Regession_Models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='red'> Project 2 : Buzz Prediction on Twitter\n",
    "\n",
    "Project Description:\n",
    "- There are two different datasets for Regression and Classification tasks. Right-most column in both the datasets is a dependent variable i.e. buzz.\n",
    "- Data description files are also provided for both the datasets.\n",
    "- Deciding which dataset is for which task is part of the project.\n",
    "- Read data into Jupyter notebook, use pandas to import data into a data frame.\n",
    "- Preprocess data: Explore data, check for missing data and apply data scaling. Justify the type of scaling used.\n",
    "\n",
    "Regression Task:\n",
    "- Apply all the regression models you've learned so far. If your model has a scaling parameter(s) use Grid Search to find the best scaling parameter. Use plots and graphs to help you get a better glimpse of the results. \n",
    "- Then use cross-validation to find average training and testing score. \n",
    "- Your submission should have at least the following regression models: KNN regressor, linear regression, Ridge, Lasso, polynomial regression, SVM both simple and with kernels. \n",
    "- Finally, find the best regressor for this dataset and train your model on the entire dataset using the best parameters and predict buzz for the test_set.\n",
    "\n",
    "Classification Task:\n",
    "- Decide about a good evaluation strategy and justify your choice.\n",
    "- Find best parameters for the following classification models: KNN classification, Logistic Regression, Linear Support Vector Machine, Kernelized Support Vector Machine, Decision Tree. \n",
    "- Which model gives the best results?\n",
    "\n",
    "Deliverables:\n",
    "- Submit IPython notebook. Use markdown to provide inline comments for this project.\n",
    "- Rename notebook with your group number and submit only one notebook. Before submitting, make sure everything runs as expected. To check that, restart the kernel (in the menubar, select Kernel > Restart) and then run all cells (in the menubar, select Cell > Run All).\n",
    "- Visualization encouraged.\n",
    "\n",
    "Questions regarding the project:\n",
    "- We have created a discussion board under Projects folder on e-learning. Create threads over there and post your queries related to project there.\n",
    "- There is a high possibility that your classmate has also faced the same problem and knows the solution. So this is an effort to encourage collaborative learning, reducing mails for frequently asked queries and also making all the information available to everyone.\n",
    "- Please check existing threads for your query before creating a new one. It goes without saying that do not share your code or complete solutions there.\n",
    "- We will also answer queries there. We will not be answering any project related queries through the mail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instructions given in class: (11/07)\n",
    "Steps to be followed:\n",
    "1. read csv files\n",
    "2. explore dataset\n",
    "3. evaluation \n",
    "4. split \n",
    "5. scale\n",
    "6. model\n",
    "-GridSearchCV - best parameters\n",
    "-train, test score\n",
    "\n",
    "Proj 3 (If you need head start):\n",
    "7. ensemble\n",
    "8. dimension reduction - repeat step 4 to 7\n",
    "9. Deep learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Dataset For Regression Task\n",
    "  \n",
    "Following steps were taken to read and explore the dataset which we are going to use in this Project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Dataset = pd.read_csv('Twitter.data', header = None)  #Reading Regression Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 583250 entries, 0 to 583249\n",
      "Data columns (total 78 columns):\n",
      "0     583250 non-null int64\n",
      "1     583250 non-null int64\n",
      "2     583250 non-null int64\n",
      "3     583250 non-null int64\n",
      "4     583250 non-null int64\n",
      "5     583250 non-null int64\n",
      "6     583250 non-null int64\n",
      "7     583250 non-null int64\n",
      "8     583250 non-null int64\n",
      "9     583250 non-null int64\n",
      "10    583250 non-null int64\n",
      "11    583250 non-null int64\n",
      "12    583250 non-null int64\n",
      "13    583250 non-null int64\n",
      "14    583250 non-null float64\n",
      "15    583250 non-null float64\n",
      "16    583250 non-null float64\n",
      "17    583250 non-null float64\n",
      "18    583250 non-null float64\n",
      "19    583250 non-null float64\n",
      "20    583250 non-null float64\n",
      "21    583250 non-null float64\n",
      "22    583250 non-null float64\n",
      "23    583250 non-null float64\n",
      "24    583250 non-null float64\n",
      "25    583250 non-null float64\n",
      "26    583250 non-null float64\n",
      "27    583250 non-null float64\n",
      "28    583250 non-null int64\n",
      "29    583250 non-null int64\n",
      "30    583250 non-null int64\n",
      "31    583250 non-null int64\n",
      "32    583250 non-null int64\n",
      "33    583250 non-null int64\n",
      "34    583250 non-null int64\n",
      "35    583250 non-null float64\n",
      "36    583250 non-null float64\n",
      "37    583250 non-null float64\n",
      "38    583250 non-null float64\n",
      "39    583250 non-null float64\n",
      "40    583250 non-null float64\n",
      "41    583250 non-null float64\n",
      "42    583250 non-null float64\n",
      "43    583250 non-null float64\n",
      "44    583250 non-null float64\n",
      "45    583250 non-null float64\n",
      "46    583250 non-null float64\n",
      "47    583250 non-null float64\n",
      "48    583250 non-null float64\n",
      "49    583250 non-null float64\n",
      "50    583250 non-null float64\n",
      "51    583250 non-null float64\n",
      "52    583250 non-null float64\n",
      "53    583250 non-null float64\n",
      "54    583250 non-null float64\n",
      "55    583250 non-null float64\n",
      "56    583250 non-null int64\n",
      "57    583250 non-null int64\n",
      "58    583250 non-null int64\n",
      "59    583250 non-null int64\n",
      "60    583250 non-null int64\n",
      "61    583250 non-null int64\n",
      "62    583250 non-null int64\n",
      "63    583250 non-null float64\n",
      "64    583250 non-null float64\n",
      "65    583250 non-null float64\n",
      "66    583250 non-null float64\n",
      "67    583250 non-null float64\n",
      "68    583250 non-null float64\n",
      "69    583250 non-null float64\n",
      "70    583250 non-null int64\n",
      "71    583250 non-null int64\n",
      "72    583250 non-null int64\n",
      "73    583250 non-null int64\n",
      "74    583250 non-null int64\n",
      "75    583250 non-null int64\n",
      "76    583250 non-null int64\n",
      "77    583250 non-null float64\n",
      "dtypes: float64(43), int64(35)\n",
      "memory usage: 347.1 MB\n"
     ]
    }
   ],
   "source": [
    "Dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0   1   2   3   4   5   6   7   8   9  ...    68   69  70  71  72  73  74  \\\n",
       "0   0   2   0   0   1   1   1   0   1   0 ...   1.0  1.0   0   2   0   0   1   \n",
       "1   2   1   0   0   0   0   4   2   1   0 ...   0.0  1.0   2   1   0   0   0   \n",
       "2   1   0   0   0   0   4   1   1   0   0 ...   1.0  1.0   1   0   0   0   0   \n",
       "3   1   0   0   1   0   0   1   1   0   0 ...   0.0  1.0   1   0   0   1   0   \n",
       "4   0   1   0   0   1   2   3   0   1   0 ...   1.0  1.0   0   1   0   0   1   \n",
       "\n",
       "   75  76   77  \n",
       "0   1   1  0.0  \n",
       "1   0   4  0.5  \n",
       "2   4   1  0.0  \n",
       "3   0   1  2.5  \n",
       "4   2   3  0.5  \n",
       "\n",
       "[5 rows x 78 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0,    0,    2, ..., 4960, 4618, 2129])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dataset.columns\n",
    "Dataset[77].unique().astype(int)  #This is Dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5        20770\n",
       "1.0        19299\n",
       "0.0        19094\n",
       "1.5        17510\n",
       "2.0        15254\n",
       "2.5        13557\n",
       "3.0        11920\n",
       "3.5        10781\n",
       "4.0         9594\n",
       "4.5         8609\n",
       "5.0         7926\n",
       "5.5         7422\n",
       "6.0         6760\n",
       "6.5         6294\n",
       "7.0         5888\n",
       "7.5         5433\n",
       "8.0         5273\n",
       "8.5         4971\n",
       "9.0         4692\n",
       "9.5         4614\n",
       "10.0        4287\n",
       "10.5        4025\n",
       "11.0        3956\n",
       "11.5        3816\n",
       "12.0        3713\n",
       "12.5        3643\n",
       "13.0        3457\n",
       "13.5        3315\n",
       "14.0        3275\n",
       "14.5        3215\n",
       "           ...  \n",
       "3623.0         1\n",
       "3584.5         1\n",
       "14486.0        1\n",
       "3621.0         1\n",
       "3618.0         1\n",
       "3617.0         1\n",
       "14463.5        1\n",
       "3614.5         1\n",
       "3613.0         1\n",
       "3611.5         1\n",
       "4498.0         1\n",
       "14427.5        1\n",
       "9324.0         1\n",
       "6791.0         1\n",
       "3603.5         1\n",
       "4500.0         1\n",
       "3602.5         1\n",
       "3599.5         1\n",
       "3599.0         1\n",
       "3597.0         1\n",
       "6290.0         1\n",
       "3593.0         1\n",
       "3591.0         1\n",
       "6798.5         1\n",
       "3589.0         1\n",
       "2834.0         1\n",
       "3587.5         1\n",
       "3150.0         1\n",
       "3585.0         1\n",
       "8191.0         1\n",
       "Name: 77, Length: 8123, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dataset[77].value_counts()     #Count of rows with unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x234a60995f8>]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(Dataset[77])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Trying to understand distribution of dependent variable\n",
    "np.log(Dataset[77]+1).hist()   #transforming to log and adding 1 to avoid -Inf\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>68</th>\n",
       "      <th>69</th>\n",
       "      <th>70</th>\n",
       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>73</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>76</th>\n",
       "      <th>77</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "      <td>583250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>140.339640</td>\n",
       "      <td>136.770147</td>\n",
       "      <td>159.679271</td>\n",
       "      <td>181.592091</td>\n",
       "      <td>201.097445</td>\n",
       "      <td>220.175371</td>\n",
       "      <td>219.388214</td>\n",
       "      <td>71.038051</td>\n",
       "      <td>69.829631</td>\n",
       "      <td>82.198203</td>\n",
       "      <td>...</td>\n",
       "      <td>1.136688</td>\n",
       "      <td>1.140372</td>\n",
       "      <td>140.789860</td>\n",
       "      <td>137.181270</td>\n",
       "      <td>160.105922</td>\n",
       "      <td>182.057440</td>\n",
       "      <td>201.596482</td>\n",
       "      <td>220.705900</td>\n",
       "      <td>219.936864</td>\n",
       "      <td>191.279493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>431.772639</td>\n",
       "      <td>432.305129</td>\n",
       "      <td>502.057428</td>\n",
       "      <td>574.883713</td>\n",
       "      <td>630.448432</td>\n",
       "      <td>669.205930</td>\n",
       "      <td>672.182204</td>\n",
       "      <td>196.876718</td>\n",
       "      <td>202.199758</td>\n",
       "      <td>239.523042</td>\n",
       "      <td>...</td>\n",
       "      <td>1.432327</td>\n",
       "      <td>1.552313</td>\n",
       "      <td>432.624954</td>\n",
       "      <td>433.026611</td>\n",
       "      <td>502.774408</td>\n",
       "      <td>575.658022</td>\n",
       "      <td>631.258318</td>\n",
       "      <td>670.050977</td>\n",
       "      <td>673.032541</td>\n",
       "      <td>612.352354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>25.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>104.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.090909</td>\n",
       "      <td>1.091296</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>148.000000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>24210.000000</td>\n",
       "      <td>29574.000000</td>\n",
       "      <td>37505.000000</td>\n",
       "      <td>72366.000000</td>\n",
       "      <td>79079.000000</td>\n",
       "      <td>79079.000000</td>\n",
       "      <td>79079.000000</td>\n",
       "      <td>18654.000000</td>\n",
       "      <td>22035.000000</td>\n",
       "      <td>29402.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>295.000000</td>\n",
       "      <td>24301.000000</td>\n",
       "      <td>29574.000000</td>\n",
       "      <td>37505.000000</td>\n",
       "      <td>72366.000000</td>\n",
       "      <td>79083.000000</td>\n",
       "      <td>79083.000000</td>\n",
       "      <td>79083.000000</td>\n",
       "      <td>75724.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0              1              2              3   \\\n",
       "count  583250.000000  583250.000000  583250.000000  583250.000000   \n",
       "mean      140.339640     136.770147     159.679271     181.592091   \n",
       "std       431.772639     432.305129     502.057428     574.883713   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         3.000000       3.000000       4.000000       4.000000   \n",
       "50%        18.000000      17.000000      21.000000      24.000000   \n",
       "75%       104.000000     100.000000     115.000000     131.000000   \n",
       "max     24210.000000   29574.000000   37505.000000   72366.000000   \n",
       "\n",
       "                  4              5              6              7   \\\n",
       "count  583250.000000  583250.000000  583250.000000  583250.000000   \n",
       "mean      201.097445     220.175371     219.388214      71.038051   \n",
       "std       630.448432     669.205930     672.182204     196.876718   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         5.000000       6.000000       6.000000       2.000000   \n",
       "50%        27.000000      31.000000      30.000000      11.000000   \n",
       "75%       147.000000     166.000000     164.000000      59.000000   \n",
       "max     79079.000000   79079.000000   79079.000000   18654.000000   \n",
       "\n",
       "                  8              9       ...                   68  \\\n",
       "count  583250.000000  583250.000000      ...        583250.000000   \n",
       "mean       69.829631      82.198203      ...             1.136688   \n",
       "std       202.199758     239.523042      ...             1.432327   \n",
       "min         0.000000       0.000000      ...             0.000000   \n",
       "25%         2.000000       2.000000      ...             1.000000   \n",
       "50%        11.000000      13.000000      ...             1.000000   \n",
       "75%        57.000000      65.000000      ...             1.090909   \n",
       "max     22035.000000   29402.000000      ...           262.000000   \n",
       "\n",
       "                  69             70             71             72  \\\n",
       "count  583250.000000  583250.000000  583250.000000  583250.000000   \n",
       "mean        1.140372     140.789860     137.181270     160.105922   \n",
       "std         1.552313     432.624954     433.026611     502.774408   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         1.000000       3.000000       3.000000       4.000000   \n",
       "50%         1.000000      18.000000      17.000000      21.000000   \n",
       "75%         1.091296     104.000000     101.000000     115.000000   \n",
       "max       295.000000   24301.000000   29574.000000   37505.000000   \n",
       "\n",
       "                  73             74             75             76  \\\n",
       "count  583250.000000  583250.000000  583250.000000  583250.000000   \n",
       "mean      182.057440     201.596482     220.705900     219.936864   \n",
       "std       575.658022     631.258318     670.050977     673.032541   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%         4.000000       5.000000       6.000000       6.000000   \n",
       "50%        24.000000      27.000000      31.000000      31.000000   \n",
       "75%       131.000000     148.000000     167.000000     165.000000   \n",
       "max     72366.000000   79083.000000   79083.000000   79083.000000   \n",
       "\n",
       "                  77  \n",
       "count  583250.000000  \n",
       "mean      191.279493  \n",
       "std       612.352354  \n",
       "min         0.000000  \n",
       "25%         4.500000  \n",
       "50%        25.500000  \n",
       "75%       139.000000  \n",
       "max     75724.500000  \n",
       "\n",
       "[8 rows x 78 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dataset.isnull().values.any()    #No Missing values\n",
    "Dataset.isnull().sum().sum()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset does not have any missing values. Thus, no treatment is needed.  \n",
    "We have plotted correlation between variables below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "corr = Dataset.corr()    #calculate correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20b4e1156a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Developing correlation plot\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Title of Database: Buzz prediction on Twitter\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "2. Sources:\n",
      "\n",
      "   -- Creators : \n",
      "\n",
      "        FranÃ§ois Kawala (1,2) and \n",
      "\n",
      "        Ahlame Douzal (1) and \n",
      "\n",
      "        Eric Gaussier (1) and\n",
      "\n",
      "        Eustache Diemert (2)\n",
      "\n",
      "\n",
      "\n",
      "   -- Institutions : \n",
      "\n",
      "        (1) UniversitÃ© Joseph Fourier (Grenoble I)\n",
      "\n",
      "            Laboratoire d'informatique de Grenoble (LIG)\n",
      "\n",
      "        (2) BestofMedia Group\n",
      "\n",
      "\n",
      "\n",
      "   -- Donor: BestofMedia (ediemert@bestofmedia.com)\n",
      "\n",
      "   -- Date: May, 2013\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "3. Past Usage:\n",
      "\n",
      "   -- References : \n",
      "\n",
      "        Predicting Buzz Magnitude in Social Media (in submission (ECML-PKDD 13))\n",
      "\n",
      "\n",
      "\n",
      "   -- Predicted attribute : \n",
      "\n",
      "         Mean Number of active discussion (NAD). This attribute is a positive integer \n",
      "\n",
      "         that describe the popularity of the instance's topic. It is stored is \n",
      "\n",
      "         the rightmost column.\n",
      "\n",
      "\n",
      "\n",
      "   -- Study results : \n",
      "\n",
      "        The results achieved are acceptable, nevertheless the unbalanced nature\n",
      "\n",
      "        of this dataset leaves some room for improvement. The data may be scaled\n",
      "\n",
      "        and normalized. First order discrete difference over features may also \n",
      "\n",
      "        be considered as additional features. Please see the reference to get\n",
      "\n",
      "        further details regarding the results.\n",
      "\n",
      "\n",
      "\n",
      "4. Relevant Information Paragraph:\n",
      "\n",
      "   -- Observations : \n",
      "\n",
      "        Each instance covers height weeks of observation for a specific topic (eg. \n",
      "\n",
      "        overclocking...). The predicted attribute's mean value is computed on the \n",
      "\n",
      "        the couple weeks following this initial observation.\n",
      "\n",
      "\n",
      "\n",
      "        Observations are Independent and identically distributed.\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "5. Number of Instances\n",
      "\n",
      "   -- Total number of instances : 38393\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "6. Number of Attributes \n",
      "\n",
      "   -- Total number of attributes : 77. \n",
      "\n",
      "\n",
      "\n",
      "   -- Time representation : \n",
      "\n",
      "        Each instance is described by 77 features, those describe the evolution\n",
      "\n",
      "        of 77 `primary features' through time. Hence each feature name is \n",
      "\n",
      "        postfixed with the relative time of observation. For instance, the value\n",
      "\n",
      "        of the feature `Nb_Active_Discussion' at time t is given in \n",
      "\n",
      "        'Nb_Active_Discussion_t'.\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "7. Attributes\n",
      "\n",
      "\n",
      "\n",
      "    -- Number of Created Discussions (NCD) (columns [0,6])\n",
      "\n",
      "\n",
      "\n",
      "       -- Type : Numeric, integers only \n",
      "\n",
      "       -- Description : This feature measures the number of discussions created \n",
      "\n",
      "          at time step t and involving the instance's topic.\n",
      "\n",
      "       -- Columns : From column 0 (NCD at relative time 0) to column 6 (NCD at \n",
      "\n",
      "          relative time 6)\n",
      "\n",
      "       -- Abbreviations : NCD_0, NCD_1, NCD_2, NCD_3, NCD_4, NCD_5, NCD_6  \n",
      "\n",
      "       -- Statistics : \n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | feature | min | max   | mean    | std     |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NCD_0   | 0   | 24210 | 140.340 | 431.772 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NCD_1   | 0   | 29574 | 136.770 | 432.305 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NCD_2   | 0   | 37505 | 159.679 | 502.057 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NCD_3   | 0   | 72366 | 181.592 | 574.883 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NCD_4   | 0   | 79079 | 201.097 | 630.448 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NCD_5   | 0   | 79079 | 220.175 | 669.205 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NCD_6   | 0   | 79079 | 219.388 | 672.182 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "    -- Author Increase (AI) (columns [7,13])\n",
      "\n",
      "\n",
      "\n",
      "       -- Type : Numeric, integers only \n",
      "\n",
      "       -- Description : This featurethe number of new authors interacting on\n",
      "\n",
      "          the instance's topic at time t (i.e. its popularity)\n",
      "\n",
      "       -- Columns : From column 7 (AI at relative time 0) to column 13 (AI at \n",
      "\n",
      "          relative time 6)\n",
      "\n",
      "       -- Abbreviations : AI_0, AI_1, AI_2, AI_3, AI_4, AI_5, AI_6  \n",
      "\n",
      "       -- Statistics : \n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | feature | min | max   | mean    | std     |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | AI_0    | 0   | 18654 | 71.038  | 196.877 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | AI_1    | 0   | 22035 | 69.830  | 202.200 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | AI_2    | 0   | 29402 | 82.198  | 239.523 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | AI_3    | 0   | 57617 | 93.775  | 278.892 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | AI_4    | 0   | 63147 | 103.896 | 309.197 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | AI_5    | 0   | 63147 | 113.611 | 326.077 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | AI_6    | 0   | 63147 | 112.941 | 326.015 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "    -- Attention Level (measured with number of authors) (AS(NA)) \n",
      "\n",
      "       (columns [14,20])\n",
      "\n",
      "\n",
      "\n",
      "       -- Type : Numeric, real in [0,1]\n",
      "\n",
      "       -- Description : This feature is a measure of the attention payed to a \n",
      "\n",
      "          the instance's topic on a social media.\n",
      "\n",
      "       -- Columns : From column 14 (AS(NA) at relative time 0) to column 20 (AS(NA)\n",
      "\n",
      "          at relative time 6)\n",
      "\n",
      "       -- Abbreviations : AS(NA)_0, AS(NA)_1, AS(NA)_2, AS(NA)_3, AS(NA)_4,\n",
      "\n",
      "          AS(NA)_5, AS(NA)_6\n",
      "\n",
      "       -- Statistics : \n",
      "\n",
      "          +----------+-----+-------+-------+-------+\n",
      "\n",
      "          | feature  | min | max   | mean  | std   |\n",
      "\n",
      "          +----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NA)_0 | 0   | 0.025 | 0.000 | 0.001 |\n",
      "\n",
      "          +----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NA)_1 | 0   | 0.029 | 0.000 | 0.001 |\n",
      "\n",
      "          +----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NA)_2 | 0   | 0.040 | 0.000 | 0.001 |\n",
      "\n",
      "          +----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NA)_3 | 0   | 0.081 | 0.000 | 0.001 |\n",
      "\n",
      "          +----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NA)_4 | 0   | 0.095 | 0.000 | 0.001 |\n",
      "\n",
      "          +----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NA)_5 | 0   | 0.095 | 0.000 | 0.001 |\n",
      "\n",
      "          +----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NA)_6 | 0   | 0.095 | 0.000 | 0.001 |\n",
      "\n",
      "          +----------+-----+-------+-------+-------+\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "    -- Burstiness Level (BL) (columns [21,27])\n",
      "\n",
      "\n",
      "\n",
      "       -- Type : Numeric, defined on [0,1] \n",
      "\n",
      "       -- Description : The burstiness level for a topic z at a time t is \n",
      "\n",
      "          defined as the ratio of ncd and nad\n",
      "\n",
      "       -- Columns : From column 21 (BL at relative time 0) to column 27 (BL at \n",
      "\n",
      "          relative time 6)\n",
      "\n",
      "       -- Abbreviations : BL_0, BL_1, BL_2, BL_3, BL_4, BL_5, BL_6  \n",
      "\n",
      "       -- Statistics : \n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | feature | min | max | mean  | std   |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | BL_0    | 0   | 1   | 0.925 | 0.256 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | BL_1    | 0   | 1   | 0.918 | 0.268 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | BL_2    | 0   | 1   | 0.907 | 0.285 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | BL_3    | 0   | 1   | 0.920 | 0.265 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | BL_4    | 0   | 1   | 0.931 | 0.247 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | BL_5    | 0   | 1   | 0.956 | 0.196 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | BL_6    | 0   | 1   | 0.955 | 0.197 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "    -- Number of Atomic Containers (NAC) (columns [28,34])\n",
      "\n",
      "\n",
      "\n",
      "       -- Type : Numeric, integer\n",
      "\n",
      "       -- Description : This feature measures the total number of atomic \n",
      "\n",
      "          containers generated through the whole social media on the instance's topic until time t.\n",
      "\n",
      "       -- Columns : From column 28 (NAC at relative time 0) to column 34 (NAC at \n",
      "\n",
      "          relative time 6)\n",
      "\n",
      "       -- Abbreviations : NAC_0, NAC_1, NAC_2, NAC_3, NAC_4, NAC_5, NAC_6\n",
      "\n",
      "       -- Statistics : \n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | feature | min | max   | mean    | std     |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAC_0   | 0   | 26644 | 150.662 | 453.689 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAC_1   | 0   | 29574 | 146.642 | 452.039 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAC_2   | 0   | 37505 | 170.721 | 523.801 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAC_3   | 0   | 72397 | 193.786 | 598.287 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAC_4   | 0   | 79136 | 214.196 | 654.468 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAC_5   | 0   | 79136 | 234.218 | 694.165 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAC_6   | 0   | 79136 | 233.236 | 696.419 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "    -- Attention Level (measured with number of contributions) (AS(NAC)) \n",
      "\n",
      "       (columns [35,41])\n",
      "\n",
      "\n",
      "\n",
      "       -- Type : Numeric, real in [0,1]\n",
      "\n",
      "       -- Description : This feature is a measure of the attention payed to a \n",
      "\n",
      "          the instance's topic on a social media.\n",
      "\n",
      "       -- Columns : From column 35 (AS(NA) at relative time 0) to column 42 \n",
      "\n",
      "          (AS(NAC) at relative time 6)\n",
      "\n",
      "       -- Abbreviations : AS(NAC)_0, AS(NAC)_1, AS(NAC)_2, AS(NAC)_3, AS(NAC)_4,\n",
      "\n",
      "          AS(NAC)_5, AS(NAC)_6\n",
      "\n",
      "       -- Statistics : \n",
      "\n",
      "          +-----------+-----+-------+-------+-------+\n",
      "\n",
      "          | feature   | min | max   | mean  | std   |\n",
      "\n",
      "          +-----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NAC)_0 | 0   | 0.022 | 0.000 | 0.000 |\n",
      "\n",
      "          +-----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NAC)_1 | 0   | 0.022 | 0.000 | 0.000 |\n",
      "\n",
      "          +-----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NAC)_2 | 0   | 0.022 | 0.000 | 0.000 |\n",
      "\n",
      "          +-----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NAC)_3 | 0   | 0.040 | 0.000 | 0.000 |\n",
      "\n",
      "          +-----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NAC)_4 | 0   | 0.046 | 0.000 | 0.000 |\n",
      "\n",
      "          +-----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NAC)_5 | 0   | 0.046 | 0.000 | 0.000 |\n",
      "\n",
      "          +-----------+-----+-------+-------+-------+\n",
      "\n",
      "          | AS(NAC)_6 | 0   | 0.046 | 0.000 | 0.000 |\n",
      "\n",
      "          +-----------+-----+-------+-------+-------+\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "    -- Contribution Sparseness (CS) (columns [42,48])\n",
      "\n",
      "\n",
      "\n",
      "       -- Type : Numeric, real in [0,1] \n",
      "\n",
      "       -- Description : This feature is a measure of spreading of contributions\n",
      "\n",
      "          over discussion for the instance's topic at time t.\n",
      "\n",
      "       -- Columns : From column 42 (CS at relative time 0) to column 48 \n",
      "\n",
      "          (CS at relative time 6)\n",
      "\n",
      "       -- Abbreviations : CS_0, CS_1, CS_2, CS_3, CS_4, CS_5, CS_6\n",
      "\n",
      "       -- Statistics :\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | feature | min | max | mean  | std   |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | CS_0    | 0   | 1   | 0.931 | 0.254 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | CS_1    | 0   | 1   | 0.923 | 0.266 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | CS_2    | 0   | 1   | 0.912 | 0.284 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | CS_3    | 0   | 1   | 0.925 | 0.264 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | CS_4    | 0   | 1   | 0.935 | 0.246 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | CS_5    | 0   | 1   | 0.961 | 0.194 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | CS_6    | 0   | 1   | 0.961 | 0.194 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "    -- Author Interaction (AT) (columns [49,55])\n",
      "\n",
      "\n",
      "\n",
      "       -- Type : Numeric, integer.\n",
      "\n",
      "       -- Description : This feature measures the average number of authors\n",
      "\n",
      "          interacting on the instance's topic within a discussion.\n",
      "\n",
      "       -- Columns : From column 49 (AT at relative time 0) to column 55 \n",
      "\n",
      "          (AT at relative time 6)\n",
      "\n",
      "       -- Abbreviations : AT_0, AT_1, AT_2, AT_3, AT_4, AT_5, AT_6\n",
      "\n",
      "       -- Statistics :\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | feature | min | max | mean  | std   |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | AT_0    | 0   | 282 | 1.045 | 1.359 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | AT_1    | 0   | 283 | 1.035 | 1.470 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | AT_2    | 0   | 283 | 1.017 | 1.208 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | AT_3    | 0   | 263 | 1.033 | 1.250 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | AT_4    | 0   | 282 | 1.046 | 1.286 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | AT_5    | 0   | 249 | 1.078 | 1.300 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | AT_6    | 0   | 283 | 1.082 | 1.423 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "    -- Number of Authors (NA) (columns [56,62])\n",
      "\n",
      "\n",
      "\n",
      "       -- Type : Numeric, integer.\n",
      "\n",
      "       -- Description : This feature measures the number of authors interacting\n",
      "\n",
      "          on the instance's topic at time t.\n",
      "\n",
      "       -- Columns : From column 49 (NA at relative time 0) to column 55 (NA at\n",
      "\n",
      "          relative time 6)\n",
      "\n",
      "       -- Abbreviations : NA_0, NA_1, NA_2, NA_3, NA_4, NA_5, NA_6\n",
      "\n",
      "       -- Statistics :\n",
      "\n",
      "       +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | feature | min | max   | mean    | std     |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NA_0    | 0   | 21723 | 123.064 | 350.215 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NA_1    | 0   | 26134 | 119.580 | 347.236 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NA_2    | 0   | 34085 | 138.959 | 403.603 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NA_3    | 0   | 64984 | 157.424 | 460.884 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NA_4    | 0   | 72159 | 173.702 | 503.660 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NA_5    | 0   | 72159 | 189.521 | 532.315 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NA_6    | 0   | 72159 | 188.410 | 531.152 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "    -- Average Discussions Length (ADL) (columns [63,69])\n",
      "\n",
      "\n",
      "\n",
      "       -- Type : Numeric, real.\n",
      "\n",
      "       -- Description : This feature directly measures the average length of a \n",
      "\n",
      "          discussion belonging to the instance's topic.\n",
      "\n",
      "       -- Columns : From column 63 (ADL at relative time 0) to column 69 (ADL at\n",
      "\n",
      "          relative time 6)\n",
      "\n",
      "       -- Abbreviations : ADL_0, ADL_1, ADL_2, ADL_3, ADL_4, ADL_5, ADL_6\n",
      "\n",
      "       -- Statistics :\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | feature | min | max | mean  | std   |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | ADL_0   | 0   | 294 | 1.095 | 1.488 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | ADL_1   | 0   | 295 | 1.084 | 1.587 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | ADL_2   | 0   | 295 | 1.067 | 1.316 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | ADL_3   | 0   | 273 | 1.085 | 1.371 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | ADL_4   | 0   | 294 | 1.100 | 1.406 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | ADL_5   | 0   | 262 | 1.137 | 1.432 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "          | ADL_6   | 0   | 295 | 1.140 | 1.552 |\n",
      "\n",
      "          +---------+-----+-----+-------+-------+\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "    -- Average Discussions Length (NAD) (columns [70,76])\n",
      "\n",
      "\n",
      "\n",
      "       -- Type : Numeric, integer.\n",
      "\n",
      "       -- Description : This features measures the number of discussions\n",
      "\n",
      "          involving the instance's topic until time t.\n",
      "\n",
      "       -- Columns : From column 70 (NAD at relative time 0) to column 76 (NAD at\n",
      "\n",
      "          relative time 6)\n",
      "\n",
      "       -- Abbreviations : NAD_0, NAD_1, NAD_2, NAD_3, NAD_4, NAD_5, NAD_6\n",
      "\n",
      "       -- Statistics :\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | feature | min | max   | mean    | std     |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAD_0   | 0   | 24301 | 140.790 | 432.625 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAD_1   | 0   | 29574 | 137.181 | 433.026 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAD_2   | 0   | 37505 | 160.106 | 502.774 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAD_3   | 0   | 72366 | 182.057 | 575.658 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAD_4   | 0   | 79083 | 201.596 | 631.258 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAD_5   | 0   | 79083 | 220.706 | 670.050 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "          | NAD_6   | 0   | 79083 | 219.937 | 673.032 |\n",
      "\n",
      "          +---------+-----+-------+---------+---------+\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "    -- Annotation (column 77)\n",
      "\n",
      "       -- Type : Numeric, real\n",
      "\n",
      "       -- Description : See 3. and 4.  \n",
      "\n",
      "       -- Columns : 96\n",
      "\n",
      "\n",
      "\n",
      "8. Missing Attribute Values:\n",
      "\n",
      "   -- There is not any missing values.  \n",
      "\n",
      "\n",
      "\n",
      "9. Class Distribution: \n",
      "\n",
      "   -- This is a regression task.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Reading .names file\n",
    "file = open(\"Twitter.names\", \"r\")\n",
    "for aline in file:\n",
    "    #items = aline.split()\n",
    "    print(aline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taking a random sample and Pre-process data\n",
    "We take 10% random sample of dataset to try all the models and decide which model is the best.  \n",
    "We will use that model for training the entire dataset then."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "subData = Dataset.sample(frac=0.1, random_state = 0)    #Selecting a random sample of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(58325, 78)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Separating dependent and independent vars\n",
    "X = pd.DataFrame(subData.iloc[:,0:77])\n",
    "Y = subData[77]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use MinMaxScaler to transform all the values in the dataset to range between 0 and 1. That way, it will be standardized. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train_org, X_test_org, y_train, y_test = train_test_split(X,Y, random_state = 0)\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "X_train_scaled = scaler.fit_transform(X_train_org)\n",
    "X_test_scaled = scaler.transform(X_test_org)\n",
    "X_train =  pd.DataFrame(X_train_scaled)\n",
    "X_test = pd.DataFrame(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Nearest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 4}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "params = {'n_neighbors':[2,3,4,5,6,7,8]}\n",
    "\n",
    "knn = KNeighborsRegressor()\n",
    "\n",
    "model = GridSearchCV(knn, params, cv=5)\n",
    "model.fit(X_train, y_train)\n",
    "model.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get n_neighbors = 4 from the GridSearch.   \n",
    "Thus, we will train using n_neighbors = 4, which should be optimal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9502728099335964\n",
      "0.9336116181369742\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "knn_reg = KNeighborsRegressor(4)\n",
    "knn_reg.fit(X_train, y_train)\n",
    "knn_train_score = knn_reg.score(X_train, y_train)\n",
    "knn_test_score = knn_reg.score(X_test, y_test)\n",
    "\n",
    "print(knn_train_score)\n",
    "print(knn_test_score)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_1 = ['knn', 'n_neighbors = 4', knn_train_score, knn_test_score]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training R^2: 0.9605233015179824\n",
      "Testing R^2: 0.9543604976065105\n",
      "Cross Validation Scores: [0.9543462  0.95941212 0.95522252]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "lreg = LinearRegression()\n",
    "lreg.fit(X_train, y_train)\n",
    "print(\"Training R^2:\", lreg.score(X_train, y_train))\n",
    "print(\"Testing R^2:\", lreg.score(X_test, y_test))\n",
    "\n",
    "Score = cross_val_score(lreg, X_train, y_train)\n",
    "print(\"Cross Validation Scores:\", Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_2 = ['Linear Regression', 'Simple', lreg.score(X_train, y_train), lreg.score(X_test, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polynomial Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have fitted second order polynomial regression. Third order polynomial wasn't fitting even on a smaller dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9831302634193698\n",
      "0.0277068238736512\n"
     ]
    }
   ],
   "source": [
    "from  sklearn.preprocessing  import PolynomialFeatures\n",
    "\n",
    "poly = PolynomialFeatures(2)\n",
    "X_train_poly = poly.fit_transform(X_train)\n",
    "X_test_poly = poly.transform(X_test)\n",
    "lreg.fit(X_train_poly, y_train)\n",
    "poly_train_score = (lreg.score(X_train_poly, y_train))\n",
    "poly_test_score = (lreg.score(X_test_poly, y_test))\n",
    "    \n",
    "print(poly_train_score)\n",
    "print(poly_test_score)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From the above test score we can infer that the Polynomial Regression model is overfitting.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_3 = ['Polynomial Regression', 'Order = 2', poly_train_score, poly_test_score]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RIDGE Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from  sklearn.linear_model import Ridge\n",
    "\n",
    "x_range = [0.01, 0.1, 1, 10, 100]\n",
    "train_score_list = []\n",
    "test_score_list = []\n",
    "\n",
    "for alpha in x_range: \n",
    "    ridge = Ridge(alpha)\n",
    "    ridge.fit(X_train,y_train)\n",
    "    train_score_list.append(ridge.score(X_train,y_train))\n",
    "    test_score_list.append(ridge.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: [0.9601913383453053, 0.9598676131183407, 0.9566996987556929, 0.9354317544171002, 0.8780316853348806]\n",
      "Testing Score: [0.9544625858412362, 0.9535175021449984, 0.9498840739215438, 0.9396666775782121, 0.903172549315234]\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Score:\", train_score_list)\n",
    "print(\"Testing Score:\", test_score_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can notice that the accuracy reduces as the alpha increases.**   \n",
    "We will plot these values for better understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;base64,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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'$\\\\alpha$')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(x_range, train_score_list, c = 'g', label = 'Train Score')\n",
    "plt.plot(x_range, test_score_list, c = 'b', label = 'Test Score')\n",
    "plt.xscale('log')\n",
    "plt.legend(loc = 3)\n",
    "plt.xlabel(r'$\\alpha$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the optimum value of alpha to design the model, alpha value is 0.01 obtained from above graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.9602\n",
      "Test score: 0.9545\n"
     ]
    }
   ],
   "source": [
    "ridge = Ridge(alpha = 0.01)\n",
    "ridge.fit(X_train,y_train)\n",
    "print('Train score: {:.4f}'.format(ridge.score(X_train,y_train)))\n",
    "print('Test score: {:.4f}'.format(ridge.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_4 = ['Ridge Regression', 'Alpha = 0.01', ridge.score(X_train, y_train), ridge.score(X_test, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the Coefficients of the ridge regression below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e58aff30f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "x_range1 = np.linspace(0.001, 1, 100).reshape(-1,1)\n",
    "x_range2 = np.linspace(1, 10000, 10000).reshape(-1,1)\n",
    "\n",
    "x_range = np.append(x_range1, x_range2)\n",
    "coeff = []\n",
    "\n",
    "for alpha in x_range: \n",
    "    ridge = Ridge(alpha)\n",
    "    ridge.fit(X_train,y_train)\n",
    "    coeff.append(ridge.coef_ )\n",
    "    \n",
    "coeff = np.array(coeff)\n",
    "\n",
    "for i in range(0,13):\n",
    "    plt.plot(x_range, coeff[:,i], label = 'feature {:d}'.format(i))\n",
    "\n",
    "plt.axhline(y=0, xmin=0.001, xmax=9999, linewidth=1, c ='gray')\n",
    "plt.xlabel(r'$\\alpha$')\n",
    "plt.xscale('log')\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.5),\n",
    "          ncol=3, fancybox=True, shadow=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LASSO Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "x_range = [0.01, 0.1, 1, 10]\n",
    "train_score_list = []\n",
    "test_score_list = []\n",
    "\n",
    "for alpha in x_range: \n",
    "    lasso = Lasso(alpha, max_iter = 30000)\n",
    "    lasso.fit(X_train,y_train)\n",
    "    train_score_list.append(lasso.score(X_train,y_train))\n",
    "    test_score_list.append(lasso.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I increased the max iterations in the above model as it was giving convergence warning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: [0.9596167126500394, 0.9532111054670308, 0.9353340267284557, 0.5805546005450664]\n",
      "Testing Score: [0.9536621115113779, 0.95105667295716, 0.9320058806122609, 0.6246270187387353]\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Score:\", train_score_list)\n",
    "print(\"Testing Score:\", test_score_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Similar to ridge, We can notice that the accuracy reduces as the alpha increases.**   \n",
    "We will plot these values for better understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'$\\\\alpha$')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e58b122908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_range, train_score_list, c = 'g', label = 'Train Score')\n",
    "plt.plot(x_range, test_score_list, c = 'b', label = 'Test Score')\n",
    "plt.xscale('log')\n",
    "plt.legend(loc = 3)\n",
    "plt.xlabel(r'$\\alpha$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the optimum value of alpha to design the model, alpha value is 0.01 obtained from above graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score: 0.9596\n",
      "Test score: 0.9537\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso(alpha = 0.01, max_iter=30000)\n",
    "lasso.fit(X_train,y_train)\n",
    "print('Train score: {:.4f}'.format(lasso.score(X_train,y_train)))\n",
    "print('Test score: {:.4f}'.format(lasso.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_5 = ['Lasso Regression', 'Alpha = 0.01', lasso.score(X_train, y_train), lasso.score(X_test, y_test)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'epsilon': 0.5, 'gamma': 1e-07, 'kernel': 'linear'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "parameters = {'kernel': ('linear', 'rbf','poly'), 'gamma': [1e-7, 1e-4],'epsilon':[0.1,0.2,0.5,0.3]}\n",
    "svr = svm.SVR()\n",
    "clf = GridSearchCV(svr, parameters)\n",
    "clf.fit(X_train, y_train)\n",
    "print(clf.best_params_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the optimum parameters obtained from the Gridsearch as above to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6905860244119069 0.7382286391320088\n"
     ]
    }
   ],
   "source": [
    "clf = svm.SVR(kernel='linear', gamma=1e-07, epsilon=0.5)\n",
    "clf.fit(X_train, y_train)\n",
    "svr_train_score = clf.score(X_train, y_train)\n",
    "svr_test_score = clf.score(X_test, y_test)\n",
    "print(svr_train_score, svr_test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_6 = ['Support Vector Regression', 'Linear Kernel, gamma = 1e-07, epsilon = 0.5', svr_train_score, svr_test_score]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SGD Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'alpha': 1e-06, 'learning_rate': 'constant', 'loss': 'squared_loss', 'penalty': 'l1'}\n",
      "0.8801184390865069\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "sgd_reg = SGDRegressor(random_state= 0)\n",
    "\n",
    "param_grid = {\n",
    "    'alpha': 10.0 ** -np.arange(1, 7),\n",
    "    'loss': ['squared_loss', 'huber'],\n",
    "    'penalty': ['l2', 'l1', 'elasticnet'],\n",
    "    'learning_rate': ['constant', 'optimal'],\n",
    "}\n",
    "\n",
    "clf = GridSearchCV(sgd_reg, param_grid)\n",
    "clf.fit(X_train, y_train)\n",
    "print(clf.best_params_)\n",
    "print(clf.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9573269112132466\n",
      "0.9514598805288702\n"
     ]
    }
   ],
   "source": [
    "sgd_reg = SGDRegressor(random_state= 0, alpha = 1e-06, max_iter = 3000, learning_rate = 'constant', loss = 'squared_loss', penalty = 'l1')\n",
    "sgd_reg.fit(X_train, y_train)\n",
    "sgd_train_score = sgd_reg.score(X_train, y_train)\n",
    "sgd_test_score = sgd_reg.score(X_test, y_test)\n",
    "print(sgd_train_score)\n",
    "print(sgd_test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table_7 = ['SGD Regressor', 'squared_loss, alpha = 1e-06', sgd_train_score, sgd_test_score]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating a Report table\n",
    "For comparing all the models, we will create a table and a plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table = pd.DataFrame(list(zip(report_table_1,\n",
    "             report_table_2,\n",
    "             report_table_3,\n",
    "             report_table_4,\n",
    "             report_table_5,\n",
    "             report_table_6,\n",
    "             report_table_7))).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "report_table.columns = ['Model', 'Model Parameter', 'Train Score', 'Test Score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Model Parameter</th>\n",
       "      <th>Train Score</th>\n",
       "      <th>Test Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>knn</td>\n",
       "      <td>n_neighbors = 4</td>\n",
       "      <td>0.950273</td>\n",
       "      <td>0.933612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>Simple</td>\n",
       "      <td>0.960523</td>\n",
       "      <td>0.95436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Polynomial Regression</td>\n",
       "      <td>Order = 2</td>\n",
       "      <td>0.98313</td>\n",
       "      <td>0.0277068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>Alpha = 0.01</td>\n",
       "      <td>0.960191</td>\n",
       "      <td>0.954463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>Alpha = 0.01</td>\n",
       "      <td>0.959617</td>\n",
       "      <td>0.953662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Support Vector Regression</td>\n",
       "      <td>Linear Kernel, gamma = 1e-07, epsilon = 0.5</td>\n",
       "      <td>0.690586</td>\n",
       "      <td>0.738229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SGD Regressor</td>\n",
       "      <td>squared_loss, alpha = 1e-06</td>\n",
       "      <td>0.957327</td>\n",
       "      <td>0.95146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Model                              Model Parameter  \\\n",
       "0                        knn                              n_neighbors = 4   \n",
       "1          Linear Regression                                       Simple   \n",
       "2      Polynomial Regression                                    Order = 2   \n",
       "3           Ridge Regression                                 Alpha = 0.01   \n",
       "4           Lasso Regression                                 Alpha = 0.01   \n",
       "5  Support Vector Regression  Linear Kernel, gamma = 1e-07, epsilon = 0.5   \n",
       "6              SGD Regressor                  squared_loss, alpha = 1e-06   \n",
       "\n",
       "  Train Score Test Score  \n",
       "0    0.950273   0.933612  \n",
       "1    0.960523    0.95436  \n",
       "2     0.98313  0.0277068  \n",
       "3    0.960191   0.954463  \n",
       "4    0.959617   0.953662  \n",
       "5    0.690586   0.738229  \n",
       "6    0.957327    0.95146  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3AAAAHfCAYAAAARPzrFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3X2QXXWd7/vP1ySQCAEkSelVZEBAi8cAZlAHkUEZBDw+zxyVB1FhKEGvoKJDKSiCx6PW6B0Vrh5GQUVFPSoMysUH9FiC1JwQHIIGDjCOgwZ5iAFiwgQw8Lt/9E6mCR3SJJ10funXq6rL3mv9eq1f721Tee+19lrVWgsAAACbvieN9wQAAAAYHQEHAADQCQEHAADQCQEHAADQCQEHAADQCQEHAADQCQEHAADQCQEHwAZTVT+tqnurasvxngsAbA4EHAAbRFXtlOSgJC3JKzbyvidvzP0BwMYi4ADYUN6Y5J+TfDHJccNXVNW0qvpEVd1WVUuq6uqqmjZY98Kquqaq7quq31XVmwbLf1pVJwzbxpuq6uphj1tVva2qbk1y62DZpwbb+GNVXVdVBw0bP6mq3ldVv66qpYP1z6yq86rqE6vN97tVdepIv2RV7VlVP6qqe6rqrqp632D5llX1D1X1+8HXP6w8EllVf1lVC6vqvVV1d1XdUVWvqqojq+qWwbbeN2wfZ1XVt6rqG4O5/qKqZq/LiwJA3wQcABvKG5N8dfD10qp66rB1f5/kuUn+Isn2Sd6b5JGq2jHJFUk+k2RWkn2TXP8E9vmqJM9Lssfg8bWDbWyf5GtJ/mdVTR2se1eSNyQ5Msk2Sd6S5D+SfCnJG6rqSUlSVTOTvCTJxavvrKqmJ7kyyfeTPD3Jrkl+PFj9/iTPH+x/dpIDkpwx7MeflmRqkmck+UCSf0xyzOB5OSjJB6rqWcPGvzLJ/xz2u1xaVVOewHMDwGZAwAEw5qrqhUn+LMk3W2vXJfl1kqMG656UoVg6pbV2e2vt4dbaNa21B5McneTK1trFrbU/tdYWt9aeSMD999baPa215UnSWvvKYBsrWmufSLJlkucMxp6Q5IzW2s1tyPzB2LlJlmQo2pLk9Ul+2lq7a4T9/Zckd7bWPtFae6C1trS19r8H645OcnZr7e7W2qIkH0py7LCf/VOS/9Za+1OSryeZmeRTg20sSLIgyT7Dxl/XWvvWYPwnMxR/z38Czw0AmwEBB8CGcFySH7bW/jB4/LX852mUMzMUH78e4eeeuYblo/W74Q+q6t1VddPgNM37kmw72P/a9vWlDB0Ny+B/L1rDuMfbxtOT3Dbs8W2DZSstbq09PPh++eB/h0fi8iRbD3u86ndrrT2SZOFq2wNgAvAhbwDG1OCzbP81yaSqunOweMsk2w0+t/XLJA8k2SXJ/NV+/HcZOtVwJPcnefKwx08bYUwbNo+Dkvxdho6kLWitPVJV9yapYfvaJcmvRtjOV5L8ajDf3ZNcuoY5/S5Dp2GO5PcZOgq5YPB4x8GydfXMld8MjmLusJ7bA6BDjsABMNZeleThDH0Obd/B1+5JrkryxsHRowuSfLKqnj64mMgLBhf4+GqSQ6vqv1bV5KqaUVX7DrZ7fZLXVNWTq2rXJMevZR7Tk6xIsijJ5Kr6QIY+67bS55OcU1W71ZB9qmpGkrTWFmbo83MXJfn2ylMyR/C9JE+rqlMHFy2ZXlXPG6y7OMkZVTVr8Dm6D2QoDNfVc6vqNYMrbJ6a5MEMXSQGgAlEwAEw1o5LcmFr7bettTtXfiU5N8nRgwA5LUNH4q5Nck+SjyV5Umvttxm6qMi7B8uvz9AFQJLk/0nyUIZOM/xShmLv8fwgQxdEuSVDpy8+kEefYvnJJN9M8sMkf0zyhSTThq3/UpK9s+bTJ9NaW5rkr5K8PMmdGbr65SGD1R9OMi/JDYPf9ReDZevqn5K8Lsm9Gfos3WsGn4cDYAKp1traRwHABFNVL8rQEbOdBkcNx3MuZyXZtbV2zNrGArB5cwQOAFYzuDz/KUk+P97xBgDDCTgAGKaqdk9yX5L/K8k/jPN0AOBRnEIJAADQCUfgAAAAOrFJ3Adu5syZbaeddhrvaQAAAIyL66677g+ttVlrG7dJBNxOO+2UefPmjfc0AAAAxkVV3TaacU6hBAAA6ISAAwAA6ISAAwAA6MQm8Rm4kfzpT3/KwoUL88ADD4z3VCaEqVOnZocddsiUKVPGeyoAAMAabLIBt3DhwkyfPj077bRTqmq8p7NZa61l8eLFWbhwYXbeeefxng4AALAGm+wplA888EBmzJgh3jaCqsqMGTMc7QQAgE3cJhtwScTbRuS5BgCATd8mHXAAAAD8p032M3Cr2+n0y8d0e//+0Zc97vrFixfnJS95SZLkzjvvzKRJkzJr1tCN0efOnZsttthirft485vfnNNPPz3Pec5zRjWnO+64I8cff3xuv/32/OlPf8quu+6ayy67bFQ/CwAAbP5GFXBV9fYkb0qyd5KLW2tvepyx70zyd0mmJfl2kpNaaw+u90w3shkzZuT6669Pkpx11lnZeuutc9pppz1qTGstrbU86UkjH8i88MILn9A+zzjjjLzsZS/L2972tiTJDTfcsA4zf7QVK1Zk8uRuOh0AAHgcoz2F8vdJPpzkgscbVFUvTXJ6kpck2SnJs5J8aD3mt8n513/91+y1115561vfmv333z933HFHTjzxxMyZMyd77rlnzj777FVjX/jCF+b666/PihUrst122+X000/P7Nmz84IXvCB33333Y7Z9xx13ZIcddlj1eJ999ln1/Uc+8pHsvffemT17dt7//vcnSX7xi1/kec97XvbZZ5+89rWvzZIlS1bt9/3vf39e9KIX5dxzz81dd92V17zmNZkzZ04OOOCA/PM///OGenoAAIANaFQB11r7Tmvt0iSL1zL0uCRfaK0taK3dm+ScDB2526zceOONOf744/Mv//IvecYznpGPfvSjmTdvXubPn58f/ehHufHGGx/zM0uWLMnBBx+c+fPn5wUveEEuuOCxLfz2t789xx13XF784hfnIx/5SO64444kyXe/+91cccUVmTt3bubPn593v/vdSZJjjjkmn/jEJ3LDDTfkOc95Ts4555xV2/rjH/+Yn/3sZzn11FPzjne8I+9973szb968fPOb38wJJ5ywgZ4ZAABgQxrrc+v2TPJPwx7PT/LUqprRWntU/FXViUlOTJIdd9xxjKexYe2yyy758z//81WPL7744nzhC1/IihUr8vvf/z433nhj9thjj0f9zLRp03LEEUckSZ773Ofmqquuesx2jzzyyPz617/O97///VxxxRXZb7/9smDBglx55ZV5y1vekmnTpiVJtt9++yxevDgPPPBAXvjCFyZJjjvuuBx77LGrtvX6179+1fdXXnllbr755lWP77333ixfvnzV9gAAgD6MdcBtnWTJsMcrv5+e1Y7etdbOT3J+ksyZM6eN8Tw2qK222mrV97feems+9alPZe7cudluu+1yzDHHjHg/teEXPZk0aVJWrFgx4rZnzJiRo48+OkcffXQOP/zwXH311WmtPeYy/609/lM2fI6ttVFfeAUAANh0jfVtBJYl2WbY45XfLx3j/Wwy/vjHP2b69OnZZpttcscdd+QHP/jBOm/rxz/+cZYvX75qu7/5zW+y44475rDDDssXvvCFVevuueeezJw5M9OmTcs111yTJLnoooty8MEHj7jdQw89NOedd96qxysvzgIAAPRlrI/ALUgyO8k3B49nJ7lr9dMn18XaLvs/Xvbff//sscce2WuvvfKsZz0rBx544Dpv69prr83b3/72TJkyJY888khOOumk7Lffftlvv/0yf/78zJkzJ1OmTMnLX/7ynHPOObnoooty0kknZfny5dl1113XeNXL8847LyeddFIuvPDCrFixIocccsijgg4AAOhDre1UvCSpqskZir0PJtkhyd8mWdFaW7HauMOTfDHJi5PckaHbCMxtrZ3+eNufM2dOmzdv3qOW3XTTTdl9991H/Yuw/jznAAAwPqrqutbanLWNG+0plGckWZ6hWwQcM/j+jKrasaqWVdWOSdJa+36Sjyf5X0luG3x9cB3mDwAAwGpGdQpla+2sJGetYfXWq439ZJJPrtesAAAAeIyx/gwcwAa30+mXj8t+N9XP4gIAE8dYX4USAACADUTAAQAAdELAAQAAdKKfz8Cdte0Yb2/J465evHhxXvKSlyRJ7rzzzkyaNCmzZs1KksydOzdbbLHFqHZzwQUX5Mgjj8zTnva0x6z7+c9/nne961158MEH8+CDD+aoo47KmWee+QR/EQAAYKLoJ+A2shkzZuT6669Pkpx11lnZeuutc9pppz3h7VxwwQXZf//9Rwy44447Lpdeemn22muvPPzww7n55pvXe94PP/xwJk2atN7bAQAANj1OoVwHX/rSl3LAAQdk3333zcknn5xHHnkkK1asyLHHHpu99947e+21Vz796U/nG9/4Rq6//vq87nWvy7777puHHnroUdtZtGjRqrCbNGlS9thjjyTJ0qVLc9xxx2XvvffOPvvsk0svvTRJ8pWvfGXV9t/3vvclSVasWJHtttsuZ5xxRg444IDMnTs31157bQ4++OA897nPzRFHHJG77rprIz47AADAhuII3BP0q1/9KpdcckmuueaaTJ48OSeeeGK+/vWvZ5dddskf/vCH/PKXv0yS3Hfffdluu+3ymc98Jueee2723Xffx2zr1FNPzW677ZZDDjkkRxxxRN74xjdmyy23zFlnnZVZs2bll7/8ZVprue+++7Jw4cKcccYZmTdvXrbddtsceuih+d73vpfDDz88S5Ysyf77758Pf/jDefDBB3PIIYfksssuy8yZM/PVr341Z555Zs4///yN/VQBAABjTMA9QVdeeWWuvfbazJkzJ0myfPnyPPOZz8xLX/rS3HzzzTnllFNy5JFH5rDDDlvrtj70oQ/l2GOPzQ9/+MN8+ctfzje+8Y1ceeWVufLKK1cddauqPOUpT8lPfvKTvPjFL87MmTOTJEcddVR+9rOf5fDDD88WW2yRV7/61UmSm266KQsWLMihhx6aZOiUyh122GFDPBUAAMBGJuCeoNZa3vKWt+Scc855zLobbrghV1xxRT796U/n29/+9qiOeu26667Zdddd87d/+7eZMWNGlixZktZaquox+12TadOmrRrfWss+++yTq6666gn+ZgAAwKZOwD1Bhx56aP76r/86p5xySmbOnJnFixfn/vvvz7Rp0zJ16tT8zd/8TXbeeee89a1vTZJMnz49S5cuHXFbl19+eY488shUVW655ZZsueWWmT59eg477LCce+65+fu///tVp1A+//nPz3ve854sXrw42267bb7+9a+PeFGVPfbYI7fffnvmzp2bAw44IA899FBuvfXW7Lnnnhv0edkU7XT65eOy33//6MvGZb+wufC3u/ny2gKsv34Cbi2X/d9Y9t5773zwgx/MoYcemkceeSRTpkzJ5z73uUyaNCnHH3/8qqNnH/vYx5Ikb37zm3PCCSdk2rRpj7n9wBe/+MW8853vzJOf/ORMmTIlX/va1/KkJz0pH/zgB3PyySdnr732yqRJk3LOOefkFa94Rc4+++z85V/+ZVprefnLX56XvexlWbFixaPmt+WWW+Zb3/pW3vGOd2Tp0qVZsWJF3v3ud0/IgAMAYGyMxxsw3nwZWT3eqXkby5w5c9q8efMeteymm27K7rvvPk4zmpg2t+d83N7pnXrUxt/pJvIGx8biXfzN24T6200m1N+v1xb6JeA2vKq6rrU2Z23j+jkCBwAATBxnbTtO+92033yZ0AF3w8L7xmW/++yw3bjsFwAA6NsmfSPvTeH0zonCcw0AAJu+TTbgpk6dmsWLFwuLjaC1lsWLF2fq1KnjPRUAAOBxbLKnUO6www5ZuHBhFi1atMH2cde9yzfYth/PTUunjct+H8/UqVPd8BsAADZxm2zATZkyJTvvvPMG3ccRrmQHAAB0ZJM9hRIAAIBHE3AAAACd2GRPodysuacFAACwDhyBAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6ISAAwAA6MSoAq6qtq+qS6rq/qq6raqOWsO4Lavqc1V1V1XdU1XfrapnjO2UAQAAJqbRHoE7L8lDSZ6a5Ogkn62qPUcYd0qSFyTZJ8nTk9yX5DNjME8AAIAJb60BV1VbJXltkjNba8taa1cnuSzJsSMM3znJD1prd7XWHkjy9SQjhR4AAABP0GiOwD07ycOttVuGLZufkcPsC0kOrKqnV9WTM3S07oqRNlpVJ1bVvKqat2jRoic6bwAAgAlnNAG3dZIlqy1bkmT6CGNvSfLbJLcn+WOS3ZOcPdJGW2vnt9bmtNbmzJo1a/QzBgAAmKBGE3DLkmyz2rJtkiwdYexnk0xNMiPJVkm+kzUcgQMAAOCJGU3A3ZJkclXtNmzZ7CQLRhg7O8kXW2v3tNYezNAFTA6oqpnrP1UAAICJba0B11q7P0NH0s6uqq2q6sAkr0xy0QjDr03yxqratqqmJDk5ye9ba38Yy0kDAABMRKO9jcDJSaYluTvJxUlOaq0tqKqDqmrZsHGnJXkgya1JFiU5Msmrx3C+AAAAE9bk0Qxqrd2T5FUjLL8qQxc5Wfl4cYauPAkAAMAYG+0ROAAAAMaZgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOiEgAMAAOjE5PGeAAAArLOzth2n/S4Zn/0y4TkCBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0AkBBwAA0InJ4z0BAAD6t9Ppl4/Lfv996rjsFsaNI3AAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdGFXAVdX2VXVJVd1fVbdV1VGPM3b/qvpZVS2rqruq6pSxmy4AAMDENXmU485L8lCSpybZN8nlVTW/tbZg+KCqmpnk+0nemeRbSbZIssPYTRcAAGDiWusRuKraKslrk5zZWlvWWrs6yWVJjh1h+LuS/KC19tXW2oOttaWttZvGdsoAAAAT02hOoXx2kodba7cMWzY/yZ4jjH1+knuq6pqquruqvltVO4600ao6sarmVdW8RYsWPfGZAwAATDCjCbitkyxZbdmSJNNHGLtDkuOSnJJkxyS/SXLxSBttrZ3fWpvTWpsza9as0c8YAABgghrNZ+CWJdlmtWXbJFk6wtjlSS5prV2bJFX1oSR/qKptW2urRyAAAABPwGiOwN2SZHJV7TZs2ewkC0YYe0OSNuzxyu9r3aYHAADASmsNuNba/Um+k+Tsqtqqqg5M8sokF40w/MIkr66qfatqSpIzk1zdWrtvLCcNAAAwEY32Rt4nJ5mW5O4MfabtpNbagqo6qKqWrRzUWvtJkvcluXwwdtcka7xnHAAAAKM3qvvAtdbuSfKqEZZflaGLnAxf9tkknx2T2QEAALDKaI/AAQAAMM4EHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCcEHAAAQCdGFXBVtX1VXVJV91fVbVV11FrGb1FV/6eqFo7NNAEAAJg8ynHnJXkoyVOT7Jvk8qqa31pbsIbx70lyd5Kt13+KAAAAJKM4AldVWyV5bZIzW2vLWmtXJ7ksybFrGL9zkmOS/PexnCgAAMBEN5pTKJ+d5OHW2i3Dls1Psucaxn8myfuSLH+8jVbViVU1r6rmLVq0aFSTBQAAmMhGE3BbJ1my2rIlSaavPrCqXp1kcmvtkrVttLV2fmttTmttzqxZs0Y1WQAAgIlsNJ+BW5Zkm9WWbZNk6fAFg1MtP57kyLGZGgAAAMONJuBuSTK5qnZrrd06WDY7yeoXMNktyU5JrqqqJNkiybZVdWeS57fW/n1MZgwAADBBrTXgWmv3V9V3kpxdVSdk6CqUr0zyF6sN/VWSZw57/BdJzk2yfxIfcgMAAFhPo72R98lJpmXo1gAXJzmptbagqg6qqmVJ0lpb0Vq7c+VXknuSPDJ4/PAGmT0AAMAEMqr7wLXW7knyqhGWX5U13OuttfbTJDusz+QAAAD4T6M9AgcAAMA4E3AAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdEHAAAACdGFXAVdX2VXVJVd1fVbdV1VFrGPeeqvpVVS2tqt9U1XvGdroAAAAT1+RRjjsvyUNJnppk3ySXV9X81tqC1cZVkjcmuSHJLkl+WFW/a619fawmDAAAMFGt9QhcVW2V5LVJzmytLWutXZ3ksiTHrj62tfbx1tovWmsrWms3J/mnJAeO9aQBAAAmotGcQvnsJA+31m4Ztmx+kj0f74eqqpIclGT1o3Qr159YVfOqat6iRYtGO18AAIAJazQBt3WSJastW5Jk+lp+7qzB9i8caWVr7fzW2pzW2pxZs2aNYhoAAAAT22g+A7csyTarLdsmydI1/UBVvT1Dn4U7qLX24LpPDwAAgJVGcwTuliSTq2q3YctmZ82nRr4lyelJXtJaW7j+UwQAACAZRcC11u5P8p0kZ1fVVlV1YJJXJrlo9bFVdXSSjyT5q9bav431ZAEAACay0d7I++Qk05LcneTiJCe11hZU1UFVtWzYuA8nmZHk2qpaNvj63NhOGQAAYGIa1X3gWmv3JHnVCMuvytBFTlY+3nnspgYAAMBwoz0CBwAAwDgTcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ0QcAAAAJ2YPN4TAOjGWduO036XjM9+AYBNjiNgv2nPAAAGbklEQVRwAAAAnRBwAAAAnRBwAAAAnRBwAAAAnRBwAAAAnRBwAAAAnRBwAAAAnRhVwFXV9lV1SVXdX1W3VdVRaxhXVfWxqlo8+Pp4VdXYThkAAGBiGu2NvM9L8lCSpybZN8nlVTW/tbZgtXEnJnlVktlJWpIfJfm3JJ8bm+kCAABMXGs9AldVWyV5bZIzW2vLWmtXJ7ksybEjDD8uySdaawtba7cn+USSN43hfAEAACasaq09/oCq/ZJc01qbNmzZaUkObq29fLWxS5Ic1lr734PHc5L8r9ba9BG2e2KGjtglyXOS3Lw+v0hnZib5w3hPgg3G67v58tpu3ry+my+v7ebN67v5mmiv7Z+11matbdBoTqHcOsmS1ZYtSfKYKBth7JIkW1dVtdVKsbV2fpLzR7H/zU5VzWutzRnvebBheH03X17bzZvXd/Pltd28eX03X17bkY3mIibLkmyz2rJtkiwdxdhtkixbPd4AAAB44kYTcLckmVxVuw1bNjvJ6hcwyWDZ7FGMAwAA4Alaa8C11u5P8p0kZ1fVVlV1YJJXJrlohOFfTvKuqnpGVT09ybuTfHEM57u5mJCnjk4gXt/Nl9d28+b13Xx5bTdvXt/Nl9d2BGu9iEkydB+4JBck+aski5Oc3lr7WlUdlOSK1trWg3GV5GNJThj86OeT/J1TKAEAANbfqAIOAACA8Teaz8ABAACwCRBwAAAAnRBwAAAAnRjNjbxZD1W1e5Jjk+yZoZufL83QrRUuaq3dNJ5zAx5fVe2Y5LlJFrTWbllt3RtaaxePz8xYX1W1X5Jdkvx/SR5MctLg8Y9ba98bz7kx9qpqXpLDWmv3jPdcGDtVtXOSI5NUkh+01m4d5ymxHgZXuv+31todVbVlkjMy9PomyXeTfKS19tC4TXAT4iImG1BVvSHJZ5NclmR+kiUZurn57CSvSPLW1to3xm+GbChVNSnJ+1trZ4/3XFg3VXV4km8m+U2S3TJ0S5T/u7X28GD9H1tr24zfDFlXVXV8kg8naUl+n6Fb5TwzQ29qvj7JKa21C8ZvhqyrqvryGlb9dZLvJXmgtfbGjTglxlBV3dRa233w/cEZ+kf9zzP0t3xQkle21n4yjlNkPVTVrUleNAi4zyTZL8knB6tPTXJda+2d4zbBTYiA24Cq6jdJjmmt/XyEdQcm+WprbaeNPjE2uME7R//RWps03nNh3VTVdUk+0Fq7vKqemuQrGTpS85rW2kNVtbS1Nn18Z8m6qKr/k6E30SrJTUle2Fq7ZrDupUk+3lqbPY5TZB1V1fIkc5P8OEOv70qnJflckmWttQ+Nx9xYf8P/u1tVVyX5x9balwePj07yttbaX4znHFl3VbVs2K3Jfptk35VHzavqKRk6G+bp4znHTYWA24CqalmSWa215SOse3KSu1f+H5X+VNXjvUM/OcnRAq5fVbWktbbtsMeTMxRxMzP0j/+7BFyfhr+2VXV/kq1X3q+0qp6U5J7W2nbjOUfWTVXtluTcJPcmeXdr7fbB8juSzG6t3T2e82P9DD/zoaruTvKM1tqfBo8nJVnUWtt+POfIuquqG5Mc11q7dnA07sCVf7NVNSvJLa21p4zrJDcRLmKyYf0oyQVVtcvwhYPH/zhYT7+OSrI8ye0jfC0cx3kxNu6tqmeufNBaW5HkDUl+m+TKJOK8X/dX1ZTB919sj34nc1qSR8ZhToyB1tqtrbWXJrk0yU+q6rTBmy/erd48TKmqN1fVWzL0mm4xbN3k+O9y785O8s2qenOSzyf5XlUdU1XHZOgU6K+N6+w2IY7AbUCDw73/b5LXJFmR//wM3OQMfebiba21e8dvhqyPqro2yTmttctGWDc1Q6dQepOkU1X1+SS/HelzjFX1uSQnen37VFUXZejD8I+5kFRVvS7JSa21v9zoE2NMVdU2GfoH4aFJ/izJLo7A9a2qfppHx/h7W2vXDtYdluTDrbUDxmNujI2q+qskZyWZk2TlG20Lk1yYoX9zrRinqW1SBNxGMDhd8tlJtk6yLEOHgP9jfGfF+qqqtyW5vbV26QjrJiU5w2ct+lVVWySZvKa/1arasbX22408LTawwWk6rbX2h/GeC2OjqvZNcnCS/9Fae2C858OGUVXbJpnib3fzMDid/alJlrfW7hvv+WxqBBwAAEAnnP4DAADQCQEHAADQCQEHAADQCQEHAADQif8fXGsgqWK+ce8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e58b3fc9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = report_table[['Train Score','Test Score']].plot(kind='bar', title =\"Accuracy comp\", figsize=(15, 8), legend=True, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From the above chart and accuracy values, We will choose Ridge regression as the model to train the whole dataset.**\n",
    "## Final Model\n",
    "### Training the whole dataset using Ridge Regression\n",
    "We will separate the train and test data and perform initial transformation and pre-processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating dependent and independent vars of the whole Dataset\n",
    "X = pd.DataFrame(Dataset.iloc[:,0:77])\n",
    "Y = Dataset[77]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train_org, X_test_org, y_train, y_test = train_test_split(X,Y, random_state = 0)\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "X_train_scaled = scaler.fit_transform(X_train_org)\n",
    "X_test_scaled = scaler.transform(X_test_org)\n",
    "X_train =  pd.DataFrame(X_train_scaled)\n",
    "X_test = pd.DataFrame(X_test_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**After applying the transformation, we will apply Ridge regression on the whole data and predict values for y_test.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 142.01637950337982\n",
      "Train score: 0.9330\n",
      "Test score: 0.9433\n"
     ]
    }
   ],
   "source": [
    "from  sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "ridge = Ridge(alpha = 0.01)\n",
    "ridge.fit(X_train,y_train)\n",
    "\n",
    "#Predicting values for the test dataset\n",
    "predict = ridge.predict(X_test)\n",
    "\n",
    "#Calulate RMSE Value\n",
    "rmse = sqrt(mean_squared_error(y_test, predict))\n",
    "\n",
    "print('RMSE:', rmse)\n",
    "print('Train score: {:.4f}'.format(ridge.score(X_train, y_train)))\n",
    "print('Test score: {:.4f}'.format(ridge.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We obtained Training accuracy of 93.3% and Testing accuracy of 94.3% on the whole dataset using Ridge regression.**  \n",
    "**We Predicted values for Y_test and obtained RMSE of 142.01.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Dataset For Classification Task\n",
    "*Classification analysis is in another Kernel*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate a beep to notify when code finishes running\n",
    "import winsound\n",
    "duration = 1800  # millisecond\n",
    "freq = 440  # Hz\n",
    "winsound.Beep(freq, duration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dataset_2 = pd.read_csv('Twitter-Absolute-Sigma-500.data', header = None)   #Reading Data For classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dataset_2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dataset_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dataset.columns\n",
    "#Dataset_2[77].unique().astype(int)   #Dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Dataset_2.isnull().sum().sum()    #No Missing values"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}