LifeExpectancy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from scipy.optimize import minimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Life_Expectancy_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>country</th>\n",
       "      <th>year</th>\n",
       "      <th>status</th>\n",
       "      <th>life_expect</th>\n",
       "      <th>adult_mort</th>\n",
       "      <th>infant_deaths</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>percent_expend</th>\n",
       "      <th>hepatitis_b</th>\n",
       "      <th>measles</th>\n",
       "      <th>...</th>\n",
       "      <th>polio</th>\n",
       "      <th>total_expend</th>\n",
       "      <th>diphtheria</th>\n",
       "      <th>hiv/aids</th>\n",
       "      <th>gdp</th>\n",
       "      <th>population</th>\n",
       "      <th>thinness_1-19</th>\n",
       "      <th>thinness_5-9</th>\n",
       "      <th>income_comp</th>\n",
       "      <th>schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2015</td>\n",
       "      <td>Developing</td>\n",
       "      <td>65.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>62</td>\n",
       "      <td>0.01</td>\n",
       "      <td>71.279624</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.16</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>584.259210</td>\n",
       "      <td>33736494.0</td>\n",
       "      <td>17.2</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.479</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2014</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>271.0</td>\n",
       "      <td>64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.523582</td>\n",
       "      <td>62.0</td>\n",
       "      <td>492</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.18</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>612.696514</td>\n",
       "      <td>327582.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0.476</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2013</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.9</td>\n",
       "      <td>268.0</td>\n",
       "      <td>66</td>\n",
       "      <td>0.01</td>\n",
       "      <td>73.219243</td>\n",
       "      <td>64.0</td>\n",
       "      <td>430</td>\n",
       "      <td>...</td>\n",
       "      <td>62.0</td>\n",
       "      <td>8.13</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>631.744976</td>\n",
       "      <td>31731688.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.470</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2012</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.5</td>\n",
       "      <td>272.0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.01</td>\n",
       "      <td>78.184215</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2787</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>8.52</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>669.959000</td>\n",
       "      <td>3696958.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>9.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2011</td>\n",
       "      <td>Developing</td>\n",
       "      <td>59.2</td>\n",
       "      <td>275.0</td>\n",
       "      <td>71</td>\n",
       "      <td>0.01</td>\n",
       "      <td>7.097109</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3013</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.87</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>63.537231</td>\n",
       "      <td>2978599.0</td>\n",
       "      <td>18.2</td>\n",
       "      <td>18.2</td>\n",
       "      <td>0.454</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year      status  life_expect  adult_mort  infant_deaths  \\\n",
       "0  Afghanistan  2015  Developing         65.0       263.0             62   \n",
       "1  Afghanistan  2014  Developing         59.9       271.0             64   \n",
       "2  Afghanistan  2013  Developing         59.9       268.0             66   \n",
       "3  Afghanistan  2012  Developing         59.5       272.0             69   \n",
       "4  Afghanistan  2011  Developing         59.2       275.0             71   \n",
       "\n",
       "   alcohol  percent_expend  hepatitis_b  measles  ...  polio  total_expend  \\\n",
       "0     0.01       71.279624         65.0     1154  ...    6.0          8.16   \n",
       "1     0.01       73.523582         62.0      492  ...   58.0          8.18   \n",
       "2     0.01       73.219243         64.0      430  ...   62.0          8.13   \n",
       "3     0.01       78.184215         67.0     2787  ...   67.0          8.52   \n",
       "4     0.01        7.097109         68.0     3013  ...   68.0          7.87   \n",
       "\n",
       "   diphtheria  hiv/aids         gdp  population  thinness_1-19  thinness_5-9  \\\n",
       "0        65.0       0.1  584.259210  33736494.0           17.2          17.3   \n",
       "1        62.0       0.1  612.696514    327582.0           17.5          17.5   \n",
       "2        64.0       0.1  631.744976  31731688.0           17.7          17.7   \n",
       "3        67.0       0.1  669.959000   3696958.0           17.9          18.0   \n",
       "4        68.0       0.1   63.537231   2978599.0           18.2          18.2   \n",
       "\n",
       "   income_comp  schooling  \n",
       "0        0.479       10.1  \n",
       "1        0.476       10.0  \n",
       "2        0.470        9.9  \n",
       "3        0.463        9.8  \n",
       "4        0.454        9.5  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2938, 22)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>life_expect</th>\n",
       "      <th>adult_mort</th>\n",
       "      <th>infant_deaths</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>percent_expend</th>\n",
       "      <th>hepatitis_b</th>\n",
       "      <th>measles</th>\n",
       "      <th>bmi</th>\n",
       "      <th>under5_death</th>\n",
       "      <th>polio</th>\n",
       "      <th>total_expend</th>\n",
       "      <th>diphtheria</th>\n",
       "      <th>hiv/aids</th>\n",
       "      <th>gdp</th>\n",
       "      <th>population</th>\n",
       "      <th>thinness_1-19</th>\n",
       "      <th>thinness_5-9</th>\n",
       "      <th>income_comp</th>\n",
       "      <th>schooling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2744.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2385.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2712.00000</td>\n",
       "      <td>2919.000000</td>\n",
       "      <td>2938.000000</td>\n",
       "      <td>2490.000000</td>\n",
       "      <td>2.286000e+03</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2904.000000</td>\n",
       "      <td>2771.000000</td>\n",
       "      <td>2775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2007.518720</td>\n",
       "      <td>69.224932</td>\n",
       "      <td>164.796448</td>\n",
       "      <td>30.303948</td>\n",
       "      <td>4.602861</td>\n",
       "      <td>738.251295</td>\n",
       "      <td>80.940461</td>\n",
       "      <td>2419.592240</td>\n",
       "      <td>38.321247</td>\n",
       "      <td>42.035739</td>\n",
       "      <td>82.550188</td>\n",
       "      <td>5.93819</td>\n",
       "      <td>82.324084</td>\n",
       "      <td>1.742103</td>\n",
       "      <td>7483.158469</td>\n",
       "      <td>1.275338e+07</td>\n",
       "      <td>4.839704</td>\n",
       "      <td>4.870317</td>\n",
       "      <td>0.627551</td>\n",
       "      <td>11.992793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.613841</td>\n",
       "      <td>9.523867</td>\n",
       "      <td>124.292079</td>\n",
       "      <td>117.926501</td>\n",
       "      <td>4.052413</td>\n",
       "      <td>1987.914858</td>\n",
       "      <td>25.070016</td>\n",
       "      <td>11467.272489</td>\n",
       "      <td>20.044034</td>\n",
       "      <td>160.445548</td>\n",
       "      <td>23.428046</td>\n",
       "      <td>2.49832</td>\n",
       "      <td>23.716912</td>\n",
       "      <td>5.077785</td>\n",
       "      <td>14270.169342</td>\n",
       "      <td>6.101210e+07</td>\n",
       "      <td>4.420195</td>\n",
       "      <td>4.508882</td>\n",
       "      <td>0.210904</td>\n",
       "      <td>3.358920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.37000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.681350</td>\n",
       "      <td>3.400000e+01</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2004.000000</td>\n",
       "      <td>63.100000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.877500</td>\n",
       "      <td>4.685343</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>4.26000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>463.935626</td>\n",
       "      <td>1.957932e+05</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.493000</td>\n",
       "      <td>10.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>72.100000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.755000</td>\n",
       "      <td>64.912906</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>43.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>5.75500</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1766.947595</td>\n",
       "      <td>1.386542e+06</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>0.677000</td>\n",
       "      <td>12.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>75.700000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>7.702500</td>\n",
       "      <td>441.534144</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>360.250000</td>\n",
       "      <td>56.200000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>7.49250</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>5910.806335</td>\n",
       "      <td>7.420359e+06</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>0.779000</td>\n",
       "      <td>14.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>723.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>17.870000</td>\n",
       "      <td>19479.911610</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>212183.000000</td>\n",
       "      <td>87.300000</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>17.60000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>50.600000</td>\n",
       "      <td>119172.741800</td>\n",
       "      <td>1.293859e+09</td>\n",
       "      <td>27.700000</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>0.948000</td>\n",
       "      <td>20.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year  life_expect   adult_mort  infant_deaths      alcohol  \\\n",
       "count  2938.000000  2928.000000  2928.000000    2938.000000  2744.000000   \n",
       "mean   2007.518720    69.224932   164.796448      30.303948     4.602861   \n",
       "std       4.613841     9.523867   124.292079     117.926501     4.052413   \n",
       "min    2000.000000    36.300000     1.000000       0.000000     0.010000   \n",
       "25%    2004.000000    63.100000    74.000000       0.000000     0.877500   \n",
       "50%    2008.000000    72.100000   144.000000       3.000000     3.755000   \n",
       "75%    2012.000000    75.700000   228.000000      22.000000     7.702500   \n",
       "max    2015.000000    89.000000   723.000000    1800.000000    17.870000   \n",
       "\n",
       "       percent_expend  hepatitis_b        measles          bmi  under5_death  \\\n",
       "count     2938.000000  2385.000000    2938.000000  2904.000000   2938.000000   \n",
       "mean       738.251295    80.940461    2419.592240    38.321247     42.035739   \n",
       "std       1987.914858    25.070016   11467.272489    20.044034    160.445548   \n",
       "min          0.000000     1.000000       0.000000     1.000000      0.000000   \n",
       "25%          4.685343    77.000000       0.000000    19.300000      0.000000   \n",
       "50%         64.912906    92.000000      17.000000    43.500000      4.000000   \n",
       "75%        441.534144    97.000000     360.250000    56.200000     28.000000   \n",
       "max      19479.911610    99.000000  212183.000000    87.300000   2500.000000   \n",
       "\n",
       "             polio  total_expend   diphtheria     hiv/aids            gdp  \\\n",
       "count  2919.000000    2712.00000  2919.000000  2938.000000    2490.000000   \n",
       "mean     82.550188       5.93819    82.324084     1.742103    7483.158469   \n",
       "std      23.428046       2.49832    23.716912     5.077785   14270.169342   \n",
       "min       3.000000       0.37000     2.000000     0.100000       1.681350   \n",
       "25%      78.000000       4.26000    78.000000     0.100000     463.935626   \n",
       "50%      93.000000       5.75500    93.000000     0.100000    1766.947595   \n",
       "75%      97.000000       7.49250    97.000000     0.800000    5910.806335   \n",
       "max      99.000000      17.60000    99.000000    50.600000  119172.741800   \n",
       "\n",
       "         population  thinness_1-19  thinness_5-9  income_comp    schooling  \n",
       "count  2.286000e+03    2904.000000   2904.000000  2771.000000  2775.000000  \n",
       "mean   1.275338e+07       4.839704      4.870317     0.627551    11.992793  \n",
       "std    6.101210e+07       4.420195      4.508882     0.210904     3.358920  \n",
       "min    3.400000e+01       0.100000      0.100000     0.000000     0.000000  \n",
       "25%    1.957932e+05       1.600000      1.500000     0.493000    10.100000  \n",
       "50%    1.386542e+06       3.300000      3.300000     0.677000    12.300000  \n",
       "75%    7.420359e+06       7.200000      7.200000     0.779000    14.300000  \n",
       "max    1.293859e+09      27.700000     28.600000     0.948000    20.700000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Things to note:\n",
    "1. count is different for different features\n",
    "1. 'adult_mort', 'hepatitis_b', 'bmi', 'polio' 'infant_death', 'alcohol', 'percent_expend', 'measles', 'under5_death', 'total_expend', 'diphtheria', 'hiv/aids', 'gdp', 'population', 'thinness_1-19', 'thinness_5-9' has outliers and skewness as SD is high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x2985673c908>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='infant_deaths', y='life_expect', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x29857b76908>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='alcohol', y='life_expect', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x29857b7b188>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5wBN7Wj5HbcIFdvkEfXKva7xSvz5uM+YniPp087CKCzNVrU3wwBN7eGjVOfx03XnMGSzP+DkHLP40+RWLh5l9WOvPTo3JrH4SrR+j+km08f2K6lqr1ib47Ld3xHglwYVKm5+r8b2rr9wapWgrumZZPSKv1WzDCsxecdaxgds//+HT+Om683ho1TlKSBlTUkpovFoLPdHNZFJEq8fuHq964xB3bJvSqhmv1lh5+7ZM136qf6IcGR3jF69OTyblUrIlPg656S2COYPl0Bl1L7doPTQnszgn0VbJZN+BWuQJsn4yvW7DVo4oD1Aph/8r1d+7xkQZpfnvKm9ZVY+I8+EhKysWD7P24kUMD1UwvJmSay9exI0rFgVuVyLqHHXfzUDzWEbcyQ5Bn9JbPXbAzBuHCOjGah6byML1I9t54Ik9gfuafdgsZh8+K/ZEjyCDfmso6ALUVsfG8MZi6rPxwu7beBKtv2e/f1t4Qq+PtzV32TR3Z+07UIuc33vMUCWyJdysnbGtLCWtHhF3/CnuZJesNHaHx9neDeoVH7poavg0ainNUOM/a+OEhDBhs3TOPmVe5IltwrlcxxtueXhX5NjJ3v2vzej565+Sgz41t2rVHKgdmtKCDdN8El2xeJjPf/i00Ps798YMw+s2bOX6kfCZg1EfC1YuX5i4VVEf2ypCiynJWkpJWj+q3ydBlJRmqGQ22ZUTrwr59NNX2CSHJIaHKly1dMHkarklM5adNHdKN8RVSxcwVGlvTKhVbNX6YlQNmv+4KuWByP1HfWq+5Izh+FcbBgg6idY/0cfh8KrMH7/q7kQtwjmD5cnWXpCBiBdVlKrfYV1dzVU3lq27n2s3bI09/qTVbCWIuu9mqP6JNn7XzKFpM5eSdO0EqU8zX7F4uOV6UTeuWJSo+sRMTE9kxvmnzW85xbxRPc4HntiTOGlHzZgKK5+Upkq5xA0XnAoET+Uvl4yJhLMK8xLVpRXnWAa9DtXvkyBqKc1QySzxia35k+NMTjxmsP6y0xL1gbfqDktLUH2wB57YM+VTd71lF6b++3aO0SHnQmdMzfSDQJiSWWBrIqi1MfuwWUxvX07VDa2GOMcy6HXEaYFJ/1FLaQYq5VLbJ7bGk2zYQHLzVeOBXPLB8MYWWtwWU6xYYtg9Xp3yqbvV9Oj6JIQkFTPqok7oUUluqFJue/yungiDNLc2Wr32bmk1tPrAEPU6unlSgWRDLaUEyiVjqFKe8qluOOLE12pGVl3YQPKVSxe0bFG0+0l6xeJhHlp1Ds+sO48vXn565OsomXHl0gWptK6a420Vfz2uoGNUPyKDAdOxW53Qw/Y7PFRh6w0f4KqlC9oaw0ryfkTdt5taDb3yOqQYMm8pmdl1wP+D90F7O/AxYD7wDWAu8CPg3znnXk9rnycfPZsnX9ifynMNmHddzXDAuERd0FjB7MNmMV6tTT6+keGNlSz+o3twzrsO58hKmSPKA4wfqMUeA5npJ+nmqbtXLV0wbbzH8NZMunHFIpYcN3fakg1B61EZ8J6T5vKjXS+3jDeqPFNjiaVWSzUkLYPTajyj8fWOjVentRTLAwbGlCn6Sd+PsBi67STeK6+jWa+VVuoWmS5dYWbDwP8B3uGcq5rZbcB3gXOBjc65b5jZl4Btzrm/jnquJEtXAJzyme/yaotB5FaC1gwK0vjHOzRY5hevHpx2PU+lPEC1diiyG6z+u3rNuQee2BN4QgSv9bL0xDk881K1rX+asCT3rgVH8g9P7Z1WbDLqBBNnCYA4S1iEdc81rz81MjrGmrt2TCbDdhZkBO/aq3rNvpLZZPKN+xph5usZ9cqJr1deR10HlkzJZOmKui64Tin09XciKT0MnAb8MzAC/AVwC/A259xBM3s3sMY5tzzquZIkpebCpDNhwBcuPz32ekBJxmnSFvZPkyTOsNpfjYkxKAmkcVKKmhVYf23AlOradeWSsf7S+BM+kpx0eu2EK611YN2yTJNSXDkmr3zWU3LOjZnZfwZ2AVXgHuARYNw5V69X83Mg8D/czK4BrgFYsCD+wUsrIYF3Ig66wjysSGWWU4xbCboaPmmcoYVbG243r8qbtGBn2Ek+Kpk3zlgMqipRrykYN1nEXacJSPTapDfkfWFv47nvLW/rr7+zTJOSmc0BLgJOAMaB24EPBdw18EzonLsJuAm8llJGYbYU9Ie45q4dgSe1vDXHGhZnWIsortqE49oWy2Jcu2Er6zftjCzR03iSbxVTnPqAccVdp+nwWQO5lsLpJr3UokxaWiltjee+E3/pnd2xPHhKsp5992vAT51ze5xzNWAj8B5gyMzqCfHtwO6M45iR5j/EkdGxQi4xANOrZYfFOeFcatcqRSWS5jIzUfXOWiXJY4YqkSeFtGa+NcYVdvyKclFrUeRZXDULSUorSbqyTkq7gKVmNmhmBrwP+DHwAHCpf5+rgW9lHMeM7N3/2pTS+kUo/RKmuVp2mJIZb59zRCdCmtL1FtUtEjUtvX5CWLl8oTfzrUm5FL54YtDyCDO9gLgbLmrtpCyXt8iDLuzNT9ZjSpvN7A68ad8HgVG8JundwDfM7EZ/21eyjGOm6nXdxsarXLtha87RhBssD0zpJosao5lwLrVp83HUk1FUt0jY9PChSpk1F06dWBF39l1Yd+Hai70lClpNTJkzWObV2qHIqe1Zdlt1S5dY3mMwWdCFvfnI/Dol59wNwA1Nm58Gzsx63/2mNuEmu0vqExGKot6yiLo+qNW1SHVJThZRn+Dr5Yda1QJsHINrvl4ty1VZu2nF17zHYKR3qMxQD6kd8tYVmn34rJbLh3dSY8uiVeIJSzjtthjifIKP+jRfX1CxPgbXvN8s1wTKe72hJFRcVdKipNRjxqu1VCZhXLV0AQ88sYfd41WOrJTZ//rBxInOIDCBJO0WmUmLIc4n+CNj1roLSghZdlt1U5dY3FauSCtKSjLN8FBlWmWDxqoLcaaTt3uRYWOL6MhKGTOmLP9eF7fFEOcTfItC5VM0J4Qsu626rUtMYzCSBhVkzUHUwm5Z77deUHaoUg6MYwACu1waC7g+tfbcWDPlkmqeVjxerQUmpLqx8erkbLowcWZRjUfso1lzQshy6rCmJUs/UkspBwEFCTLXPINt2br7A7usWq3vU5dkphzEGxNqZ42jOF15rT7Bx106JCghZNltpS4x6UdKSn3giwG1+6LGJeJ0iyU5YcYdE2p3rGSmg/9hXXyXnDE8Oa4W9fqy7LZSl5j0GyWlggurKj5UKTP78FmxxniCEsDQYDm0ayxucoh7wow7i6ydhfzqZjL4rxaJSHEoKWWoNGBMtNlX11gVO6ibzIxp18sEVc+GqVfW10+8UdIeSI87iyxqbSXwjskR5YHAZDrTmNUiESkGJaUMtZuQghYUbKxgAN6MtGs3bOWz397BDRec6m2MmECRpIp52gPpcWeRNbdY6rPvGhc+hOlJWoP/Ir1DSSmhSrnExKFDvJ7RxalBU6lXLB5m/aadgRMT9h2oTVazjrqOqGQWKyENVcqANxEira6sJBdWJukSVFebSO9RUkqgUh5g7cWL+PTGRzNLSu1cMFmtTUQmHCO6knddpVzi/NPmp17aJu0xG3W1ifQuJaUEqrVDrN746GSB1iyEjY3ErToQJCodlcw45NyUFWmzKG2jRCIicSgpJZRGQhosD2Bm7H996sk/amwkSdWByccQnZCClv++LqQKehFL24jIzNy6eVes+3Vy2XRVdMhBbcLxx7++iC9efnrs9VqSVB3Af76ohBS2v7CWWlFL24hIb1FLKQf1at5bb/hA7C6tJNfwlMx4aNU5oUsyRNWlU7VnEcmTWko5STo+tHL5wqgZ31Nccdaxk49JWjtNK26KSJ7UUuoSKxYPc/uWXTz01N7I+80+rDRZ4bvdWW+alCAieVFSysmcwXLL+zQXMd27/7WWjznQNHlCCUZEuomSUg7KJXujCgPBFbSBadcLxaEJCSLSzZSUMjZUKXP+afNDq02HVdA+fNZA4mUcNCFBRLqdklJGDLhy6YLJ8Z2R0THW3LWDsfHqlJp1YRerJk1IQfXyRES6jZJSBpoTRFAF73pB1Zkql4z1l56mZCQiPUFJKQNj41V+/7ZtbHl2LzeuWMT6TTsDl5SIMmewzKu1Q20vPCcikpa4lR9aiVMZQkkpIxPO8bWHvTcyaYmexokQqoYtIv1ESSljX9/8s8Qrqs4asMnkoyQkIv1EFR0yNuEcK5cvpDwQv6JqtXaIkdGxDKMSESkmJaUO2PLsXg6blexQ15cvFxHpJ+q+64D62FISWipCRPqRWkoFpcoMItKPlJQKoHm0SZUZRKRfqfsuR/WVX0FTv0VEQEkpN0OVMmsuPFVTv0Wkq6W9VLqSUocY4FCNOhGRKEpKGSqZccg5dcmJiMSkpJQRFUoVEUlOSSkDcwbL3HDBqUpIIiIJKSmlpHnigoiIJKfrlFIy+/BZSkgiIjOkpJQSlQUSEZk5JaWEwmp9D5hxwqq7WbbuflX4FhFpk5JSQpVy8CGbcA6Ht+rs6o3blZhERNqgiQ4JHagdanmfam2C9Zt2aoxJRHKRdpWFTlJLqYEBz6w7L5Xn0hiTiEhySkoNrlzqfbqYM1gOvU/U7xpp6QkRkeSUlHyzDytx4wqvYvcNF5xKuTR9SsNVSxdwwwWnUimXIp9LS0+IiLQn0zElM1sIbGjYdCLwh8Df+duPB54BPuyc25fWfo8oGa9OuESPKZcGuH5kOw88sYfd41WGBss4By9Xa1Nq142MjnH4rAGqtQnAazmd9875k49TnTsRkfZl2lJyzu10zp3unDsdOAM4AHwTWAXc55w7GbjP/zk1l/7rYxM/Zrxa42sP72JsvIoD9h2o8drBQ3zh8tN5aNU5kwlp9cbtjFdrk497NcbEBxERiaeT3XfvA55yzj0LXATc7G+/GViR5o6+9vCuVJ6nPouubv2mnZMtpMb73NKQzDQlXESkfZ1MSh8Bvu7ffqtz7jkA//vRQQ8ws2vMbIuZbdmzZ0+HwpxqbLw6eVHsWMiMuuaOwuZkJiKSROO575XxvXmH01EdSUpmdhhwIXB7ksc5525yzi1xzi2ZN29eNsHFiQMvOYVVcwiiKeEi0q7Gc9+bhubmHU5Hderi2Q8BP3LOPe///LyZzXfOPWdm84EXOhTHjDjeWEG2rvnnOk0JF5G83Lo53hBGES+y7VT33RW80XUHcBdwtX/7auBbHYpjxupLmpv//cqlC6ZNEdeUcBGR9mTeUjKzQeD9wO80bF4H3GZmHwd2AZdlHUdahocqPLTqnCnblhw3l/WbdmpKuIjIDGWelJxzB4Cjmra9hDcbr6uEtYBWLB5WEhIRSYEKssY0rBaQiEjmlJR8y06ayzMvVQOnfQd12YmISPpU+w6vpt0tv/1uVi5fqEkLIiI56vuW0hcvPx2AZevuZ/d4lSMrZY4oDzB+oKZJCyIiHdb3SWnLs3u585GxyfJB49UalXKJL1x+upKRiEiH9X333S0P7wqsZ6cyQSIindf3LaWwBS5UJkhEek0RKzg06/uWUhiVCRIR6TwlJZhWaFUz7kRE8hE7KZnZ4XG2dZtKucSVSxdMqWe39uJFmuQgIpKDJC2lH8Tc1jXM4JIzhrWUuYhIQbSc6GBmbwOGgYqZLeaN3q43A4MZxpY557zZd/XJDvVVYwElJhGRHMSZfbcc+E3g7cDneSMp/TPw6WzC6pygVWPX3LVDSUlEJActk5Jz7mbgZjO7xDl3Zwdiyt14tcbI6JgSk4hIhyUZUzrDzIbqP5jZHDO7MYOYCkEXz4qIdF6SpPQh59x4/Qfn3D7g3PRDKoagauEiIt3oo2ct6IoLZyFZUio1TgE3swrQ9VPCRUSkOJKUGfoacJ+Z/Q3e/IDfAm7OJCoREelLsZOSc+5PzexR4NfwZuD9J+fcpswiy1nJmus8iIhI1pIWZH0cOOic+59mNmhmb3LOvZJFYHm74qxj8w5BRKTvJCkz9NvAHcB/8zcNAyNZBJW3SnmAG1csyjsMEZG+k2SiwyeAZXgXzeKcexI4Ooug8rb24nfmHYKISF9KkpRec869Xv/BzGYRvhxR1zJTiSERkbwkSUrfN7NP49XAez9wO7KtyqMAABfMSURBVPDtbMLKz5VdMpdfRKQXJUlKq4A9wHbgd4DvAtdnEVQeSmZctXSBxpJERHKUZEr4ITO7GdiM12230znX9d13JTOeWtuzhSlEpM91SyWHuthJyczOA74EPIV3ndIJZvY7zrm/zyq4TrjirGMZGR1j/aadWlNJRCRnSa5T+jxwtnPuJwBmdhJwN9C1SemqpQtYctxcVm/cTrU2AWhNJRGRPCUZU3qhnpB8TwMvpBxPR924YhHrN+2cTEh11dqEqoSLiOQgSUtph5l9F7gNb0zpMuCHZnYxgHNuYwbxZS6sGvhuVQkXEem4JEnpCOB54N/4P+8B5gIX4CWprkpKAwYjo2MYwRdbHTNU6XRIIiJ9L8nsu481bzOzwxovqO0mh5y3kF9QQjJg5fKFnQ5JRKTvJal996CZHd/w878GfphBTB1RsvCuO4cmOYiI5CFJ991a4Htm9l/wirGeC0xrPXWLiYgrrIbVdScikosk3XebzOx3gXuBF4HFzrn/m1lkOamUS+q6E5Gu1m0XzDZK0n33/wF/AbwXWAM86F9Q21MGDK7bsJVl6+5nZHQs73BERPpKku67twBnOueqwA/M7HvAf8e7gLZn7H9dF9GKiOQldkvJOfdJ51zVzGb7Pz/rnHt/dqHlTxfRioh0VpLuu3eb2Y/xlkTHzE4zs7/KLLKC0EW0IiKdk6TM0BeB5cBLAM65bXjjSz1NF9GKiHROkqSEc+5nTZsmAu/YIzQTT0Sks5IkpZ+Z2XsAZ2aHmdmn8LvyeoXhXaNU/7724kWa5CAi0kFJZt/9LvDneBfO/hy4B/hEFkHlxQEPrTon7zBERPpWkotnXwSuDPu9ma12zq1NJaqcqJKDiEi+krSUWrkMrxRRV9L4kYh0o26u3hAk0USHFixwo9mQmd1hZk+Y2eP+1PK5ZnavmT3pf5+TYhycfPTsyN8P4K06q/EjEZFiSbOlFFbi9M+B7znnLjWzw4BB4NPAfc65dWa2ClgF/EFagZx14lE8+cL+wN8NAH92+elKQCIiBZRpS8nM3ox3LdNXAJxzrzvnxoGLgJv9u90MrEgxDr728K7Q3x0Crt2wleNX3c3pn71H9e1ERAokzaR0e8C2E/FWqP0bMxs1s//ulyl6q3PuOQD/+9FBT2hm15jZFjPbsmfPnhRD9YxXa6y8fZsSk4gUSuO575XxvXmH01FJygz9KzO7z8we839+p5ldX/+9c+5PAh42C3gX8NfOucXAfryuulicczc555Y455bMmzcv7sMSqR1yqm8nIoXSeO5709DcvMPpqCQtpS8Dq4EagHPuUeAjLR7zc+DnzrnN/s934CWp581sPoD//YUkQaetub7dyOgYy9bdzwmr7tYSFiIiHZQkKQ065/6xadvBqAf4iwD+zMzqc63fB/wYuAu42t92NfCtBHGkbmiwPHl7ZHSM1Ru3MzZexfHGEhZKTCIi2Usy++5FMzsJf5admV0KPBfjcf8BuMWfefc03hLqA8BtZvZxYBfeNU65cQ3zBtdv2km1NrWkX30JC83YExHJVpKk9AngJuAUMxsDfkpEhYc659xWYEnAr96XYN+Zerlam7wdtlSFlrAQkSK6dXP4bONuvLC2ZfedmX3SvznfOfdrwDzgFOfcrzjnns00ug4ZMJvsngtbqkJLWIiIZC/OmNLH/O9/AeCc2++ceyW7kDpvwrnJcaOVyxdSKZem/F4liEREOiNOUnrczJ4BFprZow1f283s0Yzj65jGcaNLzhimZN61wCUzLjljWONJIiId0HJMyTl3hZm9DdgEXJh9SPnZPV5lZHSMOx8ZY8Kf/TDhHHc+MsaS4+YqMYmIZCzWRAd/avdpGceSuwEz1ty1Q7PvRERy0jIpmdltzrkPm9l2phZdNcA5596ZWXQZMqZXkJ1wjvGGmXiNNPtORCR7cVpK9dl352cZSKeFlTQPo9l3IiLZizOmVC+c2hPTv9uh2XciIp0Rp/vuFYIbFvXuuzenHlXO5gyWGTxsFrvHqxwzVGHl8oUaTxIR6YA4LaU3dSKQoqiUS9xwwamBSWhkdIz1m3YqWYlI4XRj9YYgaa482yOCR5vqhVrrM/PqhVoBJSYRkZSkuchfT6jWDgUu/BdVqFVERNKhpBQgaOE/FWoVEcmeuu9C1Ks71MeQAi9sAo6slKdvFBGRtigphRgaLE8ZQwq7sMkvkSciIilQ912A8oDhHNPGkIKMHwiuACEiIskpKeFdl1Q3VCmz/rLTpiz8F8UBy9bdr+XSRURS0PdJaXiowg0XnMrwUAUDZh/u9WgmKStUnx6uxCQiMjN9nZQMGDxsgOs2bGVsvIrDSzAr79jGvv2vTbt/uWQMhUxs0PRwEclT1LLo3aSvk5IDnnxh/7Q5DLUJx4HaoSnbhipl1l96Gltv+ABhcxs0PVxEZGb6OiklMfvwWZOVG8K69lRJXERkZpSUYmpsBa1cvpBKuTTl96okLiIyc7pOKabGVlC9xaTirCIi6VJSiiGoFbRi8bCSkIhIypSUWhhWK0hEpGOUlEIY8IXLT1cyEhHpIE10CHHl0gVKSCIiHaakFOLGFYvyDkFEpO+o+05EpMv1ylLooJZSoMYCrSIi0jlKSk0GDG644NS8wxAR6Ut9nZSuWrpg2rIVf/ZhzbgTEclLX48p3bhikSY0iIgUSN+2lLSMuYhI8fRtUnLN61WIiEju+jYpldRUEhEpnL4dU5pwjpHRMVX6FhEpkL5NSnMGy6zeuJ1qbQLwlkFfvXE7gBKTiHSNXrpwFvq4+27fgdpkQqqr1iZYv2lnThGJiEjfJqUwjSvMiohIZykpNWlcYVZERDpLSalB0AqzIiLSOX070aGZVpgVEclf3yelSrnE2osXKRmJiBRAXycltY5ERIol86RkZs8ArwATwEHn3BIzmwtsAI4HngE+7Jzbl9Y+ZxkcjCgjVB4w1l92mpKRiEjBdGqiw9nOudOdc0v8n1cB9znnTgbu839OzQnzZof+rlIeUEISESmovLrvLgL+rX/7ZuBB4A/SevInX9gf+rsjyiUlJBHpar1WxaFRJ1pKDrjHzB4xs2v8bW91zj0H4H8/OuiBZnaNmW0xsy179uxJJZh9B2qMjI6l8lwiIlloPPe9Mr4373A6qhNJaZlz7l3Ah4BPmNl74z7QOXeTc26Jc27JvHnzUgto9cbtSkwiUliN5743Dc3NO5yOyjwpOed2+99fAL4JnAk8b2bzAfzvL2QdRyPVuBMRKaZMk5KZzTazN9VvAx8AHgPuAq7273Y18K0s4wgyphp3IiKFk/VEh7cC3zRvQb1ZwK3Oue+Z2Q+B28zs48Au4LKM45hGi/yJiBRPpknJOfc0cFrA9peA92W571YmtB66iEjh9G1B1qFKOe8QRESkSd8mJfXeiYgUT98mpX0Hapyw6m6Wrbtf08NFRAqib5MSeFf1jo1Xdd2SiEhB9HVSqtN1SyIixaCk5Nut65ZERHKnpOQ7ZqiSdwgiIn1PSQkwYOXyhXmHISLS9/o+KRlw5dIFWs5CRKQAtBy6lkMXESmMvm0pVcolJSQRkYLp26SkaeAiIsXTt0kJtHyFiEjR9HVSUvk7EZFi6euk5EDlhURECqSvkxKgcSURkQLp+6Sk8kIiIsXR90lJ5YVERIqjr5NS/VolEREphr6s6GB4LSRdPCsiUix9mZR+uu68vEMQEZEAfZmURES6zUfPWpB3CB3Rd2NKumBWRKS4+i4pubwDEBGRUH2XlEREpLj6LikNVcp5hyAiIiH6LimtufDUvEMQEZEQfZeUdF2SiEhx9V1SEhGR4urJpBQ27VvTwUVEiq0nk1LYtG9NBxcRKbaeTEoiIr2kX6o5gJKSiIgUSE8mpbBrkXSNkohIsfVkUjr/tPmJtouISDH0ZFJ64Ik9ibaLiEgx9GRS2j1eTbRdRESKoSeT0jFDlUTbRUSkGHoyKZ19yrxE20VEpBh6MilpTElEpDv1ZFLSmJKI9Iq5sw/LO4SO6smkdGTI9Uhh20VEpBh6MilZSOXVsO0iIlIMPZmUxg/UEm0XEZFi6MmkpCnhIiLdqSNJycxKZjZqZt/xfz7BzDab2ZNmtsHMUh3JW7l8IZVyacq2SrnEyuUL09yNiIikrFMtpU8Cjzf8/DngC865k4F9wMfT3NmKxcOsvXgRw0MVDBgeqrD24kVaCl1EpOBmZb0DM3s7cB7wx8DvmZkB5wAf9e9yM7AG+Os097ti8bCSkIhIl+lES+mLwH8EDvk/HwWMO+cO+j//HFD2EBGRbJOSmZ0PvOCce6Rxc8BdA1cqN7NrzGyLmW3Zs0fVGESkP/TzuS/rltIy4EIzewb4Bl633ReBITOrdx2+Hdgd9GDn3E3OuSXOuSXz5qlunYj0h34+92WalJxzq51zb3fOHQ98BLjfOXcl8ABwqX+3q4Fvpb3vkdExlq27nxNW3c2ydfczMjqW9i5ERCRleV2n9Ad4kx5+gjfG9JU0n3xkdIzVG7czNl7FAWPjVVZv3K7EJCJScB1LSs65B51z5/u3n3bOnemc+5fOucucc6+lua/1m3ZSrU1M2VatTbB+0840dyMiIinryYoOqhIuItKdejIpqcyQiEh36smkpDJDIiLdKfOKDnmoV3JYv2knu8erHDNUYeXyharwICJScD2ZlEBlhkREulFPdt+JiEh3UlISEZHCUFISEZHCUFISEZHCUFISEZHCUFISEZHCUFISEZHC6NnrlEZGx3TxrIhIl+nJpFRfuqJeKby+dAWgxCQiUmA92X2npStERLpTTyYlLV0hItKdejIpaekKEZHu1JNJSUtXiIh0p56c6KClK0REulNPJiXQ0hUiIt2oJ7vvRESkOykpiYhIYSgpiYhIYSgpiYhIYSgpiYhIYSgpiYhIYSgpiYhIYSgpiYhIYSgpiYhIYZhzLu8YYjGzPcCzbTz0LcCLKYczE4onmuKJpniidUs8LzrnPhjnCczse3Hv2wu6Jim1y8y2OOeW5B1HneKJpniiKZ5oiqf7qftOREQKQ0lJREQKox+S0k15B9BE8URTPNEUTzTF0+V6fkxJRES6Rz+0lEREpEsoKYmISGH0bFIysw+a2U4z+4mZrcpwP8ea2QNm9riZ7TCzT/rb15jZmJlt9b/ObXjMaj+unWa2PO2YzewZM9vu73eLv22umd1rZk/63+f4283M/ou/z0fN7F0Nz3O1f/8nzezqNmNZ2HAMtprZP5vZtZ08Pmb2VTN7wcwea9iW2vEwszP84/0T/7HWRjzrzewJf5/fNLMhf/vxZlZtOE5farXfsNeWMJ7U3h8zO8HMNvvxbDCzw9qIZ0NDLM+Y2dYOHp+w//Hc/oZ6mnOu576AEvAUcCJwGLANeEdG+5oPvMu//Sbgn4B3AGuATwXc/x1+PIcDJ/hxltKMGXgGeEvTtj8FVvm3VwGf82+fC/w9YMBSYLO/fS7wtP99jn97Tgrvy/8Fjuvk8QHeC7wLeCyL4wH8I/Bu/zF/D3yojXg+AMzyb3+uIZ7jG+/X9DyB+w17bQnjSe39AW4DPuLf/hLw75PG0/T7zwN/2MHjE/Y/ntvfUC9/9WpL6UzgJ865p51zrwPfAC7KYkfOueeccz/yb78CPA4MRzzkIuAbzrnXnHM/BX7ix5t1zBcBN/u3bwZWNGz/O+d5GBgys/nAcuBe59xe59w+4F5gpleVvw94yjkXVZkj9ePjnPtfwN6A/cz4ePi/e7Nz7gfOO7v8XcNzxY7HOXePc+6g/+PDwNujnqPFfsNeW+x4IiR6f/xP/OcAd6QRj/98Hwa+HvUcKR+fsP/x3P6GelmvJqVh4GcNP/+c6ESRCjM7HlgMbPY3/b9+8/2rDV0EYbGlGbMD7jGzR8zsGn/bW51zz4H3TwYc3cF46j7C1JNJXscH0jsew/7ttOIC+C28T8t1J5jZqJl938x+tSHOsP2Gvbak0nh/jgLGGxLuTI/PrwLPO+eebNjWsePT9D9e5L+hrtWrSSmoPzbTue9m9i+AO4FrnXP/DPw1cBJwOvAcXpdDVGxpxrzMOfcu4EPAJ8zsvVGhdyAe/HGEC4Hb/U15Hp8oSfef9nH6DHAQuMXf9BywwDm3GPg94FYze3Pa+w2Q1vuTdpxXMPWDTceOT8D/eOhdQ/ad9992V+jVpPRz4NiGn98O7M5qZ2ZWxvtjvcU5txHAOfe8c27COXcI+DJe90ZUbKnF7Jzb7X9/Afimv+/n/W6CetfGC52Kx/ch4EfOuef92HI7Pr60jsfPmdrV1nZc/sD3+cCVfjcOfjfZS/7tR/DGbf5Vi/2GvbbYUnx/XsTrvpoVEGci/nNcDGxoiLMjxyfofzzieXL7G+oFvZqUfgic7M/6OQyv2+iuLHbk93F/BXjcOfdnDdvnN9zt14H6TKK7gI+Y2eFmdgJwMt4gZyoxm9lsM3tT/TbeAPpj/nPVZ/tcDXyrIZ7f8GcMLQVe9rsiNgEfMLM5ftfNB/xt7ZryCTev49MglePh/+4VM1vq/y38RsNzxWZmHwT+ALjQOXegYfs8Myv5t0/EOx5Pt9hv2GtLEk8q74+fXB8ALp1JPL5fA55wzk12dXXi+IT9j0c8Ty5/Qz1jpjMlivqFNwPmn/A+OX0mw/38Cl5T+1Fgq/91LvA/gO3+9ruA+Q2P+Ywf104aZtmkETPe7Kdt/teO+vPg9e3fBzzpf5/rbzfgv/r73A4saXiu38IbyP4J8LEZHKNB4CXgyIZtHTs+eMnwOaCG96n042keD2AJ3kn7KeAv8SulJIznJ3jjDfW/oS/5973Efx+3AT8CLmi137DXljCe1N4f/2/yH/3XeDtweNJ4/O1/C/xu0307cXzC/sdz+xvq5S+VGRIRkcLo1e47ERHpQkpKIiJSGEpKIiJSGEpKIiJSGEpKIiJSGEpKIiJSGEpK0rfMbIWZvSPvOOIws980s7/MOw6RrCkpSVdpKFeThhV4SxCISEEoKUnHmbcw2xNmdrNfhfoOMxs0b6Gz7/vVzTc11BV70Mz+xMy+D3zSzN5q3kJ42/yv9/j3u8rM/tG8xd7+W0P5mV+Y2R/7933Yf/x78ArErvfvf1JIrCeZ2ff8mP63mZ3ib/+Wmf2Gf/t3zOyWhli/aGb/YGaPmdmZ/vbZ5lXb/qF5Fa0v8rf/pplt9PfxpJn9acO+P2Zm/+S/7mXZvBsiBZN3SQl99d8X3sJsDq+aOcBXgZXAPwDz/G2XA1/1bz8I/FXD4zfgVWoGb3G5I4FfAr4NlP3tfwX8hn/b4ZefwVuY7Xr/9t8Cl7aI9T7gZP/2WcD9/u234pWK+VW80jpzG2L9sn/7vfgL0AF/Alzl3x7yHzMb+E28xd6OBI4AnsUr2jkf2AXMw1s07yHgL/N+7/Slr6y/0uwKEUniZ865h/zbXwM+DfwycK9Xk5ISXv2zug0Nt8/BK1qJc24CeNnM/h1wBvBD//EV3qja/DrwHf/2I8D74wRo3lIF7wFutzdWpz7c3+/zZvaHeMVGf90517go3df9+/wvM3uzeUubfwC40Mw+5d/nCGCBf/s+59zL/j5/jLcy71uAB51ze/ztG/CqX4v0NCUlyUtz0cVXgB3OuXeH3H9/i+cz4Gbn3OqA39Wcc/X9TRD/734Ab4G600N+vwiv0OwxTdubX1t9zZxLnHM7pwRtdhbwWsOmxvhUmFL6jsaUJC8LzKyegK7AWwJ8Xn2bmZXN7NSQx94H/Hv/fiXzFnW7D7jUzI72t881s+NaxPAK8KawXzpvIbefmtll/nOamZ3m3z4Tb42oxcCn/GUc6i737/MreMsWvIy3bMF/8JcmwMwWt4htM/Bvzewo89byuazF/UV6gpKS5OVx4GozexSYC/wF3po7nzOzbXjLA7wn5LGfBM42s+143XGnOud+DFyPtwz8o8C9eOMyUb4BrPQnHgROdACuBD7ux7QDuMjMDsdb+O63nLeg4u8DX60nHGCfmf0D8CW8ZSAA/hNQBh41s8f8n0M5b42dNcAPgP+JtyyDSM/T0hXScWZ2PPAd59wv5xxK6szsQeBTzrktecci0o3UUhIRkcJQS0kEMLP/yvRrgf7cOfc3ecQj0q+UlEREpDDUfSciIoWhpCQiIoWhpCQiIoWhpCQiIoXx/wOUwq0c/cpQQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='percent_expend', y='life_expect', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x29857e1dbc8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='measles', y='life_expect', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df=df.dropna()\n",
    "df_copy = df.copy()\n",
    "train_set = df_copy.sample(frac=0.90, random_state=0)\n",
    "test_set = df_copy.drop(train_set.index)\n",
    "y = train_set['Life expectancy ']\n",
    "y = np.array(y.values.tolist())\n",
    "train_set = train_set.drop(['Life expectancy '], axis = 1)\n",
    "train_set = train_set.drop(['Country', 'Year', 'Status'], axis = 1)\n",
    "X = train_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = test_set['Life expectancy ']\n",
    "y_test = np.array(y_test.values.tolist())\n",
    "test_set = test_set.drop(['Life expectancy '], axis = 1)\n",
    "test_set = test_set.drop(['Country', 'Year', 'Status'], axis = 1)\n",
    "X_test = test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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       "      Adult Mortality  infant deaths  Alcohol  percentage expenditure  \\\n",
       "343             427.0              2     6.56              476.862587   \n",
       "934              83.0              3    11.80              683.919057   \n",
       "1688            127.0             35     5.21             1033.039737   \n",
       "2925            429.0             26     6.09               92.602336   \n",
       "955             288.0              2     8.01               40.537198   \n",
       "...               ...            ...      ...                     ...   \n",
       "506              76.0              2     8.00             6333.177967   \n",
       "275             251.0             25     0.01                9.804075   \n",
       "2163             48.0             29     6.66               33.302861   \n",
       "785             176.0              7     6.58               44.792478   \n",
       "2547            127.0              9     0.92              116.258594   \n",
       "\n",
       "      Hepatitis B  Measles    BMI   under-five deaths   Polio  \\\n",
       "343          94.0         0   34.2                   3   96.0   \n",
       "934          74.0     14949    6.6                   3   99.0   \n",
       "1688         93.0         0    6.1                  40   95.0   \n",
       "2925         97.0         0    3.3                  39   95.0   \n",
       "955          52.0        90   31.4                   3   44.0   \n",
       "...           ...       ...    ...                 ...    ...   \n",
       "506          14.0         6   61.3                   2   93.0   \n",
       "275           8.0       288   24.1                  39    8.0   \n",
       "2163         96.0      1064   14.5                  46   96.0   \n",
       "785          68.0       253   43.1                   9   71.0   \n",
       "2547         83.0       375   47.6                  10   83.0   \n",
       "\n",
       "      Total expenditure  Diphtheria    HIV/AIDS           GDP   Population  \\\n",
       "343                5.55         96.0       12.7   5623.379566    1946351.0   \n",
       "934               11.33         99.0        0.1   4381.288000   65342776.0   \n",
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       "955                2.88         45.0       10.7    727.777345    1444844.0   \n",
       "...                 ...          ...        ...           ...          ...   \n",
       "506                9.57         93.0        0.1  36189.588380      32312.0   \n",
       "275                4.86          8.0        1.3    837.955100     972916.0   \n",
       "2163               6.34         96.0        8.1    212.661949     868346.0   \n",
       "785                5.90         78.0        2.5    282.424200    8562622.0   \n",
       "2547               4.11          8.0        0.1   1577.457182   18294611.0   \n",
       "\n",
       "       thinness  1-19 years   thinness 5-9 years  \\\n",
       "343                     8.8                  8.6   \n",
       "934                     0.6                  0.6   \n",
       "1688                    1.6                  1.6   \n",
       "2925                    6.5                  6.4   \n",
       "955                     7.1                  6.9   \n",
       "...                     ...                  ...   \n",
       "506                     0.5                  0.4   \n",
       "275                     7.4                  7.3   \n",
       "2163                    7.0                  7.3   \n",
       "785                     3.8                  3.7   \n",
       "2547                    6.5                  6.3   \n",
       "\n",
       "      Income composition of resources  Schooling  \n",
       "343                             0.646       12.1  \n",
       "934                             0.882       16.1  \n",
       "1688                            0.739       12.5  \n",
       "2925                            0.464        9.8  \n",
       "955                             0.646       12.4  \n",
       "...                               ...        ...  \n",
       "506                             0.886       15.8  \n",
       "275                             0.458       10.0  \n",
       "2163                            0.359        7.6  \n",
       "785                             0.651       12.2  \n",
       "2547                            0.624       11.0  \n",
       "\n",
       "[1484 rows x 18 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.scatter(train_set[' BMI '], y)\n",
    "plt.show()\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def randInitializeWeights(L_in, L_out):\n",
    "    \n",
    "    W = np.zeros((L_out, 1 + L_in)) #return W as randomly intialized weigth for the NN  \n",
    "    epsilon = 6**0.5/(L_in**0.5+L_out**0.5) \n",
    "    W = - epsilon + np.random.random(size=(L_out, 1 + L_in))*2*epsilon #randomly intialized values for W\n",
    "    return W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):#sigmound function\n",
    "    \n",
    "    return 1/(1+np.exp(-z))\n",
    "\n",
    "def sigmoidGradient(z):\n",
    "    return sigmoid(z)*(1-sigmoid(z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nnCostFunction(nn_params, input_layer_size, hidden_layer_size, X, y):\n",
    "  \n",
    "    Theta1 = nn_params[0:hidden_layer_size*(input_layer_size+1)].reshape(hidden_layer_size, (input_layer_size + 1)) #unrolling parameters for layer 1\n",
    "  \n",
    "    Theta2 = nn_params[hidden_layer_size*(input_layer_size+1):hidden_layer_size*(input_layer_size+1)+hidden_layer_size*(hidden_layer_size+1)].reshape(hidden_layer_size, hidden_layer_size + 1) #unrolling parameters for layer 2\n",
    "    Theta3 = nn_params[hidden_layer_size*(input_layer_size+1)+hidden_layer_size*(hidden_layer_size+1):].reshape(1, hidden_layer_size + 1)\n",
    "    \n",
    "    m = X.shape[0] #number of inputs\n",
    "    J = 0 #return J as the cost function\n",
    "\n",
    "    Theta1_grad = np.zeros(Theta1.shape) #gradient for theta1\n",
    "    Theta2_grad = np.zeros(Theta2.shape) #gradient for theta2\n",
    "    Theta3_grad = np.zeros(Theta3.shape) #gradient for theta3  \n",
    "\n",
    "    X = np.append(np.ones((m,1)), X, axis=1) #input layer with baised values\n",
    "    a1 = X\n",
    "    z2 = a1.dot(np.transpose(Theta1)) #Z2 = A1*Theta1(transpose) \n",
    "    y = y.reshape(y.size, 1)\n",
    "    a2 = z2 #assigning input for layer 2\n",
    "    a2 = np.append(np.ones((a2.shape[0], 1)), a2, axis=1) #input layer 2 with baised values\n",
    "\n",
    "    z3 = a2.dot(np.transpose(Theta2)) #Z3 = A2*Theta2(transpose)\n",
    "    a3 = z3\n",
    "    \n",
    "    a3 = np.append(np.ones((a3.shape[0], 1)), a3, axis=1) #input layer 2 with baised values\n",
    "    z4 = a3.dot(np.transpose(Theta3))\n",
    "    a4 = z4\n",
    "    \n",
    "    h_x = a4 #output of the NN\n",
    "    \n",
    "    J = np.sum(np.sum((y-h_x)**2))/(2*m)\n",
    "    ###Backpropagation vectorized###\n",
    "    Delta4 = a4 - y\n",
    "    Delta3 = (Delta4.dot(Theta3))*np.ones((z3.shape[0], 1))\n",
    "    Delta3 = Delta3[:, 1:]\n",
    "    \n",
    "    Delta2 = (Delta3.dot(Theta2))*np.ones((z2.shape[0], 1))\n",
    "    Delta2 = Delta2[:, 1:]\n",
    "    \n",
    "    \n",
    "    Theta1_grad = (1/m) * np.transpose(Delta2).dot(a1)\n",
    "    Theta2_grad = (1/m) * np.transpose(Delta3).dot(a2)\n",
    "    Theta3_grad = (1/m) * np.transpose(Delta4).dot(a3)\n",
    "    \n",
    "    #Unroll gradients\n",
    "    grad = np.append(Theta1_grad[:], Theta2_grad[:])\n",
    "    grad = np.append(grad[:], Theta3_grad[:])\n",
    "\n",
    "    return J, grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18.129268983231924,\n",
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       "        -9.14503970e+00, -6.87666593e-02,  8.42370592e+00,  3.95304435e+00,\n",
       "         6.76796955e-02,  2.21022322e-01, -1.37929219e+00, -9.83373278e+00,\n",
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       "         1.74185147e+00, -1.69179485e-01,  9.80197157e-01, -2.85408956e+00,\n",
       "         3.98631529e+00,  8.03771947e-01, -4.81669896e+00, -3.62194487e-02,\n",
       "         4.43677194e+00,  2.08207129e+00,  3.56469441e-02,  1.16412615e-01,\n",
       "        -7.26474185e-01, -5.17943410e+00, -9.97241150e-02,  5.77784558e-01,\n",
       "        -1.68236448e+00,  2.34976341e+00,  4.73789396e-01, -2.83923929e+00,\n",
       "        -2.13498254e-02,  2.61528846e+00,  1.22729252e+00,  2.10123583e-02,\n",
       "         6.86202883e-02, -4.28225651e-01, -3.05305623e+00, -1.58710413e-01,\n",
       "         9.19541132e-01, -2.67747435e+00,  3.73963631e+00,  7.54033371e-01,\n",
       "        -4.51863463e+00, -3.39781366e-02,  4.16221805e+00,  1.95322969e+00,\n",
       "         3.34410594e-02,  1.09208834e-01, -6.81518907e-01, -4.85892319e+00,\n",
       "        -6.09558032e-01,  3.96539004e+00,  4.18705950e+00, -7.11429430e+00,\n",
       "        -1.55579981e+01, -1.05458363e+01,  5.19499335e+00, -1.48263348e+01,\n",
       "         8.44767590e+00, -3.58895278e+00, -1.06309971e+01,  7.21224277e+00,\n",
       "        -7.71064490e+00]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Theta1 = randInitializeWeights(18, 12) #Random theta for Layer1-2\n",
    "Theta2 = randInitializeWeights(12, 12) #Random theta for Layer 2-3\n",
    "Theta3 = randInitializeWeights(12, 1) #Random theta for Layer 3-4\n",
    "\n",
    "initial_nn_params = np.append(Theta1[:], Theta2[:]) #Rolling the parameters\n",
    "initial_nn_params = np.append(initial_nn_params[:], Theta3[:])\n",
    "\n",
    "input_layer_size = 18\n",
    "hidden_layer_size = 12\n",
    "\n",
    "options= {'maxiter': 1000}\n",
    "costFunction = lambda p: nnCostFunction(p, input_layer_size, hidden_layer_size, X, y)\n",
    "\n",
    "res = minimize(costFunction, initial_nn_params, jac=True, method='TNC', options=options)\n",
    "\n",
    "nn_params = res.x\n",
    "nnCostFunction(nn_params, input_layer_size, hidden_layer_size, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(nn_params, input_layer_size, hidden_layer_size, X, y):\n",
    "    \n",
    "    Theta1 = nn_params[0:hidden_layer_size*(input_layer_size+1)].reshape(hidden_layer_size, (input_layer_size + 1)) #unrolling parameters for layer 1\n",
    "    Theta2 = nn_params[hidden_layer_size*(input_layer_size+1):hidden_layer_size*(input_layer_size+1)+hidden_layer_size*(hidden_layer_size+1)].reshape(hidden_layer_size, hidden_layer_size + 1) #unrolling parameters for layer 2\n",
    "    Theta3 = nn_params[hidden_layer_size*(input_layer_size+1)+hidden_layer_size*(hidden_layer_size+1):].reshape(1, hidden_layer_size + 1)\n",
    "    \n",
    "    m = X.shape[0] #number of inputs\n",
    "    \n",
    "    X = np.append(np.ones((m,1)), X, axis=1) #input layer with baised values\n",
    "    a1 = X\n",
    "    z2 = a1.dot(np.transpose(Theta1)) #Z2 = A1*Theta1(transpose) \n",
    "    y = y.reshape(y.size, 1)\n",
    "    a2 = z2 #assigning input for layer 2\n",
    "    a2 = np.append(np.ones((a2.shape[0], 1)), a2, axis=1) #input layer 2 with baised values\n",
    "\n",
    "    z3 = a2.dot(np.transpose(Theta2)) #Z3 = A2*Theta2(transpose)\n",
    "    a3 = z3\n",
    "    \n",
    "    a3 = np.append(np.ones((a3.shape[0], 1)), a3, axis=1) #input layer 2 with baised values\n",
    "    z4 = a3.dot(np.transpose(Theta3))\n",
    "    a4 = z4\n",
    "    \n",
    "    h_x = a4 #output of the NN\n",
    "    \n",
    "    error = 0\n",
    "    for i in range(h_x.size):\n",
    "        error = error + (h_x[i] - y[i])/y[i]\n",
    "        print(h_x[i] ,\" \", y[i])\n",
    "    print('error is')\n",
    "    print(100*abs(error)/h_x.size)\n",
    "    return h_x\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[52.53051516]   [57.]\n",
      "[70.26109702]   [75.3]\n",
      "[66.86614397]   [72.8]\n",
      "[82.1628992]   [75.4]\n",
      "[81.05539188]   [75.4]\n",
      "[66.32905124]   [72.9]\n",
      "[92.58826638]   [81.7]\n",
      "[89.86192387]   [81.3]\n",
      "[95.18500392]   [81.2]\n",
      "[92.74469575]   [79.9]\n",
      "[81.49598573]   [81.1]\n",
      "[81.12568482]   [82.]\n",
      "[74.10299285]   [84.]\n",
      "[67.79038588]   [73.]\n",
      "[69.3613474]   [77.]\n",
      "[63.53411156]   [68.6]\n",
      "[64.16117265]   [67.8]\n",
      "[77.95542734]   [83.]\n",
      "[71.65648422]   [69.6]\n",
      "[68.75307033]   [68.4]\n",
      "[60.76793756]   [64.2]\n",
      "[64.49656137]   [62.5]\n",
      "[76.34242505]   [76.9]\n",
      "[78.36161959]   [71.4]\n",
      "[74.12054753]   [72.1]\n",
      "[73.38975834]   [72.2]\n",
      "[68.09947572]   [58.]\n",
      "[62.88163079]   [56.8]\n",
      "[57.00631492]   [55.3]\n",
      "[72.16766821]   [72.5]\n",
      "[67.58122146]   [71.8]\n",
      "[67.63798653]   [67.8]\n",
      "[55.23080276]   [55.6]\n",
      "[76.92542654]   [82.]\n",
      "[78.07799771]   [80.]\n",
      "[44.70658898]   [51.6]\n",
      "[72.16943693]   [73.6]\n",
      "[70.20296475]   [73.5]\n",
      "[67.24462217]   [72.8]\n",
      "[66.54708601]   [71.4]\n",
      "[64.26104735]   [61.3]\n",
      "[60.79604226]   [60.]\n",
      "[60.35611642]   [59.8]\n",
      "[73.15633958]   [78.]\n",
      "[70.28950008]   [78.6]\n",
      "[55.15778669]   [62.2]\n",
      "[69.1396576]   [72.3]\n",
      "[67.17927613]   [71.4]\n",
      "[49.7616204]   [59.4]\n",
      "[48.07038836]   [58.5]\n",
      "[73.40460463]   [79.3]\n",
      "[61.37605308]   [63.5]\n",
      "[63.12706206]   [62.8]\n",
      "[75.16015373]   [74.5]\n",
      "[69.96145561]   [73.8]\n",
      "[72.18498636]   [73.9]\n",
      "[71.1275049]   [73.9]\n",
      "[69.15046537]   [72.7]\n",
      "[86.96484927]   [88.]\n",
      "[83.9765936]   [80.]\n",
      "[82.9511974]   [79.9]\n",
      "[71.64861338]   [78.7]\n",
      "[67.3582848]   [71.3]\n",
      "[65.13583513]   [66.]\n",
      "[65.0782763]   [65.9]\n",
      "[63.58432821]   [66.3]\n",
      "[66.57699479]   [72.2]\n",
      "[64.36205525]   [68.9]\n",
      "[63.51795556]   [67.2]\n",
      "[63.38500732]   [65.9]\n",
      "[85.44565804]   [86.]\n",
      "[83.40296738]   [84.]\n",
      "[81.36967939]   [79.3]\n",
      "[77.52836461]   [79.8]\n",
      "[72.08632362]   [75.2]\n",
      "[80.13164294]   [73.]\n",
      "[80.80736859]   [72.8]\n",
      "[77.31432352]   [69.9]\n",
      "[74.81938378]   [65.3]\n",
      "[71.83016456]   [64.4]\n",
      "[69.05320687]   [63.9]\n",
      "[68.22730883]   [62.9]\n",
      "[64.99376061]   [62.6]\n",
      "[54.57055295]   [63.]\n",
      "[67.94049048]   [66.1]\n",
      "[74.02547169]   [65.1]\n",
      "[60.85155662]   [64.7]\n",
      "[79.78987232]   [74.4]\n",
      "[79.53776226]   [71.]\n",
      "[76.53853109]   [72.7]\n",
      "[51.04690706]   [44.5]\n",
      "[65.03309479]   [61.1]\n",
      "[50.51119735]   [67.]\n",
      "[76.92389903]   [71.2]\n",
      "[70.12386216]   [81.1]\n",
      "[73.25496031]   [78.3]\n",
      "[62.28648775]   [63.8]\n",
      "[55.02565859]   [45.1]\n",
      "[53.59095989]   [44.6]\n",
      "[49.98042964]   [44.]\n",
      "[73.36290466]   [73.8]\n",
      "[76.15071492]   [76.3]\n",
      "[79.04533959]   [81.1]\n",
      "[76.85955832]   [78.5]\n",
      "[55.45200792]   [61.2]\n",
      "[53.77941904]   [69.]\n",
      "[80.42383599]   [74.1]\n",
      "[70.80577589]   [72.8]\n",
      "[60.59345715]   [68.6]\n",
      "[61.84228474]   [65.9]\n",
      "[58.74387284]   [64.5]\n",
      "[57.63672001]   [64.2]\n",
      "[57.01793895]   [63.9]\n",
      "[72.79104993]   [66.1]\n",
      "[64.98041223]   [68.9]\n",
      "[68.11800778]   [66.6]\n",
      "[66.37613391]   [74.5]\n",
      "[64.25516922]   [73.]\n",
      "[49.87767236]   [61.4]\n",
      "[52.31952164]   [55.]\n",
      "[69.1867931]   [75.8]\n",
      "[59.28940756]   [61.8]\n",
      "[56.66029216]   [61.1]\n",
      "[71.78720792]   [73.2]\n",
      "[70.27718485]   [72.3]\n",
      "[70.40210699]   [72.1]\n",
      "[64.08794931]   [67.2]\n",
      "[61.04498896]   [66.8]\n",
      "[77.7157234]   [76.8]\n",
      "[77.25222907]   [76.3]\n",
      "[78.46972967]   [75.7]\n",
      "[77.20550484]   [75.5]\n",
      "[76.4710966]   [73.7]\n",
      "[76.59817356]   [79.6]\n",
      "[73.69708339]   [69.4]\n",
      "[73.07777]   [68.4]\n",
      "[68.7542431]   [67.3]\n",
      "[65.04431186]   [66.2]\n",
      "[55.88254536]   [64.9]\n",
      "[72.71300201]   [75.4]\n",
      "[70.43883595]   [74.4]\n",
      "[71.27814166]   [72.2]\n",
      "[50.04073182]   [45.3]\n",
      "[62.49732734]   [68.]\n",
      "[81.13399702]   [89.]\n",
      "[77.65235575]   [74.5]\n",
      "[71.57475447]   [73.]\n",
      "[44.96596105]   [50.]\n",
      "[66.98818189]   [73.2]\n",
      "[72.21822793]   [74.3]\n",
      "[66.11451897]   [71.2]\n",
      "[68.3320835]   [66.9]\n",
      "[78.23954347]   [73.2]\n",
      "[76.07660546]   [71.6]\n",
      "[69.94198157]   [69.4]\n",
      "[78.67593647]   [75.1]\n",
      "[82.87887246]   [74.9]\n",
      "[62.91818009]   [67.]\n",
      "[80.39939521]   [67.7]\n",
      "[78.62628294]   [75.4]\n",
      "[70.24115088]   [67.6]\n",
      "[69.94420033]   [67.8]\n",
      "[55.51206704]   [69.3]\n",
      "[69.32451782]   [61.1]\n",
      "[61.48686307]   [59.2]\n",
      "error is\n",
      "[0.8949305]\n"
     ]
    }
   ],
   "source": [
    "y_predict = predict(nn_params, input_layer_size, hidden_layer_size, X_test, y_test)"
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}