ObidroidLearning.ipynb

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Obidroid Learning Notebook"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Loading App Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I shall be using the previously exported `exports/appFeatures.csv` data for this notebook"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "from pandas import Series, DataFrame\n",
      "\n",
      "## Load the appFeatures file\n",
      "main_appData = pd.read_csv('exports/appFeatures.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This cell creates a dataframe with the same amount of unfair and fair apps, where the fair apps are randomly selected\n",
      "# TODO - Create a method that implements k-fold validation for app selection\n",
      "import random\n",
      "\n",
      "## create unfair apps dataframe and count\n",
      "df_unfair = main_appData[main_appData.appLabel == 'unfair']\n",
      "unfair_count = df_unfair.appLabel.count()\n",
      "\n",
      "## get same number of random fair apps\n",
      "df_randomly_fair = main_appData[main_appData.appLabel == 'fair'].ix[random.sample(main_appData[main_appData.appLabel == 'fair'].index, unfair_count)]\n",
      "#print df_randomly_fair.appLabel.count()\n",
      "\n",
      "\n",
      "# append the newly created dataframe of unfair * 2 rows\n",
      "appData = df_randomly_fair.append(df_unfair)\n",
      "\n",
      "# shuffle the dataframe\n",
      "appData = appData.ix[np.random.permutation(appData.index)]\n",
      "\n",
      "appData.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "Index([u'appName', u'adjectiveCount', u'avgRating', u'countCapital', u'exclamationCount', u'hasDeveloperEmail', u'hasDeveloperWebsite', u'Unnamed: 7', u'hasPrivacy', u'installs', u'price', u'revSent', u'revLength', u'appLabel'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "def trimDataFrame(df):\n",
      "    \"\"\"\n",
      "    Lets create a new dataframe for appFeatures and appLabels\n",
      "    \"\"\"\n",
      "\n",
      "    ## for App Features\n",
      "    appCols = set(df.columns)\n",
      "    appCols.remove('appName') # remove app Names column\n",
      "    appCols.remove('Unnamed: 7') # removing a weird unnamed column\n",
      "    appCols.remove('appLabel') # removing the label column\n",
      "    appCols.remove('price') # removing price since most of the apps are free\n",
      "    appCols.remove('exclamationCount') # remove exclamation count from features, as all values seemed to be 0\n",
      "    \n",
      "    df_trim = df[list(appCols)]\n",
      "    \n",
      "    # -- boolean\n",
      "    df_trim['hasPrivacy'].astype(bool)\n",
      "    df_trim['hasDeveloperEmail'].astype(bool)\n",
      "    df_trim['hasDeveloperWebsite'].astype(bool)\n",
      "    \n",
      "    # -- integer\n",
      "    df_trim['adjectiveCount'].astype(int)\n",
      "    df_trim['countCapital'].astype(int)\n",
      "    df_trim['installs'].astype(int)\n",
      "    df_trim['revSent'].astype(int)\n",
      "    df_trim['revLength'].astype(int)\n",
      "    \n",
      "    # -- float\n",
      "    df_trim['avgRating'].astype(float)\n",
      "    \n",
      "    return df_trim"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, I want to explicitly set types to all my columns as a better practice"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Explicitly casting column types in appFeatures dataframe\n",
      "appFeatures = trimDataFrame(main_appData)\n",
      "\n",
      "\n",
      "# -- boolean\n",
      "appFeatures['hasPrivacy'].astype(bool)\n",
      "appFeatures['hasDeveloperEmail'].astype(bool)\n",
      "appFeatures['hasDeveloperWebsite'].astype(bool)\n",
      "\n",
      "# -- integer\n",
      "appFeatures['adjectiveCount'].astype(int)\n",
      "appFeatures['countCapital'].astype(int)\n",
      "appFeatures['installs'].astype(int)\n",
      "appFeatures['revSent'].astype(int)\n",
      "appFeatures['revLength'].astype(int)\n",
      "\n",
      "# -- float\n",
      "appFeatures['avgRating'].astype(float)\n",
      "\n",
      "\n",
      "appFeatures"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>adjectiveCount</th>\n",
        "      <th>hasPrivacy</th>\n",
        "      <th>revLength</th>\n",
        "      <th>countCapital</th>\n",
        "      <th>hasDeveloperWebsite</th>\n",
        "      <th>installs</th>\n",
        "      <th>hasDeveloperEmail</th>\n",
        "      <th>avgRating</th>\n",
        "      <th>revSent</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>  4</td>\n",
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        "      <td>  True</td>\n",
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        "      <th>1 </th>\n",
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        "      <td>  True</td>\n",
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        "      <td>  True</td>\n",
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        "      <th>3 </th>\n",
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        "      <td>  True</td>\n",
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        "      <td>  True</td>\n",
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        "      <th>4 </th>\n",
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        "      <td>  True</td>\n",
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        "      <td> 16</td>\n",
        "      <td>  True</td>\n",
        "      <td>  7500000</td>\n",
        "      <td> False</td>\n",
        "      <td> 4.046</td>\n",
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        "      <th>5 </th>\n",
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        "      <td> False</td>\n",
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        "      <td>  6</td>\n",
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        "      <td>  True</td>\n",
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        "      <th>6 </th>\n",
        "      <td> 18</td>\n",
        "      <td>  True</td>\n",
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        "      <td> 60</td>\n",
        "      <td>  True</td>\n",
        "      <td> 30000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.526</td>\n",
        "      <td> -4</td>\n",
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        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 13</td>\n",
        "      <td> False</td>\n",
        "      <td> 1895</td>\n",
        "      <td> 19</td>\n",
        "      <td>  True</td>\n",
        "      <td> 30000000</td>\n",
        "      <td> False</td>\n",
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        "      <td> -5</td>\n",
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        "      <th>8 </th>\n",
        "      <td> 11</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1195</td>\n",
        "      <td> 10</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.400</td>\n",
        "      <td> -2</td>\n",
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        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 19</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1488</td>\n",
        "      <td> 11</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.935</td>\n",
        "      <td> -4</td>\n",
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        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 18</td>\n",
        "      <td> False</td>\n",
        "      <td> 1864</td>\n",
        "      <td> 35</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.075</td>\n",
        "      <td> -5</td>\n",
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        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 19</td>\n",
        "      <td> False</td>\n",
        "      <td> 2049</td>\n",
        "      <td> 14</td>\n",
        "      <td>  True</td>\n",
        "      <td>   750000</td>\n",
        "      <td> False</td>\n",
        "      <td> 3.983</td>\n",
        "      <td> -2</td>\n",
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        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>  8</td>\n",
        "      <td> False</td>\n",
        "      <td>  417</td>\n",
        "      <td>  2</td>\n",
        "      <td>  True</td>\n",
        "      <td> 30000000</td>\n",
        "      <td>  True</td>\n",
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        "      <td>  1</td>\n",
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        "      <th>13</th>\n",
        "      <td> 16</td>\n",
        "      <td> False</td>\n",
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        "      <td> 11</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.915</td>\n",
        "      <td> -3</td>\n",
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        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 13</td>\n",
        "      <td> False</td>\n",
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        "      <td>  True</td>\n",
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        "      <td>  True</td>\n",
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        "      <td> -3</td>\n",
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        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 20</td>\n",
        "      <td>  True</td>\n",
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        "      <td>  True</td>\n",
        "      <td>   750000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.795</td>\n",
        "      <td> -7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 12</td>\n",
        "      <td> False</td>\n",
        "      <td> 1044</td>\n",
        "      <td> 10</td>\n",
        "      <td>  True</td>\n",
        "      <td>  7500000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.997</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 15</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1245</td>\n",
        "      <td> 13</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.212</td>\n",
        "      <td> -5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>  2</td>\n",
        "      <td> False</td>\n",
        "      <td>  225</td>\n",
        "      <td>  2</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 2.611</td>\n",
        "      <td> -1</td>\n",
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        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> 15</td>\n",
        "      <td> False</td>\n",
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        "      <td> 10</td>\n",
        "      <td>  True</td>\n",
        "      <td> 30000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.547</td>\n",
        "      <td> -3</td>\n",
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        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> 22</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1406</td>\n",
        "      <td> 11</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 2.671</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td> 13</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1063</td>\n",
        "      <td> 10</td>\n",
        "      <td>  True</td>\n",
        "      <td>   750000</td>\n",
        "      <td> False</td>\n",
        "      <td> 4.045</td>\n",
        "      <td> -3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>  7</td>\n",
        "      <td> False</td>\n",
        "      <td>  855</td>\n",
        "      <td> 10</td>\n",
        "      <td>  True</td>\n",
        "      <td>   750000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.555</td>\n",
        "      <td> -9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td> 17</td>\n",
        "      <td>  True</td>\n",
        "      <td> 2147</td>\n",
        "      <td> 38</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.590</td>\n",
        "      <td> -5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td> 12</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1189</td>\n",
        "      <td> 13</td>\n",
        "      <td>  True</td>\n",
        "      <td>  7500000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.258</td>\n",
        "      <td> -9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td> 19</td>\n",
        "      <td> False</td>\n",
        "      <td> 1804</td>\n",
        "      <td>  8</td>\n",
        "      <td>  True</td>\n",
        "      <td>   750000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.428</td>\n",
        "      <td>-10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td> 26</td>\n",
        "      <td> False</td>\n",
        "      <td> 1514</td>\n",
        "      <td>  5</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.401</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td> 12</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1272</td>\n",
        "      <td> 12</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.275</td>\n",
        "      <td> -6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td> 17</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1413</td>\n",
        "      <td> 14</td>\n",
        "      <td>  True</td>\n",
        "      <td>   750000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.149</td>\n",
        "      <td> -8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>  7</td>\n",
        "      <td>  True</td>\n",
        "      <td>  610</td>\n",
        "      <td>  2</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.396</td>\n",
        "      <td> -3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td> 14</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1145</td>\n",
        "      <td>  5</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.113</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td> 14</td>\n",
        "      <td> False</td>\n",
        "      <td> 1413</td>\n",
        "      <td> 14</td>\n",
        "      <td>  True</td>\n",
        "      <td>    30000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.240</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
        "      <td>  2</td>\n",
        "      <td>  True</td>\n",
        "      <td>  573</td>\n",
        "      <td> 14</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.241</td>\n",
        "      <td> -4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
        "      <td> 12</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1387</td>\n",
        "      <td> 16</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.989</td>\n",
        "      <td> -6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td> 19</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1336</td>\n",
        "      <td>  7</td>\n",
        "      <td>  True</td>\n",
        "      <td>    75000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.310</td>\n",
        "      <td> -7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td>  7</td>\n",
        "      <td>  True</td>\n",
        "      <td>  817</td>\n",
        "      <td>  5</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.451</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td> 29</td>\n",
        "      <td>  True</td>\n",
        "      <td> 2205</td>\n",
        "      <td> 16</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.916</td>\n",
        "      <td> -9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td>  7</td>\n",
        "      <td> False</td>\n",
        "      <td>  541</td>\n",
        "      <td>  3</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.761</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td>  6</td>\n",
        "      <td> False</td>\n",
        "      <td>  310</td>\n",
        "      <td>  1</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.158</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td>  6</td>\n",
        "      <td> False</td>\n",
        "      <td>  880</td>\n",
        "      <td>  4</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 2.972</td>\n",
        "      <td> -4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td>  5</td>\n",
        "      <td> False</td>\n",
        "      <td>  583</td>\n",
        "      <td>  4</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.903</td>\n",
        "      <td> -6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td> 19</td>\n",
        "      <td> False</td>\n",
        "      <td> 1888</td>\n",
        "      <td> 20</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.433</td>\n",
        "      <td> -9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td> 13</td>\n",
        "      <td> False</td>\n",
        "      <td> 1122</td>\n",
        "      <td>  4</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.412</td>\n",
        "      <td> -3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td> 15</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1613</td>\n",
        "      <td> 21</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td> False</td>\n",
        "      <td> 4.461</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td>  5</td>\n",
        "      <td> False</td>\n",
        "      <td> 1014</td>\n",
        "      <td> 11</td>\n",
        "      <td> False</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.564</td>\n",
        "      <td> -2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td>  8</td>\n",
        "      <td> False</td>\n",
        "      <td> 1115</td>\n",
        "      <td>  6</td>\n",
        "      <td> False</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.131</td>\n",
        "      <td>  5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td>  6</td>\n",
        "      <td>  True</td>\n",
        "      <td>  437</td>\n",
        "      <td>  8</td>\n",
        "      <td>  True</td>\n",
        "      <td>    30000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.550</td>\n",
        "      <td> -1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td>  2</td>\n",
        "      <td>  True</td>\n",
        "      <td>  307</td>\n",
        "      <td>  0</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.435</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td> 13</td>\n",
        "      <td>  True</td>\n",
        "      <td>  991</td>\n",
        "      <td>  4</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.233</td>\n",
        "      <td> -3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td>  7</td>\n",
        "      <td> False</td>\n",
        "      <td>  578</td>\n",
        "      <td>  6</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.975</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td> 10</td>\n",
        "      <td>  True</td>\n",
        "      <td>  962</td>\n",
        "      <td>  7</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.926</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td> 13</td>\n",
        "      <td> False</td>\n",
        "      <td> 1267</td>\n",
        "      <td>  6</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.590</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td>  9</td>\n",
        "      <td> False</td>\n",
        "      <td> 1300</td>\n",
        "      <td>  8</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.601</td>\n",
        "      <td> -9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td> 16</td>\n",
        "      <td> False</td>\n",
        "      <td> 1051</td>\n",
        "      <td> 12</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.701</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td> 16</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1822</td>\n",
        "      <td> 10</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 2.931</td>\n",
        "      <td>-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td>  7</td>\n",
        "      <td> False</td>\n",
        "      <td>  535</td>\n",
        "      <td>  0</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.564</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56</th>\n",
        "      <td> 11</td>\n",
        "      <td> False</td>\n",
        "      <td> 1075</td>\n",
        "      <td>  8</td>\n",
        "      <td>  True</td>\n",
        "      <td>  7500000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.179</td>\n",
        "      <td> -7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57</th>\n",
        "      <td>  6</td>\n",
        "      <td>  True</td>\n",
        "      <td>  691</td>\n",
        "      <td>  4</td>\n",
        "      <td>  True</td>\n",
        "      <td>  3000000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.466</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58</th>\n",
        "      <td>  7</td>\n",
        "      <td>  True</td>\n",
        "      <td>  991</td>\n",
        "      <td>  5</td>\n",
        "      <td>  True</td>\n",
        "      <td>   750000</td>\n",
        "      <td> False</td>\n",
        "      <td> 4.340</td>\n",
        "      <td> -3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>59</th>\n",
        "      <td>  7</td>\n",
        "      <td> False</td>\n",
        "      <td>  805</td>\n",
        "      <td> 14</td>\n",
        "      <td>  True</td>\n",
        "      <td>   300000</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.539</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>323 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "    adjectiveCount hasPrivacy  revLength  countCapital hasDeveloperWebsite  \\\n",
        "0                4       True        601             1                True   \n",
        "1               13       True       1139            11                True   \n",
        "2               23       True       2223            20                True   \n",
        "3               10      False        804             5                True   \n",
        "4               22       True       1867            16                True   \n",
        "5               18      False       1162             6                True   \n",
        "6               18       True       1522            60                True   \n",
        "7               13      False       1895            19                True   \n",
        "8               11       True       1195            10                True   \n",
        "9               19       True       1488            11                True   \n",
        "10              18      False       1864            35                True   \n",
        "11              19      False       2049            14                True   \n",
        "12               8      False        417             2                True   \n",
        "13              16      False       1276            11                True   \n",
        "14              13      False       1210            12                True   \n",
        "15              20       True       2038            24                True   \n",
        "16              12      False       1044            10                True   \n",
        "17              15       True       1245            13                True   \n",
        "18               2      False        225             2                True   \n",
        "19              15      False       1120            10                True   \n",
        "20              22       True       1406            11                True   \n",
        "21              13       True       1063            10                True   \n",
        "22               7      False        855            10                True   \n",
        "23              17       True       2147            38                True   \n",
        "24              12       True       1189            13                True   \n",
        "25              19      False       1804             8                True   \n",
        "26              26      False       1514             5                True   \n",
        "27              12       True       1272            12                True   \n",
        "28              17       True       1413            14                True   \n",
        "29               7       True        610             2                True   \n",
        "30              14       True       1145             5                True   \n",
        "31              14      False       1413            14                True   \n",
        "32               2       True        573            14                True   \n",
        "33              12       True       1387            16                True   \n",
        "34              19       True       1336             7                True   \n",
        "35               7       True        817             5                True   \n",
        "36              29       True       2205            16                True   \n",
        "37               7      False        541             3                True   \n",
        "38               6      False        310             1                True   \n",
        "39               6      False        880             4                True   \n",
        "40               5      False        583             4                True   \n",
        "41              19      False       1888            20                True   \n",
        "42              13      False       1122             4                True   \n",
        "43              15       True       1613            21                True   \n",
        "44               5      False       1014            11               False   \n",
        "45               8      False       1115             6               False   \n",
        "46               6       True        437             8                True   \n",
        "47               2       True        307             0                True   \n",
        "48              13       True        991             4                True   \n",
        "49               7      False        578             6                True   \n",
        "50              10       True        962             7                True   \n",
        "51              13      False       1267             6                True   \n",
        "52               9      False       1300             8                True   \n",
        "53              16      False       1051            12                True   \n",
        "54              16       True       1822            10                True   \n",
        "55               7      False        535             0                True   \n",
        "56              11      False       1075             8                True   \n",
        "57               6       True        691             4                True   \n",
        "58               7       True        991             5                True   \n",
        "59               7      False        805            14                True   \n",
        "               ...        ...        ...           ...                 ...   \n",
        "\n",
        "    installs hasDeveloperEmail  avgRating  revSent  \n",
        "0   30000000              True      4.051       -3  \n",
        "1   30000000              True      4.351        2  \n",
        "2    3000000             False      4.555       -4  \n",
        "3   30000000              True      4.623        8  \n",
        "4    7500000             False      4.046      -11  \n",
        "5   30000000              True      4.595        1  \n",
        "6   30000000              True      4.526       -4  \n",
        "7   30000000             False      4.039       -5  \n",
        "8    3000000              True      4.400       -2  \n",
        "9     300000              True      3.935       -4  \n",
        "10   3000000              True      4.075       -5  \n",
        "11    750000             False      3.983       -2  \n",
        "12  30000000              True      4.238        1  \n",
        "13   3000000              True      3.915       -3  \n",
        "14    750000              True      4.050       -3  \n",
        "15    750000              True      3.795       -7  \n",
        "16   7500000              True      3.997        1  \n",
        "17   3000000              True      3.212       -5  \n",
        "18   3000000              True      2.611       -1  \n",
        "19  30000000              True      4.547       -3  \n",
        "20   3000000              True      2.671        4  \n",
        "21    750000             False      4.045       -3  \n",
        "22    750000              True      3.555       -9  \n",
        "23   3000000              True      4.590       -5  \n",
        "24   7500000              True      4.258       -9  \n",
        "25    750000              True      4.428      -10  \n",
        "26   3000000              True      4.401        5  \n",
        "27   3000000              True      4.275       -6  \n",
        "28    750000              True      4.149       -8  \n",
        "29    300000              True      4.396       -3  \n",
        "30   3000000              True      4.113        2  \n",
        "31     30000              True      4.240        2  \n",
        "32    300000              True      4.241       -4  \n",
        "33   3000000              True      3.989       -6  \n",
        "34     75000              True      4.310       -7  \n",
        "35   3000000              True      4.451        5  \n",
        "36    300000              True      3.916       -9  \n",
        "37    300000              True      4.761        1  \n",
        "38    300000              True      4.158        0  \n",
        "39    300000              True      2.972       -4  \n",
        "40    300000              True      3.903       -6  \n",
        "41   3000000              True      3.433       -9  \n",
        "42   3000000              True      4.412       -3  \n",
        "43   3000000             False      4.461       15  \n",
        "44    300000              True      3.564       -2  \n",
        "45   3000000              True      4.131        5  \n",
        "46     30000              True      3.550       -1  \n",
        "47   3000000              True      4.435        1  \n",
        "48    300000              True      4.233       -3  \n",
        "49   3000000              True      3.975        1  \n",
        "50    300000              True      3.926        4  \n",
        "51   3000000              True      4.590        2  \n",
        "52    300000              True      3.601       -9  \n",
        "53    300000              True      3.701        3  \n",
        "54    300000              True      2.931      -13  \n",
        "55   3000000              True      4.564        0  \n",
        "56   7500000              True      4.179       -7  \n",
        "57   3000000              True      4.466        7  \n",
        "58    750000             False      4.340       -3  \n",
        "59    300000              True      4.539        3  \n",
        "         ...               ...        ...      ...  \n",
        "\n",
        "[323 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Understanding the Data"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Histograms"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appFeatures['adjectiveCount'].hist(color=['#af9ecb'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x109c89750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bmw3DMIyvvvrKyMvLM44ePWrruRqqye5z1dXVZRiGYVy+fNlYtGiRsXfvXtvv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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109c89050>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appFeatures['avgRating'].hist(color=['#af9ecb'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x107e46690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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E9ZxJTiOsp5fL5VK+853vBOw3wlreSi2nEdby2rVryieffKIoiqJcvXpVWbly\npfLPf/7T7xit6zkrXxpSVlaG1157DR9++CFSUlKwY8cO3LhxAwDw4x//GOvWrcORI0eQmpqKL33p\nS9i/f/9sxNCd869//StefPFFxMbG4q677sKhQ4cikvPkyZM4ePCg721VALBz505cvnzZl9UIazqT\nnEZY0+HhYTgcDkxOTmJychKbN2/G6tWr8Yc//MGX0wjrOZOcRljPW3lrA6Ot5e2mymmEtfR4PNiw\nYQOAz64g8Oijj6KoqEjXevLDUEREUYzf8UpEFMU45ImIohiHPBFRFOOQJyKKYhzyRERRjEOeiCiK\nccgTEUUxDnkioij2/yRGA1x0VYCUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109d11910>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appFeatures['countCapital'].hist(color=['#af9ecb'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x109f1b3d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DfN/3l7/8Rdx7771CiPDyjdgYZ7A3XHV1dY3U4aOuo6MDH3zwAebOnQu/3w+L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\nExGZwP8BEvfJSiVBUPkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109ce4dd0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appFeatures['hasDeveloperEmail'].hist(color=['#af9ecb'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a1286d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10a11fad0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appFeatures['hasDeveloperWebsite'].hist(color=['#af9ecb'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a138610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10a1b55d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appFeatures['hasPrivacy'].hist(color=['#af9ecb'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a1d6a90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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evZs33niDo0ePMnnyZG644QbS0tIGYcLB058sli9fzvjx46murubDDz8kNzeX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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a481110>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appFeatures['installs'].hist(color=['#af9ecb'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a729a90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mV+oiImHS0dFBUVHRwCv1u+66i5deeomUlBT+/ve/U1VVxa5du/jyyy+5/fbb\n+fLLLzlz5gy7du0a9Sexbf9zdiIiMrK+vj4++OCDQRcSCwaDAKxZs4ZHH32Uxx9/nA8//JCHH36Y\nf/zjH6N6Xg11EZEI6O/vZ+rUqRw6dOiKx95//32qq6sBuO222zh//jwnT55kxowZIz6vOnURkQi4\n4YYbSE5O5i9/+QvwzUX6Pv74Y+Cbi3jt3LkTgE8//ZTz58+PaqCDOnURkbAoKSlhz549nDx5EqfT\nyW9+8xvy8/NZvXo13d3dXLhwgZKSEn71q19x7NgxKioq6O3txeFw8Oyzz4b8wxj/m4a6iEgUUf0i\nIhJFNNRFRKKIhrqISBTRUBcRiSIa6iIiUURDXUQkimioi4hEEQ11EZEo8v8B1mbm3wUNPE8AAAAA\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a713ed0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appFeatures['revSent'].hist(color=['#af9ecb'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a7c5d10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10a12a450>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appFeatures['revLength'].hist(color=['#af9ecb'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a7dcdd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10a487210>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "appFeatures.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>adjectiveCount</th>\n",
        "      <th>hasPrivacy</th>\n",
        "      <th>revLength</th>\n",
        "      <th>countCapital</th>\n",
        "      <th>hasDeveloperWebsite</th>\n",
        "      <th>installs</th>\n",
        "      <th>hasDeveloperEmail</th>\n",
        "      <th>avgRating</th>\n",
        "      <th>revSent</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 323.000000</td>\n",
        "      <td>       323</td>\n",
        "      <td>  323.000000</td>\n",
        "      <td> 323.000000</td>\n",
        "      <td>       323</td>\n",
        "      <td> 3.230000e+02</td>\n",
        "      <td>       323</td>\n",
        "      <td> 323.000000</td>\n",
        "      <td> 323.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  12.653251</td>\n",
        "      <td> 0.5263158</td>\n",
        "      <td> 1114.975232</td>\n",
        "      <td>  11.789474</td>\n",
        "      <td> 0.9071207</td>\n",
        "      <td> 2.634931e+07</td>\n",
        "      <td> 0.9287926</td>\n",
        "      <td>   4.141879</td>\n",
        "      <td>  -2.281734</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>   6.520670</td>\n",
        "      <td> 0.5000817</td>\n",
        "      <td>  498.856574</td>\n",
        "      <td>  10.834108</td>\n",
        "      <td> 0.2907135</td>\n",
        "      <td> 8.654108e+07</td>\n",
        "      <td>   0.25757</td>\n",
        "      <td>   0.497807</td>\n",
        "      <td>   4.932892</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>   0.000000</td>\n",
        "      <td>     False</td>\n",
        "      <td>   17.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>     False</td>\n",
        "      <td> 3.000000e+02</td>\n",
        "      <td>     False</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td> -17.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>   8.000000</td>\n",
        "      <td>         0</td>\n",
        "      <td>  748.500000</td>\n",
        "      <td>   6.000000</td>\n",
        "      <td>         1</td>\n",
        "      <td> 3.000000e+05</td>\n",
        "      <td>         1</td>\n",
        "      <td>   4.003500</td>\n",
        "      <td>  -5.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>  13.000000</td>\n",
        "      <td>         1</td>\n",
        "      <td> 1123.000000</td>\n",
        "      <td>  10.000000</td>\n",
        "      <td>         1</td>\n",
        "      <td> 3.000000e+06</td>\n",
        "      <td>         1</td>\n",
        "      <td>   4.242000</td>\n",
        "      <td>  -2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>  17.000000</td>\n",
        "      <td>         1</td>\n",
        "      <td> 1458.500000</td>\n",
        "      <td>  15.000000</td>\n",
        "      <td>         1</td>\n",
        "      <td> 7.500000e+06</td>\n",
        "      <td>         1</td>\n",
        "      <td>   4.431000</td>\n",
        "      <td>   1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  41.000000</td>\n",
        "      <td>      True</td>\n",
        "      <td> 2454.000000</td>\n",
        "      <td> 109.000000</td>\n",
        "      <td>      True</td>\n",
        "      <td> 7.500000e+08</td>\n",
        "      <td>      True</td>\n",
        "      <td>   4.845000</td>\n",
        "      <td>  15.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>8 rows \u00d7 9 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "       adjectiveCount hasPrivacy    revLength  countCapital  \\\n",
        "count      323.000000        323   323.000000    323.000000   \n",
        "mean        12.653251  0.5263158  1114.975232     11.789474   \n",
        "std          6.520670  0.5000817   498.856574     10.834108   \n",
        "min          0.000000      False    17.000000      0.000000   \n",
        "25%          8.000000          0   748.500000      6.000000   \n",
        "50%         13.000000          1  1123.000000     10.000000   \n",
        "75%         17.000000          1  1458.500000     15.000000   \n",
        "max         41.000000       True  2454.000000    109.000000   \n",
        "\n",
        "      hasDeveloperWebsite      installs hasDeveloperEmail   avgRating  \\\n",
        "count                 323  3.230000e+02               323  323.000000   \n",
        "mean            0.9071207  2.634931e+07         0.9287926    4.141879   \n",
        "std             0.2907135  8.654108e+07           0.25757    0.497807   \n",
        "min                 False  3.000000e+02             False    1.000000   \n",
        "25%                     1  3.000000e+05                 1    4.003500   \n",
        "50%                     1  3.000000e+06                 1    4.242000   \n",
        "75%                     1  7.500000e+06                 1    4.431000   \n",
        "max                  True  7.500000e+08              True    4.845000   \n",
        "\n",
        "          revSent  \n",
        "count  323.000000  \n",
        "mean    -2.281734  \n",
        "std      4.932892  \n",
        "min    -17.000000  \n",
        "25%     -5.000000  \n",
        "50%     -2.000000  \n",
        "75%      1.000000  \n",
        "max     15.000000  \n",
        "\n",
        "[8 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Variable Relations"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plots inspired from [Graphical Representations of linear models notebook](http://nbviewer.ipython.org/github/mwaskom/seaborn/blob/master/examples/linear_models.ipynb)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import seaborn as sns\n",
      "import matplotlib as mpl\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "\n",
      "\n",
      "sns.set(palette=\"Purples_r\")\n",
      "np.random.seed(9221999)\n",
      "mpl.rc(\"figure\", figsize=(10, 10))\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Review Sentiment vs Avg Rating"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = appFeatures['revSent'].astype(float)\n",
      "y = appFeatures['avgRating'].astype(float)\n",
      "\n",
      "sns.regplot(x,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6EnKFEEIIIUTWkZArhBBCCCGyjoRcIYQQQgiRdVIecmfPns3y5ctZvnw5q1ev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T179pz2/Pr16zV8+HDl5+frmWeeadtfAAAdRFO3luN2c0Bmq4vEJIm7K9igxVfXVatW6fDhw7rx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\nCwAAgIzz/wEfTaidzYBe4AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ca7cf10>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Review Sentiment vs Review Length"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = appFeatures['revSent'].astype(float)\n",
      "y = appFeatures['revLength'].astype(float)\n",
      "\n",
      "sns.regplot(x,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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iIpKRFIRFREREJCMpCIuIiIhIRlIQFhEREZGMpCAsIiIiIhlJQVhEREREMpKC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fv27UM8Hsfu3bthtVrx85//HG+++SZeffVV3HXXXejt7cUvfvELTJ8+Hfv27cOPf/xjbNiwIZ+h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\nAwAAICT9H0guRipIvMSOAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10cad45d0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Review Length vs Avg Rating"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = appFeatures['revLength'].astype(float)\n",
      "y = appFeatures['revSent'].astype(float)\n",
      "\n",
      "sns.regplot(x,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dmHn4q+fwssuZZBd4NNRQTJGSgbeoPItAIGR2GSIiImnP7rCSle8hK99zwvsH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\n8zkX9BRtp7KXTXszn/FU92U6X3K1TxDVDj0H1Uv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YBs2ra/HlsVH4blhV1l+lK+bT/7nn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\nmPASERERUV77/5L3B0ZKLK5RAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a7264d0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Pairwise correlation heatmaps"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
      "cmap = sns.blend_palette([\"#00008B\", \"#6A5ACD\", \"#F0F8FF\",\n",
      "                          \"#FFE6F8\", \"#C71585\", \"#8B0000\"], as_cmap=True)\n",
      "\n",
      "\n",
      "sns.corrplot(appFeatures, annot=False, diag_names=False, cmap=cmap, ax=ax);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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37t3KycmpqRiePn261pgFBQXq3r27JMnhcOihhx6SJEVFRSk4OFhSdfb8+eef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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10caae350>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.corrplot(appFeatures) #with values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10cc9c9d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KK1aEA+aXR27aAiAuLpYrVy5Tu/aH+RletkJDJ3LsWDS//36O99//wPB4YmIi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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a7a0410>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%doctest_mode"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Exception reporting mode: Plain\n",
        "Doctest mode is: ON\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Unsupervised Learning"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Scaling the feature vector"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# get all the rows for unsupervised learning\n",
      "appData_all = trimDataFrame(main_appData)\n",
      "\n",
      "appData_all.head()\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "   adjectiveCount hasPrivacy  revLength  countCapital hasDeveloperWebsite  \\\n",
        "0               4       True        601             1                True   \n",
        "1              13       True       1139            11                True   \n",
        "2              23       True       2223            20                True   \n",
        "3              10      False        804             5                True   \n",
        "4              22       True       1867            16                True   \n",
        "\n",
        "   installs hasDeveloperEmail  avgRating  revSent  \n",
        "0  30000000              True      4.051       -3  \n",
        "1  30000000              True      4.351        2  \n",
        "2   3000000             False      4.555       -4  \n",
        "3  30000000              True      4.623        8  \n",
        "4   7500000             False      4.046      -11  \n",
        "\n",
        "[5 rows x 9 columns]"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from sklearn.preprocessing import MinMaxScaler\n",
      "min_max_scaler = MinMaxScaler()\n",
      "\n",
      "print(appData_all.columns)\n",
      "\n",
      "# scale the dataframe\n",
      "X_scaled = min_max_scaler.fit_transform(appData_all)\n",
      "\n",
      "print (X_scaled)\n",
      "print X_scaled.astype('float64')\n",
      "Y = main_appData['appLabel']\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Index([u'adjectiveCount', u'hasPrivacy', u'revLength', u'countCapital', u'hasDeveloperWebsite', u'installs', u'hasDeveloperEmail', u'avgRating', u'revSent'], dtype='object')\n",
        "[[0 1 0 ..., 1 0.7934980494148245 0]\n",
        " [0 1 0 ..., 1 0.8715214564369311 0]\n",
        " [0 1 0 ..., 0 0.9245773732119635 0]\n",
        " ..., \n",
        " [0 1 0 ..., 1 0.8905071521456436 0]\n",
        " [0 1 0 ..., 1 0.9193758127438232 0]\n",
        " [0 1 0 ..., 1 0.9243172951885565 0]]\n",
        "[[ 0.          1.          0.         ...,  1.          0.79349805  0.        ]\n",
        " [ 0.          1.          0.         ...,  1.          0.87152146  0.        ]\n",
        " [ 0.          1.          0.         ...,  0.          0.92457737  0.        ]\n",
        " ..., \n",
        " [ 0.          1.          0.         ...,  1.          0.89050715  0.        ]\n",
        " [ 0.          1.          0.         ...,  1.          0.91937581  0.        ]\n",
        " [ 0.          1.          0.         ...,  1.          0.9243173   0.        ]]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/shreyas/anaconda/lib/python2.7/site-packages/sklearn/utils/validation.py:278: UserWarning: MinMaxScaler assumes floating point values as input, got object\n",
        "  \"got %s\" % (estimator, X.dtype))\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Change plot string for fair and unfair apps\\n\n",
      "def set_plot_symbol(fairness, fair_char = '.', unfair_char = '+'):\n",
      "    symbol = ''\n",
      "    if fairness == 'unfair':\n",
      "        symbol = unfair_char\n",
      "    elif fairness == 'fair':\n",
      "        symbol = fair_char\n",
      "    \n",
      "    return symbol"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Code reference: http://scikit-learn.org/stable/auto_examples/manifold/plot_lle_digits.html#example-manifold-plot-lle-digits-py\n",
      "#====================\n",
      "#----------------------------------------------------------------------\n",
      "# Scale and visualize the embedding vectors\n",
      "def plot_embedding(X, title=None):\n",
      "    x_min, x_max = np.min(X, 0), np.max(X, 0)\n",
      "    X = (X - x_min) / (x_max - x_min)\n",
      "\n",
      "    pl.figure()\n",
      "    ax = pl.subplot(111)\n",
      "    for i in range(X.shape[0]):\n",
      "        pl.text(X[i, 0], X[i, 1], str(set_plot_symbol(Y[i])),\n",
      "#                 color=pl.cm.Set1(y[i] / 2.),\n",

      "                fontdict={'weight': 'bold', 'size': 9})\n",
      "\n",
      "\n",
      "    pl.xticks([]), pl.yticks([])\n",
      "    if title is not None:\n",
      "        pl.title(title)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from time import time\n",
      "import pylab as pl\n",
      "from matplotlib import offsetbox\n",
      "from sklearn import (manifold, datasets, decomposition, ensemble, lda,\n",
      "                     random_projection)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(\"Computing MDS embedding\")\n",
      "clf = manifold.MDS(n_components=2, n_init=1, max_iter=100)\n",
      "t0 = time()\n",
      "X_mds = clf.fit_transform(X_scaled)\n",
      "print(\"Done. Stress: %f\" % clf.stress_)\n",
      "plot_embedding(X_mds,\n",
      "               \"MDS embedding of the digits (time %.2fs)\" %\n",
      "               (time() - t0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Computing MDS embedding\n",
        "Done. Stress: 596.376730"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure object at 0x10cc9c0d0>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#----------------------------------------------------------------------\n",
      "# Projection on to the first 2 principal components\n",
      "\n",
      "print(\"Computing PCA projection\")\n",
      "t0 = time()\n",
      "X_pca = decomposition.TruncatedSVD(n_components=2).fit_transform(X_scaled)\n",
      "plot_embedding(X_pca,\n",
      "               \"Principal Components projection of the digits (time %.2fs)\" %\n",
      "               (time() - t0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Computing PCA projection\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure object at 0x112b328d0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#----------------------------------------------------------------------\n",
      "# Projection on to the first 2 linear discriminant components\n",
      "\n",
      "\n",
      "print(\"Computing LDA projection\")\n",
      "X_converted = X_scaled.astype('float64')\n",
      "X2 = X_converted.copy()\n",
      "X2.flat[::X_converted.shape[1] + 1] += 0.01  # Make X invertible\n",
      "t0 = time()\n",
      "X_lda = lda.LDA(n_components=2).fit_transform(X2, y)\n",
      "plot_embedding(X_lda,\n",
      "               \"Linear Discriminant projection of the digits (time %.2fs)\" %\n",
      "               (time() - t0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Computing LDA projection\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure object at 0x110597590>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#----------------------------------------------------------------------\n",
      "# Isomap projection of the digits dataset\n",
      "n_neighbors = 10\n",
      "print(\"Computing Isomap embedding\")\n",
      "t0 = time()\n",
      "X_iso = manifold.Isomap(n_neighbors, n_components=2).fit_transform(X_scaled)\n",
      "print(\"Done.\")\n",
      "plot_embedding(X_iso,\n",
      "               \"Isomap projection of the digits (time %.2fs)\" %\n",
      "               (time() - t0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Computing Isomap embedding\n",
        "Done.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure object at 0x112060390>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#----------------------------------------------------------------------\n",
      "# Locally linear embedding of the digits dataset\n",
      "print(\"Computing LLE embedding\")\n",
      "clf = manifold.LocallyLinearEmbedding(n_neighbors, n_components=2,\n",
      "                                      method='standard')\n",
      "t0 = time()\n",
      "X_lle = clf.fit_transform(X_scaled.astype('float64'))\n",
      "print(\"Done. Reconstruction error: %g\" % clf.reconstruction_error_)\n",
      "plot_embedding(X_lle,\n",
      "               \"Locally Linear Embedding of the digits (time %.2fs)\" %\n",
      "               (time() - t0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Computing LLE embedding\n",
        "Done. Reconstruction error: 1.99238e-17"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure object at 0x111ee02d0>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#----------------------------------------------------------------------\n",
      "# Modified Locally linear embedding of the digits dataset\n",
      "print(\"Computing modified LLE embedding\")\n",
      "clf = manifold.LocallyLinearEmbedding(n_neighbors, n_components=2,\n",
      "                                      method='modified')\n",
      "t0 = time()\n",
      "X_mlle = clf.fit_transform(X_scaled.astype('float64'))\n",
      "print(\"Done. Reconstruction error: %g\" % clf.reconstruction_error_)\n",
      "plot_embedding(X_mlle,\n",
      "               \"Modified Locally Linear Embedding of the digits (time %.2fs)\" %\n",
      "               (time() - t0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Computing modified LLE embedding\n",
        "Done. Reconstruction error: 3.96675e-17"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure object at 0x110597310>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#----------------------------------------------------------------------\n",
      "# HLLE embedding of the digits dataset\n",
      "print(\"Computing Hessian LLE embedding\")\n",
      "clf = manifold.LocallyLinearEmbedding(n_neighbors, n_components=2,\n",
      "                                      method='hessian')\n",
      "t0 = time()\n",
      "X_hlle = clf.fit_transform(X_scaled.astype('float64'))\n",
      "print(\"Done. Reconstruction error: %g\" % clf.reconstruction_error_)\n",
      "plot_embedding(X_hlle,\n",
      "               \"Hessian Locally Linear Embedding of the digits (time %.2fs)\" %\n",
      "               (time() - t0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Computing Hessian LLE embedding\n",
        "Done. Reconstruction error: -2.1499e-18"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure object at 0x111e61e10>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#----------------------------------------------------------------------\n",
      "# MDS  embedding of the digits dataset\n",
      "print(\"Computing MDS embedding\")\n",
      "clf = manifold.MDS(n_components=2, n_init=1, max_iter=100)\n",
      "t0 = time()\n",
      "X_mds = clf.fit_transform(X_scaled.astype('float64'))\n",
      "print(\"Done. Stress: %f\" % clf.stress_)\n",
      "plot_embedding(X_mds,\n",
      "               \"MDS embedding of the digits (time %.2fs)\" %\n",
      "               (time() - t0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Computing MDS embedding\n",
        "Done. Stress: 1101.170364"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure object at 0x111e66550>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#----------------------------------------------------------------------\n",
      "# Random Trees embedding of the digits dataset\n",
      "print(\"Computing Totally Random Trees embedding\")\n",
      "hasher = ensemble.RandomTreesEmbedding(n_estimators=200, random_state=0,\n",
      "                                       max_depth=5)\n",
      "t0 = time()\n",
      "X_transformed = hasher.fit_transform(X_scaled.astype('float64'))\n",
      "pca = decomposition.TruncatedSVD(n_components=2)\n",
      "X_reduced = pca.fit_transform(X_transformed)\n",
      "\n",
      "plot_embedding(X_reduced,\n",
      "               \"Random forest embedding of the digits (time %.2fs)\" %\n",
      "               (time() - t0))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Computing Totally Random Trees embedding\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure object at 0x111fe5990>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#----------------------------------------------------------------------\n",
      "# Spectral embedding of the digits dataset\n",
      "print(\"Computing Spectral embedding\")\n",
      "embedder = manifold.SpectralEmbedding(n_components=2, random_state=0,\n",
      "                                      eigen_solver=\"arpack\")\n",
      "t0 = time()\n",
      "X_se = embedder.fit_transform(X_scaled.astype('float64'))\n",
      "\n",
      "plot_embedding(X_se,\n",
      "               \"Spectral embedding of the digits (time %.2fs)\" %\n",
      "               (time() - t0))\n",
      "\n",
      "pl.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Computing Spectral embedding\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure object at 0x111f74f50>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#\n",
      "# ---------------K-means Clustering\n",
      "#\n",
      "from sklearn.cluster import KMeans\n",
      "\n",
      "print(\"Computing K-means clustering\")\n",
      "kmeans = KMeans(init='k-means++', n_clusters=7, n_init=10)\n",
      "t0 = time()\n",
      "X_kmeans = kmeans.fit_transform(X_scaled)\n",
      "# print(\"Done. Stress: %f\" % clf.stress_)\n",
      "plot_embedding(X_kmeans,\n",
      "               \"Kmeans clusters (time %.2fs)\" %\n",
      "               (time() - t0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Computing K-means clustering\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure object at 0x110461fd0>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Code reference: http://stackoverflow.com/questions/2982929/plotting-results-of-hierarchical-clustering-ontop-of-a-matrix-of-data-in-python\n",
      "\"\"\"\n",
      "\n",
      "from __future__ import division\n",
      "\n",
      "\n",
      "# dendrogram plotting from scipy book\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as mpl\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "import scipy.cluster.hierarchy as sch\n",
      "from sklearn.metrics.pairwise import pairwise_distances as pwd\n",
      "import pylab\n",
      "\n",
      "# compute the distance matrix\n",
      "D = pwd(X_converted, metric='euclidean')\n",
      "\n",
      "# compute and plot the first dendrogram\n",
      "fig = pylab.figure(figsize=(8,8))\n",
      "ax1 = fig.add_axes([0.09, 0.1, 0.2, 0.6])\n",
      "Y_sch = sch.linkage(D, method=\"centroid\")\n",
      "Z1 = sch.dendrogram(Y_sch, orientation='right')\n",
      "ax1.set_xticks([])\n",
      "ax1.set_yticks([])\n",
      "\n",
      "\n",
      "# compute the second cluster\n",
      "ax2 = fig.add_axes([0.3, 0.71, 0.6, 0.2])\n",
      "Y_sch = sch.linkage(D, method='single')\n",
      "Z2 = sch.dendrogram(Y_sch)\n",
      "ax2.set_xticks([])\n",
      "ax2.set_yticks([])\n",
      "\n",
      "# plot the distance matrix\n",
      "axmatrix = fig.add_axes([0.3, 0.1, 0.6, 0.6])\n",
      "idx1 = Z1['leaves']\n",
      "idx2 = Z2['leaves']\n",
      "D = D[idx1, :]\n",
      "D = D[:, idx2]\n",
      "\n",
      "im = axmatrix.matshow(D, aspect='auto', origin='lower', cmap=pylab.cm.YlGnBu)\n",
      "axmatrix.set_xticks([])\n",
      "axmatrix.set_yticks([])\n",
      "\n",
      "#plot colorbar\n",
      "axcolor = fig.add_axes([0.91, 0.1, 0.02, 0.6])\n",
      "pylab.colorbar(im, cax=axcolor)\n",
      "fig.show()\n",
      "fig.savefig('dendrogram.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/Users/shreyas/anaconda/lib/python2.7/site-packages/matplotlib/figure.py:371: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
        "  \"matplotlib is currently using a non-GUI backend, \"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure object at 0x1105b9d50>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Supervised Learning"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To be able to assess our classifier better, We have resampled our data to include equal number of __fair__ and __unfair__ apps. We will shuffle the records and do 10-fold validation on it. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# first get the feature matrix\n",
      "# from Luis's random sampling of the dataframe\n",
      "\n",
      "appData\n",
      "\n",
      "X_resampled = trimDataFrame(appData)\n",
      "\n",
      "X_resampled\n",
      "\n",
      "X_for_classifier =  min_max_scaler.fit_transform(X_resampled).astype('float64')\n",
      "\n",
      "np.random.shuffle(X_for_classifier)\n",
      "\n",
      "n_sample, n_features = X_for_classifier.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# setting up the cross validation\n",
      "from sklearn import cross_validation\n",
      "kf = cross_validation.KFold(n_sample, n_folds=3)\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    }
   ],
   "metadata": {}
  }
 ]
}