MNlFULVJE4FSNtldWfxfgINYLaMRkWUiMnBvATWaVL0Z9UKygZwYJ6hoG8H4QEheiTUQrm2plbLF7hjBE1ib0bcCGGMOAiP7OKnc09YEl09b9fQRWJTdS3NXEuc67X4bDe1KXzd/6D7PTelDt8OOKHMy9PVAS01M4lIq0UWzsvjSgMf049ZodXKrVUe30955CD6B1fmxuyrY297A0vFTGZc0wvUJIpA/H2q03YRSdthNBEdF5BOAT0RKROSHgK7aGa2qNll19BhYU+CP2faVFe0jWD8wUI5uX6mUXXYTwVeARViri38BXAa+5lBMyknGWJ+U8+21nY6kvKCH7edSY1KOr2ivY/VIB4pD8uZZK4wDfbE5n1IJzO6soQ7gmyLyXeuuueJsWMoxLTVWj6GMgpicbk5mH8lJUH3FR/HE4f/SbfK3caG3gwXjYhMX4yZZt8ZDMH15bM6pVIKyO2voFhE5grXJ/BEROSQiNzsbmnJEzebgtpSxWQgmEpu21Lva61iRMRNfhO0yo5I7V7evVMoGuz91PwG+ZIyZY4yZAzwM/NSxqJRzotiW0q6yGGxfGZNpowPlFOs4gVI22G5DHWo4B2CM2QZoeWi0CfTBqe22t6W0a01BDxXNKfQOsy21MSa2A8UhuXPhzAHwd8b2vEolmEib1y8XkeXAbhH5kYisF5FbReQ5YHNcIlSx03jo/dp5DOWPM0xPD3D4ot2OJdc62dOCT5KYlZoV07hIGQ9ZM6GuIrbnVSrBRPrJ/ccB9/+m39e6jmC0qdk87LbTkYTaUi/P7Y36tTuDZSGJ0bjFNbILrVXUc2+L/bmVShBDJgJjjP70JJLKjTB5oSOnLi/o4fn30nlkYUfUr61or+OuSfMdiAqrDKZtqZUakt1ZQ1ki8oiIfF/bUI9S/k5oPGANoDpgRb6fIxeT6YjygqDXBNjX3sCK4badjiR7Nlw8CR0tzpxfqQRgd7D4VWAOcATY1++mRov6XVZvoZRxjpw+I9mwOLuXPeejmz10rPMsU1MmkJec4UhcJCVbm+/UvhP5WKXGKLuje+OMMX/uaCTKWVVvxXza6EDWeoIUbp1ivy11RXsdqzJnOxgVkBMcJ1h4j7Pvo9QoZfeK4F9F5M9EZKqI5IRujkamYqv6TcgdWdvpSMoK/FGvJ7DWDzhUFgrRcQKlhmQ3EfQATwI7eb8stNepoFSMdbRYrSWynf3kfWOOn/o2Hxe67c3+6Qj4OdZ1jtL0GY7GxYSp0H0ZLp5y9n2UGqXsJoI/B4qDK4sLgzdn6wwqdmrfserkScOb529XSpI1aLzD5lXB/vYGbhhXQLovtpvbXEeSIG8+nNzi7PsoNUrZTQTHgOjnBSpvqHrTqpPHQTR9hyra62K/mngw2pZaqUHZTQR9wMHg6mKdPjra1LxtfSKOg2i2r9zVXsdqpweKQ/LmQe02CAyzD4ZSCcxureA3wZsabVrroOsyTJgSl7crmdhHVx/UtSUxK3PwX7otvR009Fxi0fjJcYmL9BxIToOm4zBlcXzeU6lRwtYVgTHmZ8CvgApjzM9Ct0ivE5E7ReQ9EakSkceGOO4WEekTkT+yH7qypWaLtW1jLNs7D0EEym3MHtrdXk9pxgxSZGTbZUYlt0TbUisVht2Vxf8FOAi8Fry/TEQ2RHiND3gWuAtYCDwgItf1Nwge913g9agiV/ZUbbT67cTRGhvloYq2U7FvOx1JThFUvhHf91RqFLD7MfEJYAXQCmCMOQhE+u2yAqgyxtQYY3qAXwLhVvR8BXgZaLIZi7IrEICT78S87XQkZQU97GhKJTBIW0JjDDvjOVAcklcCDXug1/6CN6XGAruJoNcYc2nAY5G6j04H6vvdbwg+dpWITAc+DjxvMw4VjabjkJxq1cfjaFp6gOy0AMdbww9BNfgv0WP6mJuWG9e4SM2wxkoa9sT3fZXyOLuJ4KiIfALwiUiJiPwQ2BHhNeFWFQ1MHk8D3zDGDLnZrYg8KCJ7RWRvc3OzzZAVJ7dAbnyvBkKGmj1U0WZdDTjSdjqSnLnWLCql1FV2E8FXgEVAN/AL4DLwtQivaQD69w6YAZwZcEwp8EsRqQX+CHhORP7rwBMZY14wxpQaY0rz8/NthqyofMPapcsF1vaVKWGfs/oLxbksFJJbDFWb3HlvpTzK7qyhDmPMN40xtwR/IX/TGNMV4WV7gBIRKRSRVOB+4JoB5uAK5dA+yL/G2hf5N9H/NdR1enusEkiuM22nI1mV72f/hRS6wlzr7W53YH9iu3IKofmENaVWKQVEWEcQaWaQMeZjQzzXKyJfxpoN5ANeNMYcE5GHgs/ruICTTu+FzClWXdwFk1INJRP72H8hhTUF/muey/alMyVlgitx4Uu1Zg+d2g7z73InBqU8JtKCstVYA76/AHYRvu4/KGPMq1h7GfR/LGwCMMb8aTTnVhFUO992OpLQOMHAROBaWSgktH2lJgKlgMiloSnAXwGLgR8AHwTOG2O2GGO0g5eXVW1yrSwUsia4j/FArpWFQrQttVLXGDIRGGP6jDGvGWM+DawCqoDNIvKVuESnhqfrslUHj1OjucEsz/VTdcXHpZ5rLyRvyXC47XQkk2ZA2zm43OhuHEp5RMTBYhFJE5F7gZ8DDwPPAK84HZgagVPbrbKQ0+2dIxjns5LBzuZrZw9N9DmzXaZtkmS13dC21EoBkQeLf4ZVFvo98G1jzNG4RKVGpvqtuLeVGEx5gf39CeIqNE5w4/1uR6KU6yJdEfwxMA/4KrBDRC4Hb1dEROffeVX1m3FvKzEYaz2BBxNB3nyrAZ2JtEBeqcQ35BWBMSY+LStV7Fw5C21NVh3cAxZm9XKx24PfRhl5gIHzlZDvjaSplFs8+BOqRqRmi/VpN05tpyNJEmv2kOeIWFdN2pZaKU0ECadqE2TPcTuKa5R5MRGANaBepW2pldJEkEiMsT7hxmlbSrvKvZoI8ubBqZ3Q1+t2JEq5ShNBIjlfCQSC9W/vSE0b2MHcI9ImWC26zxxwOxKlXKWJIJGcDI0PuNDeeQgVbXVuhzC43GJtS63GPE0EiaTyDdf7C4VT0X7K7RAGl6NtqZXSRJAo+nqterdH1g+EGGOoaPfyFcFcaDwMPe1uR6KUazQRJIrGg1a9O82l9s6DqOq+wHgJv0GNJySnQfZsqNvpdiRKuUYTQaKofsu13ciGUtFex0q3205Hkl0I1TpOoMYuTQSJomqTVe/2mIq2U6xyu+10JHklVt8hpcYoTQSJoKfdqnN77IrAb/rY13H6uv0H2vzemtVE1mxoPQXtF9yORClXaCJIBHU7rTp3cprbkVzjSMdZZqVmkZ08/prHd5/32JhBks8aZNe21GqM0kSQCKo3u74JTTi72uvCloW2e7EbaagttVJjkCaCRFC9CXK9NW0UrIHicPsTezIR5M/ThWVqzNJEMNq1X4DWOsjy1oBse18P73Y1cVP69OueO9ORRFOXx771MqeAvwNaTrodiVJx57GfRhW1k1sgt8Sqc3vIvo4GFo+bzPik68cDVub72dnksXGCUFtqHSdQY5AmgtGu6k3PbEvZ3862OlZlzg77XLlXdy3LKYLKjW5HoVTcaSIY7Wre9lxbCbD6C63MmBn2ubKCHradS/XeLpF586H2HQgE3I5EqbjSRDCatZy06toTprgdyTXO+9s5529j0fjwcc2d0IcBTrZ5q5zF+CxIzYRzR9yORKm40kQwmnm07fSu9jpuyZhB8iDbZYpYVwWenD2UW2xt96nUGKKJYDSr3OjRttN1rMoIPz4QUl7Qw3avDRiDtTq7UrevVGOLJoLRKhCw6tkeGx8ItZ0Ot36gv7ICPzubUunz2jhBbjGc3ge93W5HolTcaCIYrc4dserZ47PcjuQap3paCRjDnNTsIY8rGB+gYHyAoxeT4xSZTSnpMHEa1O92OxKl4kYTwWhVvdn69OoxFe2nWJU5C7ExblHm5Wmk2pZajSGaCEarqo2e6zYK1v7EdttOlxf0sP2cBxNBbon176vUGKGJYDTyd8HpvZ67IugzAXa319tOBCvz/RxsSaaz1+HAopU9By5UQmer25EoFReaCEajht0wcYZVz/aQ453nmJySSX5Kpq3jJ6QYbsjqZe8Fj80e8qVYm/zUbnM7EqXiQhPBaFS92ZPTRndFcTUQUlbg9+Z6gpxCa/tPpcYARxOBiNwpIu+JSJWIPBbm+U+KyOHgbYeI3OhkPAmjaqPnykIQGigeev3AQOVeXViWN08TgRozHEsEIuIDngXuAhYCD4jIwgGHnQRuNcYsBf4WeMGpeBJGZytcqPJco7nOgJ/DnWcpTZ8R1euW5fo5ecXHxW5vrY5m4jToaIFLp92ORCnHOXlFsAKoMsbUGGN6gF8C9/Q/wBizwxhzMXi3Aojut8hYVLvNmi3k89b8+wMdZ5iflk+GL7pP96lJUJrnZ2ezx64KJMna1F7bUqsxwMlEMB2o73e/IfjYYD4H/N7BeBJDtTfbTle0nWJlZvhuo5GUF/TwjhenkWpbajVGOJkIwl3rh20oICK3YSWCbwzy/IMisldE9jY3N8cwxFGo+i3PtZUAq7/Q6gj9hQZTVtDDDi/2HQptVOO5ftlKxZaTiaAB6P8RcQZwZuBBIrIU+DFwjzHmQrgTGWNeMMaUGmNK8/PzHQl2VLh02qpbT5zmdiTXaO3tpK6nlaXjpw7r9fMn9dHem0R9u8cmsWXkQVIyNJ9wOxKlHOXkT94eoERECkUkFbgf2ND/ABGZBbwC/LEx5g8OxpIYTm6B/PlW/dpDdrfXszx9OinD3C4zSWBNQQ87PDl7qETbUquE59hvFGNML/Bl4HXgXeBXxphjIvKQiDwUPOxxIBd4TkQOishep+JJCJUbIdt76westtPRrR8YyLvbV2pbapX4HJ16Yox5FXh1wGPP9/v688DnnYwhYRhjXRGs+pLbkVynov0U9+eMbAlI2eQevns0k4CxrhA8I68Ejr4MfX5rxbFSCchbNQY1uOYTVr06PdftSK5xuucS7X1+StLyRnSe6ekBJqQEOHHJW9NiSc20xgpO73c7EqUco4lgtKjZbH069Zhd7XWszJxpq+10JGVe3bUsZ662pVYJTRPBaFH5hvULyWMq2oY/bXSg8gI/27y4niC3WNtSq4SmiWA06PND/S7PXREEjGFXDAaKQ1YX9LDvQgo9gZicLnZyiuDcUehuczsSpRyhiWA0OL0fMgqserWHVHafZ4IvjampE2NyvqxUQ9GEPg54rS11cpqVDOp2uh2JUo7QRDAaVL/t0bLQKVbFqCwUUubVbqTZhVD1pttRKOUITQSjQdVGyPNi2+k6VmXGpiwUUl7Q481xgrwSq8+TUglIE4HXdbdB0zHPbUTjD/RxoOMMt2QMr9HcYG7O8/PeZR+X/V5aTABMmgmXz0Bbk9uRKBVzmgi87tQOqywRZXtnpx3qbGROajaTfONiet5xPliW00tFs8fGCZJ82m5CJSxNBF7n1bbT7adYGeOyUEiZV/sOZRdpeUglJE0EXlf9luemjQLsaquP2fqBgTy7fWX+PKh5W9tSq4SjicDL2pqsuvSk2NbhR6qtr5s/dDdzU7oz7bAXZffS3JXEuU6PfXtmFFhrOlpq3I5EqZjy2E+ausbJrdbmKMNs7+yUvR0NLB0/lbQkZ/oC+QRW53vwqkDE+v+o2ex2JErFlCYCL6vaBDkeHB9oi91q4sGUFfSwzat9h3T7SpVgNBF4lTHWQjKPbku5KtOZ8YGQ8sl+tp9L9V45Pq8ETm2HQJ/bkSgVM5oIvKqlBgJ+qy7tIU3+Ns73trNgnLNbhs7O6CM5CaqveKssxrhJ1q3xkNuRKBUzmgi8qiZ4NRCD9s6xtKu9jhUZM/E5vF2mSKg85LFxAoDcuTpOoBKKJgKvqtzo0f5Czo8PhJR5td1EbrGOE6iEoonAiwJ91opij60fMMbEZH9iu9YU9LDrfAq9nmtLXQyNB8Df6XYkSsWEJgIvajz0fi3aQ072XCRZkpiZmhWX98sfZ5ie3sfhix7bvjJlnLW2o36X25EoFROaCLyoZrNVfvAYq+30rJhsS2lXeYHfe+sJwGoCWPWW21EoFROaCLyocqM3E4EDbacj8e44gbalVolDE4HX+Dut+nOutwaKe02Ave0NrIjT+EDIinw/Ry4m09Eb17eNLHu2NcW3o8XtSJQaMU0EXlNXYdWfk2Pb3nmkjnWeZWrKBHKT0+P6vhnJhsXZvexq9thVQVKyddV2cqvbkSg1YpoIvKb6Lc9tQgPxWU08GM+2pc4ptP6/lBrlNBF4TdWbVv3ZY+K5fmCg8oIetnttoxqAvPmaCFRC0ETgJR0tcLHWqj97SEfAz/Guc9ycPt2V91+a00t9m48L3d5aZc2EKdB9BVrr3I5EqRHRROAlte9Ym9Q71N55uA50nOaGcQWku7RdZkoSrMz3e688JEmQP1+3r1SjniYCL6ny6LaUbadY5dBuZHaVeXXXsuxCqNJ2E2p000TgJdVvebjttDvjAyGeXU+QNw9OvgMBr/XBUMo+TQRecfGUVW+eMMXtSK7R0ttBQ88lFo2f7GocJRP76A5AXZvHvmXTcyA5DZqOux2JUsPmsZ+qMezkFqve7HB752jtbq+nNGMGKeLuvgAiVrsJb7alLtG21GpU89ZvnbGscqOHxwfcLQuFeHacIHcuVL7hdhRKDZsmAi8IBKwZQ/nz3Y7kOlbbaW9MZw0tLAt4bfvK3GJo2AO9PW5HotSwOJoIROROEXlPRKpE5LEwz4uIPBN8/rCILHcyHs9qOg7J42F8ttuRXKO+p5Ue00dRWo7boQAwNT1ATlqA463eml5LagZMmAqn97odiVLD4lgiEBEf8CxwF7AQeEBEFg447C6gJHh7EPgnp+LxtJrN1voBjwmtJo5n2+lIyr1aHsop0lXGatRy8opgBVBljKkxxvQAvwTuGXDMPcC/GEsFkCUiUx2MyZsq3/DmtpTtdax0edroQGsKetjW5MF2E7nFULXJ7SiUGhYnr7GnA/X97jcAK20cMx1oHOykdRdO8OV/WR2rGL2h8wL4fdDQ4HYk19jTVsvXC/8rpMZ4p7T03GG/dNVseHRPEp+ryI9hQCOXYibxdMtPOfHdD7sdilJhLfvG64M+52QiCFdPGDjMZ+cYRORBrNIRQNuzn654b4SxxUIecD52p6uM1YliGtdU/iRWp4JgbLJo4IVh9N4deSz9xeTf7EcAxHT2UIy/x2JG44qON+J6TF4zxtwZ7iknE0EDMLPf/RnAmWEcgzHmBeCFWAc4EiKy1xhT6nYcA3k1LvBubBpXdDSu6Hg1rv6cHCPYA5SISKGIpAL3AxsGHLMB+JPg7KFVwCVjzKBlIaWUUrHn2BWBMaZXRL4MvA74gBeNMcdE5KHg888DrwJ3A1VAB/AZp+JRSikVnqMTso0xr2L9su//2PP9vjbAw07G4CBPlar68Wpc4N3YNK7oaFzR8WpcV4n1u1gppdRYpS0mlFJqjNNEYIOIvCgiTSJytN9jOSKyUUQqg3/GvT+EiMwUkbdF5F0ROSYiX/VCbCIyTkR2i8ihYFzf9kJc/eLzicgBEfmdV+ISkVoROSIiB0Vkr4fiyhKRX4vIieD32Wq34xKR+cF/p9Dtsoh8ze24grE9GvyePyoivwj+LLgeVySaCOz538DA+bePAW8aY0qAN4P3460X+H+NMTcAq4CHg2083I6tG7jdGHMjsAy4MzgrzO24Qr7KtUsRvBLXbcaYZf2mGnohrh8ArxljFgA3Yv27uRqXMea94L/TMuBmrIkm/8ftuERkOvAIUGqMWYw1SeZ+t+OyxRijNxs3YA5wtN/994Cpwa+nAu95IMbfAh/0UmxAOrAfa1W563FhrVV5E7gd+J1X/i+BWiBvwGOuxgVMBE4SHEv0SlwDYvkQsN0LcfF+p4QcrIk4vwvG55l/r8FuekUwfJNNcM1D8M8CN4MRkTnATcAuPBBbsPxyEGgCNhpjPBEX8DTwP4D+e0t6IS4DvCEi+4Ir6b0QVxHQDPw0WEr7sYhkeCCu/u4HfhH82tW4jDGngaeAOqw2OZeMMW+4HZcdmggSgIhkAi8DXzPGXHY7HgBjTJ+xLt1nACtEZLHLISEiHwWajDH73I4ljDJjzHKsjrwPi8g6twPC+lS7HPgnY8xNQDseKmsEF6p+DPgPt2MBCNb+7wEKgWlAhoh8yt2o7NFEMHznQp1Sg382uRGEiKRgJYF/M8a84qXYAIwxrcBmrDEWt+MqAz4mIrVY3XBvF5GfeyAujDFngn82YdW7V3ggrgagIXg1B/BrrMTgdlwhdwH7jTHngvfdjusO4KQxptkY4wdeAdZ4IK6INBEM3wbg08GvP41Vn48rERHgJ8C7xpjveyU2EckXkazg1+OxfkBOuB2XMeYvjTEzjDFzsEoKbxljPuV2XCKSISITQl9j1ZWPuh2XMeYsUC8ioa3zPgAcdzuufh7g/bIQuB9XHbBKRNKDP5sfwBpcdzuuyNwepBgNN6xvtkbAj/Up6XNALtagY2XwzxwX4irHqi0fBg4Gb3e7HRuwFDgQjOso8Hjwcdf/zfrFuJ73B4vd/vcqAg4Fb8eAb3ohrmAMy4C9wf/L3wDZHokrHbgATOr3mBfi+jbWh56jwL8CaV6IK9JNVxYrpdQYp6UhpZQa4zQRKKXUGKeJQCmlxjhNBEopNcZpIlBKqTFOE4EaU0TkvwW7aL4dvP8LETksIo8O83xzpF9X2kGOWR/qdBrmuVf7rbloC/45TUR+Hfx6mYjcPZzYlLLL0R3KlPKgzwFfMsa8LSJTgDXGmNl2XywiycaY3lgFY4y57pe8sVYZ/1Hw7jKglAE7/SkVS3pFoBKWiPwm2MTtmIg8KCKPYy3Ce15EngTeAAqCPe3XBj99VwSvEP5PqG+8iGwWkb8XkS3AV0XkZrH2WthJv61Wg1cH74jI/uBtTb9wJgbPeVxEnheRpOBrakUkb0Dcc4L97FOB7wD3BWO8L9jTPj94XJKIVA18vVLR0isClcg+a4xpCba52APcitV++i+MMXtF5Fms1cXLAETkMPAVY8wWEfkO8DfA14LnyjLG3BrmuCf7vV8T8EFjTJeIlGCtSA/tLbACWAicAl4D7sXq3TMoY0xPMHmVGmO+HHzvBcAnsbqo3gEcMsacH94/j1IWvSJQiewRETkEVAAzgZLBDhSRSVi/7LcEH/oZ0L8D6EuDHPev/Y5JAf5ZRI5gdcRc2O+53caYGmNMH1aCKB/m3+lF4E+CX38W+Okwz6PUVXpFoBKSiKzH+sS82hjTISKbgXEjOGV76NRY/Z3CeRQ4h7WTVxLQ1e+5ga8ZVm8XY0y9iJwTkduxNvv55HDOo1R/ekWgEtUk4GIwCSzA2spzUMaYS8BFEVkbfOiPgS1hjmsFLolI6BN9/1/Ek4BGY0wg+Hpfv+dWiEhhcGzgPmCbzb/HFWDCgMd+DPwc+FXwCkOpEdFEoBLVa0BysJ7/t1jloUg+DTwZfM0yrIHacD4DPBscLO7s9/hzwKdFpAKYx/tXEQA7gX/A6kp5EmvPATveBhaGBouDj20AMtGykIoR7T6q1CgjIqXA/2eMWRvxYKVs0DECpUYREXkM+CI6NqBiSK8IlFJqjNMxAqWUGuM0ESil1BiniUAppcY4TQRKKTXGaSJQSqkxThOBUkqNcf8XtIwijNGTnekAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('High income, high price, affordability score: ',round(affordability.output['affordability']))\n",
    "afford.view(sim=affordability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2ed0a8d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High income, High price, affordability score:  49\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test high income,  High price, should be medium to high affordability score\n",
    "inp_dict = {'income': 40, 'price': 300}\n",
    "affordability.inputs(inp_dict)\n",
    "affordability.compute()\n",
    "print('High income, High price, affordability score: ', round(affordability.output['affordability']))\n",
    "afford.view(sim=affordability)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fade16cd",
   "metadata": {},
   "source": [
    "Unsurpising, as the price changes little when doubling the net additions (adding dwellings) then the affordability score varies little. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe1d84c",
   "metadata": {},
   "source": [
    "### Apply to years data\n",
    "\n",
    "Have prices become more or less affordable over the years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ec3b89a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "inc_sls_yr = pd.DataFrame(sls_inc_data[['year','median_income', 'all_sls_price']])\n",
    "#inc_sls_yr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d2b10ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "aff_score_lst = []\n",
    "def affordability_score(obs_ref,income_value, price_value):\n",
    "    affordability.input['income'] = income_value\n",
    "    affordability.input['price'] = price_value\n",
    "    affordability.compute()\n",
    "    aff_score_lst.append(affordability.output['affordability'])\n",
    "    print(affordability.output['affordability'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bdfb295c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51.027885704322244\n",
      "45.22270731940068\n",
      "39.62978730177947\n",
      "37.71851003030535\n",
      "35.07806311158223\n",
      "28.042192281486017\n",
      "31.551672764414082\n",
      "44.44271808189336\n",
      "35.42755220596932\n",
      "34.04337992057814\n",
      "33.35004765666463\n",
      "32.63550899811977\n",
      "30.000000000000007\n",
      "30.0\n",
      "30.000000000000007\n",
      "30.00000000000001\n",
      "30.000000000000004\n",
      "29.999999999999996\n",
      "29.248225462428756\n",
      "21.194941199474847\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",
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       "\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>year</th>\n",
       "      <th>median_income</th>\n",
       "      <th>all_sls_price</th>\n",
       "      <th>aff_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>19.37</td>\n",
       "      <td>110.00</td>\n",
       "      <td>51.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>20.11</td>\n",
       "      <td>130.00</td>\n",
       "      <td>45.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>20.77</td>\n",
       "      <td>147.50</td>\n",
       "      <td>39.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>21.62</td>\n",
       "      <td>155.00</td>\n",
       "      <td>37.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>22.01</td>\n",
       "      <td>161.25</td>\n",
       "      <td>35.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2007</td>\n",
       "      <td>22.66</td>\n",
       "      <td>175.00</td>\n",
       "      <td>28.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008</td>\n",
       "      <td>23.60</td>\n",
       "      <td>171.00</td>\n",
       "      <td>31.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009</td>\n",
       "      <td>24.11</td>\n",
       "      <td>151.62</td>\n",
       "      <td>44.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>24.01</td>\n",
       "      <td>166.75</td>\n",
       "      <td>35.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>24.58</td>\n",
       "      <td>170.00</td>\n",
       "      <td>34.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>24.93</td>\n",
       "      <td>173.22</td>\n",
       "      <td>33.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>25.43</td>\n",
       "      <td>175.00</td>\n",
       "      <td>32.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>25.45</td>\n",
       "      <td>189.49</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>25.83</td>\n",
       "      <td>192.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>26.53</td>\n",
       "      <td>214.62</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>26.86</td>\n",
       "      <td>228.86</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>27.67</td>\n",
       "      <td>246.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "      <td>28.65</td>\n",
       "      <td>248.72</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020</td>\n",
       "      <td>29.37</td>\n",
       "      <td>254.50</td>\n",
       "      <td>29.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021</td>\n",
       "      <td>29.86</td>\n",
       "      <td>295.00</td>\n",
       "      <td>21.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  median_income  all_sls_price  aff_score\n",
       "0   2002          19.37         110.00      51.03\n",
       "1   2003          20.11         130.00      45.22\n",
       "2   2004          20.77         147.50      39.63\n",
       "3   2005          21.62         155.00      37.72\n",
       "4   2006          22.01         161.25      35.08\n",
       "5   2007          22.66         175.00      28.04\n",
       "6   2008          23.60         171.00      31.55\n",
       "7   2009          24.11         151.62      44.44\n",
       "8   2010          24.01         166.75      35.43\n",
       "9   2011          24.58         170.00      34.04\n",
       "10  2012          24.93         173.22      33.35\n",
       "11  2013          25.43         175.00      32.64\n",
       "12  2014          25.45         189.49      30.00\n",
       "13  2015          25.83         192.00      30.00\n",
       "14  2016          26.53         214.62      30.00\n",
       "15  2017          26.86         228.86      30.00\n",
       "16  2018          27.67         246.00      30.00\n",
       "17  2019          28.65         248.72      30.00\n",
       "18  2020          29.37         254.50      29.25\n",
       "19  2021          29.86         295.00      21.19"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yr_lst = (inc_sls_yr['year']).to_list()\n",
    "inc_lst = (inc_sls_yr['median_income']).to_list()\n",
    "pr_lst = (inc_sls_yr['all_sls_price']).to_list()\n",
    "data_lst_yr = list(zip(yr_lst, inc_lst, pr_lst))\n",
    "\n",
    "aff_score_lst = []\n",
    "for yr, inc, val in data_lst_yr:\n",
    "    affordability_score(yr, inc, val)\n",
    "\n",
    "inc_sls_yr['aff_score'] = aff_score_lst\n",
    "inc_sls_yr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "da314fc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>median_income</th>\n",
       "      <th>all_sls_price</th>\n",
       "      <th>aff_score</th>\n",
       "      <th>net_additions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>19.37</td>\n",
       "      <td>110.00</td>\n",
       "      <td>51.03</td>\n",
       "      <td>96048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>20.11</td>\n",
       "      <td>130.00</td>\n",
       "      <td>45.22</td>\n",
       "      <td>113361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>20.77</td>\n",
       "      <td>147.50</td>\n",
       "      <td>39.63</td>\n",
       "      <td>117216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>21.62</td>\n",
       "      <td>155.00</td>\n",
       "      <td>37.72</td>\n",
       "      <td>134571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>22.01</td>\n",
       "      <td>161.25</td>\n",
       "      <td>35.08</td>\n",
       "      <td>147035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2007</td>\n",
       "      <td>22.66</td>\n",
       "      <td>175.00</td>\n",
       "      <td>28.04</td>\n",
       "      <td>155691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008</td>\n",
       "      <td>23.60</td>\n",
       "      <td>171.00</td>\n",
       "      <td>31.55</td>\n",
       "      <td>150878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009</td>\n",
       "      <td>24.11</td>\n",
       "      <td>151.62</td>\n",
       "      <td>44.44</td>\n",
       "      <td>116236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>24.01</td>\n",
       "      <td>166.75</td>\n",
       "      <td>35.43</td>\n",
       "      <td>99959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>24.58</td>\n",
       "      <td>170.00</td>\n",
       "      <td>34.04</td>\n",
       "      <td>96309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>24.93</td>\n",
       "      <td>173.22</td>\n",
       "      <td>33.35</td>\n",
       "      <td>96688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>25.43</td>\n",
       "      <td>175.00</td>\n",
       "      <td>32.64</td>\n",
       "      <td>92187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>25.45</td>\n",
       "      <td>189.49</td>\n",
       "      <td>30.00</td>\n",
       "      <td>104278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>25.83</td>\n",
       "      <td>192.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>130730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>26.53</td>\n",
       "      <td>214.62</td>\n",
       "      <td>30.00</td>\n",
       "      <td>138401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>26.86</td>\n",
       "      <td>228.86</td>\n",
       "      <td>30.00</td>\n",
       "      <td>149831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>27.67</td>\n",
       "      <td>246.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>160870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "      <td>28.65</td>\n",
       "      <td>248.72</td>\n",
       "      <td>30.00</td>\n",
       "      <td>166197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020</td>\n",
       "      <td>29.37</td>\n",
       "      <td>254.50</td>\n",
       "      <td>29.25</td>\n",
       "      <td>157511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021</td>\n",
       "      <td>29.86</td>\n",
       "      <td>295.00</td>\n",
       "      <td>21.19</td>\n",
       "      <td>115469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  median_income  all_sls_price  aff_score  net_additions\n",
       "0   2002          19.37         110.00      51.03          96048\n",
       "1   2003          20.11         130.00      45.22         113361\n",
       "2   2004          20.77         147.50      39.63         117216\n",
       "3   2005          21.62         155.00      37.72         134571\n",
       "4   2006          22.01         161.25      35.08         147035\n",
       "5   2007          22.66         175.00      28.04         155691\n",
       "6   2008          23.60         171.00      31.55         150878\n",
       "7   2009          24.11         151.62      44.44         116236\n",
       "8   2010          24.01         166.75      35.43          99959\n",
       "9   2011          24.58         170.00      34.04          96309\n",
       "10  2012          24.93         173.22      33.35          96688\n",
       "11  2013          25.43         175.00      32.64          92187\n",
       "12  2014          25.45         189.49      30.00         104278\n",
       "13  2015          25.83         192.00      30.00         130730\n",
       "14  2016          26.53         214.62      30.00         138401\n",
       "15  2017          26.86         228.86      30.00         149831\n",
       "16  2018          27.67         246.00      30.00         160870\n",
       "17  2019          28.65         248.72      30.00         166197\n",
       "18  2020          29.37         254.50      29.25         157511\n",
       "19  2021          29.86         295.00      21.19         115469"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add net additions per year...\n",
    "\n",
    "inc_sls_yr['net_additions'] = sls_yr['net_additions']\n",
    "inc_sls_yr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b89e9da2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>median_income</th>\n",
       "      <th>all_sls_price</th>\n",
       "      <th>aff_score</th>\n",
       "      <th>net_additions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  median_income  all_sls_price  aff_score  net_additions\n",
       "0   2002           0.00           0.00       1.00           0.05\n",
       "1   2003           0.07           0.11       0.81           0.29\n",
       "2   2004           0.13           0.20       0.62           0.34\n",
       "3   2005           0.21           0.24       0.55           0.57\n",
       "4   2006           0.25           0.28       0.47           0.74\n",
       "5   2007           0.31           0.35       0.23           0.86\n",
       "6   2008           0.40           0.33       0.35           0.79\n",
       "7   2009           0.45           0.22       0.78           0.32\n",
       "8   2010           0.44           0.31       0.48           0.11\n",
       "9   2011           0.50           0.32       0.43           0.06\n",
       "10  2012           0.53           0.34       0.41           0.06\n",
       "11  2013           0.58           0.35       0.38           0.00\n",
       "12  2014           0.58           0.43       0.30           0.16\n",
       "13  2015           0.62           0.44       0.30           0.52\n",
       "14  2016           0.68           0.57       0.30           0.62\n",
       "15  2017           0.71           0.64       0.30           0.78\n",
       "16  2018           0.79           0.74       0.30           0.93\n",
       "17  2019           0.88           0.75       0.30           1.00\n",
       "18  2020           0.95           0.78       0.27           0.88\n",
       "19  2021           1.00           1.00       0.00           0.31"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scale for plotting\n",
    "aff_net_scaled = pd.DataFrame(inc_sls_yr)\n",
    "scaler = MinMaxScaler()\n",
    "aff_net_scaled[['aff_score','net_additions', 'median_income', 'all_sls_price']] =scaler.fit_transform(\n",
    "    aff_net_scaled[['aff_score','net_additions', 'median_income', 'all_sls_price']])\n",
    "\n",
    "aff_net_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "982a7b2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Affordability Score and Net Additions \\n England 2002-2021')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(2002,2022)\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "plt.plot(x,aff_net_scaled['all_sls_price'], linewidth = 2 , color = 'blue', linestyle = ':', label = 'Median Sales Price')\n",
    "plt.plot(x,aff_net_scaled['net_additions'], linewidth = 4 , color = 'darkviolet', label = 'Net Additions')\n",
    "plt.plot(x,aff_net_scaled['median_income'], linewidth = 2 , color = 'green', linestyle = ':',label = 'Median Income')\n",
    "plt.plot(x,aff_net_scaled['aff_score'], linewidth = 4 , color = 'orangered', label = 'Affordability Score')\n",
    "ax.set_xticks(range(2002,2022))\n",
    "ax.set_xticklabels(range(2002,2022),rotation = 45)\n",
    "plt.legend()\n",
    "plt.title('Affordability Score and Net Additions \\n England 2002-2021')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e58f64a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next - adjust for inflation and run fuzzy and graph again for years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "44059b71",
   "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>year</th>\n",
       "      <th>all_sls_price</th>\n",
       "      <th>median_income</th>\n",
       "      <th>rpi</th>\n",
       "      <th>net_additions</th>\n",
       "      <th>sls_adj</th>\n",
       "      <th>income_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>110.00</td>\n",
       "      <td>19.37</td>\n",
       "      <td>176.2</td>\n",
       "      <td>96048</td>\n",
       "      <td>62.43</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>130.00</td>\n",
       "      <td>20.11</td>\n",
       "      <td>181.3</td>\n",
       "      <td>113361</td>\n",
       "      <td>71.70</td>\n",
       "      <td>11.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>147.50</td>\n",
       "      <td>20.77</td>\n",
       "      <td>186.7</td>\n",
       "      <td>117216</td>\n",
       "      <td>79.00</td>\n",
       "      <td>11.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>155.00</td>\n",
       "      <td>21.62</td>\n",
       "      <td>192.0</td>\n",
       "      <td>134571</td>\n",
       "      <td>80.73</td>\n",
       "      <td>11.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>161.25</td>\n",
       "      <td>22.01</td>\n",
       "      <td>198.1</td>\n",
       "      <td>147035</td>\n",
       "      <td>81.40</td>\n",
       "      <td>11.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2007</td>\n",
       "      <td>175.00</td>\n",
       "      <td>22.66</td>\n",
       "      <td>206.6</td>\n",
       "      <td>155691</td>\n",
       "      <td>84.70</td>\n",
       "      <td>10.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008</td>\n",
       "      <td>171.00</td>\n",
       "      <td>23.60</td>\n",
       "      <td>214.8</td>\n",
       "      <td>150878</td>\n",
       "      <td>79.61</td>\n",
       "      <td>10.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009</td>\n",
       "      <td>151.62</td>\n",
       "      <td>24.11</td>\n",
       "      <td>213.7</td>\n",
       "      <td>116236</td>\n",
       "      <td>70.95</td>\n",
       "      <td>11.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>166.75</td>\n",
       "      <td>24.01</td>\n",
       "      <td>223.6</td>\n",
       "      <td>99959</td>\n",
       "      <td>74.58</td>\n",
       "      <td>10.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>170.00</td>\n",
       "      <td>24.58</td>\n",
       "      <td>235.2</td>\n",
       "      <td>96309</td>\n",
       "      <td>72.28</td>\n",
       "      <td>10.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>173.22</td>\n",
       "      <td>24.93</td>\n",
       "      <td>242.7</td>\n",
       "      <td>96688</td>\n",
       "      <td>71.37</td>\n",
       "      <td>10.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>175.00</td>\n",
       "      <td>25.43</td>\n",
       "      <td>250.1</td>\n",
       "      <td>92187</td>\n",
       "      <td>69.97</td>\n",
       "      <td>10.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>189.49</td>\n",
       "      <td>25.45</td>\n",
       "      <td>256.0</td>\n",
       "      <td>104278</td>\n",
       "      <td>74.02</td>\n",
       "      <td>9.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>192.00</td>\n",
       "      <td>25.83</td>\n",
       "      <td>258.5</td>\n",
       "      <td>130730</td>\n",
       "      <td>74.27</td>\n",
       "      <td>9.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>214.62</td>\n",
       "      <td>26.53</td>\n",
       "      <td>263.1</td>\n",
       "      <td>138401</td>\n",
       "      <td>81.58</td>\n",
       "      <td>10.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>228.86</td>\n",
       "      <td>26.86</td>\n",
       "      <td>272.5</td>\n",
       "      <td>149831</td>\n",
       "      <td>83.98</td>\n",
       "      <td>9.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>246.00</td>\n",
       "      <td>27.67</td>\n",
       "      <td>281.6</td>\n",
       "      <td>160870</td>\n",
       "      <td>87.36</td>\n",
       "      <td>9.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "      <td>248.72</td>\n",
       "      <td>28.65</td>\n",
       "      <td>288.8</td>\n",
       "      <td>166197</td>\n",
       "      <td>86.12</td>\n",
       "      <td>9.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020</td>\n",
       "      <td>254.50</td>\n",
       "      <td>29.37</td>\n",
       "      <td>293.1</td>\n",
       "      <td>157511</td>\n",
       "      <td>86.83</td>\n",
       "      <td>10.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021</td>\n",
       "      <td>295.00</td>\n",
       "      <td>29.86</td>\n",
       "      <td>305.0</td>\n",
       "      <td>115469</td>\n",
       "      <td>96.72</td>\n",
       "      <td>9.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  all_sls_price  median_income    rpi  net_additions  sls_adj  \\\n",
       "0   2002         110.00          19.37  176.2          96048    62.43   \n",
       "1   2003         130.00          20.11  181.3         113361    71.70   \n",
       "2   2004         147.50          20.77  186.7         117216    79.00   \n",
       "3   2005         155.00          21.62  192.0         134571    80.73   \n",
       "4   2006         161.25          22.01  198.1         147035    81.40   \n",
       "5   2007         175.00          22.66  206.6         155691    84.70   \n",
       "6   2008         171.00          23.60  214.8         150878    79.61   \n",
       "7   2009         151.62          24.11  213.7         116236    70.95   \n",
       "8   2010         166.75          24.01  223.6          99959    74.58   \n",
       "9   2011         170.00          24.58  235.2          96309    72.28   \n",
       "10  2012         173.22          24.93  242.7          96688    71.37   \n",
       "11  2013         175.00          25.43  250.1          92187    69.97   \n",
       "12  2014         189.49          25.45  256.0         104278    74.02   \n",
       "13  2015         192.00          25.83  258.5         130730    74.27   \n",
       "14  2016         214.62          26.53  263.1         138401    81.58   \n",
       "15  2017         228.86          26.86  272.5         149831    83.98   \n",
       "16  2018         246.00          27.67  281.6         160870    87.36   \n",
       "17  2019         248.72          28.65  288.8         166197    86.12   \n",
       "18  2020         254.50          29.37  293.1         157511    86.83   \n",
       "19  2021         295.00          29.86  305.0         115469    96.72   \n",
       "\n",
       "    income_adj  \n",
       "0        11.00  \n",
       "1        11.09  \n",
       "2        11.13  \n",
       "3        11.26  \n",
       "4        11.11  \n",
       "5        10.97  \n",
       "6        10.99  \n",
       "7        11.28  \n",
       "8        10.74  \n",
       "9        10.45  \n",
       "10       10.27  \n",
       "11       10.17  \n",
       "12        9.94  \n",
       "13        9.99  \n",
       "14       10.08  \n",
       "15        9.86  \n",
       "16        9.82  \n",
       "17        9.92  \n",
       "18       10.02  \n",
       "19        9.79  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rpi can use median (or min or max) as groupby measure as rpi is one value per year\n",
    "adj_data =pd.DataFrame(data.groupby('year').all_sls_price.median()/1000)\n",
    "adj_data['median_income'] =data.groupby('year').median_income.median()/1000\n",
    "adj_data['rpi'] =data.groupby('year').rpi.median()\n",
    "adj_data['net_additions'] = data.groupby('year').net_additions.sum()\n",
    "adj_data = adj_data.reset_index()\n",
    "\n",
    "adj_data['sls_adj'] = adj_data['all_sls_price']/(adj_data['rpi']/100)\n",
    "adj_data['income_adj'] = adj_data['median_income']/(adj_data['rpi']/100)\n",
    "\n",
    "adj_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c2094c52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60.584070796460175\n",
      "60.41465036097476\n",
      "59.67606995126495\n",
      "59.42275464089986\n",
      "59.28407754452302\n",
      "58.57715291464058\n",
      "59.61385263453413\n",
      "60.489548234501974\n",
      "60.12673227921956\n",
      "60.357285235811226\n",
      "60.4475639546157\n",
      "60.584070796460175\n",
      "60.18280725383831\n",
      "60.157020013627154\n",
      "59.24656211573603\n",
      "58.734899929380724\n",
      "57.98777276210079\n",
      "58.26368897881529\n",
      "58.10606441421991\n",
      "55.76347132529529\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>year</th>\n",
       "      <th>all_sls_price</th>\n",
       "      <th>median_income</th>\n",
       "      <th>rpi</th>\n",
       "      <th>net_additions</th>\n",
       "      <th>sls_adj</th>\n",
       "      <th>income_adj</th>\n",
       "      <th>aff_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002</td>\n",
       "      <td>110.00</td>\n",
       "      <td>19.37</td>\n",
       "      <td>176.2</td>\n",
       "      <td>96048</td>\n",
       "      <td>62.43</td>\n",
       "      <td>11.00</td>\n",
       "      <td>60.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003</td>\n",
       "      <td>130.00</td>\n",
       "      <td>20.11</td>\n",
       "      <td>181.3</td>\n",
       "      <td>113361</td>\n",
       "      <td>71.70</td>\n",
       "      <td>11.09</td>\n",
       "      <td>60.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004</td>\n",
       "      <td>147.50</td>\n",
       "      <td>20.77</td>\n",
       "      <td>186.7</td>\n",
       "      <td>117216</td>\n",
       "      <td>79.00</td>\n",
       "      <td>11.13</td>\n",
       "      <td>59.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005</td>\n",
       "      <td>155.00</td>\n",
       "      <td>21.62</td>\n",
       "      <td>192.0</td>\n",
       "      <td>134571</td>\n",
       "      <td>80.73</td>\n",
       "      <td>11.26</td>\n",
       "      <td>59.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>161.25</td>\n",
       "      <td>22.01</td>\n",
       "      <td>198.1</td>\n",
       "      <td>147035</td>\n",
       "      <td>81.40</td>\n",
       "      <td>11.11</td>\n",
       "      <td>59.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2007</td>\n",
       "      <td>175.00</td>\n",
       "      <td>22.66</td>\n",
       "      <td>206.6</td>\n",
       "      <td>155691</td>\n",
       "      <td>84.70</td>\n",
       "      <td>10.97</td>\n",
       "      <td>58.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008</td>\n",
       "      <td>171.00</td>\n",
       "      <td>23.60</td>\n",
       "      <td>214.8</td>\n",
       "      <td>150878</td>\n",
       "      <td>79.61</td>\n",
       "      <td>10.99</td>\n",
       "      <td>59.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2009</td>\n",
       "      <td>151.62</td>\n",
       "      <td>24.11</td>\n",
       "      <td>213.7</td>\n",
       "      <td>116236</td>\n",
       "      <td>70.95</td>\n",
       "      <td>11.28</td>\n",
       "      <td>60.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010</td>\n",
       "      <td>166.75</td>\n",
       "      <td>24.01</td>\n",
       "      <td>223.6</td>\n",
       "      <td>99959</td>\n",
       "      <td>74.58</td>\n",
       "      <td>10.74</td>\n",
       "      <td>60.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2011</td>\n",
       "      <td>170.00</td>\n",
       "      <td>24.58</td>\n",
       "      <td>235.2</td>\n",
       "      <td>96309</td>\n",
       "      <td>72.28</td>\n",
       "      <td>10.45</td>\n",
       "      <td>60.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012</td>\n",
       "      <td>173.22</td>\n",
       "      <td>24.93</td>\n",
       "      <td>242.7</td>\n",
       "      <td>96688</td>\n",
       "      <td>71.37</td>\n",
       "      <td>10.27</td>\n",
       "      <td>60.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>175.00</td>\n",
       "      <td>25.43</td>\n",
       "      <td>250.1</td>\n",
       "      <td>92187</td>\n",
       "      <td>69.97</td>\n",
       "      <td>10.17</td>\n",
       "      <td>60.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>189.49</td>\n",
       "      <td>25.45</td>\n",
       "      <td>256.0</td>\n",
       "      <td>104278</td>\n",
       "      <td>74.02</td>\n",
       "      <td>9.94</td>\n",
       "      <td>60.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>192.00</td>\n",
       "      <td>25.83</td>\n",
       "      <td>258.5</td>\n",
       "      <td>130730</td>\n",
       "      <td>74.27</td>\n",
       "      <td>9.99</td>\n",
       "      <td>60.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2016</td>\n",
       "      <td>214.62</td>\n",
       "      <td>26.53</td>\n",
       "      <td>263.1</td>\n",
       "      <td>138401</td>\n",
       "      <td>81.58</td>\n",
       "      <td>10.08</td>\n",
       "      <td>59.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017</td>\n",
       "      <td>228.86</td>\n",
       "      <td>26.86</td>\n",
       "      <td>272.5</td>\n",
       "      <td>149831</td>\n",
       "      <td>83.98</td>\n",
       "      <td>9.86</td>\n",
       "      <td>58.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2018</td>\n",
       "      <td>246.00</td>\n",
       "      <td>27.67</td>\n",
       "      <td>281.6</td>\n",
       "      <td>160870</td>\n",
       "      <td>87.36</td>\n",
       "      <td>9.82</td>\n",
       "      <td>57.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019</td>\n",
       "      <td>248.72</td>\n",
       "      <td>28.65</td>\n",
       "      <td>288.8</td>\n",
       "      <td>166197</td>\n",
       "      <td>86.12</td>\n",
       "      <td>9.92</td>\n",
       "      <td>58.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020</td>\n",
       "      <td>254.50</td>\n",
       "      <td>29.37</td>\n",
       "      <td>293.1</td>\n",
       "      <td>157511</td>\n",
       "      <td>86.83</td>\n",
       "      <td>10.02</td>\n",
       "      <td>58.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021</td>\n",
       "      <td>295.00</td>\n",
       "      <td>29.86</td>\n",
       "      <td>305.0</td>\n",
       "      <td>115469</td>\n",
       "      <td>96.72</td>\n",
       "      <td>9.79</td>\n",
       "      <td>55.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  all_sls_price  median_income    rpi  net_additions  sls_adj  \\\n",
       "0   2002         110.00          19.37  176.2          96048    62.43   \n",
       "1   2003         130.00          20.11  181.3         113361    71.70   \n",
       "2   2004         147.50          20.77  186.7         117216    79.00   \n",
       "3   2005         155.00          21.62  192.0         134571    80.73   \n",
       "4   2006         161.25          22.01  198.1         147035    81.40   \n",
       "5   2007         175.00          22.66  206.6         155691    84.70   \n",
       "6   2008         171.00          23.60  214.8         150878    79.61   \n",
       "7   2009         151.62          24.11  213.7         116236    70.95   \n",
       "8   2010         166.75          24.01  223.6          99959    74.58   \n",
       "9   2011         170.00          24.58  235.2          96309    72.28   \n",
       "10  2012         173.22          24.93  242.7          96688    71.37   \n",
       "11  2013         175.00          25.43  250.1          92187    69.97   \n",
       "12  2014         189.49          25.45  256.0         104278    74.02   \n",
       "13  2015         192.00          25.83  258.5         130730    74.27   \n",
       "14  2016         214.62          26.53  263.1         138401    81.58   \n",
       "15  2017         228.86          26.86  272.5         149831    83.98   \n",
       "16  2018         246.00          27.67  281.6         160870    87.36   \n",
       "17  2019         248.72          28.65  288.8         166197    86.12   \n",
       "18  2020         254.50          29.37  293.1         157511    86.83   \n",
       "19  2021         295.00          29.86  305.0         115469    96.72   \n",
       "\n",
       "    income_adj  aff_score  \n",
       "0        11.00      60.58  \n",
       "1        11.09      60.41  \n",
       "2        11.13      59.68  \n",
       "3        11.26      59.42  \n",
       "4        11.11      59.28  \n",
       "5        10.97      58.58  \n",
       "6        10.99      59.61  \n",
       "7        11.28      60.49  \n",
       "8        10.74      60.13  \n",
       "9        10.45      60.36  \n",
       "10       10.27      60.45  \n",
       "11       10.17      60.58  \n",
       "12        9.94      60.18  \n",
       "13        9.99      60.16  \n",
       "14       10.08      59.25  \n",
       "15        9.86      58.73  \n",
       "16        9.82      57.99  \n",
       "17        9.92      58.26  \n",
       "18       10.02      58.11  \n",
       "19        9.79      55.76  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yr_lst = (adj_data['year']).to_list()\n",
    "inc_lst = (adj_data['income_adj']).to_list()\n",
    "pr_lst = (adj_data['sls_adj']).to_list()\n",
    "data_lst_yr = list(zip(yr_lst, inc_lst, pr_lst))\n",
    "\n",
    "aff_score_lst = []\n",
    "for yr, inc, val in data_lst_yr:\n",
    "    affordability_score(yr, inc, val)\n",
    "\n",
    "adj_data['aff_score'] = aff_score_lst\n",
    "adj_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "936c7e27",
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_scaled = pd.DataFrame(adj_data)\n",
    "scaler = MinMaxScaler()\n",
    "adj_scaled[['aff_score','net_additions', 'sls_adj', 'income_adj']] =scaler.fit_transform(\n",
    "   adj_scaled[['aff_score','net_additions', 'sls_adj', 'income_adj']])\n",
    "\n",
    "# adj_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8d09e989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Adjusted for inflation: Affordability Score and Net Additions \\n England 2002-2021')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(2002,2022)\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "plt.plot(x,adj_scaled[ 'sls_adj'], linewidth = 2 , color = 'blue', linestyle = ':', label = 'Median Sales Price')\n",
    "plt.plot(x,adj_scaled['net_additions'], linewidth = 4 , color = 'darkviolet', label = 'Net Additions')\n",
    "plt.plot(x,adj_scaled['income_adj'], linewidth = 2 , color = 'green', linestyle = ':',label = 'Median Income')\n",
    "plt.plot(x,adj_scaled['aff_score'], linewidth = 4 , color = 'orangered', label = 'Affordability Score')\n",
    "ax.set_xticks(range(2002,2022))\n",
    "ax.set_xticklabels(range(2002,2022),rotation = 45)\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.title('Adjusted for inflation: Affordability Score and Net Additions \\n England 2002-2021')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f83db789",
   "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>obs_ref</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>368</td>\n",
       "      <td>actual</td>\n",
       "      <td>157.50</td>\n",
       "      <td>25.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>368</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>147.85</td>\n",
       "      <td>25.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>368</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>161.93</td>\n",
       "      <td>25.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>438</td>\n",
       "      <td>actual</td>\n",
       "      <td>140.00</td>\n",
       "      <td>26.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>438</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>133.08</td>\n",
       "      <td>26.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>438</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>140.91</td>\n",
       "      <td>26.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>529</td>\n",
       "      <td>actual</td>\n",
       "      <td>265.00</td>\n",
       "      <td>27.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>529</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>258.51</td>\n",
       "      <td>27.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>529</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>268.09</td>\n",
       "      <td>27.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>556</td>\n",
       "      <td>actual</td>\n",
       "      <td>235.00</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>556</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>225.26</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>556</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>239.77</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>602</td>\n",
       "      <td>actual</td>\n",
       "      <td>460.00</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>602</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>309.94</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>602</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>317.90</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>711</td>\n",
       "      <td>actual</td>\n",
       "      <td>395.00</td>\n",
       "      <td>31.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>711</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>243.53</td>\n",
       "      <td>31.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>711</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>246.56</td>\n",
       "      <td>31.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obs_ref variable   value  median_income\n",
       "0       368   actual  157.50          25.18\n",
       "1       368   d_pred  147.85          25.18\n",
       "2       368   h_pred  161.93          25.18\n",
       "3       438   actual  140.00          26.35\n",
       "4       438   d_pred  133.08          26.35\n",
       "5       438   h_pred  140.91          26.35\n",
       "6       529   actual  265.00          27.90\n",
       "7       529   d_pred  258.51          27.90\n",
       "8       529   h_pred  268.09          27.90\n",
       "9       556   actual  235.00          30.31\n",
       "10      556   d_pred  225.26          30.31\n",
       "11      556   h_pred  239.77          30.31\n",
       "12      602   actual  460.00          32.00\n",
       "13      602   d_pred  309.94          32.00\n",
       "14      602   h_pred  317.90          32.00\n",
       "15      711   actual  395.00          31.02\n",
       "16      711   d_pred  243.53          31.02\n",
       "17      711   h_pred  246.56          31.02"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply to the dummy set, import the test set to match the incomes\n",
    "\n",
    "dummy = pd.read_csv( 'mod_e__dummy_output.csv')\n",
    "dummy = dummy.drop(columns =['Unnamed: 0','pred'])\n",
    "dummy = dummy.melt('obs_ref')\n",
    "t_set = pd.read_csv('test_best.csv')\n",
    "t_data = t_set.iloc[[368,438,529,556,602,711]].reset_index(drop = True)\n",
    "t_data = t_data.loc[: ,['Unnamed: 0', 'median_income']]\n",
    "t_data['median_income'] = t_data['median_income']/1000\n",
    "t_data = t_data.rename(columns = {'Unnamed: 0': 'obs_ref'})\n",
    "dummy = dummy.merge(t_data)\n",
    "dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "70e406f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_lst = (dummy['value']).to_list()\n",
    "inc_lst = (dummy['median_income']).to_list()\n",
    "obs_lst = (dummy['obs_ref']).to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d6b67bde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(368, 25.177, 157.5),\n",
       " (368, 25.177, 147.84988),\n",
       " (368, 25.177, 161.92775),\n",
       " (438, 26.354, 139.995),\n",
       " (438, 26.354, 133.08032),\n",
       " (438, 26.354, 140.91193),\n",
       " (529, 27.896, 265.0),\n",
       " (529, 27.896, 258.50974),\n",
       " (529, 27.896, 268.08652),\n",
       " (556, 30.31, 235.0),\n",
       " (556, 30.31, 225.26492),\n",
       " (556, 30.31, 239.76878),\n",
       " (602, 31.997, 460.0),\n",
       " (602, 31.997, 309.94012),\n",
       " (602, 31.997, 317.90155),\n",
       " (711, 31.022, 395.0),\n",
       " (711, 31.022, 243.5341),\n",
       " (711, 31.022, 246.55913)]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_lst = list(zip(obs_lst, inc_lst,val_lst))\n",
    "data_lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8bb674fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43.59406072896764\n",
      "48.06206306787487\n",
      "41.01462545017965\n",
      "50.886233361229934\n",
      "52.95748453063551\n",
      "50.586300230842\n",
      "27.42503084598464\n",
      "28.572978253671298\n",
      "26.860682939694556\n",
      "33.49628775464781\n",
      "33.49628775464781\n",
      "33.49628775464781\n",
      "18.57473351433979\n",
      "32.43035841207846\n",
      "25.698629949923873\n",
      "17.154471722889532\n",
      "39.76526132874986\n",
      "39.76526132874986\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>obs_ref</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>median_income</th>\n",
       "      <th>aff_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>368</td>\n",
       "      <td>actual</td>\n",
       "      <td>157.50</td>\n",
       "      <td>25.18</td>\n",
       "      <td>43.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>368</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>147.85</td>\n",
       "      <td>25.18</td>\n",
       "      <td>48.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>368</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>161.93</td>\n",
       "      <td>25.18</td>\n",
       "      <td>41.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>438</td>\n",
       "      <td>actual</td>\n",
       "      <td>140.00</td>\n",
       "      <td>26.35</td>\n",
       "      <td>50.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>438</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>133.08</td>\n",
       "      <td>26.35</td>\n",
       "      <td>52.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>438</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>140.91</td>\n",
       "      <td>26.35</td>\n",
       "      <td>50.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>529</td>\n",
       "      <td>actual</td>\n",
       "      <td>265.00</td>\n",
       "      <td>27.90</td>\n",
       "      <td>27.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>529</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>258.51</td>\n",
       "      <td>27.90</td>\n",
       "      <td>28.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>529</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>268.09</td>\n",
       "      <td>27.90</td>\n",
       "      <td>26.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>556</td>\n",
       "      <td>actual</td>\n",
       "      <td>235.00</td>\n",
       "      <td>30.31</td>\n",
       "      <td>33.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>556</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>225.26</td>\n",
       "      <td>30.31</td>\n",
       "      <td>33.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>556</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>239.77</td>\n",
       "      <td>30.31</td>\n",
       "      <td>33.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>602</td>\n",
       "      <td>actual</td>\n",
       "      <td>460.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>18.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>602</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>309.94</td>\n",
       "      <td>32.00</td>\n",
       "      <td>32.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>602</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>317.90</td>\n",
       "      <td>32.00</td>\n",
       "      <td>25.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>711</td>\n",
       "      <td>actual</td>\n",
       "      <td>395.00</td>\n",
       "      <td>31.02</td>\n",
       "      <td>17.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>711</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>243.53</td>\n",
       "      <td>31.02</td>\n",
       "      <td>39.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>711</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>246.56</td>\n",
       "      <td>31.02</td>\n",
       "      <td>39.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obs_ref variable   value  median_income  aff_score\n",
       "0       368   actual  157.50          25.18      43.59\n",
       "1       368   d_pred  147.85          25.18      48.06\n",
       "2       368   h_pred  161.93          25.18      41.01\n",
       "3       438   actual  140.00          26.35      50.89\n",
       "4       438   d_pred  133.08          26.35      52.96\n",
       "5       438   h_pred  140.91          26.35      50.59\n",
       "6       529   actual  265.00          27.90      27.43\n",
       "7       529   d_pred  258.51          27.90      28.57\n",
       "8       529   h_pred  268.09          27.90      26.86\n",
       "9       556   actual  235.00          30.31      33.50\n",
       "10      556   d_pred  225.26          30.31      33.50\n",
       "11      556   h_pred  239.77          30.31      33.50\n",
       "12      602   actual  460.00          32.00      18.57\n",
       "13      602   d_pred  309.94          32.00      32.43\n",
       "14      602   h_pred  317.90          32.00      25.70\n",
       "15      711   actual  395.00          31.02      17.15\n",
       "16      711   d_pred  243.53          31.02      39.77\n",
       "17      711   h_pred  246.56          31.02      39.77"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aff_score_lst = []\n",
    "for obs, inc, val in data_lst:\n",
    "    affordability_score(obs, inc, val)\n",
    "\n",
    "dummy['aff_score'] = aff_score_lst\n",
    "dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "09f81763",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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adhkAAABTsWzZsqu7e4fZlg0i1C1atChLly6ddhkAAABTUVU/WNMy3S8BAAAGTKgDAAAYMKEOAABgwAZxTR0AAHDXc8stt+Tyyy/PzTffPO1SFoxNN900u+yySzbZZJOJtxHqAACAqbj88suz1VZbZdGiRamqaZczdd2da665Jpdffnl23XXXibfT/RIAAJiKm2++Odttt51AN1ZV2W677da55VKoAwAApkagu707834IdQAAwN3Ok5/85Fx33XVzrrPlllvOOv95z3teTjrppHmo6s5xTR0AAHC30d3p7px66qnTLmW90VIHAAAMzl/+5V/mPe95z6rpN73pTTn66KNz4IEHZs8998xv//Zv55RTTkmSXHLJJdltt93yx3/8x9lzzz1z2WWXZdGiRbn66quTJE9/+tOz1157Zffdd8+xxx57u+O8+tWvzp577pkDDzwwy5cvv0Mdy5Yty/7775+99torT3ziE/OjH/1oHl/17IQ6AABgcA477LB8+MMfXjX9kY98JM9//vNz8skn55xzzskXv/jFvPrVr053J0m+853v5LnPfW6+8Y1v5EEPetDt9nXcccdl2bJlWbp0aY455phcc801SZKbbrope+65Z84555zsv//+Ofroo2+33S233JI//dM/zUknnZRly5blBS94QV73utfN8yu/I90vAQCAwXnkIx+Zq666KldccUWWL1+ebbbZJjvttFNe+cpX5owzzshGG22UH/7wh7nyyiuTJA960IOyzz77zLqvY445JieffHKS5LLLLsvFF1+c7bbbLhtttFEOPfTQJMkf/uEf5pnPfObttvvOd76T888/P49//OOTJLfeemt22mmn+XrJayTUAQAAg/SsZz0rJ510Un784x/nsMMOy4knnpjly5dn2bJl2WSTTbJo0aJVtwfYYostZt3Haaedls997nM566yzsvnmm+eAAw5Y4y0FVh+Zsruz++6756yzzlq/L2wd6X4JAAAM0mGHHZYPfehDOemkk/KsZz0r119/fXbcccdssskm+eIXv5gf/OAHa93H9ddfn2222Sabb755Lrroonz1q19dtey2225bNcrlBz7wgey777632/ahD31oli9fvirU3XLLLbngggvW4yucjJY6AABgkHbffffceOON2XnnnbPTTjvlOc95Tp72tKdlyZIlWbx4cR72sIetdR8HHXRQ3vve92aPPfbIQx/60Nt10dxiiy1ywQUXZK+99srWW299u2v4kuSe97xnTjrppLz85S/P9ddfnxUrVuTP/uzPsvvuu6/31zqXWnnh4EK2ZMmSXrp06bTLAAAA1qMLL7wwu+2227TLWHBme1+qall3L5ltfd0vAQAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGACp512Ws4888xfax9bbrnleqrmV9x8HAAAWBDOu/Ly9bq/R9x3l/W6v9NOOy1bbrllHvWoR63X/f66hDpYwF71qeOnXcIdvO1Jz5t2CQAA69XTn/70XHbZZbn55pvzile8Ii95yUvy6U9/Oq997Wtz6623Zvvtt8/73ve+vPe978097nGPvP/978873/nOvO9978tTn/rUPOtZz0oyaoX76U9/mp/+9Kc55JBDcu211+aWW27JX//1X+eQQw6Zt/qFOgAA4G7tuOOOy7bbbpuf//zn+Z3f+Z0ccsghefGLX5wzzjgju+66a37yk59k2223zcte9rJsueWWec1rXpMked/73jfr/jbddNOcfPLJufe9752rr746++yzTw4++OBU1bzUL9QBAAB3a8ccc0xOPvnkJMlll12WY489Nvvtt1923XXXJMm22267Tvvr7rz2ta/NGWeckY022ig//OEPc+WVV+Z+97vfeq89EeoAAIC7sdNOOy2f+9znctZZZ2XzzTfPAQcckEc84hH5zne+s9ZtN95449x2221JRkHul7/8ZZLkxBNPzPLly7Ns2bJssskmWbRoUW6++eZ5ew1GvwQAAO62rr/++myzzTbZfPPNc9FFF+WrX/1qfvGLX+T000/P97///STJT37ykyTJVlttlRtvvHHVtosWLcqyZcuSJKecckpuueWWVfvccccds8kmm+SLX/xifvCDH8zraxDqAACAu62DDjooK1asyB577JH/+l//a/bZZ5/ssMMOOfbYY/PMZz4zj3jEI3LooYcmSZ72tKfl5JNPzuLFi/OlL30pL37xi3P66adn7733zte+9rVsscUWSZLnPOc5Wbp0aZYsWZITTzwxD3vYw+b1NVR3z+sB1oclS5b00qVLp10GbHBGvwQA7souvPDC7LbbbtMuY8GZ7X2pqmXdvWS29bXUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCAbTztAgAAAJL1f4/edb2/7pve9KZsueWWec1rXrNe65jNokWLsnTp0my//fa/9r601AEAAKwHK1asmMpxtdQBAAB3W295y1vyz//8z3nAAx6QHXbYIXvttdes6x1wwAFZvHhxzj777Nxwww057rjjsvfee+dNb3pTrrjiilxyySXZfvvt8453vCMve9nLcumllyZJ3v72t+fRj350rrnmmhx++OFZvnx59t5773T3ensNQh0AAHC3tGzZsnzoQx/KN77xjaxYsSJ77rnnGkNdktx0000588wzc8YZZ+QFL3hBzj///FX7+fKXv5zNNtssf/AHf5BXvvKV2XfffXPppZfmiU98Yi688MIcffTR2XffffOGN7wh//7v/55jjz12vb0OoQ4AALhb+tKXvpRnPOMZ2XzzzZMkBx988JzrH3744UmS/fbbLzfccEOuu+66VdttttlmSZLPfe5z+fa3v71qmxtuuCE33nhjzjjjjHz84x9PkjzlKU/JNttss95eh1AHAADcbVXVnV535fQWW2yxat5tt92Ws846a1XIu7PHWhdCHSQ578rLp10CAAAb2H777ZfnPe95Oeqoo7JixYp88pOfzEtf+tI1rv/hD384j3nMY/LlL385W2+9dbbeeus7rPOEJzwh73rXu/Lnf/7nSZJzzz03ixcvzn777ZcTTzwxr3/96/OpT30q11577Xp7HUIdAACwIKzrLQh+XXvuuWcOPfTQLF68OA960IPye7/3e3Ouv8022+RRj3rUqoFSZnPMMcfkyCOPzB577JEVK1Zkv/32y3vf+9688Y1vzOGHH54999wz+++/fx74wAeut9dR63PUlfmyZMmSXrp06bTL4C5sobbUnXDO56Zdwh1s6D+2AMBd14UXXpjddttt2mVM5IADDsjf/u3fZsmSJfN+rNnel6pa1t2zHlxLHbBOFr/hydMu4Q7OffOp0y4BAGBqhDoAAICxI488Ml/5ylduN+8Vr3hFTjvttOkUNIF5DXVVdUmSG5PcmmRFdy+pqm2TfDjJoiSXJPn97l5/VwkCAADcSe9+97unXcI622gDHOMx3b14Rv/Po5J8vrsfkuTz42kAAADuhA0R6lZ3SJITxs9PSPL0KdQAAABwlzDfoa6TfKaqllXVS8bz7tvdP0qS8c8d57kGAACAu6z5Hijl0d19RVXtmOSzVXXRpBuOQ+BLkqzXezgAAADclcxrqOvuK8Y/r6qqk5PsneTKqtqpu39UVTsluWoN2x6b5NhkdJ+6+awTAACYvvV966RJbnt0ySWX5KlPfWrOP//89XrsSSxatChLly7N9ttv/2vtZ966X1bVFlW11crnSZ6Q5Pwkn0hyxHi1I5KcMl81AAAAbEgrVqzY4Mecz5a6+yY5uapWHucD3f3pqvp6ko9U1QuTXJrk2fNYAwAAwJxuvfXWvPjFL86ZZ56ZnXfeOaeccko222yzO6x3wAEHZPHixTn77LNzww035Ljjjsvee++dN73pTbniiityySWXZPvtt8873vGOvOxlL8ull16aJHn729+eRz/60bnmmmty+OGHZ/ny5dl7773TvX46JM5bS113f6+7HzF+7N7dbxnPv6a7D+zuh4x//mS+agAAAFibiy++OEceeWQuuOCC3Oc+98nHPvaxNa5700035cwzz8x73vOevOAFL1g1f9myZTnllFPygQ98IK94xSvyyle+Ml//+tfzsY99LC960YuSJEcffXT23XfffOMb38jBBx+8KvT9uuZ7oBQAAIAFbdddd83ixYuTJHvttVcuueSSNa57+OGHJ0n222+/3HDDDbnuuuuSJAcffPCq1r3Pfe5z+fa3v71qmxtuuCE33nhjzjjjjHz84x9PkjzlKU/JNttss17qF+oAAIC7tXvd616rnt/jHvfIz3/+8zWuO7687A7TW2yxxap5t912W84666xZu3Cuvv36MI2bjwMAAAzShz/84STJl7/85Wy99dbZeuut77DOE57whLzrXe9aNX3uuecmGbXunXjiiUmST33qU7n22mvXS01a6gAAgAVhklsQTNs222yTRz3qUasGSpnNMccckyOPPDJ77LFHVqxYkf322y/vfe9788Y3vjGHH3549txzz+y///7r7X7ctb5GXJlPS5Ys6aVLl067DO7Czrvy8mmXMKsTzvnctEu4gy+c9ZFpl3AHQ/gHAAC4owsvvDC77bbbtMuY2AEHHJC//du/zZIlS+b1OLO9L1W1rLtnPbDulwAAAAOm+yUAAMAMRx55ZL7yla/cbt4rXvGKnHbaadMpaC2EOgAAgBne/e53T7uEdaL7JQAAMDVDGONjQ7oz74dQBwAATMWmm26aa665RrAb6+5cc8012XTTTddpO90vAQCAqdhll11y+eWXZ/ny5dMuZcHYdNNNs8suu6zTNkIdAAAwFZtsskl23XXXaZcxeLpfAgAADJhQBwAAMGC6X25gr/rU8dMu4Q7e9qTnTbsEAADgTtJSBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADtvG0C5hP5115+bRLAAAAmFda6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAHbeNoFMH2L3/DkaZcwq3PffOq0SwAAgAVPSx0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgM17qKuqe1TVN6rq38bT21bVZ6vq4vHPbea7BgAAgLuqDdFS94okF86YPirJ57v7IUk+P54GAADgTpjXUFdVuyR5SpJ/nDH7kCQnjJ+fkOTp81kDAADAXdl8t9S9PclfJLltxrz7dvePkmT8c8d5rgEAAOAua95CXVU9NclV3b3sTm7/kqpaWlVLly9fvp6rAwAAuGuYz5a6Ryc5uKouSfKhJI+tqvcnubKqdkqS8c+rZtu4u4/t7iXdvWSHHXaYxzIBAACGa+P52nF3/1WSv0qSqjogyWu6+w+r6n8lOSLJW8c/T5mvGgAAgA3jVZ86ftol3MEXzvrItEu4g3PffOp63+c07lP31iSPr6qLkzx+PA0AAMCdMG8tdTN192lJThs/vybJgRviuAAAAHd102ipAwAAYD0R6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABmzjaRcAAHdV5115+bRLmNUJ53xu2iXcwRfO+si0S7iDc9986rRLAJiIljoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZs42kXAADAwvSqTx0/7RLu4G1Pet60S4AFR0sdAADAgE0U6qrqQVX1uPHzzapqq/ktCwAAgEmsNdRV1YuTnJTk/4xn7ZLkX+exJgAAACY0SUvdkUkeneSGJOnui5PsOJ9FAQAAMJlJQt0vuvuXKyeqauMkPX8lAQAAMKlJQt3pVfXaJJtV1eOTfDTJJ+e3LAAAACYxSaj7yyTLk3wryUuTnJrk9fNZFAAAAJOZ8z51VbVRkm9298OT/MOGKQkAAIBJzdlS1923JTmvqh64geoBAABgHczZUje2U5ILqursJDetnNndB89bVQAAAExkklB39LxXAQAAwJ2y1lDX3adX1X2T/M541tndfdX8lgUAAMAk1jr6ZVX9fpKzkzw7ye8n+VpVPWu+CwMAAGDtJul++bokv7Oyda6qdkjyuSQnzWdhAAAArN0k96nbaLXultdMuB0AAADzbJKWuk9X1f9N8sHx9KFJPjV/JQEAADCpSQZK+fOqemaSfZNUkmO7++R5rwwAAIC1Wmuoq6pdk5za3R8fT29WVYu6+5L5Lg4AAIC5TXJt3EeT3DZj+tbxPAAAAKZsklC3cXf/cuXE+Pk917ZRVW1aVWdX1XlVdUFVHT2ev21VfbaqLh7/3ObOlw8AAHD3NkmoW15VB6+cqKpDklw9wXa/SPLY7n5EksVJDqqqfZIcleTz3f2QJJ8fTwMAAHAnTDL65cuSnFhV78pooJTLkjx3bRt1dyf56Xhyk/GjkxyS5IDx/BOSnJbkL9elaAAAAEYmGf3yP5LsU1VbJqnuvnHSnVfVPZIsS/LgJO/u7q9V1X27+0fjff+oqnZcw7YvSfKSJHngAx846SEBAADuVtba/bKqXlFV905yU5L/XVXnVNUTJtl5d9/a3YuT7JJk76p6+KSFdfex3b2ku5fssMMOk24GAABwtzLJNXUv6O4bkjwhyY5Jnp/kretykO6+LqNulgclubKqdkqS8c+r1mVfAAAA/Mokoa7GP5+c5J+6+7wZ89a8UdUOVXWf8fPNkjwuyUVJPpHkiPFqRyQ5ZR1rBgAAYGySgVKWVdVnkuya5K+qaqvc/r51a7JTkhPG19VtlOQj3f1vVXVWko9U1QuTXJrk2XeydgAAgLu9SULdCzO6JcH3uvtnVbVdRl0w59Td30zyyFnmX5PkwHWsEwAAgFlMMvrlbUnOmTF9TZJr5rMoAAAAJjPJNXUAAAAsUEIdAADAgE1yn7q/rardN0QxAAAArJtJWuouSnJsVX2tql5WVVvPd1EAAABMZq2hrrv/sbsfneS5SRYl+WZVfaCqHjPfxQEAADC3ia6pG99r7mHjx9VJzkvyqqr60DzWBgAAwFqs9ZYGVfW2JE9L8oUk/727zx4v+h9V9Z35LA4AAIC5TXLz8fOTvL67fzbLsr3Xcz0A3Amv+tTx0y7hDt72pOdNuwQAuFuYpPvlc1YPdFX1+STp7uvnpSoAAAAmssaWuqraNMnmSbavqm2S1HjRvZPcfwPUBgAAwFrM1f3ypUn+LKMAd86M+Tckefc81gQAAMCE1hjquvsdSd5RVX/a3e/cgDUBAAAwobm6Xz62u7+Q5IdV9czVl3f3x+e1MgAAANZqru6X+2d0G4OnzbKskwh1AAAAUzZX98s3jn8+f8OVAwAAwLqYq/vlq+basLvftv7LAQAAYF3M1f1yqw1WBQAAAHfKXN0vj96QhQAAALDuNlrbClX1G1X1yapaXlVXVdUpVfUbG6I4AAAA5jZX98uVPpDRzcafMZ4+LMkHk/zufBUFsJCdd+Xl0y4BAGCVtbbUJanu/pfuXjF+vD+jWxoAAAAwZXONfrnt+OkXq+qoJB/KKMwdmuTfN0BtAAAArMVc3S+XZRTiajz90hnLOsl/m6+iAAAAmMxco1/uuiELAQAAYN1NMlBKqurhSX4ryaYr53X3P89XUQAM3+I3PHnaJczq3DefOu0SAGC9Wmuoq6o3Jjkgo1B3apInJflyEqEOAABgyiYZ/fJZSQ5M8uPufn6SRyS517xWBQAAwEQmCXU/7+7bkqyoqnsnuSqJm48DAAAsAJNcU7e0qu6T5B8yGhHzp0nOns+iAAAAmMxaQ113//H46Xur6tNJ7t3d35zfsgAAAJjEXDcf33OuZd19zvyUBAAAwKTmaqn7u/HPTZMsSXJeRjci3yPJ15LsO7+lAQAAsDZrHCilux/T3Y9J8oMke3b3ku7eK8kjk3x3QxUIAADAmk0y+uXDuvtbKye6+/wki+etIgAAACY2yeiXF1XVPyZ5f5JO8odJLpzXqgAAAJjIJKHueUn+S5JXjKfPSPL381UQAAAAk5sz1FXVPZL8W3c/Lsn/3jAlAQDA7Ba/4cnTLmFW57751GmXwN3YnNfUdfetSX5WVVtvoHoAAABYB5N0v7w5ybeq6rNJblo5s7tfPm9VAQAAMJFJQt2/jx8AAAAsMGsNdd19QlXdM8lvjmd9p7tvmd+yAAAAmMRaQ11VHZDkhCSXJKkkD6iqI7r7jHmtDAAAgLWapPvl3yV5Qnd/J0mq6jeTfDDJXvNZGAAAAGs35+iXY5usDHRJ0t3/L8km81cSAAAAk5qkpW5pVb0vyb+Mp5+TZNn8lQQAAMCkJgl1/yXJkUlentE1dWckec98FgUAAMBk1hjqqurz3X1gkjd3918meduGKwsAAIBJzNVSt1NV7Z/k4Kr6UEatdKt09znzWhkAAABrNVeoe0OSo5Lskju20nWSx85XUQAAAExmrlD3o+5+UlW9obvfvMEqAgAAYGJz3dLgmPHPp2+AOgAAALgT5mqpu6Wq/inJzlV1zOoLu/vl81cWAAAAk5gr1D01yeMyunZu9fvS9bxVBAAAwMTWGOq6++okH6qqC7v7vJXzq2rfJIcn+ecNUB8AAABzWOvNx7v7vKpanOQPkvx+ku8n+dg81wUAAMAE5rr5+G8mOSyjVrlrknw4SXX3YzZQbQAAAKzFXC11FyX5UpKndfd3k6SqXrlBqgIAAGAic93S4D8n+XGSL1bVP1TVgUlqw5QFAADAJNYY6rr75O4+NMnDkpyW5JVJ7ltVf19VT9hA9QEAADCHuVrqkiTdfVN3n9jdT02yS5Jzkxw134UBAACwdmsNdTN190+6+/9092PnqyAAAAAmt06hDgAAgIVFqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABiweQt1VfWAqvpiVV1YVRdU1SvG87etqs9W1cXjn9vMVw0AAAB3dfPZUrciyau7e7ck+yQ5sqp+K8lRST7f3Q9J8vnxNAAAAHfCvIW67v5Rd58zfn5jkguT7JzkkCQnjFc7IcnT56sGAACAu7oNck1dVS1K8sgkX0ty3+7+UTIKfkl23BA1AAAA3BXNe6irqi2TfCzJn3X3Deuw3UuqamlVLV2+fPn8FQgAADBg8xrqqmqTjALdid398fHsK6tqp/HynZJcNdu23X1sdy/p7iU77LDDfJYJAAAwWPM5+mUleV+SC7v7bTMWfSLJEePnRyQ5Zb5qAAAAuKvbeB73/egkf5TkW1V17njea5O8NclHquqFSS5N8ux5rAEAAOAubd5CXXd/OUmtYfGB83VcAACAu5MNMvolAAAA80OoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGbONpFwAAQHLelZdPuwRgoLTUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADNjG0y4AAACY3HlXXj7tElhgtNQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADNi8hbqqOq6qrqqq82fM27aqPltVF49/bjNfxwcAALg7mM+WuuOTHLTavKOSfL67H5Lk8+NpAAAA7qR5C3XdfUaSn6w2+5AkJ4yfn5Dk6fN1fAAAgLuDDX1N3X27+0dJMv654wY+PgAAwF3Kgh0opapeUlVLq2rp8uXLp10OAADAgrShQ92VVbVTkox/XrWmFbv72O5e0t1Ldthhhw1WIAAAwJBs6FD3iSRHjJ8fkeSUDXx8AACAu5T5vKXBB5OcleShVXV5Vb0wyVuTPL6qLk7y+PE0AAAAd9LG87Xj7j58DYsOnK9jAgAA3N0s2IFSAAAAWDuhDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABiwqYS6qjqoqr5TVd+tqqOmUQMAAMBdwQYPdVV1jyTvTvKkJL+V5PCq+q0NXQcAAMBdwTRa6vZO8t3u/l53/zLJh5IcMoU6AAAABm8aoW7nJJfNmL58PA8AAIB1VN29YQ9Y9ewkT+zuF42n/yjJ3t39p6ut95IkLxlPPjTJdzZoocOwfZKrp10Eg+BcYV04X5iUc4V14XxhUs6V2T2ou3eYbcHGG7qSjFrmHjBjepckV6y+Uncfm+TYDVXUEFXV0u5eMu06WPicK6wL5wuTcq6wLpwvTMq5su6m0f3y60keUlW7VtU9kxyW5BNTqAMAAGDwNnhLXXevqKo/SfJ/k9wjyXHdfcGGrgMAAOCuYBrdL9PdpyY5dRrHvovRPZVJOVdYF84XJuVcYV04X5iUc2UdbfCBUgAAAFh/pnFNHQAAAOuJUAcAADBgQh0AAMCATWWgFNZdVT0wyQ3dfV1VLUqyJMlF3X3+dCtjIXK+sK6qaklG9xBdkeTi7r5oyiWxwIxvQ3RLjy/Gr6rHJNkzybe7+1NTLY4Fqao26e5bVpu3fXe7qTQTqaqH+fdoMlrqBqCqjkpyepKvVtWLknw6yZOSfLiqXjXV4lhwnC+si6rav6qWJnlrkuOSvDTJ+6rqtKp6wHSrY4H5epL7JElV/XmStyTZLMmrqupvplgXC0xVPaaqLk9yRVV9Zvyfiyt9ZkplMUzOlwkZ/XIAquqCjFpaNk9ySZLf6O7lVbVFkq9198OnWR8Li/OFdVFV30jyhPE5smuSt3X3M6rq8Un+vLufMOUSWSCq6vyVfz/G/xHwe93986raOMk53b3HdCtkoaiqryd5XndfUFXPSvI3Sf6ou79aVd/o7kdOuUQWkKo6Zk2LkhzR3ffekPUMlZa6Ybi1u3+e5LokP09yTZJ0903TLIoFy/nCurhHdy8fP780yYOSpLs/m2TnqVXFQnRDVa38T6Grk2w6fr5xfJ/g9u7Z3RckSXeflOTpSU6oqmck0ZrA6p6f5Pwky1Z7LE3yyynWNSha6gagqo5Pcs8kWyT5WUbXvHw6yWOTbNXdvz+96lhonC+si6o6LqMvWZ9PckiSH3b3q6pq84xaXx421QJZMKpqjyT/kuS88axHZ9TVe4+MWng/MK3aWFjGLblP7e4fz5i3S5J/S/KfunurqRXHglNVX0jy+u4+c5Zl3+/uXadQ1uAIdQMw7try7Iy+eJ2U5HeTHJ7R/6q/WwsMM81yvuyd5A/ifGEWVbVJkhcn+a2Mvqwf1923VtVmSXbs7h9MtUAWlKq6R5InJPnNjFroLk/yf7v7umnWxcJSVY9Lsry7z1tt/n2SHNndb5lKYSxIVbVtkpu7+2fTrmXIhLqBqqrtuvuaadcBAABMlz7wA1BVb62q7cfPl1TV9zIa2fAHVbX/lMtjgamq+1XV31fVu6tqu6p6U1V9s6o+UlU7Tbs+FpaqundV/U1V/UtV/cFqy94zrbpYeKrqoBnPt66q943/tnygqu47zdpYWMbnx1ur6qKqumb8uHA87z7Tro/hqCq3S5mQUDcMT5lxT5f/leTQ7n5Ikscn+bvplcUCdXySbye5LMkXMxos5alJvpTkvdMriwXqnzIaYexjSQ6rqo9V1b3Gy/aZXlksQP99xvO/S/KjJE/L6FYH/2cqFbFQfSTJtUkO6O7tunu7JI8Zz/voVCtjwamqPdfw2CvJ4mnXNxS6Xw5AVV2U5OHdvaKqvtrd+8xY9q3u/u0plscCM3O46Kq6tLsfOGPZud29eGrFseCsfk5U1euSPDnJwUk+2917Tqs2FpaqOmfl+TDLeeNvC6tU1Xe6+6Hruoy7p6q6NaNBl2qWxft092YbuKRB2njaBTCRdyc5taremuTTVfX2JB9PcmCSc6dYFwvTzBb4f55jGSTJvapqo+6+LUm6+y3jmwafkWTL6ZbGArNjVb0qoy9e966q6l/9z7C/Lcz0g6r6iyQndPeVSTLuovu8jHqRwEwXJnlpd1+8+oKqcr5MyB/hAejud2bU7eWlGQ05fmCSo5L8MMkLplgaC9MpVbVlknT361fOrKoHJ/l/U6uKheqTGd3uYpXuPiHJq+P+QNzePyTZKqOwf0KSldd63y/+g5HbOzTJdklOr6prq+onSU5Lsm0St9VhdW/KmjPJn27AOgZN98uBqKq9k3R3f72qdk9yUJILu/vUKZfGAFTVP3f3c6ddBwtfVe2b0W0wzu/uz0y7HhYu5wprUlW/m+Si7r5+fM/Lo5LsmeSCJP+9u6+faoEsKFX18iQnd7dWuV+DUDcAVfXGJE/KqLvsZzP6R/T0JI/L6P5A7vfCKlX1idVnZXSB+heSpLsP3uBFsWBV1dndvff4+YuTHJnk5IzuRfbJ7n7rNOtj4VjtXHlRkj+Jc4VZVNUFSR4xHgvg2CQ3ZTQY04Hj+c+caoEsKFV1fUbnyH8k+WCSj3b38ulWNTxC3QBU1bcyGv3nXkl+nGSX7r5hfHPgr3X3HtOsj4Wlqr6R0f+G/mNGNyCvjP5IHpYk3X369KpjoVltYJ2vJ3lydy+vqi2SfNVATKzkXGFSVXVhd+82fr5qgJ3xtEF1uJ3x95a9MmqsODSjgbqWZfTd5ePdfeMUyxsM19QNw4ruvrW7f5bkP7r7hiTp7p8nuW26pbEA7ZXRH8PXJbm+u09L8vPuPl2gYxYbVdU2VbVdRv/RtzxJuvumJCumWxoLjHOFSZ1fVc8fPz+vqpYkSVX9ZpJbplcWC1R3923d/ZnufmGS+yd5T0aXGn1vuqUNh9Evh+GXVbX5ONTttXJmVW0doY7VjEcx/N9V9dHxzyvjd5012zqj/wSoJF1V9+vuH48H25lteGnuvpwrTOpFSd5RVa9PcnWSs8ajGF42XgYz3e7vR3ffkuQTST4x7pXGBHS/HICquld3/2KW+dsn2am7vzWFshiIqnpKkkd392unXQvDMR7c4L7d/f1p18LC5lxhTapqqyS/kdF/LF6+8vYGMFNV/WZ3G5371yTUAQAADJhr6gAAAAZMqAMAABgwoQ4AAGDAhDoA7naqalFVnb+BjrVDVX2tqr5RVb+3IY4JwN2LYc4B4NdUVRt395ru1XZgkou6+4gNWRMAdx9GvwTgLq+qXpXkBePJf0zyr0k+neRrSR6Z5P8leW53/6yq3prk4IxuqP2Z7n7NGvZ5fJKfjLc/J6Ob5b47yQ5JfpbkxUk2zeh+S5sl+WGS/6+7f77+XyEAd2da6gC4S6uqvZI8P8nvZnST268lOT3JQ5O8sLu/UlXHJfnj8c9nJHlYd3dV3Wctu//NJI/r7lur6vNJXtbdF1fV7yZ5T3c/tqrekGRJd//J/LxCAO7uXFMHwF3dvklO7u6buvunST6e5PeSXNbdXxmv8/7xejckuTnJP1bVMzNqcZvLR8eBbsskj0ry0ao6N8n/SbLT+n8pAHBHWuoAuKurNcxf/fqD7u4VVbV3RtfBHZbkT5I8do593zT+uVGS67p78a9TKADcGVrqALirOyPJ06tq86raIqPulV9K8sCq+v/G6xye5MvjFretu/vUJH+WZPEkB+juG5J8v6qenSQ18oj1+zIAYHZCHQB3ad19TpLjk5yd0fV0/5jk2iQXJjmiqr6ZZNskf59kqyT/Np53epJXrsOhnpPkhVV1XpILkhyyvl4DAMzF6JcAAAADpqUOAABgwAyUAgBzqKrXJXn2arM/2t1vmUY9ALA63S8BAAAGTPdLAACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAH7/wH5ahlZjjuI5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x = 'obs_ref', y = 'aff_score', hue = 'variable', data = dummy, palette ='BuGn')\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Affordablity score')\n",
    "plt.title('Affordability Scores Actuals & Doubled Net Additions\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "85cc8f2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>obs_ref</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>368</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>-5.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>438</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>-0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>529</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>-2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>556</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>368</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>10.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>438</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>529</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>556</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   obs_ref variable  value\n",
       "0      368   h_diff  -5.92\n",
       "1      438   h_diff  -0.59\n",
       "2      529   h_diff  -2.06\n",
       "3      556   h_diff   0.00\n",
       "4      368   d_diff  10.25\n",
       "5      438   d_diff   4.07\n",
       "6      529   d_diff   4.19\n",
       "7      556   d_diff   0.00"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(dummy[['obs_ref','variable','aff_score']])\n",
    "df = df.pivot(index = 'obs_ref', columns = 'variable', values = 'aff_score').reset_index()\n",
    "#dropped the two poor predictions, 602 and 711\n",
    "df = df.iloc[:-2]\n",
    "                        \n",
    "df['h_diff'] = (df['h_pred']-df['actual'])/df['actual']*100\n",
    "df['d_diff'] = (df['d_pred']-df['actual'])/df['actual']*100\n",
    "new_df = pd.DataFrame(df[['obs_ref', 'h_diff','d_diff']])\n",
    "new_df = new_df.melt(id_vars = 'obs_ref')\n",
    "\n",
    "    \n",
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x = 'obs_ref', y = 'value', hue = 'variable', data = new_df, palette ='BuGn')\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('% Difference to Prediction')\n",
    "plt.title('Doubled & Halved Net additions Impact on Affordability Score\\n')\n",
    "plt.show()\n",
    "new_df.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ccc753de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>obs_ref</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92</td>\n",
       "      <td>actual</td>\n",
       "      <td>280.00</td>\n",
       "      <td>25.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>275.27</td>\n",
       "      <td>25.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>282.23</td>\n",
       "      <td>25.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>178</td>\n",
       "      <td>actual</td>\n",
       "      <td>405.50</td>\n",
       "      <td>33.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>178</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>503.97</td>\n",
       "      <td>33.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>178</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>510.61</td>\n",
       "      <td>33.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>287</td>\n",
       "      <td>actual</td>\n",
       "      <td>235.00</td>\n",
       "      <td>26.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>287</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>230.17</td>\n",
       "      <td>26.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>287</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>237.70</td>\n",
       "      <td>26.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>368</td>\n",
       "      <td>actual</td>\n",
       "      <td>157.50</td>\n",
       "      <td>25.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>368</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>159.41</td>\n",
       "      <td>25.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>368</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>156.23</td>\n",
       "      <td>25.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>566</td>\n",
       "      <td>actual</td>\n",
       "      <td>94.00</td>\n",
       "      <td>19.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>566</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>94.12</td>\n",
       "      <td>19.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>566</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>93.92</td>\n",
       "      <td>19.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>599</td>\n",
       "      <td>actual</td>\n",
       "      <td>380.00</td>\n",
       "      <td>30.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>599</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>378.54</td>\n",
       "      <td>30.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>599</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>380.98</td>\n",
       "      <td>30.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>699</td>\n",
       "      <td>actual</td>\n",
       "      <td>425.00</td>\n",
       "      <td>34.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>699</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>574.73</td>\n",
       "      <td>34.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>699</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>579.85</td>\n",
       "      <td>34.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obs_ref variable   value  median_income\n",
       "0        92   actual  280.00          25.75\n",
       "1        92   d_pred  275.27          25.75\n",
       "2        92   h_pred  282.23          25.75\n",
       "3       178   actual  405.50          33.04\n",
       "4       178   d_pred  503.97          33.04\n",
       "5       178   h_pred  510.61          33.04\n",
       "6       287   actual  235.00          26.51\n",
       "7       287   d_pred  230.17          26.51\n",
       "8       287   h_pred  237.70          26.51\n",
       "9       368   actual  157.50          25.18\n",
       "10      368   d_pred  159.41          25.18\n",
       "11      368   h_pred  156.23          25.18\n",
       "12      566   actual   94.00          19.57\n",
       "13      566   d_pred   94.12          19.57\n",
       "14      566   h_pred   93.92          19.57\n",
       "15      599   actual  380.00          30.59\n",
       "16      599   d_pred  378.54          30.59\n",
       "17      599   h_pred  380.98          30.59\n",
       "18      699   actual  425.00          34.94\n",
       "19      699   d_pred  574.73          34.94\n",
       "20      699   h_pred  579.85          34.94"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# on model_a dummy:\n",
    "\n",
    "dummy_a = pd.read_csv( 'mod_a_dummy_output.csv')\n",
    "dummy_a = dummy_a.drop(columns =['Unnamed: 0','pred'])\n",
    "dummy_a = dummy_a.melt('obs_ref')\n",
    "t_set_a = pd.read_csv('la_test_data.csv')\n",
    "t_data_a = t_set_a.iloc[[92,178,287,368,566,599,699]].reset_index(drop = True)\n",
    "t_data_a = t_data_a.loc[: ,['Unnamed: 0', 'median_income']]\n",
    "t_data_a['median_income'] = t_data_a['median_income']/1000\n",
    "t_data_a['obs_ref'] = [92,178,287,368,566,599,699]\n",
    "t_data_a = t_data_a.drop(columns = {'Unnamed: 0'})\n",
    "dummy_a = dummy_a.merge(t_data_a)\n",
    "dummy_a\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d15a7f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_lst_a = (dummy_a['value']).to_list()\n",
    "inc_lst_a = (dummy_a['median_income']).to_list()\n",
    "obs_lst_a = (dummy_a['obs_ref']).to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "22e5605e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(92, 25.755, 280.0),\n",
       " (92, 25.755, 275.27255),\n",
       " (92, 25.755, 282.22687),\n",
       " (178, 33.04, 405.5),\n",
       " (178, 33.04, 503.96713),\n",
       " (178, 33.04, 510.61337),\n",
       " (287, 26.506, 235.0),\n",
       " (287, 26.506, 230.16748),\n",
       " (287, 26.506, 237.69807),\n",
       " (368, 25.177, 157.5),\n",
       " (368, 25.177, 159.40747),\n",
       " (368, 25.177, 156.22812),\n",
       " (566, 19.567, 94.0),\n",
       " (566, 19.567, 94.119),\n",
       " (566, 19.567, 93.92364),\n",
       " (599, 30.587, 380.0),\n",
       " (599, 30.587, 378.5422),\n",
       " (599, 30.587, 380.97772),\n",
       " (699, 34.935, 425.0),\n",
       " (699, 34.935, 574.72815),\n",
       " (699, 34.935, 579.8523)]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_lst_a = list(zip(obs_lst_a, inc_lst_a,val_lst_a))\n",
    "data_lst_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9f132fa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.496487119437933\n",
      "25.468870757569466\n",
      "24.012941304057513\n",
      "20.11204432902148\n",
      "20.11204432902148\n",
      "20.11204432902148\n",
      "30.000000000000007\n",
      "30.000000000000007\n",
      "30.000000000000007\n",
      "43.59406072896764\n",
      "42.53295855950865\n",
      "44.26419823911365\n",
      "54.21602955256802\n",
      "54.18833918561882\n",
      "54.233818160018934\n",
      "16.512150296537246\n",
      "16.512150296537246\n",
      "16.512150296537246\n",
      "23.10458312764143\n",
      "23.10458312764143\n",
      "23.10458312764143\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obs_ref</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>median_income</th>\n",
       "      <th>aff_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92</td>\n",
       "      <td>actual</td>\n",
       "      <td>280.00</td>\n",
       "      <td>25.75</td>\n",
       "      <td>24.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>275.27</td>\n",
       "      <td>25.75</td>\n",
       "      <td>25.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>282.23</td>\n",
       "      <td>25.75</td>\n",
       "      <td>24.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>178</td>\n",
       "      <td>actual</td>\n",
       "      <td>405.50</td>\n",
       "      <td>33.04</td>\n",
       "      <td>20.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>178</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>503.97</td>\n",
       "      <td>33.04</td>\n",
       "      <td>20.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>178</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>510.61</td>\n",
       "      <td>33.04</td>\n",
       "      <td>20.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>287</td>\n",
       "      <td>actual</td>\n",
       "      <td>235.00</td>\n",
       "      <td>26.51</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>287</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>230.17</td>\n",
       "      <td>26.51</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>287</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>237.70</td>\n",
       "      <td>26.51</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>368</td>\n",
       "      <td>actual</td>\n",
       "      <td>157.50</td>\n",
       "      <td>25.18</td>\n",
       "      <td>43.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>368</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>159.41</td>\n",
       "      <td>25.18</td>\n",
       "      <td>42.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>368</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>156.23</td>\n",
       "      <td>25.18</td>\n",
       "      <td>44.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>566</td>\n",
       "      <td>actual</td>\n",
       "      <td>94.00</td>\n",
       "      <td>19.57</td>\n",
       "      <td>54.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>566</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>94.12</td>\n",
       "      <td>19.57</td>\n",
       "      <td>54.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>566</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>93.92</td>\n",
       "      <td>19.57</td>\n",
       "      <td>54.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>599</td>\n",
       "      <td>actual</td>\n",
       "      <td>380.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>16.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>599</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>378.54</td>\n",
       "      <td>30.59</td>\n",
       "      <td>16.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>599</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>380.98</td>\n",
       "      <td>30.59</td>\n",
       "      <td>16.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>699</td>\n",
       "      <td>actual</td>\n",
       "      <td>425.00</td>\n",
       "      <td>34.94</td>\n",
       "      <td>23.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>699</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>574.73</td>\n",
       "      <td>34.94</td>\n",
       "      <td>23.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>699</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>579.85</td>\n",
       "      <td>34.94</td>\n",
       "      <td>23.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obs_ref variable   value  median_income  aff_score\n",
       "0        92   actual  280.00          25.75      24.50\n",
       "1        92   d_pred  275.27          25.75      25.47\n",
       "2        92   h_pred  282.23          25.75      24.01\n",
       "3       178   actual  405.50          33.04      20.11\n",
       "4       178   d_pred  503.97          33.04      20.11\n",
       "5       178   h_pred  510.61          33.04      20.11\n",
       "6       287   actual  235.00          26.51      30.00\n",
       "7       287   d_pred  230.17          26.51      30.00\n",
       "8       287   h_pred  237.70          26.51      30.00\n",
       "9       368   actual  157.50          25.18      43.59\n",
       "10      368   d_pred  159.41          25.18      42.53\n",
       "11      368   h_pred  156.23          25.18      44.26\n",
       "12      566   actual   94.00          19.57      54.22\n",
       "13      566   d_pred   94.12          19.57      54.19\n",
       "14      566   h_pred   93.92          19.57      54.23\n",
       "15      599   actual  380.00          30.59      16.51\n",
       "16      599   d_pred  378.54          30.59      16.51\n",
       "17      599   h_pred  380.98          30.59      16.51\n",
       "18      699   actual  425.00          34.94      23.10\n",
       "19      699   d_pred  574.73          34.94      23.10\n",
       "20      699   h_pred  579.85          34.94      23.10"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aff_score_lst = []\n",
    "for obs, inc, val in data_lst_a:\n",
    "    affordability_score(obs, inc, val)\n",
    "\n",
    "dummy_a['aff_score'] = aff_score_lst\n",
    "dummy_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9107e0fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oU687/NeMjrkfZXT8njnDts2q/u6+IaMBcR6dUcvNtzL6/PfIaBTUV82wjnePj6MrMzrmPpnkKeOglSR/kdHAKd9K8u2Mvo9/MV7vf2fUtfGUjD6TFXXzPDaj0HJtRq1lL1pJHSvdj7P4PqzKuzIlrIx/PHhmRmHsBxm1FH4go5bTZMX7dKqDM7om7uJpx8u7x9t3R0bXaL40o2Pg+Znyf9jqbE93X5BRsP7+uKvoA6a9/72MrnP9p/F2PDPJM7v7ZzN+IiOz/X4AA1KujQVgNqrqf5P8bnefMulaAICf01IHwCpV1f/J6Dqf1W0tAQDm2UarngRgOMaDoZy8ovemDMDAaqiq0zK6jujFU7rnAQB3E7pfAgAADJjulwAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADttGkC5iNbbfdthcsWDDpMgAAACZiyZIlV3f3dit6bxChbsGCBVm8ePGkywAAAJiIqvrhyt7T/RIAAGDAhDoAAIABE+oAAAAGbBDX1AEAAOuf2267LZdeemluvfXWSZdyt7HJJptkxx13zMYbbzzreYQ6AABgIi699NJsueWWWbBgQapq0uVMXHfnmmuuyaWXXpqdd9551vPpfgkAAEzErbfemvvd734C3VhV5X73u99qt1wKdQAAwMQIdHe1Jp+HUAcAANzjPO1pT8v1118/4zRbbLHFCl9/6UtfmuOPP34eqlozrqkDAADuMbo73Z2TTjpp0qXMGS11AADA4Lz5zW/Oe9/73jufH3bYYTn88MOz//77Z4899siv//qv58QTT0ySXHTRRdlll13y+7//+9ljjz1yySWXZMGCBbn66quTJM961rOy5557Ztddd82RRx55l/W88Y1vzB577JH9998/S5cu/YU6lixZkn333Td77rlnnvzkJ+fyyy+fx61eMaEOAAAYnBe84AX5+Mc/fufz4447Li972ctywgkn5Oyzz86pp56aN77xjenuJMn3vve9vOQlL8k3v/nN7LTTTndZ1lFHHZUlS5Zk8eLFOeKII3LNNdckSW655ZbsscceOfvss7Pvvvvm8MMPv8t8t912W/7wD/8wxx9/fJYsWZKXv/zl+dM//dN53vJfpPslAAAwOI985CNz1VVX5bLLLsvSpUuz9dZbZ4cddsgb3vCGnH766dlggw3yox/9KFdeeWWSZKeddsqjH/3oFS7riCOOyAknnJAkueSSS3LhhRfmfve7XzbYYIM8//nPT5L8zu/8Tp797GffZb7vfe97Oe+88/LEJz4xSXL77bdnhx12mK9NXimhDgAAGKTnPOc5Of7443PFFVfkBS94QY455pgsXbo0S5YsycYbb5wFCxbceXuAzTfffIXLOO2003LKKafkjDPOyGabbZb99ttvpbcUmD4yZXdn1113zRlnnDG3G7aadL8EAAAG6QUveEE+9rGP5fjjj89znvOc3HDDDdl+++2z8cYb59RTT80Pf/jDVS7jhhtuyNZbb53NNtssF1xwQc4888w737vjjjvuHOXy2GOPzd57732XeR/2sIdl6dKld4a62267Ld/5znfmcAtnR0sdAAAwSLvuumtuuummPPCBD8wOO+yQF73oRXnmM5+ZRYsWZeHChXn4wx++ymU85SlPyfve977stttuedjDHnaXLpqbb755vvOd72TPPffMVlttdZdr+JLkXve6V44//vi89rWvzQ033JBly5bl9a9/fXbdddc539aZ1PILB+/OFi1a1IsXL550GQAAwBw6//zzs8suu0y6jLudFX0uVbWkuxetaHrdLwEAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgFk477bR87WtfW6tlbLHFFnNUzc+5+TgAAHC3cO6Vl87p8na//45zurzTTjstW2yxRR7zmMfM6XLXllAHAKyVuT4JW11Hn33KRNf/xTOOm+j6z3n7SRNdP6wPnvWsZ+WSSy7Jrbfemte97nV59atfnc9+9rN5y1vekttvvz3bbrttPvjBD+Z973tfNtxww3zkIx/JP/3TP+WDH/xgnvGMZ+Q5z3lOklEr3M0335ybb745Bx54YK677rrcdttt+Yu/+IsceOCB81a/UAcAANyjHXXUUdlmm23yk5/8JI961KNy4IEH5lWvelVOP/307Lzzzrn22muzzTbb5DWveU222GKLvOlNb0qSfPCDH1zh8jbZZJOccMIJuc997pOrr746j370o3PAAQekqualfqEOAAC4RzviiCNywgknJEkuueSSHHnkkdlnn32y8847J0m22Wab1Vped+ctb3lLTj/99GywwQb50Y9+lCuvvDK/9Eu/NOe1J0IdAABwD3baaafllFNOyRlnnJHNNtss++23X3bfffd873vfW+W8G220Ue64444koyD3s5/9LElyzDHHZOnSpVmyZEk23njjLFiwILfeeuu8bYPRLwEAgHusG264IVtvvXU222yzXHDBBTnzzDPz05/+NF/60pfygx/8IEly7bXXJkm23HLL3HTTTXfOu2DBgixZsiRJcuKJJ+a22267c5nbb799Nt5445x66qn54Q9/OK/bINQBAAD3WE95ylOybNmy7LbbbvnzP//zPPrRj852222XI488Ms9+9rOz++675/nPf36S5JnPfGZOOOGELFy4MF/+8pfzqle9Kl/60pey11575etf/3o233zzJMmLXvSiLF68OIsWLcoxxxyThz/84fO6DbpfAgAAdwtzfQuC2bj3ve+dk08+eYXvPfWpT73L84c+9KH51re+dZfXzjzzzDsf//Vf/3WSZNttt80ZZ5yxwmXefPPNa1PuCmmpAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABc586AADgbuGPTv7wnC7vH5760tWa/rDDDssWW2yRN73pTXNax4osWLAgixcvzrbbbrvWy9JSBwAAMAeWLVs2kfVqqQMAAO6x/vIv/zL/+q//mgc96EHZbrvtsueee65wuv322y8LFy7MWWedlRtvvDFHHXVU9tprrxx22GG57LLLctFFF2XbbbfNu971rrzmNa/JxRdfnCR55zvfmcc+9rG55pprctBBB2Xp0qXZa6+90t1ztg1CHQAAcI+0ZMmSfOxjH8s3v/nNLFu2LHvsscdKQ12S3HLLLfna176W008/PS9/+ctz3nnn3bmcr3zlK9l0003zwhe+MG94wxuy99575+KLL86Tn/zknH/++Tn88MOz9957561vfWv+8z//M0ceeeScbYdQBwAA3CN9+ctfzm//9m9ns802S5IccMABM05/0EEHJUn22Wef3Hjjjbn++uvvnG/TTTdNkpxyyin57ne/e+c8N954Y2666aacfvrp+dSnPpUkefrTn56tt956zrZDqAMAAO6xqmqNp13+fPPNN7/ztTvuuCNnnHHGnSFvTde1OgyUAgAA3CPts88+OeGEE/KTn/wkN910Uz7zmc/MOP3HP/7xJMlXvvKVbLXVVtlqq61+YZonPelJefe7333n83POOefOdR1zzDFJkpNPPjnXXXfdHG2FljoAAOBuYnVvQbC29thjjzz/+c/PwoULs9NOO+Vxj3vcjNNvvfXWecxjHnPnQCkrcsQRR+SQQw7JbrvtlmXLlmWfffbJ+973vrztbW/LQQcdlD322CP77rtvHvzgB8/ZdtRcjroyXxYtWtSLFy+edBkAwAqce+WlE13/0WefMtH1f/GM4ya6/nPeftJE1w9r4/zzz88uu+wy6TJmZb/99ss73vGOLFq0aN7XtaLPpaqWdPcKV677JQAAwIDpfgkAADB2yCGH5Ktf/epdXnvd616X0047bTIFzYJQBwAAMPae97xn0iWsNt0vAQAABkyoAwAAGDChDgAAYMCEOgAAgAEzUAoAAHC3sPCtT5vT5c3mPo4XXXRRnvGMZ+S8886b03XPxoIFC7J48eJsu+22a7UcLXUAAABzZNmyZet8nVrqAACAe7Tbb789r3rVq/K1r30tD3zgA3PiiSdm0003/YXp9ttvvyxcuDBnnXVWbrzxxhx11FHZa6+9cthhh+Wyyy7LRRddlG233Tbvete78prXvCYXX3xxkuSd73xnHvvYx+aaa67JQQcdlKVLl2avvfZKd89J/VrqAACAe7QLL7wwhxxySL7zne/kvve9bz75yU+udNpbbrklX/va1/Le9743L3/5y+98fcmSJTnxxBNz7LHH5nWve13e8IY35Bvf+EY++clP5pWvfGWS5PDDD8/ee++db37zmznggAPuDH1rS0sdAABwj7bzzjtn4cKFSZI999wzF1100UqnPeigg5Ik++yzT2688cZcf/31SZIDDjjgzta9U045Jd/97nfvnOfGG2/MTTfdlNNPPz2f+tSnkiRPf/rTs/XWW89J/UIdAABwj3bve9/7zscbbrhhfvKTn6x02qpa4fPNN9/8ztfuuOOOnHHGGSvswjl9/rmg+yUAAMAsffzjH0+SfOUrX8lWW22Vrbba6hemedKTnpR3v/vddz4/55xzkoxa94455pgkycknn5zrrrtuTmqa15a6qrooyU1Jbk+yrLsXVdU2ST6eZEGSi5I8r7vnZmsAAIDBms0tCCZt6623zmMe85g7B0pZkSOOOCKHHHJIdttttyxbtiz77LNP3ve+9+Vtb3tbDjrooOyxxx7Zd9998+AHP3hOaqq5GnFlhQsfhbpF3X31lNf+Lsm13f03VXVokq27+80zLWfRokW9ePHieasTAFhz51556UTXf/TZp0x0/V8847iJrn8IJ8GwMueff3522WWXSZcxa/vtt1/e8Y53ZNGiRfO6nhV9LlW1pLtXuOJJdL88MMnR48dHJ3nWBGoAAABYL8z3QCmd5HNV1Un+pbuPTHL/7r48Sbr78qrafkUzVtWrk7w6yZw1SwIAAKzKIYcckq9+9at3ee11r3tdTjvttMkUtArzHeoe292XjYPb56vqgtnOOA6ARyaj7pfzVSAAAMBU73nPeyZdwmqZ1+6X3X3Z+O9VSU5IsleSK6tqhyQZ/71qPmsAAADuvuZzjI8hWpPPY95CXVVtXlVbLn+c5ElJzkvy6SQHjyc7OMmJ81UDAABw97XJJpvkmmuuEezGujvXXHNNNtlkk9Wabz67X94/yQnjm+ttlOTY7v5sVX0jyXFV9YokFyd57jzWAAAA3E3tuOOOufTSS7N06dJJl3K3sckmm2THHXdcrXnmLdR19/eT7L6C169Jsv98rRcAABiGjTfeODvvvPOkyxi8SdzSAAAAgDki1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAO20aQLAID13cK3Pm2i6z/n7SdNdP0AzC8tdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFsaALBOnHvlpRNd/+7333Gi6weA+aKlDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwDaadAEAsC780ckfnnQJADAvtNQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIDNe6irqg2r6ptV9R/j59tU1eer6sLx363nuwYAAID11bpoqXtdkvOnPD80yRe6+yFJvjB+DgAAwBqY11BXVTsmeXqSD0x5+cAkR48fH53kWfNZAwAAwPpsvlvq3pnkj5PcMeW1+3f35Uky/rv9PNcAAACw3pq3UFdVz0hyVXcvWcP5X11Vi6tq8dKlS+e4OgAAgPXDfLbUPTbJAVV1UZKPJXl8VX0kyZVVtUOSjP9etaKZu/vI7l7U3Yu22267eSwTAABguOYt1HX3n3T3jt29IMkLknyxu38nyaeTHDye7OAkJ85XDQAAAOu7Sdyn7m+SPLGqLkzyxPFzAAAA1sBG62Il3X1aktPGj69Jsv+6WC8AAMD6bhItdQAAAMwRoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAhDoAAIABE+oAAAAGTKgDAAAYMKEOAABgwIQ6AACAARPqAAAABkyoAwAAGDChDgAAYMCEOgAAgAET6gAAAAZMqAMAABgwoQ4AAGDAZhXqqmqnqnrC+PGmVbXl/JYFAADAbKwy1FXVq5Icn+Rfxi/tmOTf57EmAAAAZmk2LXWHJHlskhuTpLsvTLL9fBYFAADA7Mwm1P20u3+2/ElVbZSk568kAAAAZms2oe5LVfWWJJtW1ROTfCLJZ+a3LAAAAGZjNqHuzUmWJvl2kt9NclKSP5vPogAAAJidjWZ6s6o2SPKt7v61JO9fNyUBAAAwWzO21HX3HUnOraoHr6N6AAAAWA0zttSN7ZDkO1V1VpJblr/Y3QfMW1UAAADMymxC3eHzXgUAAABrZJWhrru/VFX3T/Ko8UtndfdV81sWAAAAs7HK0S+r6nlJzkry3CTPS/L1qnrOfBcGAADAqs2m++WfJnnU8ta5qtouySlJjp/PwgAAAFi12dynboNp3S2vmeV8AAAAzLPZtNR9tqr+K8lHx8+fn+Tk+SsJAACA2ZrNQCn/t6qenWTvJJXkyO4+Yd4rAwAAYJVWGeqqauckJ3X3p8bPN62qBd190XwXBwAAwMxmc23cJ5LcMeX57ePXAAAAmLDZhLqNuvtny5+MH99r/koCAABgtmYT6pZW1QHLn1TVgUmunr+SAAAAmK3ZjH75miTHVNW7Mxoo5ZIkL5nXqgAAAJiV2Yx++b9JHl1VWySp7r5p/ssCAABgNlbZ/bKqXldV90lyS5J/rKqzq+pJ818aAAAAqzKba+pe3t03JnlSku2TvCzJ38xrVQAAAMzKbEJdjf8+LcmHuvvcKa+tfKaqTarqrKo6t6q+U1WHj1/fpqo+X1UXjv9uveblAwAA3LPNJtQtqarPZRTq/quqtsxd71u3Mj9N8vju3j3JwiRPqapHJzk0yRe6+yFJvjB+DgAAwBqYzeiXr8golH2/u39cVffLqAvmjLq7k9w8frrx+F8nOTDJfuPXj05yWpI3r07RAAAAjKyypa677+jus7v7+vHza7r7W7NZeFVtWFXnJLkqyee7++tJ7t/dl4+XdXlG1+kBAACwBmbTUrfGuvv2JAur6r5JTqiqX5vtvFX16iSvTpIHP/jB81MgcLdy7pWXTnT9R599ykTX/8Uzjpvo+s95+0kTXT8AsGZmc03dWhu38p2W5ClJrqyqHZJk/PeqlcxzZHcv6u5F22233booEwAAYHBmc5+6d1TVrqu74KrabtxCl6raNMkTklyQ5NNJDh5PdnCSE1d32QAAAIzMpvvlBUmOrKqNknwoyUe7+4ZZzLdDkqOrasOMwuNx3f0fVXVGkuOq6hVJLk7y3DWsHQAA4B5vlaGuuz+Q5ANV9bCMRr38VlV9Ncn7u/vUGeb7VpJHruD1a5Lsv+YlAwAAsNysrqkbt7Y9fPzv6iTnJvmjqvrYPNYGAADAKqyypa6q/iHJM5N8MclfdfdZ47f+tqq+N5/FAQAAMLPZXFN3XpI/6+4fr+C9vea4HgAAAFbDbLpfvmh6oKuqLyTJLAdMAQAAYJ6stKWuqjZJslmSbatq6yQ1fus+SR6wDmoDAABgFWbqfvm7SV6fUYA7e8rrNyZ5zzzWBAAAwCytNNR197uSvKuq/rC7/2kd1gQAAMAszdT98vHd/cUkP6qqZ09/v7s/Na+VAQAAsEozdb/cN6PbGDxzBe91EqEOAABgwmbqfvm28d+XrbtyAAAAWB0zdb/8o5lm7O5/mPtyAAAAWB0zdb/ccp1VAQAAwBqZqfvl4euyEAAAAFbfBquaoKp+uao+U1VLq+qqqjqxqn55XRQHAADAzFYZ6pIcm+S4JDtkdCPyTyT56HwWBQAAwOzMJtRVd/9bdy8b//tIRrc0AAAAYMJmGv1ym/HDU6vq0CQfyyjMPT/Jf66D2gAAAFiFmUa/XJJRiKvx89+d8l4n+X/zVRQAAACzM9Polzuvy0IAAABYfTO11N2pqn4tySOSbLL8te7+1/kqCgAAgNlZZairqrcl2S+jUHdSkqcm+UoSoQ4AAGDCZjP65XOS7J/kiu5+WZLdk9x7XqsCAABgVmYT6n7S3XckWVZV90lyVRI3HwcAALgbmM01dYur6r5J3p/RiJg3JzlrPosCAABgdlYZ6rr798cP31dVn01yn+7+1vyWBQAAwGzMdPPxPWZ6r7vPnp+SAAAAmK2ZWur+fvx3kySLkpyb0Y3Id0vy9SR7z29pAAAArMpKB0rp7t/q7t9K8sMke3T3ou7eM8kjk/zPuioQAACAlZvN6JcP7+5vL3/S3eclWThvFQEAADBrsxn98oKq+kCSjyTpJL+T5Px5rQoAAIBZmU2oe2mS30vyuvHz05P883wVBAAAwOzNGOqqasMk/9HdT0jyj+umJAAAAGZrxmvquvv2JD+uqq3WUT0AAACshtl0v7w1yber6vNJbln+Yne/dt6qAgAAYFZmE+r+c/wPAACAu5lVhrruPrqq7pXkoeOXvtfdt81vWQAAAMzGKkNdVe2X5OgkFyWpJA+qqoO7+/R5rQwAAIBVmk33y79P8qTu/l6SVNVDk3w0yZ7zWRgAAACrNuPol2MbLw90SdLd/51k4/krCQAAgNmaTUvd4qr6YJJ/Gz9/UZIl81fS8P3RyR+e6Pr/4akvnej6AQCAdWc2oe73khyS5LUZXVN3epL3zmdRAAAAzM5KQ11VfaG790/y9u5+c5J/WHdlAQAAMBsztdTtUFX7Jjmgqj6WUSvdnbr77HmtDAAAgFWaKdS9NcmhSXbML7bSdZLHz1dRAAAAzM5Moe7y7n5qVb21u9++zioCAABg1ma6pcER47/PWgd1AAAAsAZmaqm7rao+lOSBVXXE9De7+7XzVxYAAACzMVOoe0aSJ2R07dz0+9L1vFUEAADArK001HX31Uk+VlXnd/e5y1+vqr2THJTkX9dBfWvk3CsvnXQJAAAA68Qqbz7e3edW1cIkL0zyvCQ/SPLJea4LAACAWZjp5uMPTfKCjFrlrkny8STV3b+1jmoDAABgFWZqqbsgyZeTPLO7/ydJquoN66QqAAAAZmWmWxr8nyRXJDm1qt5fVfsnqXVTFgAAALMx00ApJyQ5oao2z+hedW9Icv+q+uckJ3T359ZNiayuhW992kTXf87bT5ro+gEA4J5kppa6JEl339Ldx3T3M5LsmOScJIfOd2EAAACs2ipD3VTdfW13/0t3P36+CgIAAGD2VivUAQAAcPci1AEAAAyYUAcAADBgQh0AAMCACXUAAAADttL71AEAAOuvc6+8dKLrP/rsUya6/i+ecdxE1z+X93bWUgcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBg8xbqqupBVXVqVZ1fVd+pqteNX9+mqj5fVReO/249XzUAAACs7+azpW5Zkjd29y5JHp3kkKp6RJJDk3yhux+S5Avj5wAAAKyBeQt13X15d589fnxTkvOTPDDJgUmOHk92dJJnzVcNAAAA67uN1sVKqmpBkkcm+XqS+3f35cko+FXV9uuiBtYP51556UTXf/TZp0x0/V8847iJrv+ct5800fUDAPCL5n2glKraIsknk7y+u29cjfleXVWLq2rx0qVL569AAACAAZvXUFdVG2cU6I7p7k+NX76yqnYYv79DkqtWNG93H9ndi7p70XbbbTefZQIAAAzWfI5+WUk+mOT87v6HKW99OsnB48cHJzlxvmoAAABY383nNXWPTfLiJN+uqnPGr70lyd8kOa6qXpHk4iTPnccaAAAA1mvzFuq6+ytJaiVv7z9f6wUAALgnmfeBUgAAAJg/Qh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAM2EaTLgAAgLuvc6+8dKLrP/rsUya6/i+ecdzE1n3O20+a2LoZFi11AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADNm+hrqqOqqqrquq8Ka9tU1Wfr6oLx3+3nq/1AwAA3BPMZ0vdh5M8Zdprhyb5Qnc/JMkXxs8BAABYQ/MW6rr79CTXTnv5wCRHjx8fneRZ87V+AACAe4J1fU3d/bv78iQZ/91+Ha8fAABgvXK3HSilql5dVYuravHSpUsnXQ4AAMDd0roOdVdW1Q5JMv571com7O4ju3tRdy/abrvt1lmBAAAAQ7KuQ92nkxw8fnxwkhPX8foBAADWK/N5S4OPJjkjycOq6tKqekWSv0nyxKq6MMkTx88BAABYQxvN14K7+6CVvLX/fK0TAADgnuZuO1AKAAAAqybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwIAJdQAAAAMm1AEAAAyYUAcAADBgQh0AAMCACXUAAAADJtQBAAAMmFAHAAAwYEIdAADAgAl1AAAAAybUAQAADJhQBwAAMGATCXVV9ZSq+l5V/U9VHTqJGgAAANYH6zzUVdWGSd6T5KlJHpHkoKp6xLquAwAAYH0wiZa6vZL8T3d/v7t/luRjSQ6cQB0AAACDN4lQ98Akl0x5fun4NQAAAFZTdfe6XWHVc5M8ubtfOX7+4iR7dfcfTpvu1UlePX76sCTfW6eFTta2Sa6edBHMC/t2/Wb/rr/s2/Wb/bt+s3/XX/e0fbtTd2+3ojc2WteVZNQy96Apz3dMctn0ibr7yCRHrqui7k6qanF3L5p0Hcw9+3b9Zv+uv+zb9Zv9u36zf9df9u3PTaL75TeSPKSqdq6qeyV5QZJPT6AOAACAwVvnLXXdvayq/iDJfyXZMMlR3f2ddV0HAADA+mAS3S/T3SclOWkS6x6Ie2S303sI+3b9Zv+uv+zb9Zv9u36zf9df9u3YOh8oBQAAgLkziWvqAAAAmCNCHQAAwIAJdQAAAAM2kYFSAIauqnbr7m9Nug7mR1U9OMmN3X19VS1IsijJBd193mQrY22Nb6d0W48HFaiq30qyR5LvdvfJEy2OOVFVizK6J/KyJBd29wUTLok5ZP+umIFSJqiq7pPkTzK6AfvJ3X3slPfe292/P7HimBNODNdfVXV7kh8k+WiSj3b3dydcEnOkqg5N8rtJfprkHUnelOSrSR6d5IPd/Q8TLI+1VFXnJtmvu6+rqv+b5LczGpF73ySLu/tPJloga6yq9k3y90muT7JnRt/brZPcluTF3X3J5Kpjbdm/MxPqJqiqPpnkwiRnJnl5RgflC7v7p1V1dnfvMdECWStODNdvVfXNJC9OclCS5ye5JaOA97HuvmiCpbGWquo7Gf0As1mSi5L8cncvrarNk3y9u39tkvWxdqrqvOX7sKoWJ3lcd/+kqjZKcnZ37zbZCllT4/+XnzT+vu6c5B+6+7er6olJ/m93P2nCJbIW7N+ZuaZusn6luw/t7n/v7gOSnJ3ki1V1v0kXxpx4cZJHJHlskn/M6MThFUn2yijEM2zd3ed19592968meVWS7ZN8uaq+NuHaWDu3d/dPMvo1+CdJrkmS7r5lkkUxZ26squXB/Ookm4wfbxTnRUO3YXcvHT++OMlOSdLdn0/ywIlVxVyxf2fgmrrJundVbdDddyRJd/9lVV2a5PQkW0y2NObA7eNff3+WaSeGVTXZypgLd9mJ3X1WkrOq6o1J9plMScyRs6vq2CSbJ/lCkqOr6rNJHp9EN9vhe02SY8bdMK9KsriqvpRktyR/NdHKWFuLq+qDGX1vD0xyWpJU1WZJNpxgXcwN+3cGul9OUFX9XZLPdfcp015/SpJ/6u6HTKYy5kJVfTjJvTI6MfxxRhf0Lj8x3LK7nze56lhbVfXCqdfBsv4Yd8N7bpJOcnyS38iom+3FSd6jxW74qmrDJE9K8tCMfuC+NMl/dff1k6yLtVNVG2fUa+IRSc5NclR3315VmybZvrt/ONECWSv278yEugmrql/J6CLtO0fxyWjQhRsmWhhrbQUnhnsleWGcGMLgVNX9uvuaSdfB/LB/gaHTd3yCquq1Sf45o/78j0qyaUbh7oyq2m9ylTEXuntZd3+0uz82fvy17v6D7v47gW74qmpRVZ1aVR+pqgdV1eer6oaq+kZVPXLS9bHmqupvqmrb8eNFVfX9JGdW1Q/Ho68xYCvZv1+3f4evqraoqrdX1Xnj/4+XVtWZVfXSSdfG2rN/Z6alboKq6ttJFo6bjjdLclJ37zceBv/E7nZiOGBVdXaST2XU8vq/k66HuVVVZyV5W5L7Jvm7JG/o7uOrav8kf9HdvznJ+lhzVfXt7v718eNTk/xxd3+jqh6a5NjuXjTZClkb9u/6q6pOTHJCklOSPC+jyx8+luTPkvyou98ywfJYS/bvzLTUTd7ywWrunWTLJOnui5NsPLGKmCtbZ3TCf2pVnVVVb6iqB0y4JubOxt19cnd/NKORMI/P6MEX8vPR9Bimjcfdp5Nk0+7+RpJ0939n9H81w2b/rr8WdPeHu/vS8W2DDujuC5O8LMmzJ1wba8/+nYFQN1kfSPKNqjoyyRlJ3p0kVbVdkmsnWRhz4rruflN3PzjJG5M8JKNR9U6tqldPuDbW3q1V9aSqem6SrqpnJXfeHPX2iVbG2npPkpOq6vFJPltV76yqfarq8CTnTLY05oD9u/66par2TpKqembG51LjUcYNOz189u8MdL+csKraNckuSc7r7gsmXQ9zp1ZwA/nxiGtPTPL87n7ZZCpjLlTV7hl1u7wjyRuS/F6Sg5P8KMmrutu96gZsfF3z7+XnoyNekuTfk3you2+bWGHMCft3/TT+f/n9Ge3X85K8vLv/e/xj+UHdfcREC2StVNVuGTWI2L8rINTBPKmqj3X3CyZdB/OnqnZJ8oAkX+/um6e8/pTu/uzkKmNtVdVeGXWr/cb4x7enJDm/u0+acGnMsap6XEajE3+7uz836XqYO+NWnb0y+uHcvh24qvqNJBd09w3jsSjenGSPjO4f+lf39JHjdb+EeTJToKsqrXQDNx699oQkf5jkvKo6cMrbbmA8YFX1tiRHJPnnqvrr8ePNkxxaVX860eJYa+NBjpY/fmVG+3eLJG+rqkMnVhhrbQX79t0ZjVdg364fjkqyfPTwdybZKsnfZnQv4A9NqKa7DS11MAFVdfH4WjsGajx67W92981VtSCjexH+W3e/q6q+afTa4Vo+MnFGg2ZckWTH7r5xfIPbr3f3bpOsj7Uz9ftZVd9I8rTuXlpVmyc5c/nImAyPfbt+q6rzu3uX8eO7XOJSVed098KJFXc3sNGqJwHWRFV9a2VvJbn/uqyFebHh8i6X3X3R+Bqd46tqp7hge+iWdfftSX5cVf/b3TcmSXf/pKrumHBtrL0NqmrrjHorVXcvTZLuvqWqlk22NNaSfbt+O6+qXtbdH0pyblUt6u7F49uR3OOvhRXqYP7cP8mTk1w37fVKYhCN4buiqhZ29zlJMm6xe0ZG3UP8GjxsP6uqzbr7x0n2XP5iVW2V0cA4DNtWSZZk9H9xV9UvdfcVVbVF/CAzdPbt+u2VSd5VVX+W5OokZ1TVJRkNdPTKiVZ2N6D7JcyTqvpgRiOpfWUF7x3b3S+cQFnMkaraMaMWnStW8N5ju/urEyiLOVBV9+7un67g9W2T7NDd355AWcyz8cAL9+/uH0y6FuaWfbt+qaotk/xyRo1Tl3b3lRMu6W5BqAMAABgwo18CAAAMmFAHAAAwYEIdAADAgAl1ANzjVNWCqjpvHa1ru6r6elV9s6oety7WCcA9i1saAMBaqqqNuntl98HaP8kF3X3wuqwJgHsOo18CsN6rqj9K8vLx0w8k+fckn03y9SSPTPLfSV7S3T+uqr9JckCSZUk+191vWskyP5zk2vH8Zyd5b5L3JNkuyY+TvCrJJkk+nWTTJD9K8pvd/ZO530IA7sm01AGwXquqPZO8LMlvZHQD4q8n+VKShyV5RXd/taqOSvL747+/neTh3d1Vdd9VLP6hSZ7Q3bdX1ReSvKa7L6yq30jy3u5+fFW9Ncmi7v6D+dlCAO7pXFMHwPpu7yQndPct3X1zkk8leVySS6bcJP4j4+luTHJrkg9U1bMzanGbySfGgW6LJI9J8omqOifJvyTZYe43BQB+kZY6ANZ3tZLXp19/0N29rKr2yug6uBck+YMkj59h2beM/26Q5PruXrg2hQLAmtBSB8D67vQkz6qqzapq84y6V345yYOr6jfH0xyU5CvjFretuvukJK9PsnA2K+juG5P8oKqemyQ1svvcbgYArJhQB8B6rbvPTvLhJGdldD3dB5Jcl+T8JAdX1beSbJPkn5NsmeQ/xq99KckbVmNVL0ryiqo6N8l3khw4V9sAADMx+iUAAMCAaakDAAAYMAOlAMAMqupPkzx32suf6O6/nEQ9ADCd7pcAAAADpvslAADAgAl1AAAAAybUAQAADJhQBwAAMGBCHQAAwID9/55Qp4Up/rCAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x = 'obs_ref', y = 'aff_score', hue = 'variable', data = dummy_a, palette ='BuGn')\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Affordablity score')\n",
    "plt.title('Model_a Affordability Scores Actuals & Doubled Net Additions\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "afb8db77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zf1qkxn+aLH9iVe08+fvJ+t55pcAWItzBdqa7/7e71y4y+zcytGh9NsmHMnypf/Vk3iuTvCfJJzKchD+/5e+XM3Rp+q8MAeusDOeeTes9Gc7n+u8M3Xu+nY136fudqro2Q9fA1ydZl+Sn5wTXV2f4cvPBDC2W355sY7r76gytRKdn+LX5ugxfAud6e4ZzRb6e4YvjI7v7+gXqeHGG1qF/mdTzkQwDL6S7L0rylAyv5Zcmz7XUtciekaHl8YNJvpph8JIHZnjd31bLGGxhnudlGCTj6gwhbP7+m+8dSX40yVe6+xMbJnb32Rlaj9486Z53YYaBTdLdVyT5xQznPV45efyHpy2wuz+cISjM9bsZWvQ+Mlnf+zJ8eU93fypDaPrspKvXQt3ZnpzhUgvXZjjH7qbBSKbYN8/LcCx+LsOPF4t2bcsQypPkyqo6b4H5ix6LU3hQkotqGO3xxRnOxfv2lI9drrdneB9dkOE4edVk+qLHzyTkPDTDgDefz/AaPnoy+18zXGrgy1V1xSLrfEyG88i+mOTsDOdhvXcTan9ckpd195fn/mX4QWLarplLfQYu5NczdG39coZz1ea2rk/zmTbtZ8xSXpzkUTWM6jr3Wn2vS/ITWfq4vSpDmPvPyfH17gz74M8n81+U4T3zL0muyXA87NrdV2YYYOsZGd7rv5PkmMlnwPeZdLt9QIYBfr6Y4fX6s3z/D2rAZqrv9bQBALZXVdVJfrS7/2fWtbD5qupeGVodD5q0+gLbAS13AAAjMmnhf1qS0wU72L4Id8BWU1VH1fcuOH2zv1nXBjAGk/PYrsrQLf6vZ1oMsNXplgkAADACWu4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARmCnWRewHLe5zW36oIMOmnUZAAAAM7Fu3borunvfheatqnB30EEHZe3atbMuAwAAYCaq6tLF5umWCQAAMALCHQAAwAgIdwAAACOwqs65AwAAth/XX399Lr/88nz729+edSlb3S677JIDDjggO++889SPEe4AAIBt0uWXX54999wzBx10UKpq1uVsNd2dK6+8MpdffnnueMc7Tv043TIBAIBt0re//e3ss88+21WwS5Kqyj777LPsFkvhDgAA2GZtb8Fug03ZbuEOAAAgyYMf/OBcddVVSy6zxx57LDj98Y9/fM4666wVqGp6zrkDAAC2a92d7s473/nOWZeyWbTcAQAAo/C7v/u7ednLXnbT/ec+97l53vOel/ve97454ogj8hM/8RN5+9vfniS55JJLcsghh+TJT35yjjjiiFx22WU56KCDcsUVVyRJHv7wh+fII4/MoYcemtNOO+1m63nGM56RI444Ive9732zfv3676tj3bp1Ofroo3PkkUfmgQ98YL70pS+t4FZ/j3AHAACMwnHHHZe///u/v+n+mWeemZNOOilnn312zjvvvHzgAx/IM57xjHR3kuTTn/50fvmXfznnn39+DjzwwJs916tf/eqsW7cua9euzamnnporr7wySXLdddfliCOOyHnnnZejjz46z3ve8272uOuvvz6/8Ru/kbPOOivr1q3LySefnGc/+9krvOUD3TIBAIBRuNvd7pavfvWr+eIXv5j169fn1re+dfbbb7/85m/+Zj74wQ9mhx12yBe+8IV85StfSZIceOCB+amf+qkFn+vUU0/N2WefnSS57LLL8pnPfCb77LNPdthhhzz60Y9Okpxwwgl55CMfebPHffrTn86FF16Y+9///kmSG2+8Mfvtt99KbfLNCHcAAMBoPOpRj8pZZ52VL3/5yznuuONyxhlnZP369Vm3bl123nnnHHTQQTddYmD33Xdf8DnOOeecvO9978u5556b3XbbLfe+970XvSzB/FEtuzuHHnpozj333C27YVPQLRMAABiN4447Lm9+85tz1lln5VGPelSuvvrq3Pa2t83OO++cD3zgA7n00ks3+hxXX311bn3rW2e33XbLpz71qXzkIx+5ad53v/vdm0bFfOMb35if/dmfvdljDz744Kxfv/6mcHf99dfnoosu2oJbuDgtdwAAwGgceuihufbaa3O7290u++23X44//vg89KEPzZo1a3L44YfnLne5y0af40EPelBe/vKX57DDDsvBBx98s66bu+++ey666KIceeSR2WuvvW52jl+S3OIWt8hZZ52Vpz71qbn66qtzww035OlPf3oOPfTQLb6t89WGkwlXgzVr1vTatWtnXQYAALAVXHzxxTnkkENmXcbMLLT9VbWuu9cstLxumQAAACMw83BXVTtW1flV9U+zrgUAAGC1mnm4S/K0JBfPuggAAIDVbKbhrqoOSPKQJKfPsg4AAIDVbtYtd3+d5HeSfHfGdQAAAKxqMwt3VXVMkq9297qNLPfEqlpbVWvXr1+/laoDAABYXWbZcvczSY6tqkuSvDnJfarqDfMX6u7TuntNd6/Zd999t3aNAAAAq8LMLmLe3c9K8qwkqap7J3lmd58wq3q2Rfd/2SmzLmGzvPfJL591CQAAjMg7Pn3BFn2+Yw8+fKPLXHLJJTnmmGNy4YUXLuu55z5u7dq1ef3rX59TTz013/nOd/KQhzwkV1xxRZ71rGdl//33zymnnJKdd9455557bnbddddN3JoZhjsAAIDtwZo1a7JmzXDd8fPPPz/XX399LrjggiTJKaeckmc+85k56aSTNns9sx5QJUnS3ed09zGzrgMAAGC+G2+8MU94whNy6KGH5gEPeEC+9a1vLbjcunXrcte73jX3vOc989KXvvSm6eecc06OOeaYfPWrX80JJ5yQCy64IIcffnhe8YpX5Mwzz8zzn//8HH/88Ztd5zYR7gAAALZVn/nMZ/KUpzwlF110Ufbee++89a1vXXC5k046KaeeemrOPffcBeff9ra3zemnn56jjjoqF1xwQZ70pCfl2GOPzQtf+MKcccYZm12ncAcAALCEO97xjjn88MOTJEceeWQuueSS71vm6quvzlVXXZWjjz46SXLiiSduxQoHwh0AAMASbnnLW950e8cdd8wNN9zwfct0d6pqa5b1fYQ7AACAzbT33ntnr732yoc+9KEk2SLdLJfLaJkAAMCqMM2lC2bpNa95TU4++eTstttueeADH7jV11/dvdVXuqnWrFnTa9eunXUZW43r3AEAsD27+OKLc8ghh8y6jJlZaPural13r1loed0yAQAARkC3TAAAgGV4ylOekg9/+MM3m/a0pz1ti1yIfHMIdwAAAMsw9wLl2xLdMgEAAEZAuAMAABgB4Q4AAGAEhDsAAIARMKAKAACwKmzp60BvynWZn/vc52aPPfbIM5/5zCWXu+SSS3LMMcfkwgsvzNq1a/P6178+p556ar7zne/kIQ95SK644oo861nPyv77759TTjklO++8c84999zsuuuum7o5wh0AAMBKWrNmTdasGa47fv755+f666/PBRdckCQ55ZRT8sxnPnOLXEZBt0wAAIAlvOAFL8jBBx+c+93vfvn0pz+96HLr1q3LXe9619zznve82eUSzjnnnBxzzDH56le/mhNOOCEXXHBBDj/88LziFa/ImWeemec///k5/vjjN7tO4Q4AAGAR69aty5vf/Oacf/75edvb3paPf/zjiy570kkn5dRTT82555674Pzb3va2Of3003PUUUflggsuyJOe9KQce+yxeeELX5gzzjhjs2sV7gAAABbx7//+73nEIx6R3XbbLbe61a1y7LHHLrjc1VdfnauuuipHH310kuTEE0/cmmUmEe4AAACWVFUbXaa7p1puJQl3AAAAi7jXve6Vs88+O9/61rdy7bXX5h//8R8XXG7vvffOXnvtlQ996ENJskW6WS6X0TIBAIBVYVMuXbC5jjjiiDz60Y/O4YcfngMPPDBHHXXUosu+5jWvycknn5zddtstD3zgA7dilYPq7q2+0k21Zs2aXrt27azL2Gq29HU8trZZvPkAABiPiy++OIcccsisy5iZhba/qtZ195qFltctEwAAYAR0ywQAAFiGpzzlKfnwhz98s2lPe9rTtsiFyDeHcAcAALAMcy9Qvi3RLRMAANhmraYxQrakTdlu4Q4AANgm7bLLLrnyyiu3u4DX3bnyyiuzyy67LOtxumUCAADbpAMOOCCXX3551q9fP+tStrpddtklBxxwwLIeI9wBAADbpJ133jl3vOMdZ13GqqFbJgAAwAgIdwAAACMg3AEAAIyAcAcAADACwh0AAMAICHcAAAAjINwBAACMgHAHAAAwAsIdAADACAh3AAAAIyDcAQAAjIBwBwAAMALCHQAAwAgIdwAAACMg3AEAAIyAcAcAADACwh0AAMAICHcAAAAjINwBAACMgHAHAAAwAsIdAADACAh3AAAAIyDcAQAAjIBwBwAAMALCHQAAwAgIdwAAACMg3AEAAIyAcAcAADACwh0AAMAICHcAAAAjINwBAACMgHAHAAAwAsIdAADACAh3AAAAIyDcAQAAjIBwBwAAMALCHQAAwAgIdwAAACMg3AEAAIzAzMJdVe1SVR+rqk9U1UVV9bxZ1QIAALDa7TTDdX8nyX26+xtVtXOSD1XVu7r7IzOsCQAAYFWaWbjr7k7yjcndnSd/Pat6AAAAVrOZnnNXVTtW1QVJvprkvd390VnWAwAAsFrNNNx1943dfXiSA5Lcvap+fP4yVfXEqlpbVWvXr1+/1WsEAABYDbaJ0TK7+6ok5yR50ALzTuvuNd29Zt99993apQEAAKwKsxwtc9+q2ntye9ck90vyqVnVAwAAsJrNcrTM/ZK8rqp2zBAyz+zuf5phPQAAAKvWLEfL/GSSu81q/QAAAGOyTZxzBwAAwOYR7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBGYW7qrq9lX1gaq6uKouqqqnzaoWAACA1W6nGa77hiTP6O7zqmrPJOuq6r3d/V8zrAkAAGBVmlnLXXd/qbvPm9y+NsnFSW43q3oAAABWs23inLuqOijJ3ZJ8dMalAAAArEozD3dVtUeStyZ5endfs8D8J1bV2qpau379+q1fIAAAwCow03BXVTtnCHZndPfbFlqmu0/r7jXdvWbffffdugUCAACsErMcLbOSvCrJxd39olnVAQAAMAazbLn7mSQnJrlPVV0w+XvwDOsBAABYtWZ2KYTu/lCSmtX6AQAAxmTmA6oAAACw+YQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAEdhpYwtU1S2T/EKSg+Yu393PX7myAAAAWI6Nhrskb09ydZJ1Sb6zsuUAAACwKaYJdwd094NWvBIAAAA22TTn3P1HVf3EilcCAADAJpum5e5nkzy+qj6XoVtmJenuPmxFKwMAAGBq04S7n1/xKgDYbrzj0xfMuoRNduzBh8+6BABY1Ea7ZXb3pUn2TvLQyd/ek2kAAABsIzYa7qrqaUnOSHLbyd8bquo3VrowAAAApjdNt8xfSXKP7r4uSarqz5Kcm+RvVrIwAAAApjfNaJmV5MY592+cTAMAAGAbMU3L3WuSfLSqzp7cf3iSV61YRQAAACzbRsNdd7+oqs7JcEmESnJSd5+/0oUBAAAwvUXDXVXdqruvqaofSHLJ5G/DvB/o7q+tfHkAAABMY6mWuzcmOSbJuiQ9Z3pN7v/wCtYFAADAMiwa7rr7mMm/d9x65QAAALApprnO3funmQYAAMDsLHXO3S5Jdktym6q6db53+YNbJdl/K9QGAADAlJY65+5JSZ6eIcity/fC3TVJXrqyZQEAALAcS51z9+IkL66q3+juv9mKNQEAALBMGz3nLsl3q2rvDXeq6tZV9eSVKwkAAIDlmibcPaG7r9pwp7u/nuQJK1YRAAAAyzZNuNuhqjacb5eq2jHJLVauJAAAAJZrqQFVNnhPkjOr6uUZLl5+SpJ3r2hVAAAALMs04e53M4yc+WsZRsz8lySnr2RRAAAALM9Gw113fzfJ307+AAAA2AYtdRHzM7v7l6rqPzN0x7yZ7j5sRSsDAABgaku13D1t8u8xW6MQAAAANt1SFzH/0uTfS7deOQAAAGyKpbplXpsFumNu0N23WpGKAAAAWLalWu72TJKqen6SLyf5uwyjZR6fZM+tUh0AAABTmeYi5g/s7pd197XdfU13/22SX1jpwgAAAJjeNOHuxqo6vqp2rKodqur4JDeudGEAAABMb5pw99gkv5TkK5O/X5xMAwAAYBsxzUXML0nysJUvBQAAgE210Za7qrpzVb2/qi6c3D+sqp6z8qUBAAAwrWm6Zb4yybOSXJ8k3f3JJMetZFEAAAAszzThbrfu/ti8aTesRDEAAABsmmnC3RVVdadMLmheVY9K8qUVrQoAAIBl2eiAKkmekuS0JHepqi8k+VyGC5kDAACwjVgy3FXVjkl+rbvvV1W7J9mhu6/dOqUBAAAwrSXDXXffWFVHTm5ft3VKAgAAYLmm6ZZ5flW9I8lbktwU8Lr7bStWFQAAAMsyTbj7gSRXJrnPnGmdZLPDXVW9OskxSb7a3T++uc8HAACwvdrYOXf7Jnlpkv/p7qtWYP2vTfKSJK9fgecGAADYbix6KYSq+tUkFyX5mySfqqpjt/TKu/uDSb62pZ8XAABge7NUy93Tkxza3eur6oeTnJHkHVulKgAAAJZlqYuY/193r0+S7v5skltunZJurqqeWFVrq2rt+vXrZ1ECAADANm+plrsDqurUxe5391NXrqzv6e7TMlxEPWvWrOmtsU4AAIDVZqlw99vz7q9byUIAAADYdIuGu+5+3UqvvKrelOTeSW5TVZcn+YPuftVKrxcAAGBsprnO3Yrp7sfMcv0AAABjsdSAKgAAAKwSwh0AAMAIbDTcVdUBVXV2Va2vqq9U1Vur6oCtURwAAADTmabl7jUZLl6+X5LbJfnHyTQAAAC2EdOEu327+zXdfcPk77VJ9l3hugAAAFiGacLdFVV1QlXtOPk7IcmVK10YAAAA05sm3J2c5JeSfDnJl5I8KslJK1kUAAAAyzPNde5u393Hzp1QVT+T5PMrUxIAAADLNU3L3d9MOQ0AAIAZWbTlrqrumeSnk+xbVb81Z9atkuy40oUBAAAwvaW6Zd4iyR6TZfacM/2aDOfdAQAAsI1YNNx1978l+beqem13X7oVawIAAGCZNnrOnWAHAACw7ZtmQBUAAAC2ccIdAADACGw03FXVnavq/VV14eT+YVX1nJUvDQAAgGlN03L3yiTPSnJ9knT3J5Mct5JFAQAAsDzThLvduvtj86bdsBLFAAAAsGmmCXdXVNWdknSSVNWjknxpRasCAABgWZa6iPkGT0lyWpK7VNUXknwuyQkrWhUAAADLstFw192fTXK/qto9yQ7dfe3KlwUAAMByTDNa5h9X1d7dfV13X1tVt66qP9oaxQEAADCdac65+/nuvmrDne7+epIHr1hFAAAALNs04W7HqrrlhjtVtWuSWy6xPAAAAFvZNAOqvCHJ+6vqNRlGzDw5yetWtCoAAACWZZoBVf68qv4zyX2TVJI/7O73rHhlAAAATG2alrt097uSvGuFawEAAGATTTNa5iOr6jNVdXVVXVNV11bVNVujOAAAAKYzTcvdnyd5aHdfvNLFAAAAsGmmGS3zK4IdAADAtm2alru1VfX3Sf4hyXc2TOzut61UUQAAACzPNOHuVkm+meQBc6Z1EuEOAABgGzHNpRBO2hqFAAAAsOmmGS3zzlX1/qq6cHL/sKp6zsqXBgAAwLSmGVDllUmeleT6JOnuTyY5biWLAgAAYHmmCXe7dffH5k27YSWKAQAAYNNME+6uqKo7ZRhEJVX1qCRfWtGqAAAAWJZpRst8SpLTktylqr6Q5HNJjl/RqgAAAFiWJcNdVe2Y5Ne6+35VtXuSHbr72q1TGgAAANNaMtx1941VdeTk9nVbpyQAAACWa5pumedX1TuSvCXJTQGvu13EHAAAYBsxTbj7gSRXJrnPnGmdRLgDAADYRmw03HX3SVujEAAAADbdRi+FUFV3rqr3V9WFk/uHVdVzVr40AAAApjXNde5emeRZSa5Pku7+ZJLjVrIoAAAAlmeacLdbd39s3rQbVqIYAAAANs004e6KqrpThkFUUlWPSvKlFa0KAACAZZlmtMynJDktyV2q6gtJPpfk+BWtCgAAgGVZNNxV1dO6+8VJ9uvu+1XV7kl26O5rt155AAAATGOpbpkbLoHwN0nS3dcJdgAAANumpbplXlxVlyTZt6o+OWd6JenuPmxFKwMAAGBqi4a77n5MVf1QkvckOXbrlQQAAMByLXXO3fu7+75V9Z7uvnRrFgUAAMDyLNUtc7+qOjrJQ6vqTRm6Y96ku89b0coAAACY2lLh7veT/F6SA5K8aN68TnKflSoKAACA5VnqnLuzkpxVVf9fd//hVqwJAACAZVrqnLu7dPenkvxzVR0xf75umQAAANuOpbpl/laSJyb5ywXm6ZYJAACwDVmqW+YTJ//+3NYrBwAAgE2xVMtdqmqfJI9NcpfJpIuTvLG7v7bShQEAADC9HRabUVWHJLkwyZFJ/jvJZ5L8ZJILq+ouiz0OAACArW+plrs/TPK07j5z7sSq+oUkL0jyCytZGAAAANNbtOUuyU/MD3ZJ0t1vTfLjK1cSAAAAy7VUuLtuE+cBAACwlS3VLfO2VfVbC0yvJPuuUD0AAABsgqXC3SuT7LnIvNNXoBYAAAA20VLXuXve1iwEAACATbfUOXcAAACsEjMNd1X1oKr6dFX9T1X93ixrAQAAWM1mFu6qasckL03y80l+LMljqurHZlUPAADAarbUgCo3U1U/leSPk9wyyQu7+x82c913T/I/3f3ZyfO/OcnDkvzXZj4vADAD7/j0BbMuYbMce/Dhsy4BYLMsGu6q6oe6+8tzJv1WkmMzXArhP5L8w2au+3ZJLptz//Ik99jM5wQAANguLdVy9/KqWpehle7bSa5K8tgk301yzRZYdy0wrb9voaonJnliktzhDndY1gpW+y+I733yy2ddAsAWt5pbR+7/slNmXcJmWen/V1bzvk3sX2D1W/Scu+5+eJILkvxTVZ2Y5OkZgt1uSR6+BdZ9eZLbz7l/QJIvLlDHad29prvX7Luva6cDAAAsZMkBVbr7H5M8MMneSd6W5NPdfWp3r98C6/54kh+tqjtW1S2SHJfkHVvgeQEAALY7i4a7qjq2qj6U5F+TXJghfD2iqt5UVXfa3BV39w1Jfj3Je5JcnOTM7r5oc58XAABge7TUOXd/lOSeSXZN8s7uvnuS36qqH03yggxhb7N09zuTvHNznwcAYHM5Zw1Y7ZYKd1dnCHC7Jvnqhond/ZlsgWAHAADAlrPUOXePyDB4yg0ZRskEAABgG7Voy113X5Hkb7ZiLQAAAGyiJUfLBAAAYHUQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIARmEm4q6pfrKqLquq7VbVmFjUAAACMyaxa7i5M8sgkH5zR+gEAAEZlp1mstLsvTpKqmsXqAQAARsc5dwAAACOwYi13VfW+JD+0wKxnd/fbl/E8T0zyxCS5wx3usIWqAwAAGJcVC3fdfb8t9DynJTktSdasWdNb4jkBAADGRrdMAACAEZjVpRAeUVWXJ7lnkn+uqvfMog4AAICxmNVomWcnOXsW6wYAABgj3TIBAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZgJuGuql5YVZ+qqk9W1dlVtfcs6gAAABiLWbXcvTfJj3f3YUn+O8mzZlQHAADAKOw0i5V297/MufuRJI9aifUce/DhK/G0AAAA25xt4Zy7k5O8a9ZFAAAArGYr1nJXVe9L8kMLzHp2d799ssyzk9yQ5IwlnueJSZ6YJHe4wx1WoFIAAIDVb8XCXXffb6n5VfW4JMckuW939xLPc1qS05JkzZo1iy4HAACwPZvJOXdV9aAkv5vk6O7+5ixqAAAAGJNZnXP3kiR7JnlvVV1QVS+fUR0AAACjMKvRMn9kFusFAAAYq21htEwAAAA2k3AHAAAwAsIdAADACAh3AAAAIyDcAQAAjIBwBwAAMALCHQAAwAgIdwAAACMg3AEAAIyAcAcAADACwh0AAMAICHcAAAAjINwBAACMgHAHAAAwAsIdAADACAh3AAAAIyDcAQAAjIBwBwAAMALCHQAAwAgIdwAAACMg3AEAAIyAcAcAADACwh0AAMAICHcAAAAjINwBAACMgHAHAAAwAsIdAADACAh3AAAAI7DTrAsAgNXivU9++axLAIBFabkDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGQLgDAAAYAeEOAABgBIQ7AACAERDuAAAARkC4AwAAGAHhDgAAYASEOwAAgBEQ7gAAAEZAuAMAABgB4Q4AAGAEhDsAAIAREO4AAABGoLp71jVMrarWJ7l01nVsRbdJcsWsi2BF2LfjZv+Ol307bvbvuNm/47W97dsDu3vfhWasqnC3vamqtd29ZtZ1sOXZt+Nm/46XfTtu9u+42b/jZd9+j26ZAAAAIyDcAQAAjIBwt207bdYFsGLs23Gzf8fLvh03+3fc7N/xsm8nnHMHAAAwAlruAAAARkC4AwAAGAHhDgAAYAR2mnUBAKtZVR3W3Z+cdR2sjKq6Q5JruvuqqjooyZokn+ruC2dbGZurqm6R5PqeDD5QVT+X5Igk/9Xd75ppcWwRVbUmye2T3JDkM939qRmXxBZk/y7MgCrbgKq6VZJnJTkgybu6+41z5r2su588s+LYInxBHK+qujHJ55K8Kcmbuvu/ZlwSW0hV/V6SJyX5TpK/SPLMJB9O8lNJXtXdL5pheWymqvpEknt399er6reTPCLJO5McnWRtdz9rpgWyyarq6CR/meSqJEdmeN/eOsn1SU7s7stmVx2by/5dmnC3Daiqtyb5TJKPJDk5w8H52O7+TlWd191HzLRANosviONWVecnOTHJY5I8Osl1GYLem7v7khmWxmaqqosy/BCzW5JLkvxwd6+vqt2TfLS7f3yW9bF5qurCDfuwqtYmOaq7v1VVOyU5r7sPm22FbKrJ5/IDJu/XOyZ5UXc/oqrun+S3u/sBMy6RzWD/Ls05d9uGO3X373X3P3T3sUnOS/KvVbXPrAtjizgxyY8l+Zkkf5XhC8SvJLl7hjDP6tbdfWF3P7u7fyTJE5LcNsm/V9V/zLg2Ns+N3f2tDL8OfyvJlUnS3dfNsii2mGuqakNAvyLJLpPbO8X3o9Vux+5eP7n9+SQHJkl3vzfJ7WZWFVuK/bsE59xtG25ZVTt093eTpLtfUFWXJ/lgkj1mWxpbwI2TX4P/L/O+IFbVbCtjS7jZTuzujyX5WFU9I8m9ZlMSW8h5VfXGJLsneX+S11XVu5PcJ4nut6vfKUnOmHTP/GqStVX1b0kOS/LHM62MzbW2ql6V4X37sCTnJElV7ZZkxxnWxZZh/y5Bt8xtQFX9eZJ/6e73zZv+oCR/090/OpvK2BKq6rVJbpHhC+I3M5z4u+EL4p7d/Uuzq47NVVWPnXueLOMx6Z73i0k6yVlJ7pGh++3nk7xUC97qV1U7JnlAkjtn+MH78iTv6e6rZlkXm6eqds7Qi+LHknwiyau7+8aq2jXJbbv70pkWyGaxf5cm3G0jqupOGU7mvmnUnwyDM1w908LYbAt8Qbx7ksfGF0RYdapqn+6+ctZ1sDLsX2C106d8G1BVT03ytxn6+/9kkl0zhLxzq+res6uMLaG7b+juN3X3mye3/6O7f727/1ywW/2qak1VfaCq3lBVt6+q91bV1VX18aq626zrY9NV1Z9W1W0mt9dU1WeTfKSqLp2M1sYqtsj+/aj9u/pV1R5V9fyqunDyeby+qj5SVY+fdW1sPvt3aVrutgFV9Z9JDp80Ke+W5J3dfe/J8Plv725fEFexqjovydsytMT+76zrYcuqqo8l+YMkeyf58yS/2d1nVdV9k/xRd99zlvWx6arqP7v7Jya3P5Dkd7r741V15yRv7O41s62QzWH/jldVvT3J2Unel+SXMpwW8eYkz0nyhe7+fzMsj81k/y5Ny922Y8PgNrdMsmeSdPfnk+w8s4rYUm6d4Yv/B6rqY1X1m1W1/4xrYsvZubvf1d1vyjBy5lkZbrw/3xt9j9Vp50m36iTZtbs/niTd/d8ZPqtZ3ezf8Tqou1/b3ZdPLjd0bHd/JslJSR4549rYfPbvEoS7bcPpST5eVaclOTfJS5KkqvZN8rVZFsYW8fXufmZ33yHJM5L8aIZR+D5QVU+ccW1svm9X1QOq6heTdFU9PLnpIqs3zrQyNtdLk7yzqu6T5N1V9ddVda+qel6SC2ZbGluA/Tte11XVzyZJVT00k+9Sk1HJDVO9+tm/S9AtcxtRVYcmOSTJhd39qVnXw5ZTC1yIfjJC2/2TPLq7T5pNZWwJVXXXDN0xv5vkN5P8WpLHJflCkid0t2vdrWKT855/Ld8bTfGyJP+Q5DXdff3MCmOLsH/HafK5/MoM+/XCJCd3939PfjR/THefOtMC2SxVdViGhhH7dwHCHaywqnpzdx836zpYOVV1SJL9k3y0u78xZ/qDuvvds6uMzVVVd8/Q3fbjkx/hHpTk4u5+54xLYwurqqMyjGb8n939L7Ouhy1n0spz9ww/oNu3q1xV3SPJp7r76slYFb+b5IgM1x/94+19pHndMmGFLRXsqkqr3So3Ge327CS/keTCqnrYnNkuhLyKVdUfJDk1yd9W1Z9Mbu+e5Peq6tkzLY7NNhkMacPtX82wf/dI8gdV9XszK4zNtsC+fUmG8Qzs23F4dZINo43/dZK9kvxZhmsJv2ZGNW0ztNzBDFXV5yfn4rFKTUa7vWd3f6OqDspwLcO/6+4XV9X5RrtdvTaMZJxhcI0vJzmgu6+ZXCj3o9192CzrY/PMfX9W1ceTPLi711fV7kk+smEkTVYf+3bcquri7j5kcvtmp75U1QXdffjMitsG7LTxRYDNUVWfXGxWkh/cmrWwInbc0BWzuy+ZnMNzVlUdGCd2r3Y3dPeNSb5ZVf/b3dckSXd/q6q+O+Pa2Hw7VNWtM/Riqu5enyTdfV1V3TDb0thM9u24XVhVJ3X3a5J8oqrWdPfayWVMtvtzZYU7WHk/mOSBSb4+b3olMdjG6vflqjq8uy9IkkkL3jEZuo34dXh1+7+q2q27v5nkyA0Tq2qvDAPosLrtlWRdhs/irqof6u4vV9Ue8cPMamffjtuvJnlxVT0nyRVJzq2qyzIMiPSrM61sG6BbJqywqnpVhpHXPrTAvDd292NnUBZbSFUdkKGF58sLzPuZ7v7wDMpiC6iqW3b3dxaYfpsk+3X3f86gLFbYZICGH+zuz826FrYs+3ZcqmrPJD+cobHq8u7+yoxL2iYIdwAAACNgtEwAAIAREO4AAABGQLgDAAAYAeEOgO1WVR1UVRdupXXtW1Ufrarzq+qorbFOALYvLoUAAFtIVe3U3YtdR+u+ST7V3Y/bmjUBsP0wWiYA242q+q0kJ0/unp7kH5K8O8lHk9wtyX8n+eXu/mZV/WmSY5PckORfuvuZizzna5N8bfL485K8LMlLk+yb5JtJnpBklyTvSLJrki8kuWd3f2vLbyEA2zMtdwBsF6rqyCQnJblHhgsZfzTJvyU5OMmvdPeHq+rVSZ48+fcRSe7S3V1Ve2/k6e+c5H7dfWNVvT/JKd39maq6R5KXdfd9qur3k6zp7l9fmS0EYHvnnDsAthc/m+Ts7r6uu7+R5G1Jjkpy2ZyLzb9hstw1Sb6d5PSqemSGFrilvGUS7PZI8tNJ3lJVFyR5RZL9tvymAMD303IHwPaiFpk+//yE7u4bquruGc6TOy7Jrye5zxLPfd3k3x2SXNXdh29OoQCwKbTcAbC9+GCSh1fVblW1e4Zul/+e5A5Vdc/JMo9J8qFJC9xe3f3OJE9Pcvg0K+jua5J8rqp+MUlqcNctuxkAsDDhDoDtQnefl+S1ST6W4Xy705N8PcnFSR5XVZ9M8gNJ/jbJnkn+aTLt35L85jJWdXySX6mqTyS5KMnDttQ2AMBSjJYJAAAwAlruAAAARsCAKgAwhap6dpJfnDf5Ld39glnUAwDz6ZYJAAAwArplAgAAjIBwBwAAMALCHQAAwAgIdwAAACMg3AEAAIzA/w+/ZKdKSkQyBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>obs_ref</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>-1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>178</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>287</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>368</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>566</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>599</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>699</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>92</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>178</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>287</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>368</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>-2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>566</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obs_ref variable  value\n",
       "0        92   h_diff  -1.97\n",
       "1       178   h_diff   0.00\n",
       "2       287   h_diff   0.00\n",
       "3       368   h_diff   1.54\n",
       "4       566   h_diff   0.03\n",
       "5       599   h_diff   0.00\n",
       "6       699   h_diff   0.00\n",
       "7        92   d_diff   3.97\n",
       "8       178   d_diff   0.00\n",
       "9       287   d_diff   0.00\n",
       "10      368   d_diff  -2.43\n",
       "11      566   d_diff  -0.05"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_a = pd.DataFrame(dummy_a[['obs_ref','variable','aff_score']])\n",
    "df_a = df_a.pivot(index = 'obs_ref', columns = 'variable', values = 'aff_score').reset_index()\n",
    "#dropped the two poor predictions, 602 and 711\n",
    "#df_a = df.iloc[:-2]\n",
    "                        \n",
    "df_a['h_diff'] = (df_a['h_pred']-df_a['actual'])/df_a['actual']*100\n",
    "df_a['d_diff'] = (df_a['d_pred']-df_a['actual'])/df_a['actual']*100\n",
    "new_df_a = pd.DataFrame(df_a[['obs_ref', 'h_diff','d_diff']])\n",
    "new_df_a = new_df_a.melt(id_vars = 'obs_ref')\n",
    "\n",
    "    \n",
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x = 'obs_ref', y = 'value', hue = 'variable', data = new_df_a, palette ='BuGn')\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('% Difference to Prediction')\n",
    "plt.title('Model_a Doubled & Halved Net additions Impact on Affordability Score\\n')\n",
    "plt.show()\n",
    "new_df_a.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8a5263f",
   "metadata": {},
   "source": [
    "On SVR Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "aeab5894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>obs_ref</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>median_income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>173</td>\n",
       "      <td>actual</td>\n",
       "      <td>132.00</td>\n",
       "      <td>23.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>173</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>128.65</td>\n",
       "      <td>23.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>173</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>126.49</td>\n",
       "      <td>23.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>304</td>\n",
       "      <td>actual</td>\n",
       "      <td>137.97</td>\n",
       "      <td>19.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>304</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>144.79</td>\n",
       "      <td>19.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>304</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>141.88</td>\n",
       "      <td>19.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>367</td>\n",
       "      <td>actual</td>\n",
       "      <td>256.50</td>\n",
       "      <td>32.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>367</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>282.39</td>\n",
       "      <td>32.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>367</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>235.90</td>\n",
       "      <td>32.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>491</td>\n",
       "      <td>actual</td>\n",
       "      <td>300.00</td>\n",
       "      <td>27.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>491</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>316.46</td>\n",
       "      <td>27.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>491</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>301.70</td>\n",
       "      <td>27.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>599</td>\n",
       "      <td>actual</td>\n",
       "      <td>380.00</td>\n",
       "      <td>30.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>599</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>387.67</td>\n",
       "      <td>30.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>599</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>369.28</td>\n",
       "      <td>30.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>602</td>\n",
       "      <td>actual</td>\n",
       "      <td>460.00</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>602</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>323.01</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>602</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>303.75</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>613</td>\n",
       "      <td>actual</td>\n",
       "      <td>290.00</td>\n",
       "      <td>31.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>613</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>404.80</td>\n",
       "      <td>31.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>613</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>397.27</td>\n",
       "      <td>31.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obs_ref variable   value  median_income\n",
       "0       173   actual  132.00          23.14\n",
       "1       173   d_pred  128.65          23.14\n",
       "2       173   h_pred  126.49          23.14\n",
       "3       304   actual  137.97          19.30\n",
       "4       304   d_pred  144.79          19.30\n",
       "5       304   h_pred  141.88          19.30\n",
       "6       367   actual  256.50          32.02\n",
       "7       367   d_pred  282.39          32.02\n",
       "8       367   h_pred  235.90          32.02\n",
       "9       491   actual  300.00          27.30\n",
       "10      491   d_pred  316.46          27.30\n",
       "11      491   h_pred  301.70          27.30\n",
       "12      599   actual  380.00          30.59\n",
       "13      599   d_pred  387.67          30.59\n",
       "14      599   h_pred  369.28          30.59\n",
       "15      602   actual  460.00          32.00\n",
       "16      602   d_pred  323.01          32.00\n",
       "17      602   h_pred  303.75          32.00\n",
       "18      613   actual  290.00          31.13\n",
       "19      613   d_pred  404.80          31.13\n",
       "20      613   h_pred  397.27          31.13"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply to the dummy set, import the test set to match the incomes\n",
    "\n",
    "dummy_svr = pd.read_csv( 'svr_dummy_output.csv')\n",
    "dummy_svr = dummy_svr.drop(columns =['Unnamed: 0','pred'])\n",
    "dummy_svr = dummy_svr.melt('obs_ref')\n",
    "t_set_svr = pd.read_csv('test_best.csv')\n",
    "t_data_svr = t_set_svr.iloc[[173,304,367,491,599,602,613]].reset_index(drop = True)\n",
    "t_data_svr = t_data_svr.loc[: ,['Unnamed: 0', 'median_income']]\n",
    "t_data_svr['median_income'] = t_data_svr['median_income']/1000\n",
    "t_data_svr = t_data_svr.rename(columns = {'Unnamed: 0': 'obs_ref'})\n",
    "dummy_svr = dummy_svr.merge(t_data_svr)\n",
    "dummy_svr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "15b17b88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(173, 23.143, 132.0),\n",
       " (173, 23.143, 128.6545501),\n",
       " (173, 23.143, 126.4890685),\n",
       " (304, 19.295, 137.973),\n",
       " (304, 19.295, 144.7865611),\n",
       " (304, 19.295, 141.8794322),\n",
       " (367, 32.015, 256.5),\n",
       " (367, 32.015, 282.3889023),\n",
       " (367, 32.015, 235.9038383),\n",
       " (491, 27.295, 300.0),\n",
       " (491, 27.295, 316.4593189),\n",
       " (491, 27.295, 301.6960676),\n",
       " (599, 30.587, 380.0),\n",
       " (599, 30.587, 387.674813),\n",
       " (599, 30.587, 369.2759991),\n",
       " (602, 31.997, 460.0),\n",
       " (602, 31.997, 323.0123131),\n",
       " (602, 31.997, 303.7515378),\n",
       " (613, 31.127, 290.0),\n",
       " (613, 31.127, 404.8017914),\n",
       " (613, 31.127, 397.265454)]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_lst_svr = (dummy_svr['value']).to_list()\n",
    "inc_lst_svr= (dummy_svr['median_income']).to_list()\n",
    "obs_lst_svr = (dummy_svr['obs_ref']).to_list()\n",
    "data_lst_svr = list(zip(obs_lst_svr, inc_lst_svr,val_lst_svr))\n",
    "data_lst_svr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e5eea6c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49.473970997550424\n",
      "50.30926428369044\n",
      "50.773294929772156\n",
      "41.87763965098145\n",
      "38.55512775508529\n",
      "40.01480447706111\n",
      "44.80777506638034\n",
      "42.40249457954847\n",
      "45.90725032931534\n",
      "20.257777777777793\n",
      "17.504544362655377\n",
      "19.959332510464247\n",
      "16.512150296537246\n",
      "16.512150296537246\n",
      "16.512150296537246\n",
      "18.57473351433979\n",
      "22.20220609421067\n",
      "37.05444800112537\n",
      "35.77121657767021\n",
      "17.307243470644583\n",
      "17.307243470644583\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obs_ref</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>median_income</th>\n",
       "      <th>aff_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>173</td>\n",
       "      <td>actual</td>\n",
       "      <td>132.00</td>\n",
       "      <td>23.14</td>\n",
       "      <td>49.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>173</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>128.65</td>\n",
       "      <td>23.14</td>\n",
       "      <td>50.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>173</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>126.49</td>\n",
       "      <td>23.14</td>\n",
       "      <td>50.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>304</td>\n",
       "      <td>actual</td>\n",
       "      <td>137.97</td>\n",
       "      <td>19.30</td>\n",
       "      <td>41.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>304</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>144.79</td>\n",
       "      <td>19.30</td>\n",
       "      <td>38.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>304</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>141.88</td>\n",
       "      <td>19.30</td>\n",
       "      <td>40.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>367</td>\n",
       "      <td>actual</td>\n",
       "      <td>256.50</td>\n",
       "      <td>32.02</td>\n",
       "      <td>44.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>367</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>282.39</td>\n",
       "      <td>32.02</td>\n",
       "      <td>42.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>367</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>235.90</td>\n",
       "      <td>32.02</td>\n",
       "      <td>45.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>491</td>\n",
       "      <td>actual</td>\n",
       "      <td>300.00</td>\n",
       "      <td>27.30</td>\n",
       "      <td>20.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>491</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>316.46</td>\n",
       "      <td>27.30</td>\n",
       "      <td>17.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>491</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>301.70</td>\n",
       "      <td>27.30</td>\n",
       "      <td>19.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>599</td>\n",
       "      <td>actual</td>\n",
       "      <td>380.00</td>\n",
       "      <td>30.59</td>\n",
       "      <td>16.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>599</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>387.67</td>\n",
       "      <td>30.59</td>\n",
       "      <td>16.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>599</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>369.28</td>\n",
       "      <td>30.59</td>\n",
       "      <td>16.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>602</td>\n",
       "      <td>actual</td>\n",
       "      <td>460.00</td>\n",
       "      <td>32.00</td>\n",
       "      <td>18.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>602</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>323.01</td>\n",
       "      <td>32.00</td>\n",
       "      <td>22.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>602</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>303.75</td>\n",
       "      <td>32.00</td>\n",
       "      <td>37.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>613</td>\n",
       "      <td>actual</td>\n",
       "      <td>290.00</td>\n",
       "      <td>31.13</td>\n",
       "      <td>35.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>613</td>\n",
       "      <td>d_pred</td>\n",
       "      <td>404.80</td>\n",
       "      <td>31.13</td>\n",
       "      <td>17.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>613</td>\n",
       "      <td>h_pred</td>\n",
       "      <td>397.27</td>\n",
       "      <td>31.13</td>\n",
       "      <td>17.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obs_ref variable   value  median_income  aff_score\n",
       "0       173   actual  132.00          23.14      49.47\n",
       "1       173   d_pred  128.65          23.14      50.31\n",
       "2       173   h_pred  126.49          23.14      50.77\n",
       "3       304   actual  137.97          19.30      41.88\n",
       "4       304   d_pred  144.79          19.30      38.56\n",
       "5       304   h_pred  141.88          19.30      40.01\n",
       "6       367   actual  256.50          32.02      44.81\n",
       "7       367   d_pred  282.39          32.02      42.40\n",
       "8       367   h_pred  235.90          32.02      45.91\n",
       "9       491   actual  300.00          27.30      20.26\n",
       "10      491   d_pred  316.46          27.30      17.50\n",
       "11      491   h_pred  301.70          27.30      19.96\n",
       "12      599   actual  380.00          30.59      16.51\n",
       "13      599   d_pred  387.67          30.59      16.51\n",
       "14      599   h_pred  369.28          30.59      16.51\n",
       "15      602   actual  460.00          32.00      18.57\n",
       "16      602   d_pred  323.01          32.00      22.20\n",
       "17      602   h_pred  303.75          32.00      37.05\n",
       "18      613   actual  290.00          31.13      35.77\n",
       "19      613   d_pred  404.80          31.13      17.31\n",
       "20      613   h_pred  397.27          31.13      17.31"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aff_score_lst = []\n",
    "for obs, inc, val in data_lst_svr:\n",
    "    affordability_score(obs, inc, val)\n",
    "\n",
    "dummy_svr['aff_score'] = aff_score_lst\n",
    "dummy_svr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e2c04d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x = 'obs_ref', y = 'aff_score', hue = 'variable', data = dummy_svr, palette ='BuGn')\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('Affordablity score')\n",
    "plt.title('SVR Affordability Scores Actuals & Doubled Net Additions\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "37b119e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>obs_ref</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>92</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>-1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>178</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>287</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>368</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>566</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>599</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>699</td>\n",
       "      <td>h_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>92</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>178</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>287</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>368</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>-2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>566</td>\n",
       "      <td>d_diff</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    obs_ref variable  value\n",
       "0        92   h_diff  -1.97\n",
       "1       178   h_diff   0.00\n",
       "2       287   h_diff   0.00\n",
       "3       368   h_diff   1.54\n",
       "4       566   h_diff   0.03\n",
       "5       599   h_diff   0.00\n",
       "6       699   h_diff   0.00\n",
       "7        92   d_diff   3.97\n",
       "8       178   d_diff   0.00\n",
       "9       287   d_diff   0.00\n",
       "10      368   d_diff  -2.43\n",
       "11      566   d_diff  -0.05"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_svr = pd.DataFrame(dummy_svr[['obs_ref','variable','aff_score']])\n",
    "df_svr= df_svr.pivot(index = 'obs_ref', columns = 'variable', values = 'aff_score').reset_index()\n",
    "#dropped the two poor predictions, 602 and 711, skewing data to see picture\n",
    "df_svr = df_svr.iloc[:-2]\n",
    "                        \n",
    "df_svr['h_diff'] = (df_svr['h_pred']-df_svr['actual'])/df_svr['actual']*100\n",
    "df_svr['d_diff'] = (df_svr['d_pred']-df_svr['actual'])/df_svr['actual']*100\n",
    "new_df_svr = pd.DataFrame(df_svr[['obs_ref', 'h_diff','d_diff']])\n",
    "new_df_svr = new_df_svr.melt(id_vars = 'obs_ref')\n",
    "\n",
    "    \n",
    "plt.figure(figsize=(15,10))\n",
    "sns.barplot(x = 'obs_ref', y = 'value', hue = 'variable', data = new_df_svr, palette ='BuGn')\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel('% Difference to Prediction')\n",
    "plt.title('SVR Doubled & Halved Net additions Impact on Affordability Score\\n')\n",
    "plt.show()\n",
    "new_df_a.head(12)"
   ]
  }
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