Machine Learning - Hispanic or Latino.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",
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       "        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>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": {
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\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": [
    "# Hispanic and Latino (%) Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sd_df.drop(columns=['Hispanic_or_Latino','Total_employed'])\n",
    "y = sd_df['Hispanic_or_Latino']"
   ]
  },
  {
   "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",
      " -562.2237534164378\n",
      "Coefficients: \n",
      " [-0.00099144 -0.02401549  0.01369867 -0.02183942  0.04075385  0.00247103\n",
      "  0.42581946 -0.02336989  0.28163897]\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": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "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": 13,
   "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='Hispanic_or_Latino', style='o')  \n",
    "plt.title('Predict Race:')  \n",
    "plt.xlabel('Year')  \n",
    "plt.ylabel('Hispanic or Latino (%)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1dd0d3447c8>"
      ]
     },
     "execution_count": 14,
     "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['Hispanic_or_Latino'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = sd_df['Year'].values.reshape(-1,1)\n",
    "# y = sd_df['Hispanic_or_Latino'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.03014683])"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "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='Hispanic_or_Latino', style='o')  \n",
    "plt.title('Predict Race:')  \n",
    "plt.xlabel('Year')  \n",
    "plt.ylabel('Hispanic or Latino (%)')   \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": 18,
   "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>5.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual  Predicted\n",
       "105     5.3        1.0\n",
       "35      5.3        1.0"
      ]
     },
     "execution_count": 18,
     "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
}