Python_DataAnalysis_Exercise_Spotify_EDA_DecisionTree.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import graphviz\n",
    "import pydotplus\n",
    "import io\n",
    "from scipy import misc\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spotify Song Attribute EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Dataset\n",
    "# Data from: https://www.kaggle.com/geomack/spotifyclassification\n",
    "data = pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2.017000e+03</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1008.000000</td>\n",
       "      <td>0.187590</td>\n",
       "      <td>0.618422</td>\n",
       "      <td>2.463062e+05</td>\n",
       "      <td>0.681577</td>\n",
       "      <td>0.133286</td>\n",
       "      <td>5.342588</td>\n",
       "      <td>0.190844</td>\n",
       "      <td>-7.085624</td>\n",
       "      <td>0.612295</td>\n",
       "      <td>0.092664</td>\n",
       "      <td>121.603272</td>\n",
       "      <td>3.968270</td>\n",
       "      <td>0.496815</td>\n",
       "      <td>0.505702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>582.402066</td>\n",
       "      <td>0.259989</td>\n",
       "      <td>0.161029</td>\n",
       "      <td>8.198181e+04</td>\n",
       "      <td>0.210273</td>\n",
       "      <td>0.273162</td>\n",
       "      <td>3.648240</td>\n",
       "      <td>0.155453</td>\n",
       "      <td>3.761684</td>\n",
       "      <td>0.487347</td>\n",
       "      <td>0.089931</td>\n",
       "      <td>26.685604</td>\n",
       "      <td>0.255853</td>\n",
       "      <td>0.247195</td>\n",
       "      <td>0.500091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.122000</td>\n",
       "      <td>1.604200e+04</td>\n",
       "      <td>0.014800</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018800</td>\n",
       "      <td>-33.097000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023100</td>\n",
       "      <td>47.859000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034800</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>504.000000</td>\n",
       "      <td>0.009630</td>\n",
       "      <td>0.514000</td>\n",
       "      <td>2.000150e+05</td>\n",
       "      <td>0.563000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.092300</td>\n",
       "      <td>-8.394000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.037500</td>\n",
       "      <td>100.189000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1008.000000</td>\n",
       "      <td>0.063300</td>\n",
       "      <td>0.631000</td>\n",
       "      <td>2.292610e+05</td>\n",
       "      <td>0.715000</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.127000</td>\n",
       "      <td>-6.248000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054900</td>\n",
       "      <td>121.427000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.492000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1512.000000</td>\n",
       "      <td>0.265000</td>\n",
       "      <td>0.738000</td>\n",
       "      <td>2.703330e+05</td>\n",
       "      <td>0.846000</td>\n",
       "      <td>0.054000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.247000</td>\n",
       "      <td>-4.746000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108000</td>\n",
       "      <td>137.849000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.691000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>0.984000</td>\n",
       "      <td>1.004627e+06</td>\n",
       "      <td>0.998000</td>\n",
       "      <td>0.976000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.969000</td>\n",
       "      <td>-0.307000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.816000</td>\n",
       "      <td>219.331000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.992000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  acousticness  danceability   duration_ms       energy  \\\n",
       "count  2017.000000   2017.000000   2017.000000  2.017000e+03  2017.000000   \n",
       "mean   1008.000000      0.187590      0.618422  2.463062e+05     0.681577   \n",
       "std     582.402066      0.259989      0.161029  8.198181e+04     0.210273   \n",
       "min       0.000000      0.000003      0.122000  1.604200e+04     0.014800   \n",
       "25%     504.000000      0.009630      0.514000  2.000150e+05     0.563000   \n",
       "50%    1008.000000      0.063300      0.631000  2.292610e+05     0.715000   \n",
       "75%    1512.000000      0.265000      0.738000  2.703330e+05     0.846000   \n",
       "max    2016.000000      0.995000      0.984000  1.004627e+06     0.998000   \n",
       "\n",
       "       instrumentalness          key     liveness     loudness         mode  \\\n",
       "count       2017.000000  2017.000000  2017.000000  2017.000000  2017.000000   \n",
       "mean           0.133286     5.342588     0.190844    -7.085624     0.612295   \n",
       "std            0.273162     3.648240     0.155453     3.761684     0.487347   \n",
       "min            0.000000     0.000000     0.018800   -33.097000     0.000000   \n",
       "25%            0.000000     2.000000     0.092300    -8.394000     0.000000   \n",
       "50%            0.000076     6.000000     0.127000    -6.248000     1.000000   \n",
       "75%            0.054000     9.000000     0.247000    -4.746000     1.000000   \n",
       "max            0.976000    11.000000     0.969000    -0.307000     1.000000   \n",
       "\n",
       "       speechiness        tempo  time_signature      valence       target  \n",
       "count  2017.000000  2017.000000     2017.000000  2017.000000  2017.000000  \n",
       "mean      0.092664   121.603272        3.968270     0.496815     0.505702  \n",
       "std       0.089931    26.685604        0.255853     0.247195     0.500091  \n",
       "min       0.023100    47.859000        1.000000     0.034800     0.000000  \n",
       "25%       0.037500   100.189000        4.000000     0.295000     0.000000  \n",
       "50%       0.054900   121.427000        4.000000     0.492000     1.000000  \n",
       "75%       0.108000   137.849000        4.000000     0.691000     1.000000  \n",
       "max       0.816000   219.331000        5.000000     0.992000     1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>time_signature</th>\n",
       "      <th>valence</th>\n",
       "      <th>target</th>\n",
       "      <th>song_title</th>\n",
       "      <th>artist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.833</td>\n",
       "      <td>204600</td>\n",
       "      <td>0.434</td>\n",
       "      <td>0.021900</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1650</td>\n",
       "      <td>-8.795</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4310</td>\n",
       "      <td>150.062</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>1</td>\n",
       "      <td>Mask Off</td>\n",
       "      <td>Future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1990</td>\n",
       "      <td>0.743</td>\n",
       "      <td>326933</td>\n",
       "      <td>0.359</td>\n",
       "      <td>0.006110</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1370</td>\n",
       "      <td>-10.401</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>160.083</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.588</td>\n",
       "      <td>1</td>\n",
       "      <td>Redbone</td>\n",
       "      <td>Childish Gambino</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0344</td>\n",
       "      <td>0.838</td>\n",
       "      <td>185707</td>\n",
       "      <td>0.412</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>-7.148</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2890</td>\n",
       "      <td>75.044</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.173</td>\n",
       "      <td>1</td>\n",
       "      <td>Xanny Family</td>\n",
       "      <td>Future</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.6040</td>\n",
       "      <td>0.494</td>\n",
       "      <td>199413</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.510000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0922</td>\n",
       "      <td>-15.236</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0261</td>\n",
       "      <td>86.468</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.230</td>\n",
       "      <td>1</td>\n",
       "      <td>Master Of None</td>\n",
       "      <td>Beach House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.1800</td>\n",
       "      <td>0.678</td>\n",
       "      <td>392893</td>\n",
       "      <td>0.561</td>\n",
       "      <td>0.512000</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4390</td>\n",
       "      <td>-11.648</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0694</td>\n",
       "      <td>174.004</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.904</td>\n",
       "      <td>1</td>\n",
       "      <td>Parallel Lines</td>\n",
       "      <td>Junior Boys</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  acousticness  danceability  duration_ms  energy  \\\n",
       "0           0        0.0102         0.833       204600   0.434   \n",
       "1           1        0.1990         0.743       326933   0.359   \n",
       "2           2        0.0344         0.838       185707   0.412   \n",
       "3           3        0.6040         0.494       199413   0.338   \n",
       "4           4        0.1800         0.678       392893   0.561   \n",
       "\n",
       "   instrumentalness  key  liveness  loudness  mode  speechiness    tempo  \\\n",
       "0          0.021900    2    0.1650    -8.795     1       0.4310  150.062   \n",
       "1          0.006110    1    0.1370   -10.401     1       0.0794  160.083   \n",
       "2          0.000234    2    0.1590    -7.148     1       0.2890   75.044   \n",
       "3          0.510000    5    0.0922   -15.236     1       0.0261   86.468   \n",
       "4          0.512000    5    0.4390   -11.648     0       0.0694  174.004   \n",
       "\n",
       "   time_signature  valence  target      song_title            artist  \n",
       "0             4.0    0.286       1        Mask Off            Future  \n",
       "1             4.0    0.588       1         Redbone  Childish Gambino  \n",
       "2             4.0    0.173       1    Xanny Family            Future  \n",
       "3             4.0    0.230       1  Master Of None       Beach House  \n",
       "4             4.0    0.904       1  Parallel Lines       Junior Boys  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2017 entries, 0 to 2016\n",
      "Data columns (total 17 columns):\n",
      "Unnamed: 0          2017 non-null int64\n",
      "acousticness        2017 non-null float64\n",
      "danceability        2017 non-null float64\n",
      "duration_ms         2017 non-null int64\n",
      "energy              2017 non-null float64\n",
      "instrumentalness    2017 non-null float64\n",
      "key                 2017 non-null int64\n",
      "liveness            2017 non-null float64\n",
      "loudness            2017 non-null float64\n",
      "mode                2017 non-null int64\n",
      "speechiness         2017 non-null float64\n",
      "tempo               2017 non-null float64\n",
      "time_signature      2017 non-null float64\n",
      "valence             2017 non-null float64\n",
      "target              2017 non-null int64\n",
      "song_title          2017 non-null object\n",
      "artist              2017 non-null object\n",
      "dtypes: float64(10), int64(5), object(2)\n",
      "memory usage: 268.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EDA to visualize data and observe structure\n",
    "train, test = train_test_split(data, test_size = 0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training size: 1714; Test size: 303\n"
     ]
    }
   ],
   "source": [
    "print(\"Training size: {}; Test size: {}\".format(len(train), len(test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom Color Palette \n",
    "red_blue = ['#9a0200', '#26538d'] # deep red, dusk blue\n",
    "palette = sns.color_palette(red_blue)\n",
    "sns.set_palette(palette)\n",
    "sns.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1714, 17), (303, 17))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pos_tempo = data[data['target'] == 1]['tempo']\n",
    "neg_tempo = data[data['target'] == 0]['tempo']\n",
    "\n",
    "pos_danceability = data[data['target'] == 1]['danceability']\n",
    "neg_danceability = data[data['target'] == 0]['danceability']\n",
    "\n",
    "pos_duration = data[data['target'] == 1]['duration_ms']\n",
    "neg_duration = data[data['target'] == 0]['duration_ms']\n",
    "\n",
    "pos_loudness = data[data['target'] == 1]['loudness']\n",
    "neg_loudness = data[data['target'] == 0]['loudness']\n",
    "\n",
    "pos_speechiness = data[data['target'] == 1]['speechiness']\n",
    "neg_speechiness = data[data['target'] == 0]['speechiness']\n",
    "\n",
    "pos_valence = data[data['target'] == 1]['valence']\n",
    "neg_valence = data[data['target'] == 0]['valence']\n",
    "\n",
    "pos_energy = data[data['target'] == 1]['energy']\n",
    "neg_energy = data[data['target'] == 0]['energy']\n",
    "\n",
    "pos_acousticness = data[data['target'] == 1]['acousticness']\n",
    "neg_acousticness = data[data['target'] == 0]['acousticness']\n",
    "\n",
    "pos_key = data[data['target'] == 1]['key']\n",
    "neg_key = data[data['target'] == 0]['key']\n",
    "\n",
    "pos_instrumentalness = data[data['target'] == 1]['instrumentalness']\n",
    "neg_instrumentalness = data[data['target'] == 0]['instrumentalness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25f9bea72b0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,8))\n",
    "plt.title(\"Song Tempo Like / Dislike Distribution\")\n",
    "pos_tempo.hist(alpha = 0.7, bins=30, label = 'positive')\n",
    "neg_tempo.hist(alpha = 0.7, bins=30, label = 'negative')\n",
    "plt.legend(loc = 'upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25f9c65a048>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize = (15,15))\n",
    "\n",
    "# Danceability \n",
    "ax3 = fig2.add_subplot(331)\n",
    "ax3.set_xlabel('Danceability')\n",
    "ax3.set_ylabel('Count')\n",
    "ax3.set_title('Song Dancebility Like Distribution')\n",
    "pos_danceability.hist(alpha = 0.5, bins = 30)\n",
    "ax4 = fig2.add_subplot(331)\n",
    "neg_danceability.hist(alpha = 0.5, bins = 30)\n",
    "\n",
    "# Duration \n",
    "ax3 = fig2.add_subplot(332)\n",
    "ax3.set_xlabel('Duration')\n",
    "ax3.set_ylabel('Count')\n",
    "ax3.set_title('Song Duration Like Distribution')\n",
    "pos_duration.hist(alpha = 0.5, bins = 30)\n",
    "ax4 = fig2.add_subplot(332)\n",
    "neg_duration.hist(alpha = 0.5, bins = 30)\n",
    "\n",
    "# Loudness \n",
    "ax3 = fig2.add_subplot(333)\n",
    "ax3.set_xlabel('Loudness')\n",
    "ax3.set_ylabel('Count')\n",
    "ax3.set_title('Song Loudness Like Distribution')\n",
    "pos_loudness.hist(alpha = 0.5, bins = 30)\n",
    "ax4 = fig2.add_subplot(333)\n",
    "neg_loudness.hist(alpha = 0.5, bins = 30)\n",
    "\n",
    "# Speachiness \n",
    "ax3 = fig2.add_subplot(334)\n",
    "ax3.set_xlabel('Speachiness')\n",
    "ax3.set_ylabel('Count')\n",
    "ax3.set_title('Song Speachiness Like Distribution')\n",
    "pos_speechiness.hist(alpha = 0.5, bins = 30)\n",
    "ax4 = fig2.add_subplot(334)\n",
    "neg_speechiness.hist(alpha = 0.5, bins = 30)\n",
    "\n",
    "# Valence \n",
    "ax3 = fig2.add_subplot(335)\n",
    "ax3.set_xlabel('Valence')\n",
    "ax3.set_ylabel('Count')\n",
    "ax3.set_title('Song Valence Like Distribution')\n",
    "pos_valence.hist(alpha = 0.5, bins = 30)\n",
    "ax4 = fig2.add_subplot(335)\n",
    "neg_valence.hist(alpha = 0.5, bins = 30)\n",
    "\n",
    "# Energy \n",
    "ax3 = fig2.add_subplot(336)\n",
    "ax3.set_xlabel('Energy')\n",
    "ax3.set_ylabel('Count')\n",
    "ax3.set_title('Song Energy Like Distribution')\n",
    "pos_energy.hist(alpha = 0.5, bins = 30)\n",
    "ax4 = fig2.add_subplot(336)\n",
    "neg_energy.hist(alpha = 0.5, bins = 30)\n",
    "\n",
    "# Key \n",
    "ax3 = fig2.add_subplot(337)\n",
    "ax3.set_xlabel('Key')\n",
    "ax3.set_ylabel('Count')\n",
    "ax3.set_title('Song Key Like Distribution')\n",
    "pos_key.hist(alpha = 0.5, bins = 30)\n",
    "ax4 = fig2.add_subplot(337)\n",
    "neg_key.hist(alpha = 0.5, bins = 30)\n",
    "\n",
    "# Acousticness \n",
    "ax3 = fig2.add_subplot(338)\n",
    "ax3.set_xlabel('Acousticness')\n",
    "ax3.set_ylabel('Count')\n",
    "ax3.set_title('Song Acousticness Like Distribution')\n",
    "pos_acousticness.hist(alpha = 0.5, bins = 30)\n",
    "ax4 = fig2.add_subplot(338)\n",
    "neg_acousticness.hist(alpha = 0.5, bins = 30)\n",
    "\n",
    "# Instrumentalness \n",
    "ax3 = fig2.add_subplot(339)\n",
    "ax3.set_xlabel('Instrumentalness')\n",
    "ax3.set_ylabel('Count')\n",
    "ax3.set_title('Song Instrumentalness Like Distribution')\n",
    "pos_instrumentalness.hist(alpha = 0.5, bins = 30)\n",
    "ax4 = fig2.add_subplot(339)\n",
    "neg_instrumentalness.hist(alpha = 0.5, bins = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train a classifier - Decision Tree\n",
    "c = DecisionTreeClassifier(min_samples_split=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['danceability', 'duration_ms', 'loudness', 'speechiness', 'valence', 'energy', 'key', 'acousticness', 'instrumentalness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = train[features]\n",
    "y_train = train['target']\n",
    "x_test = test[features]\n",
    "y_test = test['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = c.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_tree(tree, features, path):\n",
    "    f = io.StringIO()\n",
    "    export_graphviz(tree, out_file=f, feature_names = features)\n",
    "    pydotplus.graph_from_dot_data(f.getvalue()).write_png(path)\n",
    "    img = misc.imread(path)\n",
    "    plt.rcParams[\"figure.figsize\"] = (20,20)\n",
    "    plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"PATH\"] += os.pathsep + r'C:\\Users\\Gosia\\Anaconda3\\release\\bin'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_tree(dt, features, 'dec_tree_01.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict target using the trained calssifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = c.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0,\n",
       "       0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0,\n",
       "       1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "score_dt = accuracy_score(y_test, y_pred) *100 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy using Decision Tree:   68.0 %\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy using Decision Tree:  \", round(score_dt,1), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate hidden layer 15 done, accuracy 0.72\n",
      "Evaluate hidden layer 16 done, accuracy 0.71\n",
      "Evaluate hidden layer 17 done, accuracy 0.70\n",
      "Evaluate hidden layer 18 done, accuracy 0.70\n",
      "Evaluate hidden layer 19 done, accuracy 0.71\n",
      "Evaluate hidden layer 20 done, accuracy 0.70\n",
      "Evaluate hidden layer 21 done, accuracy 0.71\n",
      "Evaluate hidden layer 22 done, accuracy 0.71\n",
      "Evaluate hidden layer 23 done, accuracy 0.71\n",
      "Evaluate hidden layer 24 done, accuracy 0.72\n",
      "Evaluate hidden layer 25 done, accuracy 0.71\n",
      "Evaluate hidden layer 26 done, accuracy 0.71\n",
      "Evaluate hidden layer 27 done, accuracy 0.70\n",
      "Evaluate hidden layer 28 done, accuracy 0.70\n",
      "Evaluate hidden layer 29 done, accuracy 0.70\n"
     ]
    }
   ],
   "source": [
    "hidden_neuron_nums = list(range(15,30))\n",
    "total_performance_records = []\n",
    "for hn in hidden_neuron_nums:\n",
    "    c_ = tree.DecisionTreeClassifier(min_samples_leaf = hn)\n",
    "    perf_records = []\n",
    "    for i in range(10):\n",
    "        c_.fit(x_train, y_train)\n",
    "        tst_p_ = c_.predict(x_test)\n",
    "        performance = np.sum(tst_p_ == y_test)/float(tst_p_.size)\n",
    "        perf_records.append(performance)\n",
    "    total_performance_records.append(np.mean(perf_records))\n",
    "    print(\"Evaluate hidden layer {} done, accuracy {:.2f}\".format(hn, total_performance_records[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier(n_estimators = 100)\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest (nest:100) Accuracy:   77.2 %\n"
     ]
    }
   ],
   "source": [
    "forest_y_pred = clf.predict(x_test)\n",
    "score_rf = accuracy_score(y_test, forest_y_pred)*100\n",
    "print(\"Random Forest (nest:100) Accuracy:  \", round(score_rf,1), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Neural Network Classifier\n",
    "# MLP - Multi Layer Perceptrons\n",
    "from sklearn.neural_network import MLPClassifier \n",
    "mlpc = MLPClassifier()\n",
    "mlpc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "2\n",
      "<class 'numpy.ndarray'>\n",
      "(9, 100)\n",
      "<class 'numpy.ndarray'>\n",
      "(100, 1)\n"
     ]
    }
   ],
   "source": [
    "print(type(mlpc.coefs_))\n",
    "print(len(mlpc.coefs_))\n",
    "print(type(mlpc.coefs_[0]))\n",
    "print(mlpc.coefs_[0].shape)\n",
    "print(type(mlpc.coefs_[1]))\n",
    "print(mlpc.coefs_[1].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Network Classfier Accuracy:   54.5 %\n"
     ]
    }
   ],
   "source": [
    "mlpc_y_pred = mlpc.predict(x_test)\n",
    "score_nn = accuracy_score(y_test, mlpc_y_pred)*100\n",
    "print(\"Neural Network Classfier Accuracy:  \", round(score_nn,1), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Hidden Neurons:  10\n",
      "Accuracy:  [0.5445544554455446, 0.45544554455445546, 0.5445544554455446, 0.45544554455445546, 0.5445544554455446, 0.45544554455445546, 0.5445544554455446, 0.5445544554455446, 0.45544554455445546, 0.5445544554455446]\n",
      "Average Accuracy:  0.508910891089109\n"
     ]
    }
   ],
   "source": [
    "hn = 10\n",
    "mlpc_ = MLPClassifier(hidden_layer_sizes=(hn,))\n",
    "perf_records = []\n",
    "for i in range(10):\n",
    "    mlpc_.fit(x_train, y_train)\n",
    "    mlpc_y_pred = mlpc_.predict(x_test)\n",
    "    performance = np.sum(mlpc_y_pred == y_test)/float(mlpc_y_pred.size)\n",
    "    perf_records.append(performance)\n",
    "print(\"Number of Hidden Neurons: \", hn)\n",
    "print(\"Accuracy: \", perf_records)\n",
    "print(\"Average Accuracy: \", np.mean(perf_records))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluate hidden layer 2 done, accuracy 0.48\n",
      "Evaluate hidden layer 3 done, accuracy 0.50\n",
      "Evaluate hidden layer 4 done, accuracy 0.48\n",
      "Evaluate hidden layer 5 done, accuracy 0.51\n",
      "Evaluate hidden layer 6 done, accuracy 0.52\n",
      "Evaluate hidden layer 7 done, accuracy 0.50\n",
      "Evaluate hidden layer 8 done, accuracy 0.49\n",
      "Evaluate hidden layer 9 done, accuracy 0.52\n",
      "Evaluate hidden layer 10 done, accuracy 0.48\n",
      "Evaluate hidden layer 20 done, accuracy 0.49\n",
      "Evaluate hidden layer 30 done, accuracy 0.54\n",
      "Evaluate hidden layer 40 done, accuracy 0.50\n",
      "Evaluate hidden layer 50 done, accuracy 0.47\n",
      "Evaluate hidden layer 60 done, accuracy 0.49\n",
      "Evaluate hidden layer 70 done, accuracy 0.53\n",
      "Evaluate hidden layer 80 done, accuracy 0.52\n",
      "Evaluate hidden layer 90 done, accuracy 0.51\n",
      "Evaluate hidden layer 100 done, accuracy 0.48\n",
      "Evaluate hidden layer 125 done, accuracy 0.50\n",
      "Evaluate hidden layer 150 done, accuracy 0.50\n",
      "Evaluate hidden layer 175 done, accuracy 0.51\n"
     ]
    }
   ],
   "source": [
    "hidden_neuron_nums = list(range(2,10)) + list(range(10,100,10)) + list(range(100,200,25))\n",
    "# #[2,3,4,5,6...9, 10, 20, 30, ... 90, 100, 125, 150, 175]\n",
    "total_performance_records = []\n",
    "for hn in hidden_neuron_nums:\n",
    "    mlpc_ = MLPClassifier(hidden_layer_sizes=(hn,))\n",
    "    perf_records_ = []\n",
    "    for i in range(10):\n",
    "         mlpc_.fit(x_train, y_train)\n",
    "         tst_p_ = mlpc_.predict(x_test)\n",
    "         performance = np.sum(tst_p_ == y_test) / float(tst_p_.size)\n",
    "         perf_records_.append(performance)\n",
    "    total_performance_records.append(np.mean(perf_records_))\n",
    "    print (\"Evaluate hidden layer {} done, accuracy {:.2f}\".format(hn, total_performance_records[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ada-boost Classfier Accuracy:   70.3 %\n"
     ]
    }
   ],
   "source": [
    "# Ada-boost Classifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ada = AdaBoostClassifier()\n",
    "ada.fit(x_train,y_train)\n",
    "ada_y_pred = ada.predict(x_test)\n",
    "score_ada = accuracy_score(y_test, ada_y_pred)*100\n",
    "print(\"Ada-boost Classfier Accuracy:  \", round(score_ada,1), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian Naive Bayes Accuracy:   56.1 %\n"
     ]
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(x_train,y_train)\n",
    "gnb_y_pred = gnb.predict(x_test)\n",
    "score_gnb = accuracy_score(y_test, gnb_y_pred)*100\n",
    "print(\"Gaussian Naive Bayes Accuracy:  \", round(score_gnb,1), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rVy8b8wIAACiTStzy1axZM82YMUNt2rTRhQsXdObMGRvzAgAAKJOuu+UrPz9f77//vlasWCEvLy/l5OQoJSVF3t7eNucHAABQplx3y1f79u21Z88ezZkzR2+//baqV69OeAEAANyi6275GjRokDZt2qSjR4+qd+/eMsbYnBcAAECZdN0tX0OHDtWGDRsUHh6uTZs26dtvv9Xs2bO1d+9em/MDAAAoU0o84D4oKEizZ8/W5s2bVbNmTU2aNMnGvAAAAMqkGzrPlyT5+fkpPDxc69evd+V8AAAAyrQbji8AAADcOuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIuILAADAIk9XLLSoqEhTpkzRoUOHVK5cOc2cOVPGGEVFRcnhcKh+/fqaOnWqPDxoPwAA8Ovikvj6+OOPJUnvvvuu0tLSnPEVERGhli1bKi4uTikpKerUqZMrhgcAAHBbLtn01LFjR82YMUOSdOzYMd1zzz1KT09XUFCQJCk4OFhbt251xdAAAABuzWX7/Tw9PRUZGakZM2YoJCRExhg5HA5Jko+Pj7Kzs101NAAAgNty6UFXs2bN0t/+9jfFxsYqLy/PeXlubq78/PxcOTQAAIBbckl8rV+/XosXL5Yk3XXXXXI4HHr44YeVlpYmSUpNTVVgYKArhgYAAHBrLjngvnPnzpo8ebKefvppFRYWKjo6WnXr1lVsbKzmzZungIAAhYSEuGJoAAAAt+aS+KpYsaJee+21qy5PTk52xXAAAAB3DE60BQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYBHxBQAAYJFnaS+woKBA0dHROnr0qPLz8zV8+HDVq1dPUVFRcjgcql+/vqZOnSoPD7oPAAD8+pR6fG3YsEFVqlTR7NmzdfbsWfXq1UuNGjVSRESEWrZsqbi4OKWkpKhTp06lPTQAAIDbK/XNT126dNGYMWOcP5crV07p6ekKCgqSJAUHB2vr1q2lPSwAAMAdodTjy8fHR76+vsrJydGLL76oiIgIGWPkcDic12dnZ5f2sAAAAHcElxx4dfz4cQ0aNEh/+MMf1L179yuO78rNzZWfn58rhgUAAHB7pR5f//rXvzRkyBBNnDhRvXv3liQ1btxYaWlpkqTU1FQFBgaW9rAAAAB3hFKPr0WLFikrK0sLFixQeHi4wsPDFRERofnz56tfv34qKChQSEhIaQ8LAABwRyj1TztOmTJFU6ZMuery5OTk0h4KAADgjsPJtgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACwivgAAACxyWXzt3LlT4eHhkqQffvhBAwYM0MCBAzV16lQVFxe7algAAAC35pL4evPNNzVlyhTl5eVJkmbOnKmIiAi9/fbbMsYoJSXFFcMCAAC4PZfEV+3atTV//nznz+np6QoKCpIkBQcHa+vWra4YFgAAwO25JL5CQkLk6enp/NkYI4fDIUny8fFRdna2K4YFAABwe1YOuPfw+Pcwubm58vPzszEsAACA27ESX40bN1ZaWpokKTU1VYGBgTaGBQAAcDtW4isyMlLz589Xv379VFBQoJCQEBvDAgAAuB3Pkn/l5vj7+2vVqlWSpDp16ig5OdlVQwEAANwxOMkqAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARcQXAACARZ62BiouLta0adO0Z88eeXl56aWXXtIDDzxga3gAAAC3YG3L14cffqj8/HytXLlS48ePV2Jioq2hAQAA3Ia1+PrHP/6hxx57TJLUvHlzffvtt7aGBgAAcBvWdjvm5OTI19fX+XO5cuVUWFgoT89rT+Ho0aMKDQ21NT2UlnHjbvcMcCN4nO4MPE7uj8fozmDhcTp69OgN/661+PL19VVubq7z5+Li4uuGlySlpaXZmBYAAIBV1nY7tmjRQqmpqZKkr7/+Wg0aNLA1NAAAgNtwGGOMjYF++rTj3r17ZYzRyy+/rLp169oYGgAAwG1Yiy8AAABwklUAAACriC8AAACLiK9fIC0tTa1bt1Z4eLjCwsLUv39/ffDBB794OQkJCTp27Ng1r0tNTdXKlStveo7r169XeHi4+vbtqxYtWig8PFzh4eE6efLkTS/T3V3+uISHhys0NFQvvvii8vPzb2m5Y8eOLbVP3a5du1ZPPPGEc47h4eFKSUkplWVfbseOHfruu+9KfbmlIS0tTYGBgTp+/Ljzsjlz5mjt2rUuGe93v/vdVZe1b99ef/7zn50/HzhwQOHh4T+7nOTk5FKZT0ZGhvr27Vsqy3JHS5YsUdu2bZWXl3fVde+8847mz59/Q8tx5f1UWo9laThy5IhefPFF9e3bV4MGDdLQoUO1b98+l413+vRpTZs27ZaWER4erpkzZzp/zsvLU/v27X/2/4waNeqmx7v8dXPgwIEaPHiwTp06ddPLcyfWTjVRVrRq1Up/+tOfJEm5ubkKDw9XnTp19NBDD93wMmJiYq57XXBw8C3Nr2fPnurZs6cyMjI0btw4JSUl3dLy7hSXPy6SNH78eH300Ufq0qXLbZzVlZ588klNmDDBpWOsWbNG3bp1U6NGjVw6zs0qX768Jk+erP/+7/+Ww+G4LXNYtmyZ2rZtq4CAgBv6/YULFyosLMzFs7rzbdy4Ud26ddP777/vtudodJfH8sKFCxo+fLhmzJih//iP/5Ak7dq1S9OnT3fZa/a99957y/ElSZs2bVKHDh0UFBR0Q7//xhtv3NJ4l79urly5UosWLVJcXNwtLdMdEF+3wMfHR/369dNf//pXPfTQQ5o7d6527NghY4wGDx6srl27aufOnUpISJAxRjVq1NCcOXP0/PPPa9q0afrxxx81a9YseXp6ys/PT3PmzNH//u//6uDBg5owYYKWLl2q999/X56engoMDNTEiRM1f/58ZWRk6MyZMzp27JgmT57s/OaAkrRr104BAQEKCAjQkCFDFBsbq7y8PFWoUEEzZszQfffdp6SkJG3atEkOh0PdunXToEGDXHwvlr78/HydOnVKlStXVlFRkeLi4nTixAmdPXtWwcHBioiIUFRUlLy8vHT06FGdOnVKiYmJatKkiVasWKHVq1fr3nvv1ZkzZyRJBQUFio6O1pEjR1RUVKQ//vGP6tatm8LDw9WwYUPt27dPFStWVGBgoLZs2aKsrCwtXbpUlStXLnGuWVlZmjhxonJyclRUVKQxY8aodevWevLJJ/Xggw/Ky8tL8fHxiomJ0dmzZyVJU6ZMUcOGDRUVFaXDhw8rLy9Pzz77rGrXrq1PP/1U6enpqlevnn7zm9+49H6+Ga1atVJxcbFWrFhx1ZvgtZ57UVFR6tatm4KDg5WamqoPPvhAiYmJVzyX+/Tpo8TERBUXFysrK0tTpkxRixYtrjuHqKgoRUVF6Z133rni8j179uill16SJFWpUkUvv/yykpOTde7cOU2bNk07d+7UW2+9JT8/P7Vs2VLJyclq3LixevXqpZUrVyo5Ofma6+tXX32l8+fPKyEhQZJUVFSkqKgo1a9fX0OHDi3le/j2SEtLU+3atdW/f39NnDhRoaGh+uKLL/Tyyy+rcuXK8vDwUPPmzSVJc+fO1bfffqvc3FzVrVv3ii0pP8nMzNSwYcOUmZmpxx9/XCNHjlRGRoZiYmJUWFgoh8OhKVOmqFGjRtqwYYOWL18uLy8vPfjgg5o+fboyMjI0efJkeXp6qly5cnrllVe0du1a52NZGhFyKz7++GO1atXKGV6S1LRpU+dW2b17917zOf273/1On332maRLW+b79++v6tWrX3Vby5cvr4iICBljVFBQoPj4ePn4+GjcuHFatWqVPvvsM7366quqUKGC87m+e/duvfnmmypfvrwyMjLUrVs3DR8+/Kq5x8TEKDY2VmvXrr3iXJ0/N+eNGzfq6aef1gcffCCHw6H4+Hi1adNGtWvXvmqdq1Sp0nXvt3Pnzun++++XJP31r3/VihUrnNe99tprWrZsmWrUqKGnn35a586d0x//+EetXbv2mu/NK1as0Pr16+Xh4aEWLVooMjLyFh7Rm2Bww7Zv324iIiKuuGzz5s0mNjbW/P3vf3ded/HiRdOjRw9z7tw50717d7N//35jjDHJycnm22+/NWFhYWb//v0mMTHRLFmyxBQVFZnNmzebo0ePmjVr1pjZs2eb7777zvTu3dvk5+eb4uJiM3LkSPPRRx+Z119/3UyZMsUYY8yWLVvMkCFDrjnXI0eOmD59+lxxWcOGDU1mZqYxxpgxY8aYv//978YYY7Zu3WrGjRtn9u3bZ/r3728KCwtNUVGRCQ8PNwcOHCi9O9BFtm/fblq1amXCwsJM165dze9//3uzfPlyY8yl+2HVqlXGmEuPS1BQkDHGmMjISLNw4UJjjDErV640sbGxJisry3Tu3Nnk5eWZ/Px88+STT5rt27ebpKQkk5CQYIwxJjs723Tq1MmcOXPGhIWFmffee88YY8yQIUNMcnKyMcaYSZMmmc2bN18xxzVr1pjHH3/chIWFmbCwMDN69GhjjDGJiYlm2bJlxhhjTpw4Ydq1a2eKiopMu3btTHp6ujHGmFdeecWsWLHCGGPMoUOHTP/+/U12drZ54oknzJkzZ8yZM2fMhg0bnLfrk08+ccG9fOt+Wn8yMzNNhw4dzKFDh8zs2bPNmjVrrvvcu/z2fPLJJyYyMtIYc+Vz+f333zffffedMcaYDRs2mJiYGGOMMW3atLlqDu3atTMXL140MTExZuHChWb//v0mLCzMGGNMnz59zL59+4wxxqxatcrMmzfviuXMnz/frFu3zmzbts10797dLFmyxOzbt89ERET87Po6Y8YMY8yl52KvXr1MRESE87lSVowfP958/PHHxhhj+vfvb77++msTGhpqDh48aIwxJi4uzrz++usmOzvbLFmyxBhjTFFRkenSpYs5ceLEFcs6cuSIad26tcnKyjKFhYWmX79+Zvfu3Wb06NHO9eqf//yn6dWrl8nMzDQdO3Y02dnZxhhjEhISTFJSkklOTjbTp083+fn5ZuvWrWbPnj3GmGs/J26HxYsXO1+jjDFm2LBhJiwszHTu3NkcP378hp7TERERZvv27de8rR9//LEZMWKEuXDhgvnmm2/MF1984XxPKC4uNu3atXPe78uWLTOJiYlm+/btpmvXrqagoMDk5uaaFi1aXDXvn967FixYYKZMmWIuXrzU5SZ5AAAJsElEQVRo2rVrZ4wpeT0cM2aM+fzzz01eXp7p1q2bKSgouO4695PLXzd79eplgoKCzN69e40xxixcuNCcP3/eGGNMbGysee+998zhw4dN7969jTGX3m+XLl163ffm0NBQ89VXXxljjFmxYoUpKCi4iUfy5rHl6xYdO3ZMNWvW1N69e5Wenu48fqSwsFDHjh3TmTNnnOcze/rpp6/4v8OGDdOiRYv0zDPPqEaNGmratKnzuoMHD6pZs2YqX768JCkwMNB5PMBPuzhr1qz5i45rqlq1qqpWrSrp0l8pixcv1ltvvSVjjMqXL6+9e/fq2LFjGjx4sKRLf2UcPnz4hnfP3E4/7XY8e/ashgwZIn9/f0mX/pr65ptvtH37dvn6+l5xf11+P3755Zc6ePCg6tWrJy8vL0lyPh4HDhxQmzZtJF36poa6devqyJEjkqQmTZpIkvz8/FSvXj3nv6913Mu1djseOHBA3bt3lyTVqFFDvr6+yszMlCTVqVNH0qXHavv27frLX/4i6dLWMl9fX8XGxio2NlY5OTnq0aPHLd1/NlWtWlXR0dGKiopybqG63nPvcuays+Jc/lyuXr26FixYIG9vb+Xm5l7xNWbXExUVpaeeekq1a9d2XnbgwAHFx8dLurS186f7/yedO3fWokWLdN9992ns2LFKSkqSMUadO3f+2fX18uXs2bNHvr6+On/+/A3dV3eCc+fOKTU1VZmZmUpKSlJOTo6Sk5N18uRJ521v0aKFDh8+rAoVKigzM1Pjxo1TxYoVdf78eRUUFCgmJkaHDx9W1apVNWnSJDVq1Mi5BeS3v/2tDh06pAMHDujRRx+VdGndPXHihI4cOaJ69eo5H/NHH31UW7ZsUXR0tN58800999xzqlSpksaOHXt77pzrqFmz5hXfb7xw4UJJUt++fVVYWHhDz+mf1ofevXtfdVuDg4P1/fffa8SIEfL09LxiC9bZs2fl6+urGjVqSLp0n82bN09PPPGEGjRoIE9PT3l6esrb2/u68x86dKgGDBjgPHG6VPJ62LdvX61bt06nT59W+/bt5enpWeI6J135urlt2zaNGDFCmzdv1t13363IyEj5+Pjo4MGDat68uWrVqiUfHx/t379fGzdu1IIFC7RmzZprvjfPnDlTS5cu1Zw5c9S8efMrXl9s4ID7W5CTk6PVq1erS5cuCggIUMuWLZWUlKTly5era9eu8vf3V/Xq1fX9999LunRA6ubNm53/f+PGjerVq5eSkpJUv359rVq1ynldQECAdu3apcLCQhljtGPHDucT82aPlfHw+PfDHRAQoAkTJigpKUnx8fEKCQlRQECA6tWrpz//+c9KSkpSaGjoHfdNBFWrVtXs2bM1ZcoUnTp1SmvXrlWlSpU0d+5cDRkyRBcvXnSuZP//fqxVq5b279+vixcvqqioSLt375Yk1a1bV1988YWkS4/53r17nXF3qy5f9smTJ5WVlaUqVapI+vfjFRAQoMGDByspKUmvvvqqunfvrlOnTik9PV3/+Z//qSVLlmj27NnO3TG2X0RuRvv27VWnTh2tW7dOkq773PPy8tLp06clSf/85z+d///y53JCQoJefPFFzZo1Sw0aNLih2+/r66vp06c7dwVKlyJp1qxZSkpK0sSJE/X4449L+vebXIMGDZSRkaFdu3bp8ccf1/nz55WSkqLg4OCfXV8vn2uTJk20ZMkSbdiwwW0/GPFLbdiwQU899ZSWLl2q//qv/3Lu1vLy8tKBAwckSd98842kSx8oOn78uObNm6dx48Y518eEhAQlJSXp9ddfl3QphHNzc1VYWKhdu3apfv36V6wru3fv1j333CN/f38dOHDAGbOff/656tSpo5SUFD3yyCNavny5unTporfeekuS3Gbd6NChg7Zt26avv/7aedkPP/ygEydOyOFwXPc5XVhYqNzcXOXn52v//v2SdM3bmpaWpurVq2vp0qUaPny45s2b5xynatWqysnJcR64/vnnn+vBBx+UdOPvLeXKlVNiYuIVu4xLWg9bt26t3bt3a82aNerdu7ek669z13PfffepoKBA2dnZev311/WnP/1JL730kipUqOAcr2/fvlq4cKFq1KihatWqXfe9edWqVYqPj1dycrJ2796tr7766oZue2lhy9cvtH37doWHh8vDw0NFRUUaPXq0AgICVKdOHX3++ecaOHCgzp8/r44dO8rX11fx8fGKjo6Wh4eH7r33Xg0ePNi5X/+3v/2toqKiVLFiRZUvX17Tp0/Xjh07JEkNGzZU165dNWDAABUXF+uRRx5Rx44dS+0FOzIyUtOmTVNeXp4uXryomJgYNWrUSK1bt9aAAQOUn5+vpk2bOv86upPUq1dP4eHheumllzR69GiNGzdO//jHP3TXXXfpgQceuO6nZapVq6YxY8aof//+qlatmu666y5Jl1bm2NhYDRgwQHl5eRo1apTuvvvuUpnrCy+8oOjoaP3tb3/TxYsXNX369Ku+83TYsGGKiYnRqlWrlJOTo1GjRunee+/V6dOn1bNnT1WsWFFDhgyRp6enmjVrpjlz5sjf39/tv0EiJiZG27dvl6TrPvf69Omj6Ohobdy40fkG8f/16NFDI0aM0N13362aNWs6j40rScuWLfX73//eGdnTpk1TZGSkioqKJMkZZnXr1tWECRM0Z84cPfroo8rIyJCHh4ceffRR7d+/Xz4+Pr9offX29naOtXr1aueW1jvV6tWr9corrzh/vuuuu9S5c2fVrFnTuWXCx8dHlStXVtOmTbVgwQL17dtXXl5eqlWrlk6dOqVatWpdsczKlStr7NixyszMVLdu3VSvXj1NmjRJsbGxWrp0qQoLC5WQkKBq1app9OjRGjRokDw8PFS7dm1NmDBBJ0+edB5z5+HhocmTJ0u68rG8nXx8fLRw4ULNnTtXc+bMUWFhoTw9PTVjxgzdf//9131ODxo0SP369ZO/v7/zmM6HH374qtv6m9/8RmPHjtXy5cvl4eGhkSNHOsd2OBzO10aHw6HKlStr5syZv/iTlgEBAXrmmWe0fPlySSWvhw6HQyEhIdq6daseeOABSddf5y63adMm7dy5U+XKlVNubq7i4+Pl6+urFi1aqFevXqpYsaL8/Pycr+sdO3bU9OnTNXv2bEmX/tC71ntzw4YN1bt3b1WtWlU1atRQs2bNftHtv1Wc4R4AAJQJFy5cUFhYmFavXn3FVmd3474zAwAAuEFffvml+vbtqxEjRrh1eEls+QIAALDKvdMQAACgjCG+AAAALCK+AAAALCK+AAAALCK+AAAALCK+AAAALPo/q2sQu7lvfdsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure(figsize = (10,7))\n",
    "score = [score_dt, score_rf, score_nn, score_ada, score_gnb]\n",
    "objects = ('Decision Tree', 'Random Forest', 'Neural Network', 'Ada-boost', 'Gaussion Naive Bayes')\n",
    "y_pos = np.arange(len(objects))\n",
    "plt.bar(y_pos, score, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}