Diamond Prices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from subprocess import check_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dmds = pd.read_csv('./diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dmds.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.24</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>J</td>\n",
       "      <td>VVS2</td>\n",
       "      <td>62.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>336</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.96</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.24</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>I</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>62.3</td>\n",
       "      <td>57.0</td>\n",
       "      <td>336</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.26</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>61.9</td>\n",
       "      <td>55.0</td>\n",
       "      <td>337</td>\n",
       "      <td>4.07</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.22</td>\n",
       "      <td>Fair</td>\n",
       "      <td>E</td>\n",
       "      <td>VS2</td>\n",
       "      <td>65.1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>337</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>H</td>\n",
       "      <td>VS1</td>\n",
       "      <td>59.4</td>\n",
       "      <td>61.0</td>\n",
       "      <td>338</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.05</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.30</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>339</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>VS1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>56.0</td>\n",
       "      <td>340</td>\n",
       "      <td>3.93</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.22</td>\n",
       "      <td>Premium</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>61.0</td>\n",
       "      <td>342</td>\n",
       "      <td>3.88</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>54.0</td>\n",
       "      <td>344</td>\n",
       "      <td>4.35</td>\n",
       "      <td>4.37</td>\n",
       "      <td>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.20</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>60.2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>345</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.32</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>I1</td>\n",
       "      <td>60.9</td>\n",
       "      <td>58.0</td>\n",
       "      <td>345</td>\n",
       "      <td>4.38</td>\n",
       "      <td>4.42</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.30</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>348</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.34</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.30</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.4</td>\n",
       "      <td>54.0</td>\n",
       "      <td>351</td>\n",
       "      <td>4.23</td>\n",
       "      <td>4.29</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.30</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.8</td>\n",
       "      <td>56.0</td>\n",
       "      <td>351</td>\n",
       "      <td>4.23</td>\n",
       "      <td>4.26</td>\n",
       "      <td>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.30</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.7</td>\n",
       "      <td>59.0</td>\n",
       "      <td>351</td>\n",
       "      <td>4.21</td>\n",
       "      <td>4.27</td>\n",
       "      <td>2.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    carat        cut color clarity  depth  table  price     x     y     z\n",
       "0    0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1    0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2    0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3    0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4    0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "5    0.24  Very Good     J    VVS2   62.8   57.0    336  3.94  3.96  2.48\n",
       "6    0.24  Very Good     I    VVS1   62.3   57.0    336  3.95  3.98  2.47\n",
       "7    0.26  Very Good     H     SI1   61.9   55.0    337  4.07  4.11  2.53\n",
       "8    0.22       Fair     E     VS2   65.1   61.0    337  3.87  3.78  2.49\n",
       "9    0.23  Very Good     H     VS1   59.4   61.0    338  4.00  4.05  2.39\n",
       "10   0.30       Good     J     SI1   64.0   55.0    339  4.25  4.28  2.73\n",
       "11   0.23      Ideal     J     VS1   62.8   56.0    340  3.93  3.90  2.46\n",
       "12   0.22    Premium     F     SI1   60.4   61.0    342  3.88  3.84  2.33\n",
       "13   0.31      Ideal     J     SI2   62.2   54.0    344  4.35  4.37  2.71\n",
       "14   0.20    Premium     E     SI2   60.2   62.0    345  3.79  3.75  2.27\n",
       "15   0.32    Premium     E      I1   60.9   58.0    345  4.38  4.42  2.68\n",
       "16   0.30      Ideal     I     SI2   62.0   54.0    348  4.31  4.34  2.68\n",
       "17   0.30       Good     J     SI1   63.4   54.0    351  4.23  4.29  2.70\n",
       "18   0.30       Good     J     SI1   63.8   56.0    351  4.23  4.26  2.71\n",
       "19   0.30  Very Good     J     SI1   62.7   59.0    351  4.21  4.27  2.66"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmds.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encode Cut, Color and Clarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
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       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.24</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>62.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>336</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.96</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.24</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>62.3</td>\n",
       "      <td>57.0</td>\n",
       "      <td>336</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.26</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>61.9</td>\n",
       "      <td>55.0</td>\n",
       "      <td>337</td>\n",
       "      <td>4.07</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.22</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>65.1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>337</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.23</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>59.4</td>\n",
       "      <td>61.0</td>\n",
       "      <td>338</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.05</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.30</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>339</td>\n",
       "      <td>4.25</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>62.8</td>\n",
       "      <td>56.0</td>\n",
       "      <td>340</td>\n",
       "      <td>3.93</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.22</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>60.4</td>\n",
       "      <td>61.0</td>\n",
       "      <td>342</td>\n",
       "      <td>3.88</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>62.2</td>\n",
       "      <td>54.0</td>\n",
       "      <td>344</td>\n",
       "      <td>4.35</td>\n",
       "      <td>4.37</td>\n",
       "      <td>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>345</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.32</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>60.9</td>\n",
       "      <td>58.0</td>\n",
       "      <td>345</td>\n",
       "      <td>4.38</td>\n",
       "      <td>4.42</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>348</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.34</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.30</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>63.4</td>\n",
       "      <td>54.0</td>\n",
       "      <td>351</td>\n",
       "      <td>4.23</td>\n",
       "      <td>4.29</td>\n",
       "      <td>2.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.30</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>63.8</td>\n",
       "      <td>56.0</td>\n",
       "      <td>351</td>\n",
       "      <td>4.23</td>\n",
       "      <td>4.26</td>\n",
       "      <td>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.30</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>62.7</td>\n",
       "      <td>59.0</td>\n",
       "      <td>351</td>\n",
       "      <td>4.21</td>\n",
       "      <td>4.27</td>\n",
       "      <td>2.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    carat  cut  color  clarity  depth  table  price     x     y     z\n",
       "0    0.23    0      0        0   61.5   55.0    326  3.95  3.98  2.43\n",
       "1    0.21    1      0        1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2    0.23    2      0        2   56.9   65.0    327  4.05  4.07  2.31\n",
       "3    0.29    1      1        3   62.4   58.0    334  4.20  4.23  2.63\n",
       "4    0.31    2      2        0   63.3   58.0    335  4.34  4.35  2.75\n",
       "5    0.24    3      2        4   62.8   57.0    336  3.94  3.96  2.48\n",
       "6    0.24    3      1        5   62.3   57.0    336  3.95  3.98  2.47\n",
       "7    0.26    3      3        1   61.9   55.0    337  4.07  4.11  2.53\n",
       "8    0.22    4      0        3   65.1   61.0    337  3.87  3.78  2.49\n",
       "9    0.23    3      3        2   59.4   61.0    338  4.00  4.05  2.39\n",
       "10   0.30    2      2        1   64.0   55.0    339  4.25  4.28  2.73\n",
       "11   0.23    0      2        2   62.8   56.0    340  3.93  3.90  2.46\n",
       "12   0.22    1      4        1   60.4   61.0    342  3.88  3.84  2.33\n",
       "13   0.31    0      2        0   62.2   54.0    344  4.35  4.37  2.71\n",
       "14   0.20    1      0        0   60.2   62.0    345  3.79  3.75  2.27\n",
       "15   0.32    1      0        6   60.9   58.0    345  4.38  4.42  2.68\n",
       "16   0.30    0      1        0   62.0   54.0    348  4.31  4.34  2.68\n",
       "17   0.30    2      2        1   63.4   54.0    351  4.23  4.29  2.70\n",
       "18   0.30    2      2        1   63.8   56.0    351  4.23  4.26  2.71\n",
       "19   0.30    3      2        1   62.7   59.0    351  4.21  4.27  2.66"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical_cols = ['cut', 'color', 'clarity']\n",
    "for c in categorical_cols:\n",
    "    dmds[c] = pd.factorize(dmds[c])[0]\n",
    "dmds.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat      float64\n",
       "cut          int64\n",
       "color        int64\n",
       "clarity      int64\n",
       "depth      float64\n",
       "table      float64\n",
       "price        int64\n",
       "x          float64\n",
       "y          float64\n",
       "z          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmds.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53940, 10)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['carat', 'cut', 'color', 'clarity', 'depth', 'table', 'price', 'x', 'y',\n",
       "       'z'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmds.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.797940</td>\n",
       "      <td>1.228940</td>\n",
       "      <td>3.174416</td>\n",
       "      <td>2.256136</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.538734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.474011</td>\n",
       "      <td>1.265976</td>\n",
       "      <td>2.050156</td>\n",
       "      <td>1.766539</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.705699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>2.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>3.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.040000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5324.250000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>4.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.010000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>18823.000000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>31.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              carat           cut         color       clarity         depth  \\\n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
       "mean       0.797940      1.228940      3.174416      2.256136     61.749405   \n",
       "std        0.474011      1.265976      2.050156      1.766539      1.432621   \n",
       "min        0.200000      0.000000      0.000000      0.000000     43.000000   \n",
       "25%        0.400000      0.000000      1.000000      1.000000     61.000000   \n",
       "50%        0.700000      1.000000      4.000000      2.000000     61.800000   \n",
       "75%        1.040000      3.000000      5.000000      3.000000     62.500000   \n",
       "max        5.010000      4.000000      6.000000      7.000000     79.000000   \n",
       "\n",
       "              table         price             x             y             z  \n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000  \n",
       "mean      57.457184   3932.799722      5.731157      5.734526      3.538734  \n",
       "std        2.234491   3989.439738      1.121761      1.142135      0.705699  \n",
       "min       43.000000    326.000000      0.000000      0.000000      0.000000  \n",
       "25%       56.000000    950.000000      4.710000      4.720000      2.910000  \n",
       "50%       57.000000   2401.000000      5.700000      5.710000      3.530000  \n",
       "75%       59.000000   5324.250000      6.540000      6.540000      4.040000  \n",
       "max       95.000000  18823.000000     10.740000     58.900000     31.800000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmds.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    53940.000000\n",
       "mean      3932.799722\n",
       "std       3989.439738\n",
       "min        326.000000\n",
       "25%        950.000000\n",
       "50%       2401.000000\n",
       "75%       5324.250000\n",
       "max      18823.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmds['price'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checking for NA Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x19250014ac8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3vetdbziBampqYsGCBW845oILLohFixbFiBEj4pOf/GR/znxb9+HBBx+M+vr6qKuri/PP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\nSBBMAEgQTABIEEwASBBMAEj4H2Q+PGPwLKLUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19250018b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dmds.isnull(),yticklabels=False,cbar=False,cmap='hot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no NA Values, however we will still take precaution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a Reduced Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dmds_reduced = dmds[dmds.columns.difference(['cut', 'color', 'clarity'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Uuuq/AiIiFWGlS0SqwekFIiIFecL0AkOXiFSDlS4RkYIYukRECvKE0FX/BAkRkYqw0iUi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\nUhBDl4hIQf8H78wvBGPugDkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19250840278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1925089d048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(dmds_reduced.corr())\n",
    "plt.colorbar()\n",
    "tick_marks = dmds_reduced.columns.values\n",
    "plt.xticks(np.arange(tick_marks.size), tick_marks)\n",
    "plt.yticks(np.arange(tick_marks.size), tick_marks)\n",
    "plt.figure(figsize=(15,10), dpi=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PQ2Pms/N6V5ShyPTPkd26MnQLSsXx82HSOZloqsCTh3s52xvjTG+M3lCKjsHp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\ntYgiTruF/KxZ4uyOfsNTpjtaY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j/+8Y/84he/4KKLLuo1uMdT51z8F3P0w1HnY2AQx+OE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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1925093b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(dmds_reduced, diagonal='kde')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = dmds.iloc[:, dmds.columns != 'price'].values, dmds.iloc[:, dmds.columns == 'price'].values.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.35, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression\n",
    "\n",
    "Let's use basic linear regression and random forest to compare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models = [('LR', LinearRegression(n_jobs=-1)),\n",
    "          ('RF', RandomForestRegressor(n_estimators=100, criterion='mse', random_state=1, n_jobs=-1)),\n",
    "#           ('SVR-lin', SVR(kernel='linear', C=1e3))\n",
    "#           ('SVR-rbf', SVR(kernel='rbf', C=1e3)),\n",
    "#           ('SVR-poly', SVR(kernel='poly', C=1e3, degree=2))\n",
    "         ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import cross_val_score, KFold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Models in Each Turn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = []\n",
    "names = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.871834 (0.006784)\n",
      "RF: 0.978202 (0.000777)\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=5, random_state=250)\n",
    "    cv_results = cross_val_score(model, X_train, y_train, cv=kfold, n_jobs=-1)    \n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing MSE and R^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: MSE train: 2038241.2239, test: 1975222.0696\n",
      "LR: R^2 train: 0.8723, test: 0.8752\n",
      "RF: MSE train: 46136.3864, test: 329201.7774\n",
      "RF: R^2 train: 0.9971, test: 0.9792\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    y_train_pred = model.predict(X_train)\n",
    "    y_test_pred = model.predict(X_test)\n",
    "\n",
    "    print('%s: MSE train: %.4f, test: %.4f' % (name, mean_squared_error(y_train, y_train_pred),\n",
    "                                           mean_squared_error(y_test, y_test_pred)))\n",
    "    print('%s: R^2 train: %.4f, test: %.4f' % (name, r2_score(y_train, y_train_pred),\n",
    "                                           r2_score(y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results look promising. Let's take a peek at algorithmic predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BAABIJz4BAABIJz4BAABIJz4BAABIJz4BAABIJz4BAABIJz4BAABIJz4BAABIJz4BAABI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\nnXZafPTRR3Huuee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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19254bf8b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 8))\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results, vert=False)\n",
    "ax.set_yticklabels(names)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Price Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price classes: [     0.           2091.44444444   4182.88888889   6274.33333333\n",
      "   8365.77777778  10457.22222222  12548.66666667  14640.11111111\n",
      "  16731.55555556  18823.        ]\n"
     ]
    }
   ],
   "source": [
    "# convert price data to classes\n",
    "n_classes = 10\n",
    "\n",
    "y_classes = np.linspace(0, y.max(), n_classes)\n",
    "y_train_cl = np.digitize(y_train, bins=y_classes)\n",
    "y_test_cl = np.digitize(y_test, bins=y_classes)\n",
    "\n",
    "print('Price classes: %s' % (y_classes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other Model Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "models = [('LR', LogisticRegression()),\n",
    "          ('KNN', KNeighborsClassifier()),\n",
    "          ('CART', DecisionTreeClassifier()),\n",
    "          ('NB', GaussianNB()),\n",
    "          ('SVM-lin', SVC(kernel='linear')),\n",
    "          ('SVM-rbf', SVC(kernel='rbf')),\n",
    "          ('RF', RandomForestClassifier()),\n",
    "          ('MLP', MLPClassifier(alpha=1)),\n",
    "          ('ADA', AdaBoostClassifier())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluating All Models as Above Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = []\n",
    "names = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.699438 (0.006470)\n",
      "KNN: 0.771399 (0.003694)\n",
      "CART: 0.837198 (0.005258)\n",
      "NB: 0.731240 (0.003907)\n",
      "SVM-lin: 0.771798 (0.004484)\n",
      "SVM-rbf: 0.823137 (0.003745)\n",
      "RF: 0.853883 (0.004507)\n",
      "MLP: 0.741251 (0.006281)\n",
      "ADA: 0.548927 (0.168956)\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=5, random_state=42)\n",
    "    cv_results = cross_val_score(model, X_train, y_train_cl, cv=kfold, n_jobs=-1)    \n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other Methods on it's Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yyL8Psdb/H+Ovf/1rWlpakiR33313PvShD73zdwkAAMAa603v2I4YMSLz5s3L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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x192547560b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 12))\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results, vert=False)\n",
    "ax.set_yticklabels(names)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price classes: [     0.     4705.75   9411.5   14117.25  18823.  ]\n"
     ]
    }
   ],
   "source": [
    "# Convert Price Data to Classes\n",
    "n_classes = 5\n",
    "\n",
    "y_classes = np.linspace(0, y.max(), n_classes)\n",
    "y_train_cl = np.digitize(y_train, bins=y_classes)\n",
    "y_test_cl = np.digitize(y_test, bins=y_classes)\n",
    "\n",
    "print('Price classes: %s' % (y_classes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate Models in Turn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "results = []\n",
    "names = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.848236 (0.005550)\n",
      "KNN: 0.886370 (0.001022)\n",
      "CART: 0.921594 (0.003238)\n",
      "NB: 0.817205 (0.005072)\n",
      "SVM-lin: 0.882690 (0.003069)\n",
      "SVM-rbf: 0.920995 (0.002099)\n",
      "RF: 0.931149 (0.003688)\n",
      "MLP: 0.869428 (0.005494)\n",
      "ADA: 0.792334 (0.043841)\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=5, random_state=42)\n",
    "    cv_results = cross_val_score(model, X_train, y_train_cl, cv=kfold, n_jobs=-1)    \n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gQYPWuO/Jkyfnu9/9bhYuXJjx48c3/dEAAACwyWn3QVaaO3dupk+fnptvvjkV\nFRWZNWtWxo0blx49euQPf/hDBgx4580Bpk+fngEDBqSysjLV1dV59NFH89Zbb6Vdu3Z5++238+ST\nT653P127dhW2AAAAfCgf6IxtZWVlFixYkEmTJuWVV17JTjvtlEmTJmX06NGZMmVKw3p33HFHDj/8\n8CRJu3bt8pnPfCYPPfRQkuT3v/999tlnn/Xu58UXX8zo0aOTJEOHDs0ll1zScIZ46dKlTXqAAABs\nGDU1Vev8AmhJHyhsu3XrlquvvjqPP/54Dj/88Bx44IG5//77c8ABB+SPf/xj3njjjdTV1eXVV1/N\nbrvt1rDdwQcfnHvuuSdJMm3atAwdOvQDD7Zs2bIcdNBBueGGG1JTU5MZM2Z8yIcGAMCGVFe3ZJ1f\nAC3pA12K/Pzzz6dz58659NJLkyRPPvlkTjjhhAwYMCAHHHBA7rvvvixYsCAjR45stN0ee+yRiy66\nKIsWLUp9fX0+8YlPJHknWk888cQkyT777JNu3bqtdb8777xzkmTrrbfOm2++2bRHCAAAwEbtA4Xt\ns88+m1tvvTVXX311OnTokF69eqWqqipt27bNqFGjcsUVV+S1117Ldddd12i7ioqKDBw4MOPHj88B\nBxzQsLxTp06ZOHFiw8+TJ09e634rKiqa8pgAAADYhHygsB08eHDmzZuXww47LFtssUVKpVLOPvvs\nVFZWprKyMsuXL0/v3r1TWVm5xrZDhw7NYYcdlosvvrjZhwcAAICK0ns/r6fgFi70BlMAwKbp3Tds\nas6/bz15+tn54aDL17m/9e3r/W4HaIquXdc8mZp8iM+xBQCg9erbd6dyj9BIa5sH2LgJWwCAjcCM\nGTPLPUIjrW0eYOMmbAEAACg0YQsAAEChCVsAAAAKTdgCAABQaB/oc2wBACiGdz/2pzkMvmL0Ou+v\nurq62fYD8FH5HFsAAAAKwefYAgAAsFEStgAAABSasAUAAKDQhC0AAACFJmwBAAAoNGELAABAoQlb\nAAAACk3YAgAAUGjCFgAAgEITtgAAABSasAUAAKDQhC0AAACFJmwBAAAoNGELAABAoQlbAAAACk3Y\nAgAAUGjCFgAAgEITtgAAABSasAUAAKDQhC0AAACFJmwBAAAoNGELAABAoQlbAAAACq1duQcAKJo+\nfXqkvr7+Q20z+IrR+fVZtyVJqqurM2fOCxtiNACATZKwBfiQ6uvrU1e35ENtc/L0sxu2qamp2hBj\nAQBssly3MteGAAASV0lEQVSKDAAAQKEJWwAAAApN2AIAAFBowhYAAIBCE7YArdB++w0o9wgAAIUh\nbAFaodmzZ5V7BACAwhC2AAAAFFpZw3bmzJnZY4898vLLLzcs++53v5vJkyenX79+qa2tzZgxY3Lo\noYfmrrvuKuOkAAAAtFbtyj1Ahw4dcu655+b6669PRUVFw/IuXbpk4sSJSZKlS5fmi1/8YoYNG9Zo\nHQAAACj7pch77bVXunTpkhtvvHGd67z++uupqqoStQAAAKyh7Gdsk2T8+PEZNWpUPvvZzzYsW7x4\ncWpra7N69erMmTMntbW1ZZwQoHnV1FSVewQAgI1GqwjbLbfcMuedd17GjRuX/v37J2l8KfLrr7+e\nI444Ivvss0/23HPPco4K0Czq6pas93bhCwDwwZX9UuR3DRo0KL169cqdd965xm2dOnVKZWVlVq1a\nVYbJAAAAaM1axRnbd33jG9/II488kuT/X4qcJCtXrswuu+ySvfbaq5zjAQAA0AqVNWwHDBiQAQMG\nNPzcuXPn3H///UmSQw89tFxjAQAAUCCt5lJkAAAAaAphC9AK9e27U7lHAAAoDGEL0ArNmDGz3CMA\nABSGsAUAAKDQhC0AAACFJmwBAAAoNGELAABAoZX1c2wBiqqmpupDrT/4itEN21RXV2+IkQAANlkV\npVKpVO4hmsvChUvLPQIAAAAbSNeulWtd7lJkAAAACk3YAgAAUGjCFgAAgEITtgAAABSasAUAAKDQ\nhC0AAACFJmwBAAAoNGELAABAoQlbAAAACk3YAgAAUGjCFgAAgEITtgAAABSasAUAAKDQhC0AAACF\nJmwBAAAoNGELAABAoQlbAAAACk3YAgAAUGjCFgAAgEITtgAAABSasAUAAKDQhC0AAACFJmwBAIBN\nQp8+PVJTU5WamqqM+fm/NHzfp0+Pco/GRyRsAQCATUJ9fX3q6pakrm5JunTfquH7+vr6co/GRyRs\nAQAAKDRhCwAAQKEJWwAAAApN2AIAAFBowhYAAIBCE7YAAAAUmrAFAACg0No11x39z//8T6644oqs\nWLEiy5cvz8CBA3PqqaemoqIi99xzT84777z86le/Srdu3ZIkV111VaZNm5aampok73ym1JAhQ3LS\nSSfljDPOSF1dXV566aW0b98+NTU16dOnTy644ILmGhcAAICNRLOE7ZIlSzJ27NhcddVV6dmzZ95+\n++2cdtppueWWW3LkkUfm9ttvT21tbW677baceuqpDdsdc8wxOfLII5MkK1euzJAhQzJ69Oh873vf\nS/JO/H7sYx9rWAcAAAD+UbNcivzb3/42AwYMSM+ePZMkbdu2zXe+852MHDky8+fPz+LFi3PCCSfk\nrrvuyqpVq9Z6H4sWLcpbb72Vjh07NsdIAAAAbCKa5YxtXV1dunfv3mhZp06dkiSTJk3KyJEjU1VV\nld122y2/+c1vMmTIkCTJz372s9x99915+eWX061bt0yYMCGdO3dujpEAAADYRDRL2G6zzTZ55pln\nGi2bP39+Xn755fzyl7/MJz7xiUyfPj2LFy/ODTfc0BC2716K/NRTT2Xs2LENZ3wBAADgg2qWS5E/\n//nP53e/+11eeOGFJMmqVaty2WWXZdasWenXr18mTpyY6667LpMmTcrf//73zJ49u9H2/fr1ywkn\nnJCxY8dm9erVzTESAAAAm4hmCdvOnTvnsssuy/nnn5/a2tocfvjh6du3bx5++OEMHz680bqHHXZY\nbrzxxjXuY9SoUencuXNuvvnm5hgJAACATURFqVQqlXuI5rJw4dJyjwAAALRSNTVVqatbkiQ5efrZ\n+eGgy9dYTuvWtWvlWpc3yxlbAAAAKBdhCwAAQKEJWwAAAApN2AIAAFBowhYAAIBCE7YAAAAUmrAF\nAACg0IQtAACwyaipqUpNTVUWz3+t4fvq6upyj8VHVFEqlUrlHqK5LFy4tNwjAAAAsIF07Vq51uXO\n2AIAAFBowhYAAIBCE7YAAAAUmrAFAACg0IQtAAAAhSZsAQAAKDRhCwAAQKEJWwAAAApN2AIAAFBo\nwhYAAIBCE7YAAAAUmrAFAACg0IQtAAAAhSZsAQAAKDRhCwAAQKEJWwAAAApN2AIAAFBowhYAAIBC\nE7YAAAAUmrAFAACg0IQtAAAAhSZsAQAAKDRhCwDAR9KnT4/U1FSlpqYqY37+L+nTp0e5RwI2McIW\nAICPpL6+PnV1S1JXtyRdum+V+vr6co8EbGKELQAAAIUmbAEAACg0YQsAAEChCVsAAAAKTdgCANAk\n++03oNwjACQRtgAANNHs2bPKPQJAEmELAABAwQlbAAAACm2Dh+3MmTNz+umnN/z8X//1Xzn44INz\n9NFH55RTTmm07r777pskmTx5cgYNGpTXX3+94bbTTz89M2fO3NDjAgAAUDAtesZ22rRpufbaa/Oz\nn/0s22yzTR577LFMmTJlreuuWLEi3/72t1tyPAAAAAqoxcJ2ypQp+dnPfpbrr78+H/vYx5IkY8eO\nzVVXXZX//d//XWP9Qw45JH/9619z//33t9SIAAB8SDU1VeUeAaBlwvbRRx/NbbfdlsWLF+ftt99u\nWN6tW7ecdtpp+cY3vrHGNm3bts1ll12Wb3/721m0aFFLjAkAwIdUV7ek3CMAtEzYdu3aNddff32+\n8pWv5Kyzzsrq1asbbhs2bFg6deqUm266aY3tevbsmaOPPjoXXXRRS4wJAABAAbVI2G633Xbp2LFj\nxowZk/bt2+fqq69udPv48ePz05/+NMuWLVtj2zFjxmTRokV55JFHWmJUAAAACqbFP+7n29/+dm69\n9dZG73C81VZb5ZxzzsmKFSvWWL+ioiKXXnppVq5c2ZJjAgAAUBAVpVKpVO4hmsvChUvLPQIAwCaj\npqYqdXVLGv43SU6efnZuP+LH/vYW2CC6dq1c6/IWP2MLAMDGoW/fnco9AkASYQsAQBPNmDHz/VcC\naAHCFgAAgEITtgAAABSasAUAAKDQhC0AAACFJmwBAPjIamqqUlNTlcXzX0t1dXW5xwE2MT7HFgAA\ngELwObYAAABslIQtAAAAhSZsAQAAKDRhCwAAQKEJWwAAAApN2AIAAFBowhYAAIBCE7YAAAAUmrAF\nAACg0IQtAAAAhSZsAQAAKDRhCwAAQKEJWwAAAApN2AIAAFBowhYAAIBCE7YAAAAUmrAFAACg0IQt\nAAAAhSZsAQAAKDRhCwAAQKEJWwAAAApN2AIAAFBowhYAAIBCa1fuAQCgKPr06ZH6+vq13jb4itH5\n9Vm3tfBEa1ddXZ05c14o9xgA0GKELQB8QPX19amrW7LW206efvY6b2tpNTVV5R4BAFqUS5EBAAAo\nNGELAABAoQlbAAAACk3YAgAAUGjCFgDWY7/9BpR7hFbB7wGA1kzYAsB6zJ49q9wjtAp+DwC0ZsIW\nAACAQiv759jOnDkzt9xyS6688sqGZbW1tVmxYkU233zzrF69OkuWLMmZZ56ZgQMHlnFSAAAAWqOy\nh+26fOc730nv3r2TJH/961/zta99TdgCAACwhkJcirxgwYJUVVWVewwAAABaoVZ7xnbcuHFp165d\nFixYkN122y2XXnppuUcCYBNVU1O8f1wt4swA0FStNmzfvRT5lltuybRp07L11luXeyQANlF1dUuS\nFCsW3525uRTpsQOw6Wn1lyIfccQR2XrrrRu9uRQAAAC8q1WcsX3ooYdy6KGHNvxcV1fX6PZvfOMb\nGTZsWIYPH56+ffu29HgAAAC0YmUP2wEDBuQPf/jDeteprq7OjBkzWmgiAAAAiqTVX4oMAAAA6yNs\nAWA9+vbdqdwjtAp+DwC0ZsIWANZjxoyZ5R6hVfB7AKA1E7YAAAAUmrAFAACg0IQtAAAAhSZsAQAA\nKLSyf44tABRJTU3VWpcPvmL0Om9radXV1eUeAQBaVEWpVCqVe4jmsnDh0nKPAAAAwAbStWvlWpe7\nFBkAAIBCE7YAAAAUmrAFAACg0IQtAAAAhSZsAQAAKDRhCwAAQKEJWwAAAApN2AIAAFBowhYAAIBC\nE7YAAAAUWkWpVCqVewgAAABoKmdsAQAAKDRhCwAAQKEJWwAAAApN2AIAAFBowhYAAIBCE7YAAAAU\nmrAFAACg0NqVewBa1urVqzN+/Pg8++yz6dChQyZMmJDtttsuSbJw4cKMHTu2Yd1Zs2bljDPOSMeO\nHXPnnXcmSd58883MmjUrDz30UF588cV89atfTc+ePZMkRx55ZIYMGdLij4n1H9ckmTp1aq6//vq0\nadMmI0eOzFFHHbXObZ5//vmcc845qaioyA477JBvfvObadPGv4GVS1OO7apVq3LeeeflpZdeysqV\nK3PSSSdl//33zzPPPOM520o05bgmyYgRI9K5c+ckybbbbptLL73Uc7YVacpxnTx5stfYAni/Yztl\nypRcd911qayszIgRIzJq1CivswXQlOPqNbYVK7FJ+dWvflUaN25cqVQqlf70pz+VTjzxxLWu9/jj\nj5dqa2tLb731VqPl48ePL91yyy2lUqlUuu2220rXXXfdhh2YD+T9juu+++5bWrRoUenNN98sHXDA\nAaX6+vp1bvPVr3619Mgjj5RKpVLpggsuKP36179uwUfCP2rKsZ00aVJpwoQJpVKpVFq0aFFp4MCB\npVLJc7Y1acpxfeONN0rDhw9f4748Z1uPphzX9/Ia23qt79j+/e9/L33+858vLVq0qPT222+Xamtr\nS/Pnz/c6WwBNOa5eY1sv/zy0iXnsscfy2c9+Nkmy22675amnnlpjnVKplEsuuSTjx49P27ZtG5Y/\n+eSTmTt3bg4//PAkyVNPPZUHHnggX/7yl3Peeefl9ddfb5kHwRre77juuOOOWbp0aVauXJlSqZSK\niop1bvP000/nM5/5TJJkv/32y3//93+34CPhHzXl2B544IE57bTTkrzzfH73eew523o05bjOnj07\nK1asyHHHHZejjz46f/7zn5N4zrYmTTmu7/Ia27qt79i++OKL2XHHHVNdXZ02bdpkl112yRNPPOF1\ntgCacly9xrZewnYT8/rrrzdcxpYkbdu2zVtvvdVonenTp2eHHXbI9ttv32j5Nddck5NPPrnh5113\n3TVnn312brzxxnTv3j0//OEPN+zwrNP7HdcddtghI0eOzEEHHZTPfe5zqaqqWuc27/2PrU6dOmXp\n0qUt90BYQ1OObadOndK5c+e8/vrr+drXvpavf/3rSTxnW5OmHNfNNtssxx9/fK677rpcdNFFOfPM\nMz1nW5mmHNd3eY1t3dZ3bLfbbrvMnTs3r776alasWJGHH344y5cv9zpbAE05rl5jWy9hu4np3Llz\nli1b1vDz6tWr065d4z+1njp1akaPHt1o2ZIlS/Lcc89lr732alj2hS98If369Wv4/plnntmAk7M+\n6zuus2fPzgMPPJDf/va3mT59el577bXce++969zmvX/ns2zZskb/4UXLa8qxTZKXX345Rx99dIYP\nH56hQ4cm8ZxtTZpyXHv16pVhw4aloqIivXr1SnV1dRYuXOg524o09fnqNbb1W9+x7dKlS84999yc\neuqpGTt2bD71qU9lyy239DpbAE05ronX2NZK2G5i+vfvnxkzZiRJ/vznP6dPnz5rrPPUU0+lf//+\njZb98Y9/zN57791o2fHHH5+//OUvSZKHH344n/rUpzbQ1Lyf9R3XysrKbLbZZunYsWPatm2brbba\nKkuWLFnnNjvvvHNmzpyZJJkxY0b23HPPFn40vFdTju2rr76a4447LmeddVYOO+ywhvU9Z1uPphzX\nSZMm5bLLLkuSvPLKK3n99dfTtWtXz9lWpCnHNfEaWwTrO7ZvvfVWnnnmmdx00035/ve/n7/+9a/p\n37+/19kCaMpx9RrbelWUSqVSuYeg5bz77m9z5sxJqVTKt7/97TzzzDNZvnx5Dj/88Lz22ms59thj\nc9dddzXa7j//8z/Trl27HHPMMQ3Lnn766VxyySVp3759Pvaxj+WSSy5pdDkHLef9juvNN9+cO+64\nI+3bt0+PHj1yySWXpF27dmts07t37zz33HO54IILsmrVqmy//faZMGFCo7+1pmU15dhefvnluffe\nexv9OcFPfvKTzJs3z3O2lWjKcU2Sc889NwsWLEhFRUXOPPPM9O/f33O2FWnKce3QoYPX2AJ4v2P7\nH//xH7nvvvvSsWPHHHvssTnwwAPXuo3X2dalKcd1woQJXmNbKWELAABAobkUGQAAgEITtgAAABSa\nsAUAAKDQhC0AAACFJmwBAAAoNGELAABAoQlbAAAACu3/Af0ZU+kE6PjjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x192548a36a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 8))\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results, vert=False)\n",
    "ax.set_yticklabels(names)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classes 5 and 10 have won the scenario with the Random Forest Classifier. It has been determined that the Classification Accuracy is the better method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}