Machine Learning - Asian.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import sklearn\n",
    "import seaborn as seabornInstance \n",
    "from sklearn import linear_model\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from collections import Counter\n",
    "import statsmodels.api as sm\n",
    "# from timeseries import WindowSlider"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Occupation</th>\n",
       "      <th>Total_employed</th>\n",
       "      <th>20_to_24_years</th>\n",
       "      <th>25_to_34_years</th>\n",
       "      <th>35_to_44_years</th>\n",
       "      <th>45_to_54_years</th>\n",
       "      <th>55_to_64_years</th>\n",
       "      <th>Number_of_workers</th>\n",
       "      <th>Median_weekly_earnings</th>\n",
       "      <th>Men_Number_of_workers</th>\n",
       "      <th>Men_Median_weekly_earnings</th>\n",
       "      <th>Women_Number_of_workers</th>\n",
       "      <th>Women_Median_weekly_earnings</th>\n",
       "      <th>Black</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Hispanic_or_Latino</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Computer and information systems managers</td>\n",
       "      <td>553</td>\n",
       "      <td>10</td>\n",
       "      <td>103</td>\n",
       "      <td>199</td>\n",
       "      <td>162</td>\n",
       "      <td>74</td>\n",
       "      <td>530</td>\n",
       "      <td>1579</td>\n",
       "      <td>392</td>\n",
       "      <td>1595</td>\n",
       "      <td>138</td>\n",
       "      <td>1543</td>\n",
       "      <td>5.7</td>\n",
       "      <td>12.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Web developers</td>\n",
       "      <td>182</td>\n",
       "      <td>20</td>\n",
       "      <td>61</td>\n",
       "      <td>58</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>117</td>\n",
       "      <td>1017</td>\n",
       "      <td>81</td>\n",
       "      <td>1033</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>11.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Computer and information research scientists</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Computer systems analysts</td>\n",
       "      <td>447</td>\n",
       "      <td>12</td>\n",
       "      <td>109</td>\n",
       "      <td>113</td>\n",
       "      <td>133</td>\n",
       "      <td>64</td>\n",
       "      <td>373</td>\n",
       "      <td>1328</td>\n",
       "      <td>242</td>\n",
       "      <td>1410</td>\n",
       "      <td>132</td>\n",
       "      <td>1144</td>\n",
       "      <td>7.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Computer occupations, all other</td>\n",
       "      <td>306</td>\n",
       "      <td>21</td>\n",
       "      <td>81</td>\n",
       "      <td>74</td>\n",
       "      <td>81</td>\n",
       "      <td>37</td>\n",
       "      <td>282</td>\n",
       "      <td>1127</td>\n",
       "      <td>221</td>\n",
       "      <td>1165</td>\n",
       "      <td>61</td>\n",
       "      <td>998</td>\n",
       "      <td>8.8</td>\n",
       "      <td>11.9</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>Network and computer systems administrators</td>\n",
       "      <td>199</td>\n",
       "      <td>8</td>\n",
       "      <td>48</td>\n",
       "      <td>65</td>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>194</td>\n",
       "      <td>1447</td>\n",
       "      <td>144</td>\n",
       "      <td>1541</td>\n",
       "      <td>50</td>\n",
       "      <td>1233</td>\n",
       "      <td>9.7</td>\n",
       "      <td>11.7</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>Database administrators</td>\n",
       "      <td>106</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>96</td>\n",
       "      <td>1642</td>\n",
       "      <td>67</td>\n",
       "      <td>1752</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>16.7</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>123</td>\n",
       "      <td>Computer support specialists</td>\n",
       "      <td>547</td>\n",
       "      <td>36</td>\n",
       "      <td>158</td>\n",
       "      <td>145</td>\n",
       "      <td>119</td>\n",
       "      <td>68</td>\n",
       "      <td>486</td>\n",
       "      <td>1192</td>\n",
       "      <td>365</td>\n",
       "      <td>1223</td>\n",
       "      <td>121</td>\n",
       "      <td>1103</td>\n",
       "      <td>10.5</td>\n",
       "      <td>12.7</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>124</td>\n",
       "      <td>Computer programmers</td>\n",
       "      <td>454</td>\n",
       "      <td>19</td>\n",
       "      <td>117</td>\n",
       "      <td>105</td>\n",
       "      <td>116</td>\n",
       "      <td>72</td>\n",
       "      <td>425</td>\n",
       "      <td>1586</td>\n",
       "      <td>343</td>\n",
       "      <td>1613</td>\n",
       "      <td>82</td>\n",
       "      <td>1463</td>\n",
       "      <td>8.5</td>\n",
       "      <td>21.8</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>125</td>\n",
       "      <td>Information security analysts</td>\n",
       "      <td>125</td>\n",
       "      <td>12</td>\n",
       "      <td>46</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>128</td>\n",
       "      <td>1758</td>\n",
       "      <td>103</td>\n",
       "      <td>1875</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>16.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>12.9</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       Occupation  Total_employed  \\\n",
       "0       Computer and information systems managers             553   \n",
       "1                                  Web developers             182   \n",
       "2    Computer and information research scientists              20   \n",
       "3                       Computer systems analysts             447   \n",
       "4                 Computer occupations, all other             306   \n",
       "..                                            ...             ...   \n",
       "121   Network and computer systems administrators             199   \n",
       "122                       Database administrators             106   \n",
       "123                  Computer support specialists             547   \n",
       "124                          Computer programmers             454   \n",
       "125                 Information security analysts             125   \n",
       "\n",
       "     20_to_24_years  25_to_34_years  35_to_44_years  45_to_54_years  \\\n",
       "0                10             103             199             162   \n",
       "1                20              61              58              28   \n",
       "2                 3               7               5               3   \n",
       "3                12             109             113             133   \n",
       "4                21              81              74              81   \n",
       "..              ...             ...             ...             ...   \n",
       "121               8              48              65              50   \n",
       "122               5              21              29              27   \n",
       "123              36             158             145             119   \n",
       "124              19             117             105             116   \n",
       "125              12              46              31              18   \n",
       "\n",
       "     55_to_64_years  Number_of_workers  Median_weekly_earnings  \\\n",
       "0                74                530                    1579   \n",
       "1                12                117                    1017   \n",
       "2                 2                 19                       0   \n",
       "3                64                373                    1328   \n",
       "4                37                282                    1127   \n",
       "..              ...                ...                     ...   \n",
       "121              25                194                    1447   \n",
       "122              18                 96                    1642   \n",
       "123              68                486                    1192   \n",
       "124              72                425                    1586   \n",
       "125              15                128                    1758   \n",
       "\n",
       "     Men_Number_of_workers  Men_Median_weekly_earnings  \\\n",
       "0                      392                        1595   \n",
       "1                       81                        1033   \n",
       "2                       17                           0   \n",
       "3                      242                        1410   \n",
       "4                      221                        1165   \n",
       "..                     ...                         ...   \n",
       "121                    144                        1541   \n",
       "122                     67                        1752   \n",
       "123                    365                        1223   \n",
       "124                    343                        1613   \n",
       "125                    103                        1875   \n",
       "\n",
       "     Women_Number_of_workers  Women_Median_weekly_earnings  Black  Asian  \\\n",
       "0                        138                          1543    5.7   12.9   \n",
       "1                         35                             0    5.9   11.1   \n",
       "2                          2                             0    0.0    0.0   \n",
       "3                        132                          1144    7.1   14.9   \n",
       "4                         61                           998    8.8   11.9   \n",
       "..                       ...                           ...    ...    ...   \n",
       "121                       50                          1233    9.7   11.7   \n",
       "122                       29                             0    6.8   16.7   \n",
       "123                      121                          1103   10.5   12.7   \n",
       "124                       82                          1463    8.5   21.8   \n",
       "125                       25                             0   16.6    7.6   \n",
       "\n",
       "     Hispanic_or_Latino  Year  \n",
       "0                   3.8  2011  \n",
       "1                   6.9  2011  \n",
       "2                   0.0  2011  \n",
       "3                   4.4  2011  \n",
       "4                   6.5  2011  \n",
       "..                  ...   ...  \n",
       "121                 8.5  2019  \n",
       "122                 6.2  2019  \n",
       "123                11.3  2019  \n",
       "124                 8.7  2019  \n",
       "125                12.9  2019  \n",
       "\n",
       "[126 rows x 17 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import CSV files (2019-2011)\n",
    "dfs = []\n",
    "for i in range (11,20):\n",
    "    file_path = f\"sof_dev_data/20{i}_data.csv\"\n",
    "    df = pd.read_csv(file_path)\n",
    "    df[\"Year\"] = f\"20{i}\"\n",
    "    dfs.append(df)\n",
    "combined_df = pd.concat(dfs, ignore_index=True)\n",
    "combined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd_df = combined_df.loc[combined_df[\"Occupation\"]==\"Software developers, applications and systems software\"]\n",
    "sd_df = sd_df.drop(columns=['Number_of_workers','Men_Median_weekly_earnings','Men_Number_of_workers',\\\n",
    "                          'Women_Median_weekly_earnings','Women_Number_of_workers','Occupation'])\n",
    "# sd_df = sd_df.iloc[:, 1:] used to drop the first column, when 'Number of workers' couldn't be removed --fixed .csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print relationships between data\n",
    "# sb.pairplot(sd_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = sd_df.head(16)\n",
    "df1.plot(kind='bar',figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Total_employed</th>\n",
       "      <th>20_to_24_years</th>\n",
       "      <th>25_to_34_years</th>\n",
       "      <th>35_to_44_years</th>\n",
       "      <th>45_to_54_years</th>\n",
       "      <th>55_to_64_years</th>\n",
       "      <th>Median_weekly_earnings</th>\n",
       "      <th>Black</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Hispanic_or_Latino</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1044</td>\n",
       "      <td>44</td>\n",
       "      <td>331</td>\n",
       "      <td>340</td>\n",
       "      <td>230</td>\n",
       "      <td>87</td>\n",
       "      <td>1558</td>\n",
       "      <td>4.8</td>\n",
       "      <td>27.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1084</td>\n",
       "      <td>53</td>\n",
       "      <td>338</td>\n",
       "      <td>348</td>\n",
       "      <td>226</td>\n",
       "      <td>102</td>\n",
       "      <td>1591</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.4</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>1103</td>\n",
       "      <td>49</td>\n",
       "      <td>332</td>\n",
       "      <td>320</td>\n",
       "      <td>260</td>\n",
       "      <td>117</td>\n",
       "      <td>1643</td>\n",
       "      <td>4.1</td>\n",
       "      <td>28.8</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>1235</td>\n",
       "      <td>64</td>\n",
       "      <td>373</td>\n",
       "      <td>360</td>\n",
       "      <td>290</td>\n",
       "      <td>121</td>\n",
       "      <td>1693</td>\n",
       "      <td>5.3</td>\n",
       "      <td>31.9</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>63</td>\n",
       "      <td>1353</td>\n",
       "      <td>74</td>\n",
       "      <td>438</td>\n",
       "      <td>387</td>\n",
       "      <td>280</td>\n",
       "      <td>148</td>\n",
       "      <td>1682</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77</td>\n",
       "      <td>1483</td>\n",
       "      <td>79</td>\n",
       "      <td>490</td>\n",
       "      <td>404</td>\n",
       "      <td>302</td>\n",
       "      <td>168</td>\n",
       "      <td>1776</td>\n",
       "      <td>4.1</td>\n",
       "      <td>35.7</td>\n",
       "      <td>4.8</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>1536</td>\n",
       "      <td>80</td>\n",
       "      <td>526</td>\n",
       "      <td>433</td>\n",
       "      <td>277</td>\n",
       "      <td>175</td>\n",
       "      <td>1792</td>\n",
       "      <td>5.6</td>\n",
       "      <td>32.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>1682</td>\n",
       "      <td>99</td>\n",
       "      <td>595</td>\n",
       "      <td>462</td>\n",
       "      <td>291</td>\n",
       "      <td>200</td>\n",
       "      <td>1864</td>\n",
       "      <td>3.9</td>\n",
       "      <td>35.4</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>1815</td>\n",
       "      <td>111</td>\n",
       "      <td>640</td>\n",
       "      <td>496</td>\n",
       "      <td>313</td>\n",
       "      <td>195</td>\n",
       "      <td>1894</td>\n",
       "      <td>5.8</td>\n",
       "      <td>37.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Total_employed  20_to_24_years  25_to_34_years  35_to_44_years  \\\n",
       "7              1044              44             331             340   \n",
       "21             1084              53             338             348   \n",
       "35             1103              49             332             320   \n",
       "49             1235              64             373             360   \n",
       "63             1353              74             438             387   \n",
       "77             1483              79             490             404   \n",
       "91             1536              80             526             433   \n",
       "105            1682              99             595             462   \n",
       "119            1815             111             640             496   \n",
       "\n",
       "     45_to_54_years  55_to_64_years  Median_weekly_earnings  Black  Asian  \\\n",
       "7               230              87                    1558    4.8   27.1   \n",
       "21              226             102                    1591    4.0   29.4   \n",
       "35              260             117                    1643    4.1   28.8   \n",
       "49              290             121                    1693    5.3   31.9   \n",
       "63              280             148                    1682    5.0   33.8   \n",
       "77              302             168                    1776    4.1   35.7   \n",
       "91              277             175                    1792    5.6   32.4   \n",
       "105             291             200                    1864    3.9   35.4   \n",
       "119             313             195                    1894    5.8   37.7   \n",
       "\n",
       "     Hispanic_or_Latino  Year  \n",
       "7                   4.6  2011  \n",
       "21                  5.2  2012  \n",
       "35                  5.3  2013  \n",
       "49                  5.2  2014  \n",
       "63                  5.4  2015  \n",
       "77                  4.8  2016  \n",
       "91                  6.2  2017  \n",
       "105                 5.3  2018  \n",
       "119                 5.1  2019  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Asian (%) Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sd_df.drop(columns=['Asian','Total_employed'])\n",
    "y = sd_df['Asian']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train models\n",
    "linreg = linear_model.LinearRegression()\n",
    "# linreg.fit(sd_df[['Year','20_to_24_years','25_to_34_years','35_to_44_years','45_to_54_years','55_to_64_years','Median_weekly_earnings']],sd_df.Asian)\n",
    "linreg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " -16593.79190823335\n",
      "Coefficients: \n",
      " [-0.32840858 -0.0634556   0.22742781  0.13658354 -0.21675926 -0.0966532\n",
      " -4.47297314 -1.73015761  8.32743846]\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print('Intercept: \\n', linreg.intercept_)\n",
    "print('Coefficients: \\n', linreg.coef_)\n",
    "y_pred = linreg.score(X, y)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sd_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "# sd_df = sd_df.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7, 9)\n",
      "(7,)\n",
      "(2, 9)\n",
      "(2,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "sd_df.plot(x='Year', y='Asian', style='o')  \n",
    "plt.title('Predict Race:')  \n",
    "plt.xlabel('Year')  \n",
    "plt.ylabel('Asian (%)')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2153bd3f148>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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One0d10dvrlRDRa7rSEkhKzWgO9YV6kTHiLpGplzHAZJaYCEXWWufkvTUFa998bK3pzU3ruHKzxu/2usAEt+zTT062jasextKtaOmwHWcpLJ7XbFebx3Qc6d69enbalzHAZIWE98BLLs3zg/oZ2f6dEttge6qZ5/lSktP8evOdUU61TWq9qFJ13GApEXJArCsTneN6smjnVpfmq0Ht1VobmQeVtoda4uUHvTruVM9rqMASYuSBWDZdAxN6TtvXVJFXro+uatafh8Fy5W0oF976op0pmdcHUPszQJcoGQBWBajUyH94+sXlJkS0K/dXqPUgN91pKR365pCpQZ8OniW+YOAC5QsAEsWikT1zTcuajoU1advr1F2Ggc+x4K0oF+3ri7QyY4RpsADDlCyACyJtVZPHG5Xx9CUPnFLtcpz011HwmXuWFskn8/o5ZZ+11GApEPJArAkLzT36kTHiO7dVKaN5Tmu4+AKOelB3VSdp8MXhzQ+E3YdB0gqlCwAN6yxc0TPn+rVTdV52lNX5DoOruHOuiJFolavneNuFrCSKFkAbkjv2LT+6XC7qvLT9ZGbKhnVEMNKstO0sTxHr7cOaiYccR0HSBqULACLNhOK6FuvX1LQZ/Qrt9Yo6OdbSazbU1ekqVBEhy4MuY4CJA2+MwJYFGutnni7XQMTM3pk1yrlpvMkYTxYVZip2sIMvdzSr0jUuo4DJAVKFoBFOXi2X42do7p/U5nWFGe5joNFuHNdkUamQjrVNeo6CpAUKFkAFuxc37iebezWlspcvW8dG93jzYbyHOWlB/V664DrKEBSoGQBWJCx6ZC+91abirJT9dGb2egej3zG6NY1hWrtn1DP6LTrOEDCo2QBuK6otfqnw+2aDkX0yK5VHJkTx3bW5CvgM9zNAlYAJQvAdR0806eW3nF9eFuFynLSXMfBEmSmBrS1Kk9HLg1rOsQ4B8BLlCwA7+lC/4SeO9WjrVW52lmT7zoOlsHtawo1G4nq7UuMcwC8RMkCcE2TM2F971Cb8jNS9JHt7MNKFJX56VpVkKHXWwcUtYxzALxCyQJwVdZa/fBoh8anw/rkrlVKC7IPK5HctqZQ/eOzOtc77joKkLAoWQCu6kjbsBo7R3VPQ6kq89Jdx8Ey21yZo6zUgF5jAzzgGUoWgHcZmpjVj491qrYwU3dy8HNCCvh8uqU2X83dYxqenHUdB0hIlCwAv+CdcQ2S9PEdVfKxDyth7awpkCQdZgM84AlKFoBf8EpLvy4MTOjDWyuUn5niOg48lJ+ZorUlWTp8YYgN8IAHKFkAfq57ZFrPNvVoU0WOblqV5zoOVsDOmnwNT4XYAA94gJIFQJIUiVr94O12pQX9jGtIIg3lOcpI8eutiywZAsuNkgVA0twyYcfwlB7cVqHM1IDrOFghAb9PN1Xn6VTnqCZmwq7jAAmFkgVA/WMzeu5UjxrKc7S5Isd1HKywHbUFilirI23DrqMACYWSBSS5qLX65yPtCviNHtxewTJhEirLSVN1froOXRiUZQM8sGwoWUCSe/P8oC4MTOpDW8qVkxZ0HQeO7KwtUO/YjNoGJ11HARIGJQtIYsOTs3q6sVt1JVm6eRWHPyezrZW5SvH7dIgN8MCyoWQBSexfj3fJWsvThFBq0K8tVbk63j6imXDEdRwgIVCygCR1untUTV2juntDKUNHIUm6eVW+ZiNRNXWOuo4CJARKFpCEQpGofnysU8XZqXrfukLXcRAjagozlJcR1FGeMgSWBSULSEIHmvs0NBnSg9sqFPDxbQBzfMZoe3WeWnrHNTodch0HiHt8dwWSTP/4jA6e7dO2qlytLc5yHQcx5qbqfFlJx7ibBSwZJQtIItZa/fhYpwI+owe2lLuOgxhUnJ2qqvx0lgyBZUDJApJIY+eozvaO656GUmZi4Zq2V+epa2Ra3SPTrqMAcY2SBSSJUCSqn57sUkl2qm5dzWZ3XNvWqjz5jHS0jZlZwFJQsoAk8dq5AQ1NhvShLeXy+5iJhWvLSg2oriRbR9uGFeWYHeCGUbKAJDA+E9aLzb1aX5qtutJs13EQB25alafR6bBa+yZcRwHiFiULSALPNfUoFInqgS1lrqMgTmwsz1FqwMeSIbAElCwgwXWPTuutC4O6dXWhSrLTXMdBnAj6fdpcmauTnaMKRaKu4wBxiZIFJDBrrX56oktpQb/2bShxHQdxZltVnmbDUTV3j7mOAsQlShaQwM70jOts77ju3lCijNSA6ziIM6uLMpWZGtDxjhHXUYC4RMkCElTUWj3b1K2CzBTduqbAdRzEIb/PaHNFjpq7RzUTjriOA8QdShaQoE50jKhrZFof2FjC+YS4YVur8hSKWJ3uYskQWCy+8wIJKBK1eq6pR2U5adpalec6DuJYTWGGctJYMgRuBCULSECHLw5pYGJW9zSUymcYPIob5zNGWypzdaZnTNMhlgyBxaBkAQkmFInqhdM9WlWQoQ1lDB7F0m2pylMkatXUOeo6ChBXKFlAgnnt3IBGp8O6b1OZDHexsAyq89OVlxHU8Y5h11GAuELJAhLIdCiin53pU31pllYXZbqOgwRhjNHWyly19I5rcibsOg4QNyhZQAJ55Vy/pkIR3dPA8TlYXluq8hS1UiNLhsCCUbKABDEdiuiVln5tLM9RZV666zhIMBW5aSrMTGHJEFgEShaQIF5rHdB0KKq713N8DpafMUZbq3LV2jehcZYMgQWhZAEJYCYU0ctn+7WhLFuV+dzFgjc2VeTKSjrVxZIhsBCULCABvH5+UFOhiN7PXSx4qDw3TQWZKWrsZDApsBCULCDOzYajeuns3BOF1QUZruMggRljtKkiR+d6JzQ1y2BS4HooWUCce+P8gCZnI+zFworYVJGriLU63c2SIXA9lCwgjs2Gozp4tl/rirO0qpC5WPBeVX66ctICjHIAFoCSBcSxQxcHNTET1vs3cBcLK8NnjDZVzJ1lOBNmyRB4L5QsIE5FolYvt/RrVUEG092xojZV5igctTrTM+46ChDTKFlAnDrZMaLhyZD21BW7joIkU1uYqcwUv0528JQh8F4oWUAcstbq4Nk+FWWlakN5tus4SDI+Y9RQkaPmnjGFIlHXcYCYRckC4lBL37i6Rqa1p65IPmNcx0ES2lSRq9lwVC29LBkC10LJAuLQS2f6lZ0a0PbqPNdRkKTWFGcqLehjMCnwHihZQJzpGJ5SS9+47lhXpICfv8JwI+DzaWNZjk51jSkSta7jADGJ79BAnHnpbJ9SAz7durrAdRQkuYaKHE2FIrowMOE6ChCTKFlAHBmcmNWJ9hHtWl2gtKDfdRwkubqSbAV8Rk0cGA1cFSULiCOvneuXMdIda4tcRwGUEvBpXUmWTnWNylqWDIErUbKAODETjujQxSFtrsxVbnrQdRxAkrSxPEfDkyF1j067jgLEHEoWECfevjSsmXCUu1iIKRvKsmUklgyBq6BkAXEgaq1eO9evqvx0rSrIcB0H+LnstKCqCzJ0ipIFvAslC4gDLb3j6h+f1R1rC11HAd5lY3mOOoenNTw56zoKEFMoWUAcePVcv7LTAtpcmes6CvAuG+ePdjrVPeY4CRBbKFlAjOsbm9GZnnHdurpAAR9/ZRF7SrLTVJSVwpIhcAW+YwMx7rXWfvl9RrfUMnwUsWtjeY7O901oOhRxHQWIGZQsIIZNzUb09sVhbavKVXYaYxsQuxrKcxSxVs09LBkC76BkATHsSNuQZiNR3c7YBsS46oIMZab41dTJkiHwDkoWEKOstXrj/KCq89NVmZfuOg7wnnzGaEN5js70jCkcjbqOA8QEShYQoy4MTKpvbEa7VjO2AfFhY1m2ZsJRXRyYdB0FiAmULCBGvXF+QGlBn7YwtgFxYm1JlgI+o9M8ZQhIomQBMWl8JqzGjlHdvCpfKQH+miI+pAb8WlOcqdPMywIkUbKAmHT44pAi1moXYxsQZ9aX5WhgYlb9YzOuowDOUbKAGBO1Vm+eH9DqokyV5KS5jgMsyobSuenvp7tZMgQoWUCMaekd19BkSLeu5i4W4k9+ZopKc1I5YgcQJQuIOW+eH1Rmil8NFTmuowA3ZENZji4OTGhqlunvSG6ULCCGjEyFdLp7VDtrOacQ8WtDWbaiVjrby90sJDe+iwMx5PDFIUWtOKcQca26IEMZKX6eMkTSo2QBMSJqrQ5fHNSa4kwVZKa4jgPcMJ8xWl+arebuMUWtdR0HcGZBJcsYc78xptkY02KM+cJVPp5qjPne/MffMMbUXvaxrcaY14wxjcaYE8YYHpcCruJ8/4SGJkPaWcNdLMS/9WXZmgpF1DbI9Hckr+uWLGOMX9JXJD0gqUHSI8aYhisu+4ykIWvtOklflvQX858bkPRNSf+ztXaTpL2SQsuWHkgghy8OKS3o0yY2vCMB1JVky2fEkiGS2kLuZO2S1GKtbbXWzkr6rqSHrrjmIUlfn3/7CUn7jDFG0r2Sjltrj0mStXbAWsvjJsAVpmYjOtkxom1VeQr6WcVH/EtP8au2MFOnOGIHSWwh380rJbVd9n77/GtXvcZaG5Y0IqlQUr0ka4x5xhjztjHmD5ceGUg8x9qHFY5algqRUNaXZat3bEbDk7OuowBOLKRkmau8duVOxmtdE5B0p6Rfmf/vLxtj9r3rCxjzWWPMIWPMob6+vgVEAhLL4YtDKs9NU0UeWxaROOrnp78397BkiOS0kJLVLqn6sverJHVe65r5fVi5kgbnX/+ZtbbfWjsp6SlJN1/5Bay1j1lrd1prdxYXFy/+TwHEsa6RKXUMT2lHTb7mVtmBxFCSnaq89KDO9Iy7jgI4sZCS9ZakOmPMamNMiqRPSnryimuelPTo/NsPS3rBWmslPSNpqzEmY7583SWpaXmiA4nh0MUh+X1G26vyXEcBlpUxRvWl2TrXN65wNOo6DrDirluy5vdYfV5zhemUpO9baxuNMV8yxjw4f9nXJBUaY1ok/b6kL8x/7pCkv9JcUTsq6W1r7U+W/48BxKdwJKqjl4bVUJ6jjNSA6zjAsqsvzdZsOKqLA4xyQPJZ0Hd1a+1Tmlvqu/y1L1729rSkj1/jc7+puTEOAK5wqntMU6GIdtTku44CeGJtcab8xuhM95jWFme5jgOsKJ4VBxw6cmlIOWkBrSvhhw8SU2rQr5qiDDa/IylRsgBHxmfCOtMzpm3VefKx4R0JbH0poxyQnChZgCPH24cVtdJNq1gqRGJ7Z5QDTxki2VCyAEeOXBpWRW6aynKYjYXEVpKdqtz0oM6wZIgkQ8kCHOgZnVbH8BR3sZAU3hnl0MIoByQZShbgwNG2YfmMtLUq13UUYEWsL81ilAOSDiULWGFRa3W0bVh1JdnKTgu6jgOsiLXFWXOjHFgyRBKhZAEr7Hz/hEamQrppFRPekTxSg37VFGZQspBUKFnACjtyaVipAZ82lue4jgKsqPrSbPWMMsoByYOSBayg2XBUJztHtKUyV0E/f/2QXOrL5kY5nGWUA5IE3+WBFdTUNarZcFTbWSpEEiqdH+XA9HckC0oWsIKOtw8rNz2o2sJM11GAFTc3yiFL5xjlgCRByQJWyOT8MTpbq3I5RgdJq740WzPhqC4xygFJgJIFrJCTnaOKWmlbFUuFSF5ri7PkM+IpQyQFShawQo61D6s4K1XluRyjg+SVFvSrpjCTcwyRFChZwAoYmQrpQv+EtlbnyrBUiCS3vjRb3aPTGpkKuY4CeIqSBayA4+3DsmKpEJDm9mVJLBki8VGygBVwvH1ElXnpKspKdR0FcK40J1U5aQFKFhIeJQvwWP/YjDqGp7StmrtYgPTOKIdstfSOKxK1ruMAnqFkAR471j4sI2lrZa7rKEDMeGeUw8XBCddRAM9QsgAPWWt1rH1Yq4sylZMedB0HiBnrSuZHOXTzlCESFyUL8FDnyLT6x2fZ8A5c4f8f5cC+LCQuShbgoRPtw/IZaVNljusoQMypnx/lMDrNKAckJkoW4BFrrU50jGhdSZYyUgKu4wAxp64kS5LU0suSIRITJQvwSMfwlIYmQ9pSyVIhcDVluWnKTA1QspCwKFmAR060j8hvjBrKWSoErsZnjOpKsnS2Z0xRyygHJB5KFuCBy5cK01P8ruMAMWtdSXOutzsAACAASURBVJYmZiPqHpl2HQVYdpQswANtQ1MangppaxWzsYD3sm5+X9ZZnjJEAqJkAR440T4sv89oI0uFwHvKSQuqLCdNZ9mXhQREyQKWWdRanewcVX1JltKCLBUC11NXmqWLA5OaDUddRwGWFSULWGZtg5MamQppC0uFwILUlWQrYq1a+7mbhcRCyQKW2fGOEQV8RhvKWCoEFqKmMENBv2HJEAmHkgUso6i1OtkxovrSbJYKgQUK+n1aXZSpsz2ULCQWShawjC4OTGpsOqwtlSwVAouxriRb/eMzGpqcdR0FWDaULGAZnex8Z6kw23UUIK5wxA4SESULWCZRa9XUOaq60mylslQILEpJdqpy0gLMy0JCoWQBy6R9aEojUyFtrmDDO7BYxhjVlWSrpW9ckShH7CAxULKAZdLYMXdWIU8VAjemrjRL06GojrcPu44CLAtKFrAMrLU62TmitSWZnFUI3KC1xVkykg6e6XcdBVgWlCxgGXSOTGtoMqTNFTxVCNyozNSAKvLS9dLZPtdRgGVByQKWQWPHiHxGnFUILFFdaZaOtA1rdDrkOgqwZJQsYIneWSpcXZSpzNSA6zhAXKsryVYkavVqy4DrKMCSUbKAJeoZm1H/+Kw2sVQILFl1QboyU/wsGSIhULKAJTrZMSIjaROjG4AlC/h8un1tkQ6e7ZO1jHJAfKNkAUvU2DmimsIMZacFXUcBEsKe+iK1DU7p4sCk6yjAklCygCXoH5tRz+iMNnNWIbBs9tQVS5IOsmSIOEfJApagsWtUktTAU4XAsqkpzFB1QTrzshD3KFnAEpzqGlVFXpryMlJcRwEShjFGu+uK9dq5foUiUddxgBtGyQJuUO/YtNoGJ5mNBXhgT12xJmYjevvikOsowA2jZAE36PlTvbJiqRDwwu1rC+X3Gb10liVDxC9KFnCD9jf1KD8jqLKcNNdRgISTmx7U9uo8Nr8jrlGygBswMRPWyy392lieI2OM6zhAQtpTV6wTHSManJh1HQW4IZQs4Aa8dLZPs+Eo+7EAD+2uL5K10istLBkiPlGygBvwbGOPctODqi3MdB0FSFhbK3OVkxbQwTMsGSI+UbKARQpHonqhuVf7NpTI72OpEPBKwO/TnXVFeulsP0fsIC5RsoBFeuvCkIYnQ7qnodR1FCDh7a4rVvfotFp6x11HARaNkgUs0v6mHqUEfNpTX+w6CpDwdtcVSZJ+xpIh4hAlC1gEa62eberWneuKlJkacB0HSHhV+RlaU5zJvCzEJUoWsAinu8fUPjTFUiGwgvbUFeuN8wOaDkVcRwEWhZIFLML+ph4ZI+3bWOI6CpA09tQXaToU1aELHLGD+ELJAhZhf1OPbqrOU0k2U96BlXLr6kIF/UYvMf0dcYaSBSxQ5/CUTnSM6J6GMtdRgKSSmRrQjpp8Nr8j7lCygAV67lSPJLEfC3BgT32xTnePqXd02nUUYMEoWcAC7W/q0ZqiTK0ryXIdBUg6e+rmRqbwlCHiCSULWICRqZBeOzegezZxFwtwoaE8R4WZKezLQlyhZAELcKC5V+Go1b0sFQJO+Hzm50fsRKMcsYP4QMkCFmB/U4+KslK0vTrfdRQgae2uK9bAxKyaukZdRwEWhJIFXMdsOKqfNfdp34ZSDoQGHNozf8QO+7IQLyhZwHW83jqgsZmw7mU/FuBUSU6aNpRl6yCjHBAnKFnAdTzb1K30oF/vW1fkOgqQ9PbUF+vQxUFNzoZdRwGui5IFvAdrrZ5r6tWe+iKlBf2u4wBJb3ddkUIRq9dbB1xHAa6LkgW8hxMdI+oenWbKOxAjbqktUFrQp4Nn2JeF2EfJAt7D/qYe+Yy0bwMHQgOxIC3o162rC3WQeVmIA5Qs4D0829ijW2oLlJ+Z4joKgHm764rU2jeh9qFJ11GA90TJAq7h0sCkmnvGOKsQiDF31XPEDuIDJQu4hmebuiVJ97IfC4gp60qyVJaTxhE7iHmULOAa9jf1aENZtlYVZriOAuAyxhjtqS/Sy2f7FY5EXccBromSBVzF4MSs3rowyFIhEKN21xVrdDqs4x0jrqMA10TJAq7ihdO9ilpRsoAYdee6Ihkjpr8jplGygKvY39Stspw0banMdR0FwFXkZ6Zoa2Uum98R0yhZwBWmQxEdPNOvexpKZQwHQgOxak99sY62DWtkKuQ6CnBVlCzgCi+f7ddUKMJSIRDjdtcVKxK1eu0cd7MQmyhZwBX2N/UoOzWg29YUuo4C4D3ctCpPWakBHWTJEDGKkgVcJhK1ev50j/ZuKFFKgL8eQCwL+n26fW2hDp7pk7XWdRzgXfgpAlzmaNuQ+sdnWSoE4sSe+mK1D03pwgBH7CD2ULKAyzzb2KOg32jv+mLXUQAswJ66IkmMckBsomQBl9nf1KPb1hQqJy3oOgqABagpzFRNYQZH7CAmUbKAeS2942rtn9C9LBUCcWV3XZFeOzeg2TBH7CC2ULKAefubeiRJH6BkAXFlT12xJmYjevvSkOsowC+gZAHznm3q1pbKXJXnpruOAmARbl9bqIDPsC8LMWdBJcsYc78xptkY02KM+cJVPp5qjPne/MffMMbUXvHxVcaYcWPMv1me2MDy6h2b1tG2YZ4qBOJQdlpQN6/K54gdxJzrlixjjF/SVyQ9IKlB0iPGmIYrLvuMpCFr7TpJX5b0F1d8/MuSfrr0uIA3nj/VK2ulezdRsoB4tLuuSCc7RzQwPuM6CvBzC7mTtUtSi7W21Vo7K+m7kh664pqHJH19/u0nJO0z84e+GWM+IqlVUuPyRAaW3/6mHlUXpGt9abbrKABuwJ76YlkrvdzC3SzEjoWUrEpJbZe93z7/2lWvsdaGJY1IKjTGZEr6I0n/YelRAW9MzIT1cku/7tlYxoHQQJzaXJmr/IygDp6hZCF2LKRkXe2nzpXnF1zrmv8g6cvW2vH3/ALGfNYYc8gYc6ivj42LWFkHz/RpNhxlPxYQx/w+o/etK9JLZzliB7FjISWrXVL1Ze9XSeq81jXGmICkXEmDkm6V9P8YYy5I+l8l/R/GmM9f+QWstY9Za3daa3cWFzNpGytrf1OP8jKCuqU233UUAEtwV32xesdmdKprzHUUQNLCStZbkuqMMauNMSmSPinpySuueVLSo/NvPyzpBTtnt7W21lpbK+k/S/oza+1fL1N2YMnCkaieP92ruzeUKOBnogkQz+6qn/tH+oEzvY6TAHOu+1Nlfo/V5yU9I+mUpO9baxuNMV8yxjw4f9nXNLcHq0XS70t615gHIBa9eWFQI1MhprwDCaAkJ00N5Tk60My2E8SGwEIustY+JempK1774mVvT0v6+HV+jz+5gXyAp/Y39Sgl4NPuOpapgUSwd32xHjvYqtHpEGeQwjnWR5C0rLXa39Sj3euKlJm6oH9vAIhxe9eXKBy1epVRDogBlCwkrVNdY2ofmuKpQiCB3LQqT9mpAZYMERMoWUha+5t6ZIy0byMlC0gUQb9Pd9YV6UAzoxzgHiULSWv/qW7dVJ2n4uxU11EALKO964vVPTqtMz3vOaIR8BwlC0mpc3hKJztGde+mMtdRACyzu+pLJEkHmhnlALcoWUhK+5t6JIn9WEACKstN04aybPZlwTlKFpLS/qYerSnO1NriLNdRAHjgrvXFOnRxUOMzYddRkMQoWUg6I1Mhvd46oHsbWCoEEtXe+hKFIoxygFuULCSdA829CkctS4VAAttRk6/MFL8OnGHJEO5QspB0nm3qUVFWqm6qznMdBYBHUgI+vW9dkX7GKAc4RMlCUpkJR/Sz5j59YGOJfD7jOg4AD+1dX6KO4Smd62OUA9ygZCGpvN46txH23k0sFQKJ7q71c2eS8pQhXKFkIansb+pWRopfd6wtch0FgMcq89JVV5JFyYIzlCwkjWh07kDoPXXFSgv6XccBsAL2ri/Wm+cHNcEoBzhAyULSONExop7RGZ4qBJLIXfUlmo1E9XrrgOsoSEKULCSN/U098vuM7t5Q4joKgBVyy+p8ZaT4WTKEE5QsJI39TT26pTZf+ZkprqMAWCGpAb/uWFuoA2d6GeWAFUfJQlK4ODCh5p4x3cOUdyDp3FVfrLbBKZ3vn3AdBUmGkoWk8M6B0PeyHwtIOnvXz20RYMkQK42ShaTwbFOPNpRlq7ogw3UUACusuiBDa4ozOWIHK46ShYQ3ODGrQxcGuYsFJLG99SV6vXVAU7MR11GQRChZSHjPn+pR1Ir9WEASu2t9sWbDUb1+nlEOWDmULCS8/U09Ks9N0+bKHNdRADhy6+oCpQV9+hn7srCCKFlIaNOhiF462697GkplDAdCA8kqLejX7WsKdaC513UUJBFKFhLaS2f7NRWKMOUdgN6/oUQXBibV2jfuOgqSBCULCe2Zxm7lpAV025pC11EAOPb++VEOL5zmbhZWBiULCSscieq5Uz3at7FUQT//qwPJrrogQ/WlWZQsrBh+8iBhvXl+UMOTId23iacKAcy5e0Op3jw/qNHpkOsoSAKULCSsZxq7lRb06a76YtdRAMSIuzeUKBy1evlsv+soSAKULCSkaNTqmcYe7akrVnqK33UcADHi5lV5yk0P6vlTLBnCe5QsJKTjHSPqHp1mqRDALwj4fdq7vlgHmnsVjVrXcZDgKFlISM80divgM9q3scR1FAAx5u4NJRqYmNWx9mHXUZDgKFlISM80duu2NYXKy0hxHQVAjLmrvlg+wygHeI+ShYTT0jum1r4J3beJAaQA3i0vI0U7avIpWfAcJQsJ5+mT3ZI4EBrAtd29oVSNnaPqHpl2HQUJjJKFhPNMY49uWpWnstw011EAxKi7N8zt13yRswzhIUoWEkrH8JROdIzwVCGA91RfmqXKvHRGOcBTlCwklGcb55YKKVkA3osxc08fv9zSp+lQxHUcJChKFhLK0ye7VV+apdVFma6jAIhxH9hYqulQVK+0MP0d3qBkIWEMjM/orQuDup+7WAAW4NY1BcpKDei5Uz2uoyBBUbKQMJ4/1auole6lZAFYgNSAX3fVF+u5U0x/hzcoWUgYzzR2qzIvXZsqclxHARAn7mkoVd/YjI53jLiOggREyUJCGJ8J66Wz/bp/c5mMMa7jAIgTe9cXy+8z2t/U7ToKEhAlCwnhQHOvZiNRnioEsCh5GSm6pTZfzzUxygHLj5KFhPBMY48KM+eOygCAxfjAxlI194zp0sCk6yhIMJQsxL2ZcEQvnu7VPQ2l8vtYKgSwOPc0zJ1zylOGWG6ULMS9V1sGND4T1n2bWSoEsHg1hZmqK8miZGHZUbIQ955p7FZWakB3rC10HQVAnLqnoVRvnB/UyGTIdRQkEEoW4lokarW/qUfv31Ci1IDfdRwAceoDDaWKRK0OnGEDPJYPJQtx7dCFQQ1MzOq+TaWuowCIY9ur8lSUlaL9TSwZYvlQshDXnjrRpdSAT+9fX+I6CoA45vMZ7dtQqgPNfZoJc2A0lgclC3ErGrX66cluvX99iTJTA67jAIhz920u1fhMWK+eG3AdBQmCkoW4dfjSkHrHZvTAFp4qBLB0d6wtUlZqQM82Mv0dy4OShbj1k+NdSgn4tG8j+7EALF1a0K+964v1bGOPIhwYjWVAyUJcikatnj7Zrbvqi5XFUiGAZXL/5jINTMzq8MUh11GQAChZiEtH2obUPTqtD20pdx0FQALZu75EKQGfnj7JkiGWjpKFuPST491K8fu0byNPFQJYPlmpAd25rkjPNHbLWpYMsTSULMSduacKu7SnvkjZaUHXcQAkmPs3laljeEqNnaOuoyDOUbIQd462D6trZFofZKkQgAf2bSyRz8wd2QUsBSULceenJ7oU9BueKgTgicKsVO1aXUDJwpJRshBXrLV66kS3dtcVKzedpUIA3rhvU5nO9IyrtW/cdRTEMUoW4sqx9hF1DE/pgc0MIAXgnfs2zX2PeaaRswxx4yhZiCs/PtapoN/o3k2ULADeqchL19aqXD3NkiGWgJKFuBGNWv3keJfuqi9hqRCA5+7fXKZjbcPqGJ5yHQVxipKFuHHo4twA0g9v46lCAN57Z9jxT090OU6CeEXJQtz48bFOpQV9+gBPFQJYATWFmdpUkaOfULJwgyhZiAvhSFQ/PdmlfRtKlclZhQBWyAe3lOvIJZYMcWMoWYgLr7cOqn98lqVCACuKJUMsBSULceHHxzqVlRrQ3vWcVQhg5dQWsWSIG0fJQsybDUf1dGO37mkoVVrQ7zoOgCTzzpJhJ0uGWCRKFmLeyy19GpkKsVQIwIl3lgyf4m4WFomShZj342Ndyk0P6s51xa6jAEhCtUWZaijPoWRh0ShZiGnToYj2N/Xo/k1lSgnwvysANz60tVxvs2SIReKnFmLaC6d7NT4T1oe3VbiOAiCJfZAlQ9wAShZi2r8c6VBJdqpuX1voOgqAJLZ6fsmQpwyxGJQsxKzhyVm92NyrB7dVyO8zruMASHK/tG3uKcO2wUnXURAnKFmIWT850aVQxOojN1W6jgIA+vDWuW0LTx7rdJwE8YKShZj1oyOdqivJ0qaKHNdRAEDVBRnaWZOvH1OysECULMSktsFJvXlhUB+5qVLGsFQIIDY8uL1Cp7vHdLp71HUUxAFKFmLSO7fjH+SpQgAx5INbyuX3GT15lLtZuD5KFmKOtVY/PNKhW2rzVV2Q4ToOAPxcUVaq7lxXpB8d7ZS11nUcxDhKFmJOY+eoWnrH2fAOICY9tL1CHcNTevvSkOsoiHGULMScHx3tUNBvfn5eGADEkns3lSk14GPJENdFyUJMiUStfnS0U3vXlygvI8V1HAB4l6zUgD6wsVT/erxL4UjUdRzEMEoWYsqr5/rVOzajj2xnqRBA7Hpwe4UGJmb1yrkB11EQwyhZiClPHG5XbnpQ+zaWuI4CANe0d32xstMC+tHRDtdREMMoWYgZI1MhPX2yWw9tr1Ba0O86DgBcU2rArw9uLtczJ7s1ORt2HQcxipKFmPGT412aCUf18I4q11EA4Lo+tqNKE7MRPX2y23UUxChKFmLGE4fbVF+apS2Vua6jAMB13VKbr1UFGfrB2+2uoyBGUbIQE1p6x/X2pWE9vKOKY3QAxAVjjD56c6VePTegzuEp13EQgxZUsowx9xtjmo0xLcaYL1zl46nGmO/Nf/wNY0zt/Ov3GGMOG2NOzP/37uWNj0Txg7fb5fcZBpACiCsfu7lK1ko/PMIGeLzbdUuWMcYv6SuSHpDUIOkRY0zDFZd9RtKQtXadpC9L+ov51/slfdhau0XSo5L+cbmCI3FEolb//Ha79tYXqyQ7zXUcAFiw6oIM7VpdoB8cbueYHbzLQu5k7ZLUYq1ttdbOSvqupIeuuOYhSV+ff/sJSfuMMcZae8Ra+85I3EZJacaY1OUIjsTx0tk+9YzOsOEdQFx6+OYqtfZP6EjbsOsoiDELKVmVktoue799/rWrXmOtDUsakVR4xTUfk3TEWjtzY1GRqJ443K78jKD2bSx1HQUAFu2DW8uVHvTricNsgMcvWkjJutou5Cvvib7nNcaYTZpbQvzcVb+AMZ81xhwyxhzq6+tbQCQkipHJkJ5t6tFD2yuVEuA5DADxJys1oPs3l+lfj3VqOhRxHQcxZCE/1dolVV/2fpWkK0/F/Pk1xpiApFxJg/PvV0n6oaRfs9aeu9oXsNY+Zq3daa3dWVxcvLg/AeLavxzt0CyzsQDEuY/dXKXR6bCeO9XjOgpiyEJK1luS6owxq40xKZI+KenJK655UnMb2yXpYUkvWGutMSZP0k8k/Vtr7SvLFRqJwVqr77x5SVurcrWZ2VgA4tjtawtVnpvGkiF+wXVL1vweq89LekbSKUnft9Y2GmO+ZIx5cP6yr0kqNMa0SPp9Se+Mefi8pHWS/tgYc3T+F4fSQZJ0pG1Yp7vH9MiuVa6jAMCS+H1GH7u5SgfP9KlrhJlZmLOgTTDW2qestfXW2rXW2j+df+2L1ton59+ettZ+3Fq7zlq7y1rbOv/6/2WtzbTWbr/sV693fxzEk++8cUmZKX59eFuF6ygAsGSfuKVaUSt9/y3uZmEOO43hxOh0SD8+3qkHt1cqKzXgOg4ALFl1QYZ21xXpe29dUiTKzCxQsuDIj450aDoU1adYKgSQQB7ZtUqdI9M6eJYn5UHJggPWWn3rjUvaXJmjLVVseAeQOD6wsVRFWSn6zhuXXEdBDKBkYcUdax9hwzuAhJQS8OljO6r0/Ole9Y5Ou44DxyhZWHHfeeOSMlL8epAN7wAS0CdvWaVI1OqfGOeQ9ChZWFFj0yE9eaxTD26rUHZa0HUcAFh2q4sydfuaQn33rUuKsgE+qVGysKJ+cLhdU6EIS4UAEtojt65S2+CUXjnX7zoKHKJkYcVEo1bfeO2itlfnaVt1nus4AOCZ+zaVKj8jqO+8yQb4ZEbJwop5qaVfrf0T+vU7al1HAQBPpQb8enhHlZ5t7FEPG+CTFiULK+Ybr15QUVaqPril3HUUAPDcp2+rVcRafev1i66jwBFKFlbEpYFJvdDcq0/tqlZKgP/tACS+VYUZunt9ib795iXNhCOu48ABftphRXzjtQvyG6Nfua3GdRQAWDGP3lGr/vFZPXWiy3UUOEDJgucmZ8P6/qE23b+5TKU5aa7jAMCKuXNdkdYUZ+ofXmXJMBlRsuC5fznSqdHpMBveASQdn8/o0dtrdaxtWEfbhl3HwQqjZMFT1lp9/dUL2lSRox01+a7jAMCK++jNlcpM8esbr15wHQUrjJIFT712bkDNPWN69PZaGWNcxwGAFZedFtTDO6r0r8e71D8+4zoOVhAlC5567KVWFWWl6sHtnFMIIHn92h21mo1E9Z03GE6aTChZ8Exz95gONPfp1++oUVrQ7zoOADiztjhLu+uK9I+vX2ScQxKhZMEzjx1sVXrQr19lbAMA6Dd3r1Hv2Ix+dLTTdRSsEEoWPNE9Mq0nj3XoE7dUKy8jxXUcAHBud12RNpbn6LGDrYpGres4WAGULHji7189r0jU6jN3rnYdBQBigjFGn9uzRi2943qxudd1HKwAShaW3dh0SN9+/ZIe2FKu6oIM13EAIGZ8aGu5KvPS9dWftbqOghVAycKy+95bbRqbCetze9a4jgIAMSXo9+k37lytNy8M6u1LQ67jwGOULCyrUCSqx18+r1tXF2hrVZ7rOAAQcz55S7Vy04N6jLtZCY+ShWX1wyMd6hyZ1ufu4i4WAFxNZmpAv3rbKj3T1K3WvnHXceAhShaWTTgS1VdebNGmihy9f32J6zgAELMevaNWQb9P/99L511HgYcoWVg2Tx7r1MWBSf3uvjqO0AGA91CSnaaHd1TpB4fb1TUy5ToOPELJwrKIRK3++oUWbSzP0b0Npa7jAEDM+6271ipqrf72wDnXUeARShaWxb8e71Rr/4R+9+513MUCgAWoLsjQx3dW6btvtnE3K0FRsrBk0ajVf3uhRfWlWbpvU5nrOAAQN3577zpFrdX/y92shETJwpL99GS3WnrH9Tt318nn4y4WACzUO3ezvvNmm7pHpl3HwTKjZGFJ5u5indXa4kx9cEu56zgAEHfeuZv1twdaXEfBMqNkYUmeOtml091j+p276+TnLhYALFp1QYYe3lGl77zF3axEQ8nCDQtFovrLZ5q1oSxbH95W4ToOAMSt/+X96xSNcjcr0VCycMO++1abLgxM6g/vX89dLABYgsv3ZrUNTrqOg2VCycINmZgJ6788d1a7aguY7g4Ay+D39tXL55P+8tlm11GwTChZuCGPv3xe/eMz+qMHNjAXCwCWQVlumj5z52r96GinTnaMuI6DZUDJwqINTszqqwdbdU9DqXbU5LuOAwAJ43N3rVVBZor+7KlTsta6joMlomRh0b7yYosmZ8P6w/vWu44CAAklJy2o3717nV49N6CfnelzHQdLRMnCorQNTuofX7uoh3dUqa4023UcAEg4n7q1RjWFGfrzn55WJMrdrHhGycKi/OlPTsnvM/rf7ql3HQUAElJKwKf//b71Ot09ph8e6XAdB0tAycKCvXy2X083duvzd69TeW666zgAkLA+tKVc26py9ZfPNGtiJuw6Dm4QJQsLEopE9Sc/btSqggx95s7VruMAQEIzxuiLH96k7tFp/dcXzrqOgxtEycKCfOO1i2rpHdcf/1KD0oJ+13EAIOHtqMnX/7CzSl976bxaesdcx8ENoGThuvrHZ/Sf95/RnvpifWAjg0cBYKX80X9v786jq6zuNY5/f5kDSQiEkAESoAwGCAaQSbGTokWsAq0DaNWKLWoHtNc6da22Ttyr1Va01lpFwalQqt6WWq5UFEVUpkiAMgSQhMkQhgBJgMz7/nEONE0JRk3ynuH5rJWVc97znviLe23Ok3fvd+9xOXSIieSXCzZoSYcgpJAln+rhNwo5XlvPLy8ZqIVHRUTaUUpCLLd/4wze33aQhev3el2OfEYKWXJa+TsOMT9/F1PP7U2f1ASvyxERCTtXjerJoMwk7n99oybBBxmFLGlWdV09d766joykOKaf38/rckREwlJkhHHfhFxNgg9CClnSrN8t+Zht+yqZMWkwCbFRXpcjIhK2zurZmSuHZzHrvSLtaxhEFLLklDaVlPPkkm1MGtqdr+dosruIiNd+Nn4AXRNi+Omf11JT1+B1OdICClnyH+obHHe9uo6k+Gh+/s2BXpcjIiJApw7R/PekwWzeW8ETS7Z5XY60gEKW/IfZ7xexdvcR7rl0EF06xnhdjoiI+J0/II1vDe3Ok0u2adgwCChkyb/Zvr+SR/5RyNgB3bjkzAyvyxERkSZ+cclAOneM4fZX1mnYMMApZMlJNXUN3DKvgLjoSB6YOFhrYomIBKDkDjHMmJjrmzv7joYNA5lClpz06zcLWb/nCA9+60zSO8V5XY6IiDTjwkHpTBySyW/f3sbq4jKvy5FmKGQJAMu2HuAP727nqlHZjMtN97ocERH59xhrsQAAEEJJREFUFPdPzKV7cjzT567h8LEar8uRU1DIEsqO1vBf8wvok9qRn1+suwlFRIJBYlw0T1w1lP2V1fz0z+u0t2EAUsgKc8457nhlHYeP1fL4lKHEx0R6XZKIiLTQmT2SueuiASzeVMqcD4q9LkeaUMgKc39Yup3Fm0q5Y9wZDMrs5HU5IiLyGU0d04uxA7rxPws3a1mHAKOQFcaWbtnPr97YzMWDM7jh3N5elyMiIp+DmfHwZXmkJMTwg5c/4tBRzc8KFApZYWrHwaP8eO4a+qcl8qvLztRyDSIiQaxzxxievHoYe8uruPnlfGrrtX5WIFDICkNHq+u48cV8AJ6+ZjgdtfmziEjQG5rdmYe+PZjl28v45YINmggfAPTpGmZOTHTfUlrBnOtHkp3SweuSRESklUwa2oPCvZU89e7H5KQncu3ZvbwuKazpSlaYeeiNQv6+voQ7x+Xwlf6pXpcjIiKt7PZvnMHYAd24928bWbb1gNflhDWFrDDy3LIinnr3Y64elc20r3zJ63JERKQNREYYMycPpW9qAje/lK87Dj2kkBUmFqz9hPte38i4QencNyFXE91FREJYQmwUs68fQVJ8NNc9t5KP91d6XVJYUsgKA+9t3c9t8wsY1bsLMycPITJCAUtEJNRlJsfz0vdGYQbXzFrBnsPHvS4p7ChkhbjVxWXc9GI+fVITePra4cRFa0V3EZFw0btrR16YOoqK6jqumbWCA5XVXpcUVhSyQtjy7Qe59rmVpCXF8fzUkXSKj/a6JBERaWcDM5OY/d0RfHLkON+ZtYL9FQpa7UUhK0S9v+0A3529ku7J8cy7cTRpSXFelyQiIh4Z3qsLz143gh0Hj3HFHz7U0GE7UcgKQe8U7mPqnFX0SunI3Gmj6ZaogCUiEu7G9O3KS98byYHKai7//QcUHTjqdUkhTyErxPy1YA/TXsinb7cE5n5/NF0TYr0uSUREAsRZPbsw9/ujqapr4PKnPmRTSbnXJYU0hawQ4ZzjscVbuWVeAUOzk/nj90bTuWOM12WJiEiAye3eifk3nk1UhHHFUx+yZPM+r0sKWQpZIaC6rp7b5q/l0cVb+Naw7rx4wyg6ddAkdxERObW+3RJ47QfnkJ3SganPr+KZpdu112EbUMgKcvsrqrnm2ZW8tmYPt13Qn19fnkdMlJpVREROLzM5nj/fdDYX5aYzY+Embn9lHdV19V6XFVK0QXQQ+2DbAabPK6CiqpbHJg9hwpDuXpckIiJBpENMFE9MGcbjaVuZuXgrW/dV8tvJQ8lO6eB1aSFBlzyCUH2D4zdvbuHqZ1fQKT6Kv/5ojAKWiIh8LhERxq1j+/PUd4ZRtL+S8Y+/x/+u2e11WSFBV7KCzK6yY9z+ylqWby/j28N6cP/EQXSIUTOKiMgXMy43g8E9kvnJvAJ+8qe1vFu4n/sm5pIUpzm+n5c+nYNEfYNjzgfFPLKokAiDhy87k8uHZ3ldloiIhJDuyfHMnTaaJ5dsY+ZbW1lZVMY9lw7iwkHpXpcWlBSygkDh3grufHUdBbsO8/UzUnlg0mC6J8d7XZaIiISgyAjjx+f349x+Xbn7tfVMezGfCwamce+lg8jUZ89nopAVwMqO1vD4W1t5afkOkuKjeWzyEC7Ny8TMvC5NRERC3NDszvztx+fy3LIiZi7eytjfvMv08/vx3XN6ERcd6XV5QUEhKwBV1dYz+/1inlyyjaM1dUwemc1tF/QnRau3i4hIO4qOjODGr/Zh/OAM7v3bBh78v83Meb+YW8f247KzehAVqfvnTkchK4Acr6nnT6t28vTS7XxypIrzc7px10U59EtL9Lo0EREJY1ldOjDruhF8+PFBfrVoM3e9tp6n39vOrWP7Mz43XWGrGQpZAeDIsVpe+LCY2R8UU3a0huE9O/PIFXmc06er16WJiIicdHafFF67+Rze3FjKI/8oZPrcNTyUHM/Uc3tz5YgsEmIVKxrT/w2POOdYu/sI81buZMHaTzhWU895Od24+Wt9GNGri9fliYiInJKZceGgdMYOSOOtzft4Zul27n99IzMXb+HK4VlcMSKL/hqBARSy2t3+imoWri9h3qpdbCopJz46kkvyMrh+TG8GZCR5XZ6IiEiLREQYFwxM44KBaazZeYhZ7xUx54NiZi0rIi8rmSuG9+CbZ2bSKT5819myQNsQcvjw4W716tVel9Gq9pVXsWjDXv6+voSVRWU0OBiUmcSUkdlMGJJJohZ6C1p/XLHT6xJEQs5Vo7K9LkE+pwOV1fxlzR7mr97FltJKoiONs/t05RuDfGGsW2Kc1yW2OjPLd84NP+VrClmtr6q2nvwdh1i6dT/vbTnAxpJywLfr+fjBGYwfnE5Ouq5ahQKFLJHWp5AV/JxzrNt9hIXrS3hjw152HDyGGeT1SGZM3xTG9OnKsJ6dQ2IpCIWsNuSco7S8mjU7D/HRzkN8tPMw6/ccoaaugehI46yenflyv1QuHJimuwRDkEKWSOtTyAotzjkKSytY9M9Slm7dT8Guw9Q3OGKjIhiSlcyQ7GSG9EgmLyuZjE5xQbcW5OlCluZktVBdfQOfHK5iZ9kxig8eZUtpBZv3VrCltILDx2oBiImMILd7EteO7sk5fVMY1TuFjrrTQkREwpiZkZOeRE56EreM7UdldR0riw7y/raD5O84xOxlxdTUNwDQpWMM/bol0D8tkf5pCXwpNYGszh3ISI4jOgiXiWhRAjCzccBjQCQwyzn3YJPXY4EXgLOAg8CVzrli/2t3AzcA9cB059yiVqv+c2hocBw8WkN1XT1VtQ0nv1dU1VJeVUf58VqOHK9lf0U1+yuqKS2vorSiipLDVdQ1/OuqX8eYSPqnJ3JRbjpnpCWSl5XMwMwkYqOC/9KniIhIW0mIjeK8nDTOy0kDoLquns0lFRTsOsymknK2lFbwlzV7qKiuO/meCIOMTvFkJseRmhhLakIsqYmxJHeIITEuiqS4aJLio4iPjiImKoJY/1d8TKSn854/NWSZWSTwO+ACYDewyswWOOc2NjrtBuCQc66vmU0GHgKuNLOBwGRgEJAJLDaz/s65+tb+RVqqvKqWETMWf+p5ibFRpCbF0i0xlqFZnbk0L57sLh3I7tKR7JQOZCTFERERXJc0RUREAk1sVCR5Wb7hwhOcc5QcqaL44FF2lx1n16Fj7Co7xt7yKgr3VrCs4gDlVXWn+ak+X+2fyvNTR7Zl+afVkitZI4FtzrntAGY2D5gANA5ZE4B7/I9fAZ4w36DqBGCec64aKDKzbf6f92HrlP/ZdYyN4v4Jg4iNiiQ2OuLk96S4KDrFR5MUH01SXHRITMYTEREJRmZGZnK8b0PqPqc+p6q2niPHa0+ORFVU1XG8po7qugZq6hqoqW8gzeO7GVsSsroDuxo93w2Mau4c51ydmR0BUvzHlzd5b/fPXW0riI6M4Jqze3lZgoiIiHxBcdGRxEVHkpYUuMtCtCRknWpMrOktic2d05L3YmbTgGn+p5VmVtiCulpTV+BAO/835fTUJoFHbRJ4QrJNrva6gC8mJNskyLV1m/Rs7oWWhKzdQFaj5z2AT5o5Z7eZRQGdgLIWvhfn3NPA0y2opU2Y2ermbr8Ub6hNAo/aJPCoTQKP2iTweNkmLbkfchXQz8x6m1kMvonsC5qcswC4zv/4MuBt51uAawEw2cxizaw30A9Y2Tqli4iIiASuT72S5Z9j9SNgEb4lHJ5zzm0ws/uA1c65BcCzwIv+ie1l+IIY/vPm45skXwf80Ms7C0VERETaS4vWyXLOLQQWNjn2i0aPq4DLm3nvDGDGF6ixPXg2VCnNUpsEHrVJ4FGbBB61SeDxbjpSoG2rIyIiIhIKgm+NehEREZEgEFYhy8yyzGyJmW0ysw1mdov/eBcze9PMtvq/d/a61nBxmja5x8z2mFmB/2u817WGCzOLM7OVZrbW3yb3+o/3NrMV/n7yJ/+NMNIOTtMmc8ysqFE/GeJ1reHGzCLNbI2Zve5/rn7isVO0iWf9JKxCFr7J97c55wYAo4Ef+rf+uQt4yznXD3jL/1zaR3NtAvCoc26I/2th8z9CWlk1cJ5zLg8YAowzs9H4tst61N9PDuHbTkvaR3NtAnB7o35S4F2JYesWYFOj5+on3mvaJuBRPwmrkOWcK3HOfeR/XIGvEbrj2/7nef9pzwMTvakw/JymTcQjzqfS/zTa/+WA8/BtmwXqJ+3qNG0iHjKzHsDFwCz/c0P9xFNN28RrYRWyGjOzXsBQYAWQ5pwrAd+HPtDNu8rCV5M2AfiRma0zs+c0hNu+/JfbC4B9wJvAx8Bh59yJHVk93yIr3DRtE+fciX4yw99PHjWzWA9LDEczgTuABv/zFNRPvNa0TU7wpJ+EZcgyswTgVeBW51y51/XIKdvk9/i2BR0ClAC/9rC8sOOcq3fODcG3S8NIYMCpTmvfqsJb0zYxs1zgbiAHGAF0Ae70sMSwYmbfBPY55/IbHz7Fqeon7aSZNgEP+0nYhSwzi8b3Yf6yc+41/+FSM8vwv56B7y9FaSenahPnXKn/Q6UBeAbfB720M+fcYeAdfPPlkv3bZkEzW2RJ22vUJuP8w+3OOVcNzEb9pD2NAS41s2JgHr5hwpmon3jpP9rEzF7ysp+EVcjyj5c/C2xyzv2m0UuNtwW6Dvhre9cWrpprkxOh128S8M/2ri1cmVmqmSX7H8cDY/HNlVuCb9ssUD9pV820yeZGfxwavrk/6iftxDl3t3Ouh3OuF75dTt52zl2N+olnmmmT73jZT1q04nsIGQNcA6z3z20A+BnwIDDfzG4AdtLM6vXSJpprkyn+22wdUAzc6E15YSkDeN7MIvH9ITbfOfe6mW0E5pnZA8AafOFY2kdzbfK2maXiG6YqAG7yskgBfENR6ieB5WWv+olWfBcRERFpA2E1XCgiIiLSXhSyRERERNqAQpaIiIhIG1DIEhEREWkDClkiIiIibUAhS0SClplNMjNnZjmfct7CE+tMiYi0Fy3hICJBy8zm41tD6i3n3D0elyMi8m90JUtEgpJ/v8sxwA34VnfGzDLMbKmZFZjZP83sy/7jxWbW1f/4L2aWb2YbzGxao59XaWYzzGytmS03szQPfi0RCSEKWSISrCYCbzjntgBlZjYMuApY5N9IOQ/f6s5NTXXOnQUMB6abWYr/eEdguXMuD1gKfL/NfwMRCWkKWSISrKbg2wQW//cpwCrgejO7BxjsnKs4xfumm9laYDmQBfTzH68BXvc/zgd6tU3ZIhIuwm3vQhEJAf6rT+cBuWbmgEh8+1zeAXwFuBh40cweds690Oh9X8O3ufLZzrljZvYOEOd/udb9a5JqPfr3UUS+IF3JEpFgdBnwgnOup3Oul3MuCyjCF7D2Oeeewbcx77Am7+sEHPIHrBxgdLtWLSJhRX+piUgwmgI82OTYq8Ac4KiZ1QKVwLVNznkDuMnM1gGF+IYMRUTahJZwEBEREWkDGi4UERERaQMKWSIiIiJtQCFLREREpA0oZImIiIi0AYUsERERkTagkCUiIiLSBhSyRERERNqAQpaIiIhIG/h/qGbFER8J5d0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.tight_layout()\n",
    "seabornInstance.distplot(sd_df['Asian'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  X = sd_df['Year'].values.reshape(-1,1)\n",
    "# y = sd_df['Asian'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([26.03078556])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_min = np.array([X.min()])\n",
    "X_max = np.array([X.max()])\n",
    "y_min = linreg.predict(X_min)\n",
    "y_max = linreg.predict(X_max)\n",
    "y_min\n",
    "# X.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "sd_df.plot(x='Year', y='Asian', style='o')  \n",
    "plt.title('Predict Race:')  \n",
    "plt.xlabel('Year')  \n",
    "plt.ylabel('Asian (%)')   \n",
    "plt.plot([X_min[0], X_max[0]], [y_min[0], y_max[0]], color='red', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = sd_df['Year'].values.reshape(-1,1)\n",
    "# y = sd_df['Asian'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>35.4</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>28.8</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual  Predicted\n",
       "105    35.4        1.0\n",
       "35     28.8        1.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd_df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})\n",
    "sd_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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