Machine Learning - Black.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": {
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       "</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": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x18dcc37edc8>"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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TjYQvP8OC0nF+i+/x/niOR4Hf4APlzYQvTISPb/H+f8UnCMzFLT7d2OA7WCHO3YJ/+5nAzo3ooSJgkiRJkiRJkiRJkiRJkiRJkiRJkiRJknQ9g8lceZIkSZIkSZIkSZIkSZIkSZIkSZIkSTLIyUHuJEmSJEmSJEmSJEmSJEmSJEmSJEmSZMCQg9xJkiRJkiRJkiRJkiRJkiRJkiRJkiTJgCEHuZMkSZIkSZIkSZIkSZIkSZIkSZIkSZIBQw5yJ0mSJEmSJEmSJEmSJEmSJEmSJEmSJAOGHOROkiRJkiRJkiRJkiRJkmRIIuk1STMkzZQ0XdI24T9G0pwW45wsafO+lTRJqiNpL0kmaYNerrtW0krtkitJkmQoIOcPkj5U8vuYpImdlGuokIPcbULSqlFhniHpr5L+Uvq/TJXrV5F0SAPxDpf0bP9IXfe+60ma0c/3+IOksf15j6RxJK0p6SZJ90q6W9Lh4b+KpN9JejB+V64Tx45FY7GF+/+PpHskzZI0SdLaFedXiHR1RivxJ4OfOjp8XEWevFudOMbWO9/L/fcI/Z0h6U5J21WcTx1OaiJpOUl3RMfb3ZKOD/8Jkh4t6W/NcnNJ8uBSHOOj82TzCv+1JM2X9KUliT8Z3EgaJukuSdfE/7bor6QDJf29dJ9PV5zP/DfpFUlzJc0uyvHwa0sdIsLvGPe4W9LvK84tkraSgUWUqxeW/g+PPKup76nSgFq3D2KEPi/2fJFfd1Ve3O3vMukzFpjZWDPbBDgK+FanBUqSJvkE8Afg4/UuMrPdzKzt/chJdyJpfsX/heWwpEMk7d8mOU6Q9P523Cvu19QEJkl7SnpX6X9b5U26HzMz4BDgtOi/Gwl8Azh0SeKVNLwv5Bvs5CB3mzCzp6PCPBb4MfDd4r+Z/atKkFXwhJEk3cKrwP+a2TuBrYBDo4D/CjDJzN4OTIr/tdgRaHWA5S5gczPbGLgMOKXi/InA7xcL1QYkDevEfZOmqaXDsGiefG2dOMYCrXZQTwI2iXLgIOCcivOpw0k9XgZ2io63scAHJW0V544o6W+9CWg70noejKRRwGHAlCqnvwv8ttW4l4TU3wHF4cC9FX5t0V/gF6X7ZP6btMr7QofKE336vQ4RA2xnAuPMbENg74pLqqWttpAdL33CC8BGkkbE/12AvyxJhDmI0Ti96XC+yyHJCsAzlZ4xKHKLfKX3wtXece7LMRFqpqSTK8ItJel8SSe1QfZkCCJpeWBb4GBikFvSGpJujglycyRtH/5zJa0W7islTYsJdJ8txTdf0jdCn2+X9KYOPFbSYczsx2Z2QZvudayZ3dDX8fZhPXVPYOEgd3/JmwxszGwOcDVwJPB14AIze1jSAbFoZYakMyUtBSDpLPkiqLslHVvEI2mepK9JuhXYqyMPM8DIQe4uICrDc+L4fHifDLwjlP/kWGFyY1SkZ0n6SBPxfyUS0qwiwchXYs+RdG4kpAskfUDSbZIeKM0APykq4zfJV+oeVCX+EXHN7JBvh/C/TdJGpeumSNpQ0vLylTt3yFcc7B7n3yDp0pDz58ByLb/UpM8xsyfMbHq4/4l3pL0V2AM4Py47Hy/4F0PSGHzixhdDr7eXtLZ8VXaxOnutOve/ycxejL+3A6NLcW8GvAm4vt4zSDpY0ndL/z8j6bRw71cqcH5SdDpL+lGpwDm+FHaupGMl/QHYW9Jh6llp/vN6ciSdoY4ON4Tc6sYJwD6hJ/vILRlcGd/9dkkb17n//JjZBzASKNypw0mvmFPMsl46DqsTZBGWNA8OTsQnGL1UEfeewCPA3b3IcKLCgkL8/4akw8J9hKSpIUtZT+t1vJwgaQqwddSVCv39TiPvJGkvkkYDH2bxCT6NhB3Dkutvrbgz/036nSWtQwD/AVxuZn8GMLO/leJuKG1J2lnSFaX/u0i6PNy7SvqjvC13qbyznNDTqfJ241mSFP6TJX1TvqL8cEl7xzUzJd3c0ktKfot/R/DVeD8rTkgaKW+3T5W3n/cI/xGSfh469AtgRClMnw1iyC0FPCJnJUmvq6fNf4u8b6GWjMMknVoq4/+zSvxbRJh1S36j5JY+lo7/K8QzLV1DxrdJmhjPeYvCXK+k3eX9EHdJuqF4RrkVhrMkXQ9cIF+5dnnE8aCkU0pxz5W0mnyA815JZ8e7vF4xMSGeYVako1MVq8Pk/R9F+TBL0ttrfP+k84yI73Qfnp+eWOWavwG7mNmmwD7ADwDkpkn3BN4TE1LLE/KHAxcDD5jZMf35AMmQZk9gopk9APxD0qZ43eG6mGS/CVBtMulBZrYZsDlwmKRVw38kcHvo883AZ/r9CZKuI8rKL4V7sfZKnL9QPl7xoKTPhP/y8vbZdPlYQVEnqFeOTpA0PtxbyMcUZkYZOqqGfMtJOi/ucZek94X/gfL67NX00sarEudnos4yU9Kv5GMV2wDjgFOjnHhbhbxzJR1fet6iDtJMXT8ZPByP578fAk6Rj43tBWwT+fFweixufCUmT28C7KKStQDgBTPb1swubaPsAxczy6PNB3Ac8KVwbwnMBN4AjMIHXTYG1gNmlMIsDYwK9xuBB8M9HHi2zr12w2f9C5/UMBFfBbMe8Ao+C2kpvLJzdoT5KHBZuE8CpuMDzm8E5uEdgQvlw2enFGE3BP4ELIPPIPxO+L8LmBLuU4CPh3tl4IGI/8vAWeH/buA1YGynv1ceVfVqDPBnfIbzsxXnnqkTbqHux/+rgQPCfRBwZYP3PwM4JtxLAZOBNYEDgTPqhBsJPAwsHf9vA/4NeGfIUvifCewf7lXid1jcZ+P4Pxf4cinux4Flw71Sp79RHk3p8HHxPWcB5wIr1wm3iI4BpwNfD/dOlPLtGuH3Au4D/gFsHX6pw3k0qrfD8PJ6PvDt8JsA3B/6+93iG9YI33IejJfLvwr3ZNyyRqGTfwSWr4y/ShxjgOnhXip0eVVgV+Aseuoq1wA7xHWF/o4A5gCrxn8DPlZcE+9A8T/1twsP3ArLZviK7GvCr136eyDwRNznMmDNkh5OJvPfPBo4gEfxdtE04LMlvZxLP9chgO8BPww9mlboWJxbLG3ViEN4HWT1+H8JsDuwGt6BPTL8jwSODfcqpfAXAruHezJwZuncbOCt4U4dbl635uN9AJfh7eIZLJpXfhPYr3i/ePt5JPA/wLnhvzFutagon+cCq5W/I9XL0uKbnkK0r2rIOBFv638EmAocDSwLPNqLjJ+lp922LHAnsE7xfHjfxDRgrcp0ApwH7BnuzwL/V0e+ScDbw/0e4MZwr0xP/eDTRRx42p0GjCjd9xFgxfgGf6KnrJgb6WRMvOOx4f/L0jPPwTsvwRcszAn36cC+4V6muF8e3XcA80vurfHJm4rvXnzPFfG8cDaeTl8M//8DPlMlzsl4f9/RnX6+PAb3AfwGn4ABbnnrVGAH4KHI78aWrp1LT/lwXOjoTOA5YKvwf7mUd+4DnNPpZ8yj33TntcjPiuPPpXL4OHrGLxZrr5T0Z0SUk48Bb8HHKlaIa1YLPSzy01rl6ARgfJSVjwBbhP8KwPAasv8vcF64NwjZl8PL9HmU6rFVwi7M2yv8Vy25TwI+X5avdG7h/0hTxXWfK9ILTfYX5jF4Dnxy85fD/QXcQlORxu4HvhbnDsXbl7OAp0o6NY9oW+XR2JEruTvP9nin8YvmKwuvBLarcp2Ab0uahc9CWlMxM7sXdsVnjtyFJ5r1gPXj3ENmdo+ZvQ7cAxRmNmbjmX3BlWb2kvmKgZuBLSrusR1e0cfM7sYLvvWAnwN7yE2DHIQ3UguZjpbv6X0TXgCthVfALop47qKXFWFJZ5Cv7PgV8AUze34Jo9sa72AD16Fqul95//3wWaanhtfngGvN7LHewprZC8CNwEdiZt3SZjYb2BnvHJwaerkzUKwk+Jik6Xga2pCSeRrgFyX3LODikO/V3mRJOkcVHf4R8DbcjOgTeCdFo5TzvxuBVSWtWOtiM7vCzDbAZ1oXqwNSh5OGMLPXzGd+jga2jBmhR+ENui3wwd4jm4iyoTxYbkrpu3gjspLjcVO986ucq5R/LvC0pHfjdYG7zOzpcO9KT11lA6BY6XSYpJm4BY81S/6v4ekY4Hl8dfk5kv4dKKx+JF2C3ALR38xsWsWpftff4GpgjPmWJzfQY4Em89+kGbY1X733IXzLkx1oXx1iOK5nHwY+AHxN0vp10tZimPeYXAjsJzd/vjW+engrXDdvDR0+AFg7gr0vVsHOxjvnNixFWdbhW4EJsYInTfC3gJnNwtvgnwAqzd7vCnwlvs9kqrefZ+F5STVqlaX/wgeawQd8x9QR8Za43w74XsXb4Xn31F5k3BXYP/yn4JPbivu/E5/ktruFlYIKzgE+Fe5P0dOfsAhRt98GuDTu8xNgjTg9GrgudPgIFtXhq8xsQen/JDN7zsxewvtH1mZxHrWerTWmAWMiPY0ys9vC/5LS9X8EvirpSGDtivslXYqZ/REfmFm94tQXgSfxFVeb44Mx4P11ViO62/C8NC0VJv1CrL7eCW8LzcXzun3oybf/Alyoir2VJe0IvB+ffL8JXl8t9PSVqDeAt7tya5LBywLr2XZnLHBsjetqtVd+bWYLzOwpvI9/SzxP/GaMYdyAW1AsrMUsVo5W3OcdwBNmNhXAzJ43s1rto3Jd+j58glox5vE7M/tHL89ejY3kFmFmA/uyaL2hHpfHb/mZmuovTAYVr8cBnh7OLaWzd5jZiWHd53B8W8KN8Qml5brCC+0VeWCThVTnUYPX7Y/PGt3UzF6VNI/GzHkLOMnMfrqIp7QePjOv4PXS/9dZVDcqK+uV/6s+g5m9IGkybtLjo3jnT3H9nmb2cIVM1eJOugi5ebhfARebWVGAPylpDTN7QtIauAmvVqn7/SW9H1818F4zK/R1a2B7SZ/DVxIuI2m+mdXaG/wc4Kv4Spaio0TA+WZ2VMX91gG+hM8gfEbSBGoXOB/GGxDj8I7HDetUxJIOUU2HzezJ0vmz6ensayjKKn695mNmdnOYOFqN1OGkSczs2ShfP2hmhWnulyWdh3/vlqOu4T8K2AiYHGX1m4GrJI3DV0uNl5v1XAl4XdJLZnZGjbjOwWdWvxlf9Qiuv98ys5+UL6zoeHkxnrnQ35fM7DWAqBdtiQ8ufhz4b7yjJ+ketgXGSdoN/4YrSLrIzPaL8/2pv8RkioKzgW+HO/PfpGHM7PH4/Zvc7PeWZrbQNHc/1yHmAU/FZIsX5CbBNwE2pX7aquQ8fNLHS8ClkX8K7wj8xCLC+YDMmfjK4MckHUcNHTazQyS9B9flGZLGVqS7pDGuAr6Dr3JeteQv4KNmdn/54kbaz72Upc0MYtyCbxvxFrwD/IiQs0gDtWQUvrrpuipyPRGyvBufKL8IZnar3LTpe4Fh5vscVmMp3LrY2CrnTgdOM7Or4p7Hlc5Vdh6W+0dqvY/Ka0ZQp0/HzC6Rb63yYXyw/dPR0Z10MTGZbRjwNG51sWBFYJ6ZvS7pAHom9VwPHCvpkkhnq5QGV36Kl/GXStory/ekHxiP7/u6cDsI+XYiOwC3mtnZkkbidYby/sor4pYYXwyd36qdQicDjsXaK+FfbcxgX3yS0GZm9kpMvijqHtXK0TL1Jg1VUm9MpdUBwgn4mMVMSQfidZ1GKJ6rXH9oqb8wGXTcAFwm6ftm9lRMTBqJWyn4J/B8jKd8AB/oTlogV3J3npuBveT7aS2P7298C67k5T0nVsRn6b8qaRca30f2OuDgqNAgabQaWwFeZk9Jy0a47XETY5XPsG/E/0581vRDce4c3LT0bWb2XEmmw4rAsaKrMp5NaHy2VNIGooPip8C9ZnZa6dRV+IoP4vfXdaKp1Ovb6NmHYl/gD3Xu/258Vv44K+1DaGb7mtlaZjYG70y+oE7nNGY2BV/B8B/07HU3CR+keWPcaxVJa+MFzgvAc/L92z5UQ7alcHN2N+Fm91fCO8uTLqKWDkdlomAv3NxgLSp1uJxv7Yh3QFe1cCDfr7DYy3JTfOb/06nDSSNIWj1WCiHft+r9wH2F/oZu7Ulz+ttQHhyrmlYzszGhp7fjefGdZrZ9yf97wDfrDHADXAF8EF/9VXR4XwccpJ49YN8autxQx0uEW9HMrsVNQVXr5E46iJkdZWajQ08+jpuR3a8d+hvxl/P5cfj2QFmHSBpGvt/wqMKNr06d0646BF6/3l7ScElvwCcY3VsrbdUSIAbqHweOwTvxwPP0beWToJHvPbg+PZ2RT0U+O75WvJLeZmZTzOxY3NTemrWuTepyLnCCuZWIMtcBny/VI6u1nzfCTZZX0leDGFPw1dKvm690ngH8J953UU/G64D/Us/e2usXfRPAs3in+TcjDVTjAjy/rbqKG3yVF/CopL3jHor+BPDn/0u4D6gWfkkxs2eAf0oq3m1RNiHfZ/wRM/sB3m7O/Ti7l2JP7hm4pYoDigmVJc4EDpB0O75a8AUAM5uIf987I/wik/ai7TkdX02b/bBJX/MJvI1V5ld4OT9D0l34wqPvV1wzERguX217Il4fSJLF6KW9sod8b+xV8QHhqfSMYbwi3ye7mmWUWtwHvEXSFnHvUXILsdUo14PWxy3I3F/j2kYZBTwR9ZZ9S/6V9fhGaKaunwxSol5/PHCDeiw0vwmvF9yDtx/Pxi1jJS2SK7k7jJndIeln9Jj5+lHRqJV0p9w8xm+A04CrJd2JJ4IHG4z/2mjM3h7tzX/iHXPNMBU3ZbcmvpfEk0UnT3A68JOQ9RV8j7h/xf2nSHqRRRulxwPfi+uXwgfE98AHw8+PBD+dxQfTk86yLfBJYHY03MBXM50M/FLSwfj+J3vXieNqfPbSHsDn8ckO50o6Avg7PeboqnEqXom6NHT5z2Y2rsVn+SW+B8wzAGZ2j6RjgOuj8vYKcKiZ3R4NgrvxPWFqFTjDgIvkZmeEm+59tkXZkv6jlg5/QtJYfEblXLzDrhY30WOK8Vv4apDzIt96kfqdZx/FzTW+AiwA9imtnmmW1OGhxxp4GTkMLzt/aWbXSLpR0ur4d5uBr7KqxZLkwX2Cmf1L0k34iqtiJfb18klyf4z8fT6wH97xckikr/up3fEyCvi1fNWhcFOSycDg4jbp72FyywOvAv/ArQm0Sua/Q5M3AVdEHjUcuMTMJkq6sB11CDO7V9JE3FTk6/hef/UG1OtxMb4v9z0R99/lK1V+JmnZuOYYM3tAvjp9djzb1GqRBafKTe4Jn/gxs0XZhjRmNo/FByHABx++B8yKQeS5+N7YP6JHh2YAd1QJ22hZ2ptsL0t6rBT+FnxgpRiQryXjObjZzunh/3d8UlMR75OSdgd+K+mgKre+GN8T82dVzpXZF/hR5MdL41unzcTT2aWS/hKyr9P4UzfFwcDZkl7AzbUXE20305wAAALCSURBVPz3wbcIeAX4K75HY9KFmFnVrRbMt9vZKNwPsuhEhaNK152M942Uw+5Ycn+976RNkh7Kelby+wHwgxrXjyn9rToJ08yWL7kvAy5bIiGTgU7V9krUi+/Axy3WAk40s8clXUzPGMYMfOC6IaK/YB/gdPnk/gX4BP9q26OdCfw4xhdeBQ6M+kqjt3uH3FJuwReBr+ET+/6E13GK8Y+f4+X8YdSZ+FnBcTTeX5gMIszsuIr/l7DodjYFn6wRfnQ/iDWoUev968lQQNJJ+Eyj77UYfk3gd8A7l2AwJ0n6FEnX4JWySZ2WJUlaIXU4GajEIOB0YO/oKEySAUXmv8lAR9IZwF1WsZ1VknQjksYDe5hZ1U7AbkHS8mY2P9xfAdYws8M7LFaSJEmS9BvyrWzmW88WakmSJB0hzeQk/YakT+GmJL+aA9xJNyBpJUkPAAuyczoZiKQOJwMZSe/CrbdMygHuZKCR+W8yGJA0DV+FeFGnZUmS3pB0Or4y9sROy9IAHw5T13PwLd5O6rRASZIkSZIkSTIUyJXcg4QwkzehwvtFM9umA+IkQ5yY4FA5c/1WMzu0gbBHs7jJ80vN7BtNyjAFWLbC+5NV9rhLksWQ9AHg2xXej5rZXg2EbVn/K+JJHU5aotN5cOzHVW0QcGcze7rReJKhSaf1N+LJ/DdpmS6pQ1zB4maZjzSz65qJJxl69FU+2l9I+iG+BVGZ75tZzT27kyRJkiQZuCxh3frfgAsrvF82s/f0lXxJknSeHOROkiRJkiRJkiRJkiRJkiRJkiRJkiRJBgxprjxJkiRJkiRJkiRJkiRJkiRJkiRJkiQZMOQgd5IkSZIkSZIkSZIkSZIkSZIkSZIkSTJgyEHuJEmSJEmSJEmSJEmSJEmSJEmSJEmSZMCQg9xJkiRJkiRJkiRJkiRJkiRJkiRJkiTJgOH/AUfjCS2PIKKaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1980x1980 with 132 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print relationships between data\n",
    "sb.pairplot(sd_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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": [
    "# Black (%) Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sd_df.drop(columns=['Black','Total_employed'])\n",
    "y = sd_df['Black']"
   ]
  },
  {
   "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(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " -1608.4494713403803\n",
      "Coefficients: \n",
      " [-0.04177642  0.01530042  0.01616526  0.03920475 -0.06819731 -0.0150056\n",
      " -0.1072433   0.75583729  0.80768336]\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": [
    "# plt.figure(figsize=(10,10))\n",
    "# plt.tight_layout()\n",
    "# seabornInstance.distplot(sd_df['Black'])"
   ]
  },
  {
   "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='Black', style='o')  \n",
    "plt.title('Predict Race:')  \n",
    "plt.xlabel('Year')  \n",
    "plt.ylabel('Black (%)')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18dd315da08>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\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['Black'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = sd_df['Year'].values.reshape(-1,1)\n",
    "# y = sd_df['Black'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.31987586])"
      ]
     },
     "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='Black', style='o')  \n",
    "plt.title('Predict Race:')  \n",
    "plt.xlabel('Year')  \n",
    "plt.ylabel('Black (%)')   \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['Black'].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>3.9</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual  Predicted\n",
       "105     3.9        1.0\n",
       "35      4.1        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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}