diff --git a/ObidroidMDSPlots.ipynb b/ObidroidMDSPlots.ipynb
index 9098dcc..aa3a0c6 100644
--- a/ObidroidMDSPlots.ipynb
+++ b/ObidroidMDSPlots.ipynb
@@ -47,7 +47,7 @@
        ]
       }
      ],
-     "prompt_number": 149
+     "prompt_number": 1110
     },
     {
      "cell_type": "code",
@@ -67,7 +67,7 @@
        ]
       }
      ],
-     "prompt_number": 150
+     "prompt_number": 1111
     },
     {
      "cell_type": "code",
@@ -83,7 +83,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 151
+     "prompt_number": 1112
     },
     {
      "cell_type": "code",
@@ -108,7 +108,7 @@
       "# shuffle the dataframe\n",
       "appData = appData.ix[np.random.permutation(appData.index)]\n",
       "\n",
-      "appData.columns"
+      "main_appData.columns"
      ],
      "language": "python",
      "metadata": {},
@@ -116,13 +116,13 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 152,
+       "prompt_number": 1113,
        "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": 152
+     "prompt_number": 1113
     },
     {
      "cell_type": "code",
@@ -165,7 +165,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 153
+     "prompt_number": 1114
     },
     {
      "cell_type": "markdown",
@@ -961,7 +961,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 154,
+       "prompt_number": 1115,
        "text": [
         "    adjectiveCount hasPrivacy  revLength  countCapital hasDeveloperWebsite  \\\n",
         "0                4       True        601             1                True   \n",
@@ -1093,7 +1093,7 @@
        ]
       }
      ],
-     "prompt_number": 154
+     "prompt_number": 1115
     },
     {
      "cell_type": "markdown",
@@ -1117,7 +1117,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 154
+     "prompt_number": 1115
     },
     {
      "cell_type": "markdown",
@@ -1241,7 +1241,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 155,
+       "prompt_number": 1116,
        "text": [
         "   adjectiveCount hasPrivacy  revLength  countCapital hasDeveloperWebsite  \\\n",
         "0               4       True        601             1                True   \n",
@@ -1261,7 +1261,7 @@
        ]
       }
      ],
-     "prompt_number": 155
+     "prompt_number": 1116
     },
     {
      "cell_type": "code",
@@ -1287,7 +1287,7 @@
        ]
       }
      ],
-     "prompt_number": 156
+     "prompt_number": 1117
     },
     {
      "cell_type": "code",
@@ -1305,7 +1305,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 157
+     "prompt_number": 1118
     },
     {
      "cell_type": "code",
@@ -1317,13 +1317,8 @@
       "    X = (X - x_min) / (x_max - x_min)\n",
       "    \n",
       "    fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))\n",
-      "\n",
-      "#     scatter = ax.scatter(X[:,0],\n",
-      "#                          X[:,1],\n",
-      "#                          c=np.random.random(size=N),\n",
-      "#                          s = 10,\n",
-      "#                          alpha=0.3\n",
-      "#                          cmap=plt.cm.jet)\n",
+      "    fig.set_figwidth(12)\n",
+      "    fig.set_figheight(8)\n",
       "\n",
       "    scatter = ax.scatter(X[:,0],\n",
       "                         X[:,1],\n",
@@ -1333,34 +1328,22 @@
       "                         cmap=plt.cm.jet)\n",
       "    ax.grid(color='white', linestyle='solid')\n",
       "\n",
-      "    ax.set_title(\"Scatter Plot of Unfair/Fair Apps\", size=20)\n",
+      "    ax.set_title(\"Scatter Plot of Unfair/Fair Apps\", size=24)\n",
       "\n",
-      "    #[x+1 if x >= 45 else x+5 for x in l]\n",
-      "    labels = ['app {0}'.format(i) if main_appData.iloc[i]['appLabel'] == 'unfair' else '' for i in range(X.shape[0])]\n",
+      "    labels = [main_appData.iloc[i]['appName'].decode('ascii', 'replace') for i in range(X.shape[0])]\n",
       "    tooltip = plugins.PointLabelTooltip(scatter, labels=labels)\n",
       "    plugins.connect(fig, tooltip)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 158
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#dir(plt)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 159
+     "prompt_number": 1119
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print(\"Computing MDS embedding with D3\")\n",
+      "print(\"Computing MDS embedding and plotting with D3\")\n",
       "clf = manifold.MDS(n_components=2, n_init=1, max_iter=100, verbose=1)\n",
       "t0 = time()\n",
       "\n",
@@ -1377,16 +1360,15 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Computing MDS embedding with D3\n",
-        "breaking at iteration 95 with stress 644.63537748"
+        "Computing MDS embedding and plotting with D3\n",
+        "Done. Stress: 546.646566"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "\n",
-        "Done. Stress: 644.635377\n"
+        "\n"
        ]
       },
       {
@@ -1396,7 +1378,7 @@
         "\n",
         "</style>\n",
         "\n",
-        "<div id=\"fig44346231797284640865465\"></div>\n",
+        "<div id=\"fig44346174000169188884232\"></div>\n",
         "<script>\n",
         "function mpld3_load_lib(url, callback){\n",
         "  var s = document.createElement('script');\n",
@@ -1407,37 +1389,37 @@
         "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
         "}\n",
         "\n",
-        "function create_fig44346231797284640865465(){\n",
+        "function create_fig44346174000169188884232(){\n",
         "  \n",
-        "  mpld3.draw_figure(\"fig44346231797284640865465\", {\"width\": 480.0, \"plugins\": [{\"voffset\": 10, \"labels\": [\"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", \"\", 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         "}\n",
         "\n",
         "if(typeof(mpld3) !== \"undefined\"){\n",
         "   // already loaded: just create the figure\n",
-        "   create_fig44346231797284640865465();\n",
+        "   create_fig44346174000169188884232();\n",
         "}else if(typeof define === \"function\" && define.amd){\n",
         "   // require.js is available: use it to load d3/mpld3\n",
         "   require.config({paths: {d3: \"http://d3js.org/d3.v3.min\"}});\n",
         "   require([\"d3\"], function(d3){\n",
         "      window.d3 = d3;\n",
-        "      mpld3_load_lib(\"http://mpld3.github.io/js/mpld3.v0.1.js\", create_fig44346231797284640865465);\n",
+        "      mpld3_load_lib(\"http://mpld3.github.io/js/mpld3.v0.1.js\", create_fig44346174000169188884232);\n",
         "    });\n",
         "}else{\n",
         "    // require.js not available: dynamically load d3 & mpld3\n",
         "    mpld3_load_lib(\"http://d3js.org/d3.v3.min.js\", function(){\n",
-        "        mpld3_load_lib(\"http://mpld3.github.io/js/mpld3.v0.1.js\", create_fig44346231797284640865465);})\n",
+        "        mpld3_load_lib(\"http://mpld3.github.io/js/mpld3.v0.1.js\", create_fig44346174000169188884232);})\n",
         "}\n",
         "</script>"
        ],
        "metadata": {},
        "output_type": "display_data",
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9dla7lYbWBrxebx8nkySpN8jCL3USCATQaE+/awidCHXPIUnSwCILv9SJXq9H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\nPx8JCQk4ceLEnPd169YtVzMYY4wtksvH+AsKCtD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ERERE4lDSLCIiIiISh5JmEREREZE4lDSLiIiIiMShpFlEREREJA4lzSIiIiIi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\nLpdr0q4u03tnjTc0M+UcgGmttLRUs1talG2ul2/VNeo3pDk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        "text": [
-        "<matplotlib.figure.Figure at 0x113901fd0>"
+        "<matplotlib.figure.Figure at 0x11337eed0>"
        ]
       }
      ],
-     "prompt_number": 160
+     "prompt_number": 1120
     },
     {
      "cell_type": "code",
@@ -1456,7 +1438,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 161
+     "prompt_number": 1121
     },
     {
      "cell_type": "code",
@@ -1486,7 +1468,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 162
+     "prompt_number": 1122
     },
     {
      "cell_type": "code",
@@ -1498,9 +1480,9 @@
       "\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))\n",
+      "#plot_embedding(X_mds,\n",
+      "#               \"MDS embedding of the digits (time %.2fs)\" %\n",
+      "#               (time() - t0))\n",
       "\n",
       "#print clf.get_params(True)"
      ],
@@ -1512,7 +1494,7 @@
        "stream": "stdout",
        "text": [
         "Computing MDS embedding\n",
-        "breaking at iteration 94 with stress 606.440932964"
+        "breaking at iteration 83 with stress 784.894512328"
        ]
       },
       {
@@ -1520,58 +1502,11 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "Done. Stress: 606.440933\n"
-       ]
-      },
-      {
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UFRXV/oupp5kzZ2LFihX4+++/oVQqkZqairCwsGrX9fPzQ1RUFD7//HPEx8fjvffew7Rp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\n4wM9Pb3Gah5jrJ74buxqatS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-       "text": [
-        "<matplotlib.figure.Figure at 0x1138fddd0>"
+        "Done. Stress: 784.894512\n"
        ]
       }
      ],
-     "prompt_number": 163
+     "prompt_number": 1123
     },
     {
      "cell_type": "code",
@@ -1596,7 +1531,7 @@
        "stream": "stdout",
        "text": [
         "Computing MDS embedding\n",
-        "Done. Stress: 666.779549"
+        "Done. Stress: 814.870262"
        ]
       },
       {
@@ -1607,138 +1542,7 @@
        ]
       }
      ],
-     "prompt_number": 164
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))\n",
-      "N = 100\n",
-      "\n",
-      "x, y = np.random.normal(0, 1, (2, 200))\n",
-      "#print x\n",
-      "#print y\n",
-      "\n",
-      "scatter = ax.scatter(np.random.normal(size=N),\n",
-      "                     np.random.normal(size=N),\n",
-      "                     c=np.random.random(size=N),\n",
-      "                     s = 1000 * np.random.random(size=N),\n",
-      "                     alpha=0.3,\n",
-      "                     cmap=plt.cm.jet)\n",
-      "ax.grid(color='white', linestyle='solid')\n",
-      "\n",
-      "ax.set_title(\"Scatter Plot (with tooltips!)\", size=20)\n",
-      "\n",
-      "labels = ['point {0}'.format(i + 1) for i in range(N)]\n",
-      "tooltip = plugins.PointLabelTooltip(scatter, labels=labels)\n",
-      "plugins.connect(fig, tooltip)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "\n",
-        "<style>\n",
-        "\n",
-        "</style>\n",
-        "\n",
-        "<div id=\"fig44346233036966827529014\"></div>\n",
-        "<script>\n",
-        "function mpld3_load_lib(url, callback){\n",
-        "  var s = document.createElement('script');\n",
-        "  s.src = url;\n",
-        "  s.async = true;\n",
-        "  s.onreadystatechange = s.onload = callback;\n",
-        "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
-        "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
-        "}\n",
-        "\n",
-        "function create_fig44346233036966827529014(){\n",
-        "  \n",
-        "  mpld3.draw_figure(\"fig44346233036966827529014\", {\"width\": 480.0, \"plugins\": [{\"voffset\": 10, \"labels\": [\"point 1\", \"point 2\", \"point 3\", \"point 4\", \"point 5\", \"point 6\", \"point 7\", \"point 8\", \"point 9\", \"point 10\", \"point 11\", \"point 12\", \"point 13\", \"point 14\", \"point 15\", \"point 16\", \"point 17\", \"point 18\", \"point 19\", \"point 20\", \"point 21\", \"point 22\", \"point 23\", \"point 24\", \"point 25\", \"point 26\", \"point 27\", \"point 28\", \"point 29\", \"point 30\", \"point 31\", \"point 32\", \"point 33\", \"point 34\", \"point 35\", \"point 36\", \"point 37\", \"point 38\", \"point 39\", \"point 40\", \"point 41\", \"point 42\", \"point 43\", \"point 44\", \"point 45\", \"point 46\", \"point 47\", \"point 48\", \"point 49\", \"point 50\", \"point 51\", \"point 52\", \"point 53\", \"point 54\", \"point 55\", \"point 56\", \"point 57\", \"point 58\", \"point 59\", \"point 60\", \"point 61\", \"point 62\", \"point 63\", 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-        "\n",
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-        "   // already loaded: just create the figure\n",
-        "   create_fig44346233036966827529014();\n",
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-        "   require.config({paths: {d3: \"http://d3js.org/d3.v3.min\"}});\n",
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-        "      window.d3 = d3;\n",
-        "      mpld3_load_lib(\"http://mpld3.github.io/js/mpld3.v0.1.js\", create_fig44346233036966827529014);\n",
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-        "    // require.js not available: dynamically load d3 & mpld3\n",
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-        "        mpld3_load_lib(\"http://mpld3.github.io/js/mpld3.v0.1.js\", create_fig44346233036966827529014);})\n",
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BSCWTEIzhdDrx+/28/vop+vsNmEyF2O2VaLXvi7SiZIhEghw40IlWe4BNm3ws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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x113920410>"
-       ]
-      }
-     ],
-     "prompt_number": 165
-    },
-    {
-     "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": 166
-    },
-    {
-     "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": 167
+     "prompt_number": 1124
     }
    ],
    "metadata": {}
diff --git a/ObidroidMDSPlotsWithD3.ipynb b/ObidroidMDSPlotsWithD3.ipynb
new file mode 100644
index 0000000..7bab1d8
--- /dev/null
+++ b/ObidroidMDSPlotsWithD3.ipynb
@@ -0,0 +1,1404 @@
+{
+ "metadata": {
+  "name": ""
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Obidroid Multi Dimensional Scaling (MDS) 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": [
+      "%pylab --no-import-all inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      }
+     ],
+     "prompt_number": 56
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%doctest_mode"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Exception reporting mode: Context\n",
+        "Doctest mode is: OFF\n"
+       ]
+      }
+     ],
+     "prompt_number": 57
+    },
+    {
+     "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": 58
+    },
+    {
+     "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",
+      "main_appData.columns"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 59,
+       "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": 59
+    },
+    {
+     "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": 60
+    },
+    {
+     "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": [
+      {
+       "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>0 </th>\n",
+        "      <td>  4</td>\n",
+        "      <td>  True</td>\n",
+        "      <td>  601</td>\n",
+        "      <td>  1</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 30000000</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 4.051</td>\n",
+        "      <td> -3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1 </th>\n",
+        "      <td> 13</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 1139</td>\n",
+        "      <td> 11</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 30000000</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 4.351</td>\n",
+        "      <td>  2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2 </th>\n",
+        "      <td> 23</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 2223</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  True</td>\n",
+        "      <td>  3000000</td>\n",
+        "      <td> False</td>\n",
+        "      <td> 4.555</td>\n",
+        "      <td> -4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3 </th>\n",
+        "      <td> 10</td>\n",
+        "      <td> False</td>\n",
+        "      <td>  804</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 30000000</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 4.623</td>\n",
+        "      <td>  8</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4 </th>\n",
+        "      <td> 22</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 1867</td>\n",
+        "      <td> 16</td>\n",
+        "      <td>  True</td>\n",
+        "      <td>  7500000</td>\n",
+        "      <td> False</td>\n",
+        "      <td> 4.046</td>\n",
+        "      <td>-11</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5 </th>\n",
+        "      <td> 18</td>\n",
+        "      <td> False</td>\n",
+        "      <td> 1162</td>\n",
+        "      <td>  6</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 30000000</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 4.595</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6 </th>\n",
+        "      <td> 18</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 1522</td>\n",
+        "      <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",
+        "    </tr>\n",
+        "    <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",
+        "      <td> 4.039</td>\n",
+        "      <td> -5</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <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",
+        "    </tr>\n",
+        "    <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",
+        "    </tr>\n",
+        "    <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",
+        "    </tr>\n",
+        "    <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",
+        "    </tr>\n",
+        "    <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",
+        "      <td> 4.238</td>\n",
+        "      <td>  1</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>13</th>\n",
+        "      <td> 16</td>\n",
+        "      <td> False</td>\n",
+        "      <td> 1276</td>\n",
+        "      <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",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>14</th>\n",
+        "      <td> 13</td>\n",
+        "      <td> False</td>\n",
+        "      <td> 1210</td>\n",
+        "      <td> 12</td>\n",
+        "      <td>  True</td>\n",
+        "      <td>   750000</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 4.050</td>\n",
+        "      <td> -3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>15</th>\n",
+        "      <td> 20</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 2038</td>\n",
+        "      <td> 24</td>\n",
+        "      <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",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>19</th>\n",
+        "      <td> 15</td>\n",
+        "      <td> False</td>\n",
+        "      <td> 1120</td>\n",
+        "      <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",
+        "    </tr>\n",
+        "    <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": 61,
+       "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": 61
+    },
+    {
+     "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": [
+      {
+       "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>0</th>\n",
+        "      <td>  4</td>\n",
+        "      <td>  True</td>\n",
+        "      <td>  601</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> True</td>\n",
+        "      <td> 30000000</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 4.051</td>\n",
+        "      <td> -3</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> 13</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 1139</td>\n",
+        "      <td> 11</td>\n",
+        "      <td> True</td>\n",
+        "      <td> 30000000</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 4.351</td>\n",
+        "      <td>  2</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> 23</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 2223</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> True</td>\n",
+        "      <td>  3000000</td>\n",
+        "      <td> False</td>\n",
+        "      <td> 4.555</td>\n",
+        "      <td> -4</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> 10</td>\n",
+        "      <td> False</td>\n",
+        "      <td>  804</td>\n",
+        "      <td>  5</td>\n",
+        "      <td> True</td>\n",
+        "      <td> 30000000</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 4.623</td>\n",
+        "      <td>  8</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> 22</td>\n",
+        "      <td>  True</td>\n",
+        "      <td> 1867</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> True</td>\n",
+        "      <td>  7500000</td>\n",
+        "      <td> False</td>\n",
+        "      <td> 4.046</td>\n",
+        "      <td>-11</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "<p>5 rows \u00d7 9 columns</p>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 62,
+       "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": 62
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.preprocessing import MinMaxScaler\n",
+      "min_max_scaler = MinMaxScaler()\n",
+      "\n",
+      "print(appData_all.columns)\n",
+      "#print appData_all\n",
+      "\n",
+      "# scale the dataframe\n",
+      "X_scaled = min_max_scaler.fit_transform(appData_all)"
+     ],
+     "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"
+       ]
+      }
+     ],
+     "prompt_number": 63
+    },
+    {
+     "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",
+      "from mpld3 import enable_notebook\n",
+      "from mpld3 import plugins\n",
+      "enable_notebook()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 64
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Plot D3 scatter\n",
+      "def plot_d3(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",
+      "    fig, ax = plt.subplots(subplot_kw=dict(axisbg='#EEEEEE'))\n",
+      "    fig.set_figwidth(12)\n",
+      "    fig.set_figheight(8)\n",
+      "\n",
+      "    scatter = ax.scatter(X[:,0],\n",
+      "                         X[:,1],\n",
+      "                         c=['red' if main_appData.iloc[i]['appLabel'] == 'unfair' else 'green' for i in range(X.shape[0])],\n",
+      "                         s=75,\n",
+      "                         alpha=0.2,\n",
+      "                         cmap=plt.cm.jet)\n",
+      "    ax.grid(color='white', linestyle='solid')\n",
+      "\n",
+      "    ax.set_title(\"Scatter Plot of Unfair/Fair Apps\", size=24)\n",
+      "\n",
+      "    labels = [main_appData.iloc[i]['appName'].decode('ascii', 'replace') for i in range(X.shape[0])]\n",
+      "    tooltip = plugins.PointLabelTooltip(scatter, labels=labels)\n",
+      "    plugins.connect(fig, tooltip)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 65
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(\"Computing MDS embedding and plotting with D3\")\n",
+      "clf = manifold.MDS(n_components=2, n_init=1, max_iter=100, verbose=1)\n",
+      "t0 = time()\n",
+      "\n",
+      "X_mds = clf.fit_transform(X_scaled)\n",
+      "print(\"Done. Stress: %f\" % clf.stress_)\n",
+      "plot_d3(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 and plotting with D3\n",
+        "Done. Stress: 776.610632"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      },
+      {
+       "html": [
+        "\n",
+        "<style>\n",
+        "\n",
+        "</style>\n",
+        "\n",
+        "<div id=\"fig224645714683048765893769\"></div>\n",
+        "<script>\n",
+        "function mpld3_load_lib(url, callback){\n",
+        "  var s = document.createElement('script');\n",
+        "  s.src = url;\n",
+        "  s.async = true;\n",
+        "  s.onreadystatechange = s.onload = callback;\n",
+        "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
+        "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+        "}\n",
+        "\n",
+        "function create_fig224645714683048765893769(){\n",
+        "  \n",
+        "  mpld3.draw_figure(\"fig224645714683048765893769\", {\"width\": 960.0, \"plugins\": [{\"voffset\": 10, \"labels\": [\"Facebook Pages Manager\", \"Job Search\", \"Square Register\", \"File Manager (Explorer)\", \"Quickoffice\", \"Kingsoft Office 5.9.1 (Free)\", \"Vault-Hide SMS, Pics & Videos\", \"Documents To Go 3.0 Main App\", \"Box\", \"ADP Mobile Solutions\", \"UPS Mobile\", \"Microsoft Remote Desktop\", \"OfficeSuite 7 (PDF & HD)\", \"PrinterShare\\ufffd\\ufffd\\ufffd\\ufffd_\\ufffd\\ufffd Mobile Print\", \"USPS MOBILE\\ufffd\\ufffd\\ufffd\\ufffd\", \"PayPal Here: Get Paid Anywhere\", \"Olive Office Premium (free)\", \"Monster Job Search\", \"GoToMeeting\", \"ES Task Manager(Task Killer)\", \"Citrix Receiver\", \"FedEx Mobile\", \"Facetime Plus Trendy\", \"Splashtop 2 Remote Desktop\", \"Call Blocker\", \"Concur\", \"CamCard Free - Business Card R\", \"TouchDown for Smartphones\", \"GoDaddy Mobile\", \"Job Search, Salaries & Reviews\", \"Cisco WebEx Meetings\", \"Authy\", \"Job Search - Snagajob\", \"Intuit GoPayment Card Reader\", \"Pro App - The Home Depot\", \"Mobile Doc Scanner Lite\", \"Lync 2013\", \"App Lock\", \"AppLock Theme Space\", \"AirWatch MDM Agent\", \"GALAXY Note 3 Experience\", \"Lync 2010\", \"Task Manager (Task Killer)\", \"PocketCloud Remote RDP / VNC\", \"BackPage Classifieds Free\", \"Apps 2 SD (Move app 2 sd)\", \"Antivirus & Security\", \"Easy Installer - Apps On SD\", \"QuickBooks Online\", \"Office Documents Viewer (Free)\", \"TouchDown HD\", \"File Manager HD (Tablet)\", \"POLARIS Office for Good\", \"eFax - Mobile phone fax app\", \"Mobile@Work\", \"Kingsoft Office (Multi-Lan)\", \"OfficeSuite Pro 7 (Trial)\", \"2X Client RDP/Remote Desktop\", \"Adobe\\ufffd\\ufffd\\ufffd\\ufffd Connect\\ufffd\\ufffd\\ufffd\\ufffd_\\ufffd\\ufffd Mobile\", \"App Lock ( HI AppLock )\", \"Comics\", \"Pocket Manga - Manga Reader\", \"Marvel Comics\", \"Manga Searcher | Manga Reader\", \"Manga Plus Reader\", \"Drawing cartoons\", \"Perfect Viewer\", \"Anime Wallpapers\", \"Princess Girls Jigsaw Puzzle\", \"DC Comics\", \"Tango Talk Free Guide\", \"Funny Pics LOL!\", \"Funny Facts Free 8000+\", \"Children\\ufffd\\ufffd\\ufffd\\ufffd\\ufffd\\ufffd\\ufffd\\ufffds Bible\", \"The Walking Dead Comics 1\", \"A Comic Viewer\", \"Comic Font Pack FlipFont\\ufffd\\ufffd\\ufffd\\ufffd\", \"Crunchyroll Manga\", \"The Avengers-Iron Man Mark VII\", \"Anime HD Wallpapers\", \"300+ SexPositions\", \"ComicRack Free\", \"Yo Momma Jokes 1000+\", \"Rage Comic Maker\", \"Komik Reader - Free\", \"Tattoos 4 Men\", \"Rage Comics\", \"LOL Social Funny Pictures!\", \"Kids Movie (English)\", \"funny backgrounds jokes\", \"Pokemon Love\", \"Dark Horse Comics\", \"Guns & Destruction\", \"Peppa Pig\", \"Cupcakes - GO Launcher Theme\", \"Marvel Unlimited\", \"Manga Reader\", \"\\ufffd\\ufffd\\ufffd\\ufffd_\\ufffd_\\ufffd\\ufffd\\ufffd\\ufffd_\\ufffd\\ufffd\\ufffd\\ufffd\\ufffd_\\ufffd\\ufffd_ \\ufffd\\ufffd\\ufffd\\ufffd\\ufffd\\ufffd_\\ufffd_\\ufffd\\ufffd\\ufffd\\ufffd - Naver Webtoon\", \"The Walking Dead Comics 2\", \"GoComics\", \"xkcd Browser\", \"My Little Pony Wallpaper\", \"Manga Rock - Best Manga Reader\", \"Hello Kitty Lock Screen\", \"The Walking Dead Comics 3\", \"Lovely Kitty Go Launcher Theme\", \"Pink Kitty bow Clock Widget\", \"The Walking Dead Comics 4\", \"Manga Reader\", \"Photo Fun - Funny Pics Creator\", \"Supreme Heroes Wallpapers\", \"The Walking Dead Comics 7\", \"The Walking Dead Comics 5\", \"VIZ Manga\", \"The Walking Dead Comics 6\", \"Cartoon Font Pack FlipFont\\ufffd\\ufffd\\ufffd\\ufffd\", \"Love Quotes\", \"Best BitStrips\", \"MangaZoo - Best Manga Reader\", \"Dragon Ball Z Channel\", \"Facebook Messenger\", \"Skype - free IM & video calls\", \"Kik Messenger\", \"WhatsApp Messenger\", \"Yahoo Mail\", \"GO SMS Pro\", \"AntiVirus Security - FREE\", \"Viber\", \"Chrome Browser - Google\", \"LINE: Free Calls & Messages\", \"Firefox Browser for Android\", \"Google Voice\", \"Pinger: Text Free + Call Free\", \"Voxer Walkie-Talkie PTT\", \"Text Me!  Free Texting & Call\", \"Dolphin Browser\", \"Portable Wi-Fi hotspot Free\", \"WeChat\", \"Yahoo Messenger\", \"Handcent SMS\", \"KakaoTalk: Free Calls & Text\", \"Glide - Video Texting\", \"Outlook.com\", \"BBM\", \"Talkatone free calls + texting\", \"Handcent Emoji Plugin\", \"Mr. Number-Block calls, texts\", \"Gmail\", \"FoxFi (WiFi Tether w/o Root)\", \"Opera Mini browser for Android\", \"Photon Flash Player & Browser\", \"Current Caller ID\", \"Hangouts (replaces Talk)\", \"Samsung Push Service\", \"chomp SMS\", \"XFINITY Connect\", \"HeyWire FREE Texting\", \"Wifi Hotspot & USB Tether Lite\", \"WiFi HotSpot / WiFi Tether\", \"Next Browser for Android\", \"Opera browser for Android\", \"Omegle Android FREE\", \"XDA-Developers\", \"Hotmail Connect\", \"ChatON\", \"FoxFi (Sprint/AT&T only)\", \"TiKL Touch Talk Walkie Talkie\", \"chomp SMS emoji add-on\", \"YouMail Visual Voicemail\", \"Yahoo Messenger Plug-in\", \"PHONE for Google Voice & GTalk\", \"Bluetooth File Transfer\", \"Vonage Mobile\\ufffd\\ufffd\\ufffd\\ufffd Call Video Text\", \"Verizon Messages\", \"GO SMS Pro ChristmasEveThemeEX\", \"FREE Calls & Text by Mo+ Beta\", \"Contacts +\", \"Lango Messaging\", \"BOSS Revolution US\", \"yuilop: Free Calls & Free SMS\", \"Cartwheel\", \"Zillow Real Estate & Rentals\", \"Domino's Pizza USA\", \"Starbucks\", \"Daily Horoscope\", \"Birthday Cards for Facebook\", \"Coca-Cola Freestyle\", \"Horoscopes + daily fortune\", \"Tinder\", \"BeNaughty - Flirty Dating\", \"Wrapp\", \"Sleep as Android\", \"Pizza Hut\", \"realtor.com real estate, homes\", \"Papa John's Pizza\", \"Neon Alarm Clock Free\", \"Life360 - Family Locator +more\", \"Michaels Stores\", \"Real Estate & Homes by Trulia\", \"Countdown Widget\", \"Dunkin' Donuts\", \"Get Free Stuff on Listia\", \"Kohl's\", \"KicksOnFire\", \"Adfun - Friends, Prizes, Fun\", \"KBB.com Car Prices & Reviews\", \"Mirror\", \"Horoscope\", \"Jo-Ann\", \"Hobby Lobby Stores\", \"Houzz Interior Design Ideas\", \"Birthday 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0.5769971639017542], [0.7380853961213182, 0.5368184362509549], [0.18174136919483055, 0.47579515490038554], [0.1808962719212396, 0.48802981905942494], [0.7172991587233672, 0.5989325235475731], [0.17933543872724048, 0.49941774544560297], [0.7214218393601934, 0.5817476663711763], [0.7668712016696883, 0.4935667347930164], [0.9409223699612993, 0.193619148511442], [0.18013733211140273, 0.49416928966612533], [0.8946435489700734, 0.1515318226780294], [0.317585528824911, 0.014720251792458575], [0.18113960174370258, 0.46095108370842397], [0.17929264489930144, 0.45109062977880826], [0.17884119144049088, 0.4493903954599423]]}, \"id\": \"22464571468304\", \"toolbar\": [\"reset\", \"move\"], \"height\": 640.0});\n",
+        "}\n",
+        "\n",
+        "if(typeof(mpld3) !== \"undefined\"){\n",
+        "   // already loaded: just create the figure\n",
+        "   create_fig224645714683048765893769();\n",
+        "}else if(typeof define === \"function\" && define.amd){\n",
+        "   // require.js is available: use it to load d3/mpld3\n",
+        "   require.config({paths: {d3: \"http://d3js.org/d3.v3.min\"}});\n",
+        "   require([\"d3\"], function(d3){\n",
+        "      window.d3 = d3;\n",
+        "      mpld3_load_lib(\"http://mpld3.github.io/js/mpld3.v0.1.js\", create_fig224645714683048765893769);\n",
+        "    });\n",
+        "}else{\n",
+        "    // require.js not available: dynamically load d3 & mpld3\n",
+        "    mpld3_load_lib(\"http://d3js.org/d3.v3.min.js\", function(){\n",
+        "        mpld3_load_lib(\"http://mpld3.github.io/js/mpld3.v0.1.js\", create_fig224645714683048765893769);})\n",
+        "}\n",
+        "</script>"
+       ],
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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iCBiaiYiIiIgiYGgmIiIiIoqAoZmIiIiIKAKGZiIiIiKiCBiaiYiIiIgiYGgm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\nD1/7oX70/o/U5euSfaVdptNUSimloqlrky67JdM0ZRQZyrX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+       "text": [
+        "<matplotlib.figure.Figure at 0x1107b1210>"
+       ]
+      }
+     ],
+     "prompt_number": 66
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file