Chris Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cycling to work - a data fitting challenge\n",
    "I am an avid cyclist, and ride to work every day rain or shine (though hail can make me call my wife to pick me up!). With access to census data in Australia I thought it would be interesting to see if I could fit a model to predict cyling to work based on other census factors. This insight could help town planners with making changes to encourage cycling to work.\n",
    "\n",
    "# Problem Statement: \n",
    "I will aim to fit an ML model to census data to predict the proportion of people cycling to work using R2 regression as my evaluation metric. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First we need to import a number of libraries including our custom functions\n",
    "There is some overlap in our plotting, but it's nice to be able to both make quick histograms of our data and to have more interactive versions using Bokeh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'cleanfunc' from 'C:\\\\Users\\\\cartechr\\\\OneDrive - Mars Inc\\\\GitHub\\\\ABS-Region-Data\\\\cleanfunc.py'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import importlib\n",
    "import operator\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "import seaborn as sns\n",
    "import os\n",
    "from textwrap import wrap\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "import eli5\n",
    "import cleanfunc\n",
    "import re\n",
    "from sklearn.metrics import r2_score\n",
    "from bokeh.models import HoverTool\n",
    "from bokeh.plotting import figure, output_notebook, show, ColumnDataSource\n",
    "importlib.reload(cleanfunc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to just read in the pre-prepared data. Functions are included in cleanfunc.py to read the raw excel files you can get from the ABS, but this can take significant time to process, so we have exported the cleaned data for use here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NUMBER OF BUSINESSES Number of non-employing businesses no.</th>\n",
       "      <th>NUMBER OF BUSINESSES 1-4 employees no.</th>\n",
       "      <th>NUMBER OF BUSINESSES 5-19 employees no.</th>\n",
       "      <th>NUMBER OF BUSINESSES 20 or more employees no.</th>\n",
       "      <th>NUMBER OF BUSINESSES Total number of businesses no.</th>\n",
       "      <th>BUSINESS ENTRIES Number of non-employing business entries  no.</th>\n",
       "      <th>BUSINESS ENTRIES 1-4 employees no.</th>\n",
       "      <th>BUSINESS ENTRIES 5-19 employees no.</th>\n",
       "      <th>BUSINESS ENTRIES 20 or more employees no.</th>\n",
       "      <th>BUSINESS ENTRIES Total number of business entries no.</th>\n",
       "      <th>...</th>\n",
       "      <th>RELIGIOUS AFFILIATION Judaism   %</th>\n",
       "      <th>RELIGIOUS AFFILIATION Other Religions   %</th>\n",
       "      <th>RELIGIOUS AFFILIATION Secular Beliefs, Other Spiritual Beliefs and No Religious Affiliation  %</th>\n",
       "      <th>RELIGIOUS AFFILIATION Inadequately described or not stated   %</th>\n",
       "      <th>AUSTRALIAN CITIZENSHIP Australian citizen  %</th>\n",
       "      <th>AUSTRALIAN CITIZENSHIP Not an Australian citizen  %</th>\n",
       "      <th>AUSTRALIAN CITIZENSHIP Australian Citizenship - Not stated  %</th>\n",
       "      <th>SPEAKS A LANGUAGE OTHER THAN ENGLISH AT HOME Proportion of total population  %</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LABEL</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Queanbeyan</th>\n",
       "      <td>402.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>712.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.855263</td>\n",
       "      <td>111.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>2.1</td>\n",
       "      <td>28.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>80.6</td>\n",
       "      <td>11.2</td>\n",
       "      <td>8.2</td>\n",
       "      <td>21.2</td>\n",
       "      <td>-35.352466</td>\n",
       "      <td>149.233725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyong</th>\n",
       "      <td>322.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>631.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.855263</td>\n",
       "      <td>84.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>28.6</td>\n",
       "      <td>9.4</td>\n",
       "      <td>86.1</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.6</td>\n",
       "      <td>8.2</td>\n",
       "      <td>-33.285641</td>\n",
       "      <td>151.425306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bathurst</th>\n",
       "      <td>1017.0</td>\n",
       "      <td>574.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>211.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.4</td>\n",
       "      <td>24.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>86.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>-33.474707</td>\n",
       "      <td>149.641320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orange</th>\n",
       "      <td>983.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1832.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.855263</td>\n",
       "      <td>196.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.748598</td>\n",
       "      <td>0.4</td>\n",
       "      <td>22.6</td>\n",
       "      <td>9.7</td>\n",
       "      <td>88.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-33.329242</td>\n",
       "      <td>149.108147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maitland</th>\n",
       "      <td>836.0</td>\n",
       "      <td>277.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1241.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.855263</td>\n",
       "      <td>172.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>26.7</td>\n",
       "      <td>9.8</td>\n",
       "      <td>89.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>-32.708177</td>\n",
       "      <td>151.526844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 455 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            NUMBER OF BUSINESSES Number of non-employing businesses no.  \\\n",
       "LABEL                                                                     \n",
       "Queanbeyan                                              402.0             \n",
       "Wyong                                                   322.0             \n",
       "Bathurst                                               1017.0             \n",
       "Orange                                                  983.0             \n",
       "Maitland                                                836.0             \n",
       "\n",
       "            NUMBER OF BUSINESSES 1-4 employees no.  \\\n",
       "LABEL                                                \n",
       "Queanbeyan                                   193.0   \n",
       "Wyong                                        193.0   \n",
       "Bathurst                                     574.0   \n",
       "Orange                                       515.0   \n",
       "Maitland                                     277.0   \n",
       "\n",
       "            NUMBER OF BUSINESSES 5-19 employees no.  \\\n",
       "LABEL                                                 \n",
       "Queanbeyan                                     89.0   \n",
       "Wyong                                          85.0   \n",
       "Bathurst                                      254.0   \n",
       "Orange                                        270.0   \n",
       "Maitland                                      114.0   \n",
       "\n",
       "            NUMBER OF BUSINESSES 20 or more employees no.  \\\n",
       "LABEL                                                       \n",
       "Queanbeyan                                           27.0   \n",
       "Wyong                                                23.0   \n",
       "Bathurst                                             58.0   \n",
       "Orange                                               61.0   \n",
       "Maitland                                             18.0   \n",
       "\n",
       "            NUMBER OF BUSINESSES Total number of businesses no.  \\\n",
       "LABEL                                                             \n",
       "Queanbeyan                                              712.0     \n",
       "Wyong                                                   631.0     \n",
       "Bathurst                                               1895.0     \n",
       "Orange                                                 1832.0     \n",
       "Maitland                                               1241.0     \n",
       "\n",
       "            BUSINESS ENTRIES Number of non-employing business entries  no.  \\\n",
       "LABEL                                                                        \n",
       "Queanbeyan                                               75.0                \n",
       "Wyong                                                    40.0                \n",
       "Bathurst                                                122.0                \n",
       "Orange                                                  119.0                \n",
       "Maitland                                                132.0                \n",
       "\n",
       "            BUSINESS ENTRIES 1-4 employees no.  \\\n",
       "LABEL                                            \n",
       "Queanbeyan                                32.0   \n",
       "Wyong                                     34.0   \n",
       "Bathurst                                  80.0   \n",
       "Orange                                    60.0   \n",
       "Maitland                                  38.0   \n",
       "\n",
       "            BUSINESS ENTRIES 5-19 employees no.  \\\n",
       "LABEL                                             \n",
       "Queanbeyan                                  5.0   \n",
       "Wyong                                       7.0   \n",
       "Bathurst                                   12.0   \n",
       "Orange                                     10.0   \n",
       "Maitland                                    8.0   \n",
       "\n",
       "            BUSINESS ENTRIES 20 or more employees no.  \\\n",
       "LABEL                                                   \n",
       "Queanbeyan                                   3.855263   \n",
       "Wyong                                        3.855263   \n",
       "Bathurst                                     3.000000   \n",
       "Orange                                       3.855263   \n",
       "Maitland                                     3.855263   \n",
       "\n",
       "            BUSINESS ENTRIES Total number of business entries no.     ...      \\\n",
       "LABEL                                                                 ...       \n",
       "Queanbeyan                                              111.0         ...       \n",
       "Wyong                                                    84.0         ...       \n",
       "Bathurst                                                211.0         ...       \n",
       "Orange                                                  196.0         ...       \n",
       "Maitland                                                172.0         ...       \n",
       "\n",
       "            RELIGIOUS AFFILIATION Judaism   %  \\\n",
       "LABEL                                           \n",
       "Queanbeyan                           0.100000   \n",
       "Wyong                                0.100000   \n",
       "Bathurst                             0.100000   \n",
       "Orange                               0.748598   \n",
       "Maitland                             0.100000   \n",
       "\n",
       "            RELIGIOUS AFFILIATION Other Religions   %  \\\n",
       "LABEL                                                   \n",
       "Queanbeyan                                        2.1   \n",
       "Wyong                                             0.7   \n",
       "Bathurst                                          0.4   \n",
       "Orange                                            0.4   \n",
       "Maitland                                          0.6   \n",
       "\n",
       "            RELIGIOUS AFFILIATION Secular Beliefs, Other Spiritual Beliefs and No Religious Affiliation  %  \\\n",
       "LABEL                                                                                                        \n",
       "Queanbeyan                                               28.5                                                \n",
       "Wyong                                                    28.6                                                \n",
       "Bathurst                                                 24.0                                                \n",
       "Orange                                                   22.6                                                \n",
       "Maitland                                                 26.7                                                \n",
       "\n",
       "            RELIGIOUS AFFILIATION Inadequately described or not stated   %  \\\n",
       "LABEL                                                                        \n",
       "Queanbeyan                                               10.5                \n",
       "Wyong                                                     9.4                \n",
       "Bathurst                                                 12.0                \n",
       "Orange                                                    9.7                \n",
       "Maitland                                                  9.8                \n",
       "\n",
       "            AUSTRALIAN CITIZENSHIP Australian citizen  %  \\\n",
       "LABEL                                                      \n",
       "Queanbeyan                                          80.6   \n",
       "Wyong                                               86.1   \n",
       "Bathurst                                            86.2   \n",
       "Orange                                              88.1   \n",
       "Maitland                                            89.1   \n",
       "\n",
       "            AUSTRALIAN CITIZENSHIP Not an Australian citizen  %  \\\n",
       "LABEL                                                             \n",
       "Queanbeyan                                               11.2     \n",
       "Wyong                                                     6.3     \n",
       "Bathurst                                                  4.0     \n",
       "Orange                                                    3.8     \n",
       "Maitland                                                  3.5     \n",
       "\n",
       "            AUSTRALIAN CITIZENSHIP Australian Citizenship - Not stated  %  \\\n",
       "LABEL                                                                       \n",
       "Queanbeyan                                                8.2               \n",
       "Wyong                                                     7.6               \n",
       "Bathurst                                                  9.9               \n",
       "Orange                                                    8.0               \n",
       "Maitland                                                  7.4               \n",
       "\n",
       "            SPEAKS A LANGUAGE OTHER THAN ENGLISH AT HOME Proportion of total population  %  \\\n",
       "LABEL                                                                                        \n",
       "Queanbeyan                                               21.2                                \n",
       "Wyong                                                     8.2                                \n",
       "Bathurst                                                  4.9                                \n",
       "Orange                                                    5.3                                \n",
       "Maitland                                                  3.8                                \n",
       "\n",
       "                  lat        long  \n",
       "LABEL                              \n",
       "Queanbeyan -35.352466  149.233725  \n",
       "Wyong      -33.285641  151.425306  \n",
       "Bathurst   -33.474707  149.641320  \n",
       "Orange     -33.329242  149.108147  \n",
       "Maitland   -32.708177  151.526844  \n",
       "\n",
       "[5 rows x 455 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('df.csv', index_col = \"LABEL\")\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're interested in looking at the cycling data. There are a number of different region sizes, so it makes sense to normalise the cyclists by the number of households in an area.\n",
    "\n",
    "The value we are trying to predict (y) is then this column. We should drop this column from our original dataframe to create X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cycle_col = 'METHOD OF TRAVEL TO WORK - EMPLOYED PERSONS Used one method - Bicycle no.'\n",
    "residents_col = 'ESTIMATED RESIDENT POPULATION (USUAL RESIDENTS, ERP AT 30 JUNE)  Total no.'\n",
    "y = df[cycle_col]/df[residents_col]\n",
    "y.index = df.index\n",
    "X = df.drop([cycle_col, residents_col], axis = 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visually explore our data to see what we're working with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a73ae370b8>"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log(y).hist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to plot with a log scale because the vast bulk of data is at the low end. There is also a couple of significant outliers at the top that look like they might have to be removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  METHOD OF TRAVEL TO WORK - EMPLOYED PERSONS Used one method - Bicycle no.  \\\n",
      "LABEL                                                                                         \n",
      "Lord Howe Island                                          54.000000                           \n",
      "Badgerys Creek                                            44.129032                           \n",
      "\n",
      "                  ESTIMATED RESIDENT POPULATION (USUAL RESIDENTS, ERP AT 30 JUNE)  Total no.  \n",
      "LABEL                                                                                         \n",
      "Lord Howe Island                                              194.0                           \n",
      "Badgerys Creek                                                 23.0                           \n",
      "Median population of each region: 6445.0\n"
     ]
    }
   ],
   "source": [
    "print(df.loc[y>0.1,[cycle_col, residents_col]])\n",
    "print(\"Median population of each region: {}\".format(df[residents_col].median()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both of these outliers have extremely small resident population which when combined with a moderate number of cyclists gives an unusual result. We will drop both these points from our analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a73b3f7c88>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = X[y<0.1]\n",
    "y = y[y<0.1]\n",
    "\n",
    "np.log(y).hist()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's a little better, though we still have a significant cluster below the 1% mark. We might deal with that later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a73afbbe48>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlations = cleanfunc.sort_series_abs(df.dropna(subset=[cycle_col]\n",
    "                         ).corr().loc[:,cycle_col])[1:10]\n",
    "\n",
    "correlations.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning - First Pass\n",
    "Now that we have a clean dataframe, we would like to apply some machine learning to it to see if we can predict the number of cyclists. Once we can make predictions, we can interrogate those predictions to see what's going on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_binned = np.digitize(y, y.quantile([0, 0.2, 0.4, 0.6, 0.8]))\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 43, stratify = y_binned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators=100)\n",
    "rf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"365c742a-785b-4179-8834-76930c478e0e\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"365c742a-785b-4179-8834-76930c478e0e\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"365c742a-785b-4179-8834-76930c478e0e\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '365c742a-785b-4179-8834-76930c478e0e' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"365c742a-785b-4179-8834-76930c478e0e\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"365c742a-785b-4179-8834-76930c478e0e\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"365c742a-785b-4179-8834-76930c478e0e\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '365c742a-785b-4179-8834-76930c478e0e' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"365c742a-785b-4179-8834-76930c478e0e\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"c29d21c0-fafd-405e-866e-220473e8c32f\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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Park - Lisarow\",\"Corrimal - Tarrawanna - Bellambi\",\"Penrith\",\"Moss Vale - Berrima\",\"Chatswood (West) - Lane Cove North\",\"North Sydney - Lavender Bay\",\"Warwick Farm\",\"Panania - Milperra - Picnic Point\",\"Albury - North\",\"Warragamba - Silverdale\",\"Parramatta - Rosehill\",\"Lawson - Hazelbrook - Linden\",\"Lake Munmorah - Mannering Park\",\"Canley Vale - Canley Heights\",\"Hunters Hill - Woolwich\",\"Randwick - South\",\"St Georges Basin - Erowal Bay\",\"Belmore - Belfield\",\"Taree Region\",\"Chatswood (East) - Artarmon\",\"Bolton Point - Teralba\",\"Casula\",\"Ulladulla\",\"Willoughby - Castle Cove - Northbridge\",\"Lalor Park - Kings Langley\",\"Tomerong - Wandandian - Woollamia\",\"St Clair\",\"Greenacre - Mount Lewis\",\"Bulahdelah - Stroud\",\"Winston Hills\",\"Southern Highlands\",\"Austral - Greendale\",\"Granville - Clyde\",\"St Ives\",\"Batemans Bay - South\",\"Redhead\",\"Roselands\",\"Wingham\",\"Woollahra\",\"Lismore\",\"Dulwich Hill - Lewisham\",\"Gymea - Grays Point\",\"Maitland - West\",\"Newtown - Camperdown - Darlington\",\"Avalon - Palm Beach\",\"Corowa Region\",\"Leichhardt - Annandale\",\"Queanbeyan Region\",\"Tuggerah - Kangy Angy\",\"Dee Why - North Curl Curl\",\"South Hurstville - Blakehurst\",\"Old Bar - Manning Point - Red Head\",\"Glen Innes\",\"Blacktown (East) - Kings Park\",\"Kogarah Bay - Carlton - Allawah\",\"Springwood - Winmalee\",\"Wentworth-Balranald Region\",\"Maitland - North\",\"Mudgee\",\"Wagga Wagga - West\",\"Huskisson - Vincentia\",\"Sawtell - Boambee\",\"Narromine\",\"Bexley\",\"Cowra Region\",\"Berry - Kangaroo Valley\",\"Hurstville\",\"Castle Hill - West\",\"Dubbo - South\",\"Adamstown - Kotara\",\"Shortland - Jesmond\",\"Woonona - Bulli - Russell Vale\",\"Balgowlah - Clontarf - Seaforth\",\"Moree\",\"Wallsend - Elermore Vale\",\"Broulee - Tomakin\",\"Waterloo - Beaconsfield\",\"Galston - Laughtondale\",\"Queanbeyan West - Jerrabomberra\",\"Minto - St Andrews\",\"Castle Hill - East\",\"Cabramatta - Lansvale\",\"Mount Hutton - Windale\",\"Cooma\",\"Wyoming\",\"Bega-Eden Hinterland\",\"Casino\",\"Campbelltown - Woodbine\",\"St Leonards - Naremburn\",\"Cremorne - Cammeray\",\"Muswellbrook Region\",\"Tumut Region\",\"Homebush\",\"Carlingford\",\"Maitland - East\",\"Lindfield - Roseville\",\"Dubbo - West\",\"Kingswood - Werrington\",\"Box Head - MacMasters Beach\",\"Parkes (NSW)\",\"Valentine - Eleebana\",\"Cobbitty - Leppington\",\"Sylvania - Taren Point\",\"Malabar - La Perouse - Chifley\",\"Ingleburn - Denham Court\",\"Grafton\",\"Mullumbimby\",\"Point Clare - Koolewong\",\"Bass Hill - Georges Hall\",\"Bankstown - South\",\"Oyster Bay - Como - Jannali\",\"Albion Park Rail\",\"Hornsby - West\",\"Beacon Hill - Narraweena\",\"Blaxland - Warrimoo - Lapstone\",\"Chipping Norton - Moorebank\",\"Coonabarabran\",\"Regents Park\",\"Saratoga - Davistown\",\"Guildford - South Granville\",\"Mudgee Region - East\",\"Tweed Heads\",\"Shellharbour - Oak Flats\",\"Charlestown - Dudley\",\"Epping - North Epping\",\"Erskineville - Alexandria\",\"Young\",\"Canterbury (North) - Ashbury\",\"Oberon\",\"Woolgoolga - Arrawarra\",\"Sydenham - Tempe - St Peters\",\"Glenmore Park - Regentville\",\"Terrigal - North Avoca\",\"Mulgoa - Luddenham - Orchard Hills\",\"Goulburn\",\"Randwick - North\",\"Tamworth - North\",\"Haberfield - Summer Hill\",\"Tea Gardens - Hawks Nest\",\"Bathurst - East\",\"Sydney - Haymarket - The Rocks\",\"Manly - Fairlight\",\"Drummoyne - Rodd Point\",\"Laurieton - Bonny Hills\",\"Dural - Kenthurst - Wisemans Ferry\",\"Beresfield - Hexham\",\"Balmain\",\"Smithfield - Wetherill Park\",\"Auburn - South\",\"West Ryde - Meadowbank\",\"Woolaware - Burraneer\",\"Inverell Region - East\",\"Turramurra\",\"Griffith Region\",\"Condell Park\",\"Gladesville - Huntleys Point\",\"Eurobodalla Hinterland\",\"Scone\",\"Chittaway Bay - Tumbi Umbi\",\"Bradbury - Wedderburn\",\"Orange\",\"Jilliby - Yarramalong\",\"Callala Bay - Currarong\",\"Horsley Park - Kemps Creek\",\"Narrabeen - Collaroy\",\"Nambucca Heads\",\"Banora Point\",\"Illawong - Alfords Point\",\"Menai - Lucas Heights - Woronora\",\"Hoxton Park - Carnes Hill - Horningsea Park\",\"Sutherland - Kirrawee\",\"Kingscliff - Fingal Head\",\"Coogee - Clovelly\",\"Castle Hill - North\",\"Wentworth - Buronga\",\"Inverell\",\"Berowra - Brooklyn - Cowan\",\"Toukley - Norah Head\",\"Nelson Bay Peninsula\",\"Kariong\",\"Dungog\",\"Queanbeyan - East\",\"Cobar\",\"Liverpool\",\"Narooma - Bermagui\",\"Mount Druitt - Whalan\",\"Pennant Hills - Cheltenham\",\"Coramba - Nana Glen - Bucca\",\"Pymble\",\"Miranda - Yowie Bay\",\"Edgeworth - Cameron Park\",\"Berala\",\"St Marys - North St Marys\",\"Cowra\",\"Nowra\",\"Forbes\",\"Rouse Hill - Beaumont Hills\",\"Katoomba - Leura\",\"Ulladulla Region\",\"Cambridge Park\",\"Wahroonga (East) - Warrawee\",\"Normanhurst - Thornleigh - Westleigh\",\"Kellyville\",\"Horsley - Kembla Grange\",\"Northmead\",\"Douglas Park - Appin\",\"Coffs Harbour - North\",\"Bowral\",\"Bellingen\",\"Morisset - Cooranbong\",\"Ashcroft - Busby - Miller\",\"Kempsey\",\"Robertson - Fitzroy Falls\",\"Grenfell\",\"Lakemba\",\"Macquarie Park - Marsfield\",\"Cecil Hills\",\"Lithgow Region\",\"Engadine\",\"Albury - South\",\"Tamworth - West\",\"Wentworthville - Westmead\",\"Port Macquarie - West\",\"Hinchinbrook\",\"Moama\",\"Lilyfield - Rozelle\",\"Evans Head\",\"Merimbula - Tura Beach\",\"Tumbarumba\",\"Bonnyrigg Heights - Bonnyrigg\",\"Berkeley - Lake Heights - Cringila\",\"Braidwood\",\"Castlereagh - Cranebrook\",\"Goulburn Region\",\"Blacktown (South)\",\"Karabar\",\"Wagga Wagga Region\",\"Kurri Kurri - Abermain\",\"Edensor Park\",\"North Rocks\",\"Punchbowl\",\"Windang - Primbee\",\"Acacia Gardens\",\"Wollongong - East\",\"Heathcote - Waterfall\",\"Eastwood - Denistone\",\"Calga - Kulnura\",\"Asquith - Mount Colah\",\"Thirroul - Austinmer - Coalcliff\",\"Kincumber - Picketts Valley\",\"Pitt Town - McGraths Hill\",\"Deniliquin Region\",\"Pendle Hill - Girraween\",\"Lurnea - Cartwright\",\"Cessnock Region\",\"Bankstown - North\",\"Griffith (NSW)\",\"Monterey - Brighton-le-Sands - Kyeemagh\",\"Mascot - Eastlakes\",\"Temora\",\"Muswellbrook\",\"Shellharbour - Flinders\",\"Batemans Bay\",\"Cronulla - Kurnell - Bundeena\",\"Forestville - Killarney Heights\",\"Bidwill - Hebersham - Emerton\",\"Port Kembla - Warrawong\",\"Culburra Beach\",\"Dapto - Avondale\",\"Swansea - Caves Beach\",\"Yass Region\",\"Wagga Wagga - East\",\"Albion Park - Macquarie Pass\",\"Freshwater - Brookvale\",\"Walcha\",\"West Wyalong\",\"Lithgow\",\"Baulkham Hills (East)\",\"West Pennant Hills\",\"Bondi - Tamarama - Bronte\",\"Grafton Region\",\"Wentworth Falls\",\"Kempsey Region\",\"Fairfield - East\",\"Quakers Hill\",\"Paddington - Moore Park\",\"Petersham - Stanmore\",\"Warilla\",\"Moruya - Tuross Head\",\"Glenhaven\",\"Ermington - Rydalmere\",\"South West Rocks\",\"Glendale - Cardiff - Hillsborough\",\"Macksville - Scotts Head\",\"Croydon Park - Enfield\",\"Redfern - Chippendale\",\"Kiama\",\"Concord - Mortlake - Cabarita\",\"Avoca Beach - Copacabana\",\"Kingsgrove (South) - Bardwell Park\",\"Erskine Park\",\"Tamworth - East\",\"Frenchs Forest - Belrose\",\"Singleton\",\"Arncliffe - Bardwell Valley\",\"Armidale\",\"Windsor - Bligh Park\",\"Forster-Tuncurry Region\",\"Greenfield Park - Prairiewood\",\"Castle Hill - Central\",\"Emu Plains - Leonay\",\"Warnervale - Wadalba\",\"Wauchope\",\"Gorokan - Kanwal - Charmhaven\",\"Mudgee Region - West\",\"Singleton Region\",\"Hill Top - Colo Vale\",\"Merewether - The Junction\",\"Peakhurst - Lugarno\",\"Deniliquin\",\"Blue Haven - San Remo\",\"Anna Bay\",\"Picton - Tahmoor - Buxton\",\"Leumeah - Minto Heights\",\"Manly Vale - Allambie Heights\",\"Eden\",\"Toongabbie - Constitution Hill\",\"Budgewoi - Buff Point - Halekulani\",\"Dubbo - East\",\"Maroubra - North\",\"Pottsville\",\"Lilli Pilli - Port Hacking - Dolans Bay\",\"Castle Hill - South\",\"Bangalow\",\"Branxton - Greta - Pokolbin\",\"Maryland - Fletcher - Minmi\",\"Byron Bay\",\"Walgett - Lightning Ridge\",\"Strathfield\",\"North Nowra - Bomaderry\",\"Cootamundra\",\"Narrandera\",\"Darlinghurst\",\"St Johns Park - Wakeley\",\"Coffs Harbour - South\",\"Caringbah\",\"Crows Nest - Waverton\",\"Lane Cove - Greenwich\",\"Macquarie Fields - Glenfield\",\"Double Bay - Bellevue Hill\",\"Oatlands - Dundas Valley\",\"Wickham - Carrington - Tighes Hill\",\"Figtree - Keiraville\",\"Gloucester\",\"Woy Woy - Blackwall\",\"Five Dock - Abbotsford\",\"Seven Hills - Toongabbie\",\"Bateau Bay - Killarney Vale\",\"Mosman\",\"Bourke - Brewarrina\",\"Albury Region\",\"Unanderra - Mount Kembla\",\"Goonellabah\",\"Bathurst Region\",\"Tumut\",\"Homebush Bay - Silverwater\",\"Lidcombe\",\"Scone Region\",\"Pagewood - Hillsdale - Daceyville\",\"Lemon Tree Passage - Tanilba Bay\",\"Albury - East\",\"Fairfield - West\",\"West Hoxton - Middleton Grange\",\"Oatley - Hurstville Grove\",\"Lethbridge Park - Tregear\",\"Mittagong\",\"Young Region\",\"West Wallsend - Barnsley - Killingworth\",\"Bilpin - Colo - St Albans\",\"Leeton\",\"Neutral Bay - Kirribilli\",\"Pyrmont - Ultimo\",\"Blackheath - Megalong Valley\",\"Wellington\",\"Prestons - Edmondson Park\",\"Bossley Park - Abbotsbury\",\"Kensington (NSW)\",\"Gunnedah\",\"Riverstone - Marsden Park\",\"Gundagai\",\"Revesby\",\"Claymore - Eagle Vale - Raby\",\"Ourimbah - Fountaindale\",\"Baulkham Hills (West) - Bella Vista\",\"Junee\",\"Surry Hills\",\"Bombala\",\"Yarramundi - Londonderry\",\"Wyong\",\"Tocumwal - Finley - Jerilderie\",\"Marrickville\",\"Warners Bay - Boolaroo\",\"Putney\",\"Fairfield\",\"Holsworthy - Wattle Grove\",\"Kingsgrove (North) - Earlwood\",\"Condobolin\",\"Colyton - Oxley Park\",\"The Oaks - Oakdale\",\"Umina - Booker Bay - Patonga\",\"Parkes Region\",\"Cooma Region\",\"Canterbury (South) - Campsie\",\"Hay\",\"Narrabri\",\"Raymond Terrace\",\"Terranora - North Tumblegum\",\"Brunswick Heads - Ocean Shores\",\"Rooty 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       "  var render_items = [{\"docid\":\"bc5356cd-41f7-475d-a6eb-57d515fc46ae\",\"elementid\":\"c29d21c0-fafd-405e-866e-220473e8c32f\",\"modelid\":\"ffddcbff-ad23-4bae-8ab7-b16698718944\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "ffddcbff-ad23-4bae-8ab7-b16698718944"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data R2 score: 0.9507424401437472\n",
      "Test data R2 score: 0.5161265551169147\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y_pred, y_train_pred = cleanfunc.evaluate_model(rf, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A 50% r2 score on out test data isn't too bad! But the fact that we're getting a 95% R2 score on our train data would suggest that we're over-fitting quite a bit. Let's do some grid-searching to see if we can improve this model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transforming data with a log function\n",
    "I read recently on a kaggle kernal that many regressors do not perform as well on skewed data. I thought I would try transforming my Y though a log transform to approach a more normal distribution and fit a standard model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"2bd999bc-a7e7-400f-9b76-a0558b166cde\">Loading BokehJS ...</span>\n",
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       "\n",
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       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
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       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
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       "\n",
       "  /**\n",
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       "\n",
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       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
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       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
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       "        iopub: {\n",
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       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
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       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
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       "      return\n",
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       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"2bd999bc-a7e7-400f-9b76-a0558b166cde\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"2bd999bc-a7e7-400f-9b76-a0558b166cde\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '2bd999bc-a7e7-400f-9b76-a0558b166cde' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"2bd999bc-a7e7-400f-9b76-a0558b166cde\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"2bd999bc-a7e7-400f-9b76-a0558b166cde\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"2bd999bc-a7e7-400f-9b76-a0558b166cde\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '2bd999bc-a7e7-400f-9b76-a0558b166cde' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"2bd999bc-a7e7-400f-9b76-a0558b166cde\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"facee9e5-6360-4fcb-b82b-1006a0dce87b\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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Application\",\"version\":\"0.12.16\"}};\n",
       "  var render_items = [{\"docid\":\"a8439045-3cc5-4cca-a801-89c18f565f16\",\"elementid\":\"facee9e5-6360-4fcb-b82b-1006a0dce87b\",\"modelid\":\"945d3077-ebe9-4fda-8244-4a788094fe95\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "945d3077-ebe9-4fda-8244-4a788094fe95"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data R2 score: 0.9465196685576699\n",
      "Test data R2 score: 0.48584481776086885\n"
     ]
    }
   ],
   "source": [
    "y_log = np.log(y)\n",
    "y_log_binned = np.digitize(y_log, y_log.quantile([0, 0.2, 0.4, 0.6, 0.8]))\n",
    "X_train, X_test, y_log_train, y_log_test = train_test_split(X, y_log, test_size = 0.2, random_state = 43, stratify = y_binned)\n",
    "rf = RandomForestRegressor(n_estimators=100)\n",
    "rf.fit(X_train, y_log_train)\n",
    "y_log_pred, y_log_train_pred = cleanfunc.evaluate_model(rf, X_train, X_test, y_log_train, y_log_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5771450231915842"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(np.exp(y_log_test), np.exp(y_log_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is important to look at the un-transformed data R2 as the absolute values can have a significant effect. It looks like this improvement has increased our model R2 quite significantly. This is worth further exploration in the later parts of this notebook, but will be left for another time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning: Refinement:\n",
    "There are a number of things we can do to refind our model, first we can do some hyper-parameter tuning using a random grid search. This will allow us to search a wide section of parameters in a relatively shorter time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [20, 40, 60, 80, 100, 120, 140, 160, 180, 200], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True, False]}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 20, stop = 200, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "\n",
    "print(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  4.3min\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  8.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=100, n_jobs=-1,\n",
       "          param_distributions={'n_estimators': [20, 40, 60, 80, 100, 120, 140, 160, 180, 200], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True, False]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the random grid to search for best hyperparameters\n",
    "# First create the base model to tune\n",
    "rf = RandomForestRegressor()\n",
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n",
    "\n",
    "# Fit the random search model\n",
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 180, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'auto', 'max_depth': None, 'bootstrap': True}\n"
     ]
    }
   ],
   "source": [
    "print(rf_random.best_params_)\n",
    "best_random = rf_random.best_estimator_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"26da7985-4394-4840-8d6d-915d390a2338\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
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       "\n",
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       "    var output_area = handle.output_area;\n",
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       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
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       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
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       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
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       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
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       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"26da7985-4394-4840-8d6d-915d390a2338\");\n",
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       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"26da7985-4394-4840-8d6d-915d390a2338\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '26da7985-4394-4840-8d6d-915d390a2338' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"26da7985-4394-4840-8d6d-915d390a2338\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
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Hall\",\"Bankstown - South\",\"Oyster Bay - Como - Jannali\",\"Albion Park Rail\",\"Hornsby - West\",\"Beacon Hill - Narraweena\",\"Blaxland - Warrimoo - Lapstone\",\"Chipping Norton - Moorebank\",\"Coonabarabran\",\"Regents Park\",\"Saratoga - Davistown\",\"Guildford - South Granville\",\"Mudgee Region - East\",\"Tweed Heads\",\"Shellharbour - Oak Flats\",\"Charlestown - Dudley\",\"Epping - North Epping\",\"Erskineville - Alexandria\",\"Young\",\"Canterbury (North) - Ashbury\",\"Oberon\",\"Woolgoolga - Arrawarra\",\"Sydenham - Tempe - St Peters\",\"Glenmore Park - Regentville\",\"Terrigal - North Avoca\",\"Mulgoa - Luddenham - Orchard Hills\",\"Goulburn\",\"Randwick - North\",\"Tamworth - North\",\"Haberfield - Summer Hill\",\"Tea Gardens - Hawks Nest\",\"Bathurst - East\",\"Sydney - Haymarket - The Rocks\",\"Manly - Fairlight\",\"Drummoyne - Rodd Point\",\"Laurieton - Bonny Hills\",\"Dural - Kenthurst - Wisemans Ferry\",\"Beresfield - Hexham\",\"Balmain\",\"Smithfield - Wetherill Park\",\"Auburn - South\",\"West Ryde - Meadowbank\",\"Woolaware - Burraneer\",\"Inverell Region - East\",\"Turramurra\",\"Griffith Region\",\"Condell Park\",\"Gladesville - Huntleys Point\",\"Eurobodalla Hinterland\",\"Scone\",\"Chittaway Bay - Tumbi Umbi\",\"Bradbury - Wedderburn\",\"Orange\",\"Jilliby - Yarramalong\",\"Callala Bay - Currarong\",\"Horsley Park - Kemps Creek\",\"Narrabeen - Collaroy\",\"Nambucca Heads\",\"Banora Point\",\"Illawong - Alfords Point\",\"Menai - Lucas Heights - Woronora\",\"Hoxton Park - Carnes Hill - Horningsea Park\",\"Sutherland - Kirrawee\",\"Kingscliff - Fingal Head\",\"Coogee - Clovelly\",\"Castle Hill - North\",\"Wentworth - Buronga\",\"Inverell\",\"Berowra - Brooklyn - Cowan\",\"Toukley - Norah Head\",\"Nelson Bay Peninsula\",\"Kariong\",\"Dungog\",\"Queanbeyan - East\",\"Cobar\",\"Liverpool\",\"Narooma - Bermagui\",\"Mount Druitt - Whalan\",\"Pennant Hills - Cheltenham\",\"Coramba - Nana Glen - Bucca\",\"Pymble\",\"Miranda - Yowie Bay\",\"Edgeworth - Cameron Park\",\"Berala\",\"St Marys - North St Marys\",\"Cowra\",\"Nowra\",\"Forbes\",\"Rouse Hill - Beaumont Hills\",\"Katoomba - Leura\",\"Ulladulla Region\",\"Cambridge Park\",\"Wahroonga (East) - Warrawee\",\"Normanhurst - Thornleigh - Westleigh\",\"Kellyville\",\"Horsley - Kembla Grange\",\"Northmead\",\"Douglas Park - Appin\",\"Coffs Harbour - North\",\"Bowral\",\"Bellingen\",\"Morisset - Cooranbong\",\"Ashcroft - Busby - Miller\",\"Kempsey\",\"Robertson - Fitzroy Falls\",\"Grenfell\",\"Lakemba\",\"Macquarie Park - Marsfield\",\"Cecil Hills\",\"Lithgow Region\",\"Engadine\",\"Albury - South\",\"Tamworth - West\",\"Wentworthville - Westmead\",\"Port Macquarie - West\",\"Hinchinbrook\",\"Moama\",\"Lilyfield - Rozelle\",\"Evans Head\",\"Merimbula - Tura Beach\",\"Tumbarumba\",\"Bonnyrigg Heights - Bonnyrigg\",\"Berkeley - Lake Heights - Cringila\",\"Braidwood\",\"Castlereagh - Cranebrook\",\"Goulburn Region\",\"Blacktown (South)\",\"Karabar\",\"Wagga Wagga Region\",\"Kurri Kurri - Abermain\",\"Edensor Park\",\"North Rocks\",\"Punchbowl\",\"Windang - Primbee\",\"Acacia Gardens\",\"Wollongong - East\",\"Heathcote - Waterfall\",\"Eastwood - Denistone\",\"Calga - Kulnura\",\"Asquith - Mount Colah\",\"Thirroul - Austinmer - Coalcliff\",\"Kincumber - Picketts Valley\",\"Pitt Town - McGraths Hill\",\"Deniliquin Region\",\"Pendle Hill - Girraween\",\"Lurnea - Cartwright\",\"Cessnock Region\",\"Bankstown - North\",\"Griffith (NSW)\",\"Monterey - Brighton-le-Sands - Kyeemagh\",\"Mascot - Eastlakes\",\"Temora\",\"Muswellbrook\",\"Shellharbour - Flinders\",\"Batemans Bay\",\"Cronulla - Kurnell - Bundeena\",\"Forestville - Killarney Heights\",\"Bidwill - Hebersham - Emerton\",\"Port Kembla - Warrawong\",\"Culburra Beach\",\"Dapto - Avondale\",\"Swansea - Caves Beach\",\"Yass Region\",\"Wagga Wagga - East\",\"Albion Park - Macquarie Pass\",\"Freshwater - Brookvale\",\"Walcha\",\"West Wyalong\",\"Lithgow\",\"Baulkham Hills (East)\",\"West Pennant Hills\",\"Bondi - Tamarama - Bronte\",\"Grafton Region\",\"Wentworth Falls\",\"Kempsey Region\",\"Fairfield - East\",\"Quakers Hill\",\"Paddington - Moore Park\",\"Petersham - Stanmore\",\"Warilla\",\"Moruya - Tuross Head\",\"Glenhaven\",\"Ermington - Rydalmere\",\"South West Rocks\",\"Glendale - Cardiff - Hillsborough\",\"Macksville - Scotts Head\",\"Croydon Park - Enfield\",\"Redfern - Chippendale\",\"Kiama\",\"Concord - Mortlake - Cabarita\",\"Avoca Beach - Copacabana\",\"Kingsgrove (South) - Bardwell Park\",\"Erskine Park\",\"Tamworth - East\",\"Frenchs Forest - Belrose\",\"Singleton\",\"Arncliffe - Bardwell Valley\",\"Armidale\",\"Windsor - Bligh Park\",\"Forster-Tuncurry Region\",\"Greenfield Park - Prairiewood\",\"Castle Hill - Central\",\"Emu Plains - Leonay\",\"Warnervale - Wadalba\",\"Wauchope\",\"Gorokan - Kanwal - Charmhaven\",\"Mudgee Region - West\",\"Singleton Region\",\"Hill Top - Colo Vale\",\"Merewether - The Junction\",\"Peakhurst - Lugarno\",\"Deniliquin\",\"Blue 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       "  var render_items = [{\"docid\":\"4873976c-e0a5-4f0e-981f-c2d18d6f1098\",\"elementid\":\"7e49d320-0807-4122-afb1-de139505025e\",\"modelid\":\"af90e4c2-1acb-4a07-a881-d55d8e97e30d\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "af90e4c2-1acb-4a07-a881-d55d8e97e30d"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data R2 score: 0.9433647854229724\n",
      "Test data R2 score: 0.5211060425740528\n"
     ]
    }
   ],
   "source": [
    "y_best_pred, y_best_train_pred = cleanfunc.evaluate_model(best_random, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compared to our old R2 score of 0.50 we have a 2% increase. Not a lot, but it's something! Mousing over the higher values shows that a large number of them are in inner sydney. The bulk of the values are below 2% cyclists. I wonder if the model is being overwhelmed by signal at the high end, meaning it isn't picking up the more subtle signals at the low end. I'm going to recut the dataframe and rebuild our analysis to check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of data below 2% cyclists in region: 95.50%\n",
      "Test data R2 score for values below 2% cyclists in region: -0.04\n"
     ]
    }
   ],
   "source": [
    "percentbelow = 2\n",
    "print('Proportion of data below {}% cyclists in region: {:0.2f}%'.format((percentbelow),100*sum(y_test<(percentbelow/100))/y_test.shape[0]))\n",
    "print('Test data R2 score for values below {}% cyclists in region: {:0.2f}'.format(percentbelow, r2_score(y_test[y_test<(percentbelow/100)], y_best_pred[y_test<(percentbelow/100)])))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With an R2 value which is negative, we can see that our model is worse than simply taking the average of the data at predicting low end values. Given that this subset holds nearly 85% of all our data, this is definitely something we want to address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_trunc = y[y<0.02]\n",
    "X_trunc = X[y<0.02]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now reuse our code from earlier to process and graph the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_binned = np.digitize(y_trunc, y_trunc.quantile([0, 0.2, 0.4, 0.6, 0.8]))\n",
    "X_trunc_train, X_trunc_test, y_trunc_train, y_trunc_test = train_test_split(X_trunc, y_trunc, test_size = 0.2, random_state = 43, stratify = y_binned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_trunc = RandomForestRegressor(n_estimators=200)\n",
    "rf_trunc.fit(X_trunc_train, y_trunc_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"d0017a0a-9eaf-4079-9be1-bda89d28859d\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
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       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
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       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"d0017a0a-9eaf-4079-9be1-bda89d28859d\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"d0017a0a-9eaf-4079-9be1-bda89d28859d\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'd0017a0a-9eaf-4079-9be1-bda89d28859d' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"d0017a0a-9eaf-4079-9be1-bda89d28859d\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"d0017a0a-9eaf-4079-9be1-bda89d28859d\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"d0017a0a-9eaf-4079-9be1-bda89d28859d\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'd0017a0a-9eaf-4079-9be1-bda89d28859d' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"d0017a0a-9eaf-4079-9be1-bda89d28859d\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"006298e7-8b30-450a-8ba1-43624f2fd97d\"></div>\n",
       "</div>"
      ]
     },
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       "  var render_items = [{\"docid\":\"4d42cb40-03cd-4116-9966-32757ca3dd67\",\"elementid\":\"006298e7-8b30-450a-8ba1-43624f2fd97d\",\"modelid\":\"1fc46189-21c5-4b18-9df5-c1e98cdcf696\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1fc46189-21c5-4b18-9df5-c1e98cdcf696"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data R2 score: 0.9038103479109401\n",
      "Test data R2 score: 0.46662873428427754\n"
     ]
    }
   ],
   "source": [
    "y_best_pred, y_best_train_pred = cleanfunc.evaluate_model(rf_trunc, X_trunc_train, X_trunc_test, y_trunc_train, y_trunc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the correlations in this dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "    table.eli5-weights tr:hover {\n",
       "        filter: brightness(85%);\n",
       "    }\n",
       "</style>\n",
       "\n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "        <table class=\"eli5-weights eli5-feature-importances\" style=\"border-collapse: collapse; border: none; margin-top: 0em; table-layout: auto;\">\n",
       "    <thead>\n",
       "    <tr style=\"border: none;\">\n",
       "        <th style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">Weight</th>\n",
       "        <th style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">Feature</th>\n",
       "    </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.00%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0413\n",
       "                \n",
       "                    &plusmn; 0.0227\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                COMMUTING TO WORK Average commuting distance from Place of Usual Residence kms\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 80.33%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0404\n",
       "                \n",
       "                    &plusmn; 0.0094\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                COMMUTING TO WORK Median commuting distance from Place of Usual Residence kms\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 87.32%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0216\n",
       "                \n",
       "                    &plusmn; 0.0518\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                METHOD OF TRAVEL TO WORK - EMPLOYED PERSONS Used one method - Motor bike/scooter no.\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 91.41%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0124\n",
       "                \n",
       "                    &plusmn; 0.0090\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                FIELD OF STUDY Creative Arts %\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 93.24%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0088\n",
       "                \n",
       "                    &plusmn; 0.0082\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                long\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 93.99%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0074\n",
       "                \n",
       "                    &plusmn; 0.0050\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                RELIGIOUS AFFILIATION Secular Beliefs, Other Spiritual Beliefs and No Religious Affiliation  %\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.62%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0063\n",
       "                \n",
       "                    &plusmn; 0.0068\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                HOUSEHOLDS BY TYPE Lone person households no.\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.77%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0061\n",
       "                \n",
       "                    &plusmn; 0.0036\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                METHOD OF TRAVEL TO WORK - EMPLOYED PERSONS Used one method - Walked only no.\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.94%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0058\n",
       "                \n",
       "                    &plusmn; 0.0084\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                NUMBER OF BUSINESSES BY INDUSTRY Accommodation &amp; Food Services no.\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.94%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0058\n",
       "                \n",
       "                    &plusmn; 0.0010\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                ESTIMATED RESIDENT POPULATION (USUAL RESIDENTS, ERP AT 30 JUNE)  40-44 years no.\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 94.98%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0057\n",
       "                \n",
       "                    &plusmn; 0.0289\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OCCUPATION OF EMPLOYED PERSONS Community, Personal Service Workers %\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.03%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0041\n",
       "                \n",
       "                    &plusmn; 0.0038\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                SOLAR INSTALLATIONS Increase in SWH installations from previous year %\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.03%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0041\n",
       "                \n",
       "                    &plusmn; 0.0022\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                RESIDENTIAL PROPERTY PRICES Houses - number of transfers no.\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.30%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0037\n",
       "                \n",
       "                    &plusmn; 0.0043\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                FAMILIES BY TYPE Average Family Size no.\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.36%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0036\n",
       "                \n",
       "                    &plusmn; 0.0031\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                FAMILIES BY TYPE Couple families with non-dependent children only no.\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.53%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0034\n",
       "                \n",
       "                    &plusmn; 0.0056\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                SOCIAL MARITAL STATUS &amp; REGISTERED MARITAL STATUS Married %\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.54%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0034\n",
       "                \n",
       "                    &plusmn; 0.0050\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                OCCUPATION OF EMPLOYED PERSONS Machinery Operators, Drivers %\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.79%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0030\n",
       "                \n",
       "                    &plusmn; 0.0026\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                HOUSEHOLD STRESS Households where mortgage repayments are less than 30% of household income %\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 96.82%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0030\n",
       "                \n",
       "                    &plusmn; 0.0030\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                TENURE TYPE Rented %\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "        <tr style=\"background-color: hsl(120, 100.00%, 97.10%); border: none;\">\n",
       "            <td style=\"padding: 0 1em 0 0.5em; text-align: right; border: none;\">\n",
       "                0.0026\n",
       "                \n",
       "                    &plusmn; 0.0043\n",
       "                \n",
       "            </td>\n",
       "            <td style=\"padding: 0 0.5em 0 0.5em; text-align: left; border: none;\">\n",
       "                FIELD OF STUDY Society and Culture %\n",
       "            </td>\n",
       "        </tr>\n",
       "    \n",
       "    \n",
       "        \n",
       "            <tr style=\"background-color: hsl(120, 100.00%, 97.10%); border: none;\">\n",
       "                <td colspan=\"2\" style=\"padding: 0 0.5em 0 0.5em; text-align: center; border: none; white-space: nowrap;\">\n",
       "                    <i>&hellip; 433 more &hellip;</i>\n",
       "                </td>\n",
       "            </tr>\n",
       "        \n",
       "    \n",
       "    </tbody>\n",
       "</table>\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "    \n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perm = eli5.sklearn.PermutationImportance(rf_trunc).fit(X_trunc_test, y_trunc_test)\n",
    "eli5.show_weights(perm, feature_names = X.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   51.5s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  3.5min\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  6.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=100, n_jobs=-1,\n",
       "          param_distributions={'n_estimators': [20, 40, 60, 80, 100, 120, 140, 160, 180, 200], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True, False]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rf_random_trunc = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n",
    "\n",
    "# Fit the random search model\n",
    "rf_random_trunc.fit(X_trunc_train, y_trunc_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 180, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'auto', 'max_depth': None, 'bootstrap': True}\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"4dd830e1-6954-48b4-9fb8-59dd3e81c088\">Loading BokehJS ...</span>\n",
       "    </div>"
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       "  function now() {\n",
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       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
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       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
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       "  /**\n",
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       "      Bokeh.index[id].model.document.clear();\n",
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       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"4dd830e1-6954-48b4-9fb8-59dd3e81c088\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"4dd830e1-6954-48b4-9fb8-59dd3e81c088\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '4dd830e1-6954-48b4-9fb8-59dd3e81c088' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"4dd830e1-6954-48b4-9fb8-59dd3e81c088\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"4dd830e1-6954-48b4-9fb8-59dd3e81c088\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"4dd830e1-6954-48b4-9fb8-59dd3e81c088\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '4dd830e1-6954-48b4-9fb8-59dd3e81c088' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"4dd830e1-6954-48b4-9fb8-59dd3e81c088\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"7db6c1c5-708c-4bed-b714-66582f3dd7a4\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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Birrong\"],\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[408]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[408]}},\"selected\":{\"id\":\"397b9e1a-03aa-47f4-82d2-5ec5be39e8f6\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"019fff74-e05b-4656-b1ce-61a7fa200646\",\"type\":\"UnionRenderers\"}},\"id\":\"aa139865-1e30-4e7c-b5f4-5ec2b06ddfe1\",\"type\":\"ColumnDataSource\"}],\"root_ids\":[\"e3ace76a-b26b-4527-bb4b-14db78938c62\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.16\"}};\n",
       "  var render_items = [{\"docid\":\"86db0010-94f1-4611-b5e5-f323fce1a972\",\"elementid\":\"7db6c1c5-708c-4bed-b714-66582f3dd7a4\",\"modelid\":\"e3ace76a-b26b-4527-bb4b-14db78938c62\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "e3ace76a-b26b-4527-bb4b-14db78938c62"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data R2 score: 0.8865518134288911\n",
      "Test data R2 score: 0.49777998191972583\n"
     ]
    }
   ],
   "source": [
    "print(rf_random.best_params_)\n",
    "best_trunc = rf_random_trunc.best_estimator_\n",
    "y_best_pred, y_best_train_pred = cleanfunc.evaluate_model(best_trunc, X_trunc_train, X_trunc_test, y_trunc_train, y_trunc_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "lower_bound = 0.004\n",
    "upper_bound = 0.03\n",
    "\n",
    "y_trunc = y[(y<upper_bound) & (y > lower_bound)]\n",
    "X_trunc = X[(y<upper_bound) & (y > lower_bound)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_trunc = RandomForestRegressor(n_estimators=200)\n",
    "rf_trunc.fit(X_trunc_train, y_trunc_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"64d0acd5-1285-47b1-b302-09a3f668fde2\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"64d0acd5-1285-47b1-b302-09a3f668fde2\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"64d0acd5-1285-47b1-b302-09a3f668fde2\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '64d0acd5-1285-47b1-b302-09a3f668fde2' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"64d0acd5-1285-47b1-b302-09a3f668fde2\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"64d0acd5-1285-47b1-b302-09a3f668fde2\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"64d0acd5-1285-47b1-b302-09a3f668fde2\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '64d0acd5-1285-47b1-b302-09a3f668fde2' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"64d0acd5-1285-47b1-b302-09a3f668fde2\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"fcf1dc5c-de29-4e62-b5de-6fac6f749342\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = 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       "  var render_items = [{\"docid\":\"a2e47bfb-47ab-43b6-bd27-ca25e66cf8e3\",\"elementid\":\"fcf1dc5c-de29-4e62-b5de-6fac6f749342\",\"modelid\":\"8246123b-a612-4519-bd92-3ad352b6410a\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
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       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
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       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "8246123b-a612-4519-bd92-3ad352b6410a"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data R2 score: 0.906371203858099\n",
      "Test data R2 score: 0.4611196740109156\n"
     ]
    }
   ],
   "source": [
    "y_low_pred, y_low_train_pred = cleanfunc.evaluate_model(rf_trunc, X_trunc_train, X_trunc_test, y_trunc_train, y_trunc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of curiosity, how does our model trained on truncated data generalize to the full dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
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       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
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       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
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       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"875e0138-3152-4c9d-9625-1654cf95da01\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '875e0138-3152-4c9d-9625-1654cf95da01' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"875e0138-3152-4c9d-9625-1654cf95da01\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"875e0138-3152-4c9d-9625-1654cf95da01\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"875e0138-3152-4c9d-9625-1654cf95da01\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '875e0138-3152-4c9d-9625-1654cf95da01' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"875e0138-3152-4c9d-9625-1654cf95da01\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"49f4b516-d91d-49be-ab9f-cbf3ac9352db\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  var docs_json = {\"b75946c5-5763-4392-98d7-f60ce4e315e4\":{\"roots\":{\"references\":[{\"attributes\":{\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"red\"},\"line_alpha\":{\"value\":0.5},\"line_color\":{\"value\":\"red\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"88798b5a-e0a8-4e81-bafe-e258b2f202c4\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"d78dbe19-e118-46c9-905e-e116b17c8014\",\"type\":\"PanTool\"},{\"attributes\":{\"overlay\":{\"id\":\"06ef9126-b5dc-49b8-84e2-b016518cfed2\",\"type\":\"BoxAnnotation\"}},\"id\":\"aa3711e1-1e82-4333-b007-6ba45645104b\",\"type\":\"BoxZoomTool\"},{\"attributes\":{},\"id\":\"42a45bee-90a0-4cc2-bcd6-419df0bfe723\",\"type\":\"Selection\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"value\":\"#1f77b4\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"size\":{\"units\":\"screen\",\"value\":10},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"70f5674e-0184-47d4-a85a-b532c8230b63\",\"type\":\"Circle\"},{\"attributes\":{\"source\":{\"id\":\"4b1c11cd-3bda-4623-9b7c-b3d51d06eb55\",\"type\":\"ColumnDataSource\"}},\"id\":\"090d112d-c834-420e-be9c-54a9ec6f93a4\",\"type\":\"CDSView\"},{\"attributes\":{\"plot\":null,\"text\":\"\"},\"id\":\"0fc2f76f-275c-41dc-986a-745597f82956\",\"type\":\"Title\"},{\"attributes\":{\"data_source\":{\"id\":\"a1975b92-bc82-4415-81be-f701ba94622d\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"88798b5a-e0a8-4e81-bafe-e258b2f202c4\",\"type\":\"Circle\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"70f5674e-0184-47d4-a85a-b532c8230b63\",\"type\":\"Circle\"},\"selection_glyph\":null,\"view\":{\"id\":\"e87b5d68-7cf6-45f4-824d-801fe5759f61\",\"type\":\"CDSView\"}},\"id\":\"511acb2a-1589-4ce7-88c1-136c15143d24\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"a1975b92-bc82-4415-81be-f701ba94622d\",\"type\":\"ColumnDataSource\"}},\"id\":\"e87b5d68-7cf6-45f4-824d-801fe5759f61\",\"type\":\"CDSView\"},{\"attributes\":{\"axis_label\":\"Acutal\",\"formatter\":{\"id\":\"c5d640e4-59d5-4080-8964-c0b8a8cf6a49\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"22443002-3740-4572-9f69-89389cbf78bc\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"0f530c88-b460-47ab-9bdd-dcab6fbe1685\",\"type\":\"BasicTicker\"}},\"id\":\"74073005-91d0-43eb-9637-52fbea08d92a\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"6b413574-8510-4a89-a37a-77a5184231ca\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"577f913e-afee-4e62-b107-e563414818c4\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"callback\":null,\"data\":{\"desc\":[\"Quirindi\",\"Rockdale - Banksia\",\"Stockton - Fullerton Cove\",\"Gilgandra\",\"Wangi Wangi - Rathmines\",\"Helensburgh\",\"Bega - Tathra\",\"Nyngan - Warren\",\"Concord West - North Strathfield\",\"Hornsby - East\",\"Bonnells Bay - Silverwater\",\"Blacktown (North) - Marayong\",\"Dorrigo\",\"Hamilton - Broadmeadow\",\"Narara\",\"Korora - Emerald Beach\",\"Camden - Ellis Lane\",\"Niagara Park - Lisarow\",\"Corrimal - Tarrawanna - Bellambi\",\"Penrith\",\"Moss Vale - Berrima\",\"Chatswood (West) - Lane Cove North\",\"North Sydney - Lavender Bay\",\"Warwick Farm\",\"Panania - Milperra - Picnic Point\",\"Albury - North\",\"Warragamba - Silverdale\",\"Parramatta - Rosehill\",\"Lawson - Hazelbrook - Linden\",\"Lake Munmorah - Mannering Park\",\"Canley Vale - Canley Heights\",\"Hunters Hill - Woolwich\",\"Randwick - South\",\"St Georges Basin - Erowal Bay\",\"Belmore - Belfield\",\"Taree Region\",\"Chatswood (East) - Artarmon\",\"Bolton Point - Teralba\",\"Casula\",\"Ulladulla\",\"Willoughby - Castle Cove - Northbridge\",\"Lalor Park - Kings Langley\",\"Tomerong - Wandandian - Woollamia\",\"St Clair\",\"Greenacre - Mount Lewis\",\"Bulahdelah - Stroud\",\"Winston Hills\",\"Southern Highlands\",\"Austral - Greendale\",\"Granville - Clyde\",\"St Ives\",\"Batemans Bay - South\",\"Redhead\",\"Roselands\",\"Wingham\",\"Woollahra\",\"Lismore\",\"Dulwich Hill - Lewisham\",\"Gymea - Grays Point\",\"Maitland - West\",\"Newtown - Camperdown - Darlington\",\"Avalon - Palm Beach\",\"Corowa Region\",\"Leichhardt - Annandale\",\"Queanbeyan Region\",\"Tuggerah - Kangy Angy\",\"Dee Why - North Curl Curl\",\"South Hurstville - Blakehurst\",\"Old Bar - Manning Point - Red Head\",\"Glen Innes\",\"Blacktown (East) - Kings Park\",\"Kogarah Bay - Carlton - Allawah\",\"Springwood - Winmalee\",\"Wentworth-Balranald Region\",\"Maitland - North\",\"Mudgee\",\"Wagga Wagga - West\",\"Huskisson - Vincentia\",\"Sawtell - Boambee\",\"Narromine\",\"Bexley\",\"Cowra Region\",\"Berry - Kangaroo Valley\",\"Hurstville\",\"Castle Hill - West\",\"Dubbo - South\",\"Adamstown - Kotara\",\"Shortland - Jesmond\",\"Woonona - Bulli - Russell Vale\",\"Balgowlah - Clontarf - Seaforth\",\"Moree\",\"Wallsend - Elermore Vale\",\"Broulee - Tomakin\",\"Waterloo - Beaconsfield\",\"Galston - Laughtondale\",\"Queanbeyan West - Jerrabomberra\",\"Minto - St Andrews\",\"Castle Hill - East\",\"Cabramatta - Lansvale\",\"Mount Hutton - Windale\",\"Cooma\",\"Wyoming\",\"Bega-Eden Hinterland\",\"Casino\",\"Campbelltown - Woodbine\",\"St Leonards - Naremburn\",\"Cremorne - Cammeray\",\"Muswellbrook Region\",\"Tumut Region\",\"Homebush\",\"Carlingford\",\"Maitland - East\",\"Lindfield - Roseville\",\"Dubbo - West\",\"Kingswood - Werrington\",\"Box Head - MacMasters Beach\",\"Parkes (NSW)\",\"Valentine - Eleebana\",\"Cobbitty - Leppington\",\"Sylvania - Taren Point\",\"Malabar - La Perouse - Chifley\",\"Ingleburn - Denham Court\",\"Grafton\",\"Mullumbimby\",\"Point Clare - Koolewong\",\"Bass Hill - Georges Hall\",\"Bankstown - South\",\"Oyster Bay - Como - Jannali\",\"Albion Park Rail\",\"Hornsby - West\",\"Beacon Hill - Narraweena\",\"Blaxland - Warrimoo - Lapstone\",\"Chipping Norton - Moorebank\",\"Coonabarabran\",\"Regents Park\",\"Saratoga - Davistown\",\"Guildford - South Granville\",\"Mudgee Region - East\",\"Tweed Heads\",\"Shellharbour - Oak Flats\",\"Charlestown - Dudley\",\"Epping - North Epping\",\"Erskineville - Alexandria\",\"Young\",\"Canterbury (North) - Ashbury\",\"Oberon\",\"Woolgoolga - Arrawarra\",\"Sydenham - Tempe - St Peters\",\"Glenmore Park - Regentville\",\"Terrigal - North Avoca\",\"Mulgoa - Luddenham - Orchard Hills\",\"Goulburn\",\"Randwick - North\",\"Tamworth - North\",\"Haberfield - Summer Hill\",\"Tea Gardens - Hawks Nest\",\"Bathurst - East\",\"Sydney - Haymarket - The Rocks\",\"Manly - Fairlight\",\"Drummoyne - Rodd Point\",\"Laurieton - Bonny Hills\",\"Dural - Kenthurst - Wisemans Ferry\",\"Beresfield - Hexham\",\"Balmain\",\"Smithfield - Wetherill Park\",\"Auburn - South\",\"West Ryde - Meadowbank\",\"Woolaware - Burraneer\",\"Inverell Region - East\",\"Turramurra\",\"Griffith Region\",\"Condell Park\",\"Gladesville - Huntleys Point\",\"Eurobodalla Hinterland\",\"Scone\",\"Chittaway Bay - Tumbi Umbi\",\"Bradbury - Wedderburn\",\"Orange\",\"Jilliby - Yarramalong\",\"Callala Bay - Currarong\",\"Horsley Park - Kemps Creek\",\"Narrabeen - Collaroy\",\"Nambucca Heads\",\"Banora Point\",\"Illawong - Alfords Point\",\"Menai - Lucas Heights - Woronora\",\"Hoxton Park - Carnes Hill - Horningsea Park\",\"Sutherland - Kirrawee\",\"Kingscliff - Fingal Head\",\"Coogee - Clovelly\",\"Castle Hill - North\",\"Wentworth - Buronga\",\"Inverell\",\"Berowra - Brooklyn - Cowan\",\"Toukley - Norah Head\",\"Nelson Bay Peninsula\",\"Kariong\",\"Dungog\",\"Queanbeyan - East\",\"Cobar\",\"Liverpool\",\"Narooma - Bermagui\",\"Mount Druitt - Whalan\",\"Pennant Hills - Cheltenham\",\"Coramba - Nana Glen - Bucca\",\"Pymble\",\"Miranda - Yowie Bay\",\"Edgeworth - Cameron Park\",\"Berala\",\"St Marys - North St Marys\",\"Cowra\",\"Nowra\",\"Forbes\",\"Rouse Hill - Beaumont Hills\",\"Katoomba - Leura\",\"Ulladulla Region\",\"Cambridge Park\",\"Wahroonga (East) - Warrawee\",\"Normanhurst - Thornleigh - Westleigh\",\"Kellyville\",\"Horsley - Kembla Grange\",\"Northmead\",\"Douglas Park - Appin\",\"Coffs Harbour - North\",\"Bowral\",\"Bellingen\",\"Morisset - Cooranbong\",\"Ashcroft - Busby - Miller\",\"Kempsey\",\"Robertson - Fitzroy Falls\",\"Grenfell\",\"Lakemba\",\"Macquarie Park - Marsfield\",\"Cecil Hills\",\"Lithgow Region\",\"Engadine\",\"Albury - South\",\"Tamworth - West\",\"Wentworthville - Westmead\",\"Port Macquarie - West\",\"Hinchinbrook\",\"Moama\",\"Lilyfield - Rozelle\",\"Evans Head\",\"Merimbula - Tura Beach\",\"Tumbarumba\",\"Bonnyrigg Heights - Bonnyrigg\",\"Berkeley - Lake Heights - Cringila\",\"Braidwood\",\"Castlereagh - Cranebrook\",\"Goulburn Region\",\"Blacktown (South)\",\"Karabar\",\"Wagga Wagga Region\",\"Kurri Kurri - Abermain\",\"Edensor Park\",\"North Rocks\",\"Punchbowl\",\"Windang - Primbee\",\"Acacia Gardens\",\"Wollongong - East\",\"Heathcote - Waterfall\",\"Eastwood - Denistone\",\"Calga - Kulnura\",\"Asquith - Mount Colah\",\"Thirroul - Austinmer - Coalcliff\",\"Kincumber - Picketts Valley\",\"Pitt Town - McGraths Hill\",\"Deniliquin Region\",\"Pendle Hill - Girraween\",\"Lurnea - Cartwright\",\"Cessnock Region\",\"Bankstown - North\",\"Griffith (NSW)\",\"Monterey - Brighton-le-Sands - Kyeemagh\",\"Mascot - Eastlakes\",\"Temora\",\"Muswellbrook\",\"Shellharbour - Flinders\",\"Batemans Bay\",\"Cronulla - Kurnell - Bundeena\",\"Forestville - Killarney Heights\",\"Bidwill - Hebersham - Emerton\",\"Port Kembla - Warrawong\",\"Culburra Beach\",\"Dapto - Avondale\",\"Swansea - Caves Beach\",\"Yass Region\",\"Wagga Wagga - East\",\"Albion Park - Macquarie Pass\",\"Freshwater - Brookvale\",\"Walcha\",\"West Wyalong\",\"Lithgow\",\"Baulkham Hills (East)\",\"West Pennant Hills\",\"Bondi - Tamarama - Bronte\",\"Grafton Region\",\"Wentworth Falls\",\"Kempsey Region\",\"Fairfield - East\",\"Quakers Hill\",\"Paddington - Moore Park\",\"Petersham - Stanmore\",\"Warilla\",\"Moruya - Tuross Head\",\"Glenhaven\",\"Ermington - Rydalmere\",\"South West Rocks\",\"Glendale - Cardiff - Hillsborough\",\"Macksville - Scotts Head\",\"Croydon Park - Enfield\",\"Redfern - Chippendale\",\"Kiama\",\"Concord - Mortlake - Cabarita\",\"Avoca Beach - Copacabana\",\"Kingsgrove (South) - Bardwell Park\",\"Erskine Park\",\"Tamworth - East\",\"Frenchs Forest - Belrose\",\"Singleton\",\"Arncliffe - Bardwell Valley\",\"Armidale\",\"Windsor - Bligh Park\",\"Forster-Tuncurry Region\",\"Greenfield Park - Prairiewood\",\"Castle Hill - Central\",\"Emu Plains - Leonay\",\"Warnervale - Wadalba\",\"Wauchope\",\"Gorokan - Kanwal - Charmhaven\",\"Mudgee Region - West\",\"Singleton Region\",\"Hill Top - Colo Vale\",\"Merewether - The Junction\",\"Peakhurst - Lugarno\",\"Deniliquin\",\"Blue 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       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
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       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
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       "      attempts++;\n",
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       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
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      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "22443002-3740-4572-9f69-89389cbf78bc"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data R2 score: 0.41855724967266705\n",
      "Test data R2 score: 0.4923217123661374\n"
     ]
    }
   ],
   "source": [
    "y_low_pred, y_low_train_pred = cleanfunc.evaluate_model(rf_trunc, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It performs very poorly! It looks like we'll have to Use a multi-layer model, as the effects which dominate at the high end don't have as much of an impact at the low end. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  33 tasks      | elapsed:   49.3s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  3.4min\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  6.9min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid=True, n_iter=100, n_jobs=-1,\n",
       "          param_distributions={'n_estimators': [20, 40, 60, 80, 100, 120, 140, 160, 180, 200], 'max_features': ['auto', 'sqrt'], 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None], 'min_samples_split': [2, 5, 10], 'min_samples_leaf': [1, 2, 4], 'bootstrap': [True, False]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rf_random_trunc = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)\n",
    "\n",
    "# Fit the random search model\n",
    "rf_random_trunc.fit(X_trunc_train, y_trunc_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 200, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'auto', 'max_depth': 90, 'bootstrap': True}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"719ba07d-4c12-4c36-b230-5b8aed74ab4a\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"719ba07d-4c12-4c36-b230-5b8aed74ab4a\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"719ba07d-4c12-4c36-b230-5b8aed74ab4a\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '719ba07d-4c12-4c36-b230-5b8aed74ab4a' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"719ba07d-4c12-4c36-b230-5b8aed74ab4a\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"719ba07d-4c12-4c36-b230-5b8aed74ab4a\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"719ba07d-4c12-4c36-b230-5b8aed74ab4a\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '719ba07d-4c12-4c36-b230-5b8aed74ab4a' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.16.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.16.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.16.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"719ba07d-4c12-4c36-b230-5b8aed74ab4a\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div class=\"bk-root\">\n",
       "    <div class=\"bk-plotdiv\" id=\"1b1f2da0-e824-4a68-a934-0f5528dbfae0\"></div>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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       "  var render_items = [{\"docid\":\"96ec316e-e5ab-4c79-bbec-06d50155c934\",\"elementid\":\"1b1f2da0-e824-4a68-a934-0f5528dbfae0\",\"modelid\":\"a05def47-0802-4f82-addf-6a21accd56ef\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
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       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
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       "    var attempts = 0;\n",
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       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "a05def47-0802-4f82-addf-6a21accd56ef"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data R2 score: 0.8866860107441148\n",
      "Test data R2 score: 0.47286519126146676\n"
     ]
    }
   ],
   "source": [
    "print(rf_random_trunc.best_params_)\n",
    "best_trunc = rf_random_trunc.best_estimator_\n",
    "y_best_pred, y_best_train_pred = cleanfunc.evaluate_model(best_trunc, X_trunc_train, X_trunc_test, y_trunc_train, y_trunc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly this model seems to have over-fit our train data a little worse, giving us an overall worse R2 score on our holdout set. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combining models\n",
    "For the final step we're going to try combining a variety of models to maintain accuracy at all levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0.02]\n",
      "(405,)\n",
      "(405, 453)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cartechr\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 1]\n",
      "(441,)\n",
      "(441, 453)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\cartechr\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
      "           max_features='auto', max_leaf_nodes=None,\n",
      "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
      "           oob_score=False, random_state=None, verbose=0, warm_start=False), RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
      "           max_features='auto', max_leaf_nodes=None,\n",
      "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
      "           oob_score=False, random_state=None, verbose=0, warm_start=False)]\n"
     ]
    }
   ],
   "source": [
    "y_binned = np.digitize(y, y.quantile([0, 0.2, 0.4, 0.6, 0.8]))\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 43, stratify = y_binned)\n",
    "\n",
    "X_strat = []\n",
    "forests = []\n",
    "ranges = [[0,0.02], [0.0, 1]]\n",
    "for minmax in ranges:\n",
    "    print(minmax)\n",
    "    y_trunc = y_train[(y>minmax[0]) & (y<minmax[1])]\n",
    "    X_trunc = X_train[(y>minmax[0]) & (y<minmax[1])]\n",
    "    print(y_trunc.shape)\n",
    "    print(X_trunc.shape)\n",
    "    rf = RandomForestRegressor(n_estimators=100)\n",
    "    rf.fit(X_trunc, y_trunc)\n",
    "    forests.append(rf)\n",
    "    \n",
    "print(forests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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   ],
   "source": [
    "predictions = pd.DataFrame(data={})\n",
    "for i, rf in enumerate(forests):\n",
    "    y_pred = rf.predict(X_test)\n",
    "    predictions = pd.concat([predictions, pd.DataFrame(y_pred)], axis = 1)\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions\n",
    "\n",
    "def chooseprediction(x):\n",
    "    [low, high] = x\n",
    "    prediction = high\n",
    "    if prediction < 0.02:\n",
    "        prediction = low\n",
    "    return prediction\n",
    "\n",
    "def multipredict(forests):\n",
    "    \n",
    "    predictions = pd.DataFrame(data={})\n",
    "    for i, rf in enumerate(forests):\n",
    "        y_pred = rf.predict(X_test)\n",
    "        predictions = pd.concat([predictions, pd.DataFrame(y_pred)], axis = 1)\n",
    "    \n",
    "\n",
    "        \n",
    "    prediction = predictions.apply(chooseprediction, axis = 1)\n",
    "    \n",
    "    return prediction\n",
    "\n",
    "y_pred_multi = multipredict(forests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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fit\"},\"renderers\":[{\"id\":\"7d857949-9881-4502-b7e4-91fd38553001\",\"type\":\"GlyphRenderer\"}]},\"id\":\"d2649865-6ef3-4075-b14f-522cef9d2a3d\",\"type\":\"LegendItem\"},{\"attributes\":{\"overlay\":{\"id\":\"1c9d171f-7e6a-4af9-85a7-abe88ef62f5e\",\"type\":\"BoxAnnotation\"}},\"id\":\"1994a059-06f2-47fa-829f-0420a50a9948\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":[0,0.042347548824232764],\"y\":[0,0.042347548824232764]},\"selected\":{\"id\":\"ee21bd05-d24a-40d7-8213-df4f906ac09a\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"849ef12f-c957-4017-af3c-3f24baefbea4\",\"type\":\"UnionRenderers\"}},\"id\":\"713bdb8d-cbdc-45a8-83c3-45b846c0fd1c\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"aabcaac6-d752-450c-9857-a3f5c5843aeb\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"92f2a57c-91e1-48f5-9d16-35cfeaabc48a\",\"type\":\"WheelZoomTool\"},{\"attributes\":{},\"id\":\"2c1694f6-1597-43eb-9931-e34fd8602208\",\"type\":\"ResetTool\"}],\"root_ids\":[\"fcef4a51-f1f4-4f9f-b819-3623dc22fcc1\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.16\"}};\n",
       "  var render_items = [{\"docid\":\"d852d3b8-804e-47ab-9f34-f24079b5fdc1\",\"elementid\":\"43b1b138-3ad1-45bd-90a6-4c036efbc91d\",\"modelid\":\"fcef4a51-f1f4-4f9f-b819-3623dc22fcc1\"}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "fcef4a51-f1f4-4f9f-b819-3623dc22fcc1"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data R2 score: 0.4703469722242435\n"
     ]
    }
   ],
   "source": [
    "test_source = ColumnDataSource(data=dict(\n",
    "    x=y_test,\n",
    "    y=y_pred_multi,\n",
    "    desc=y_test.index,\n",
    "))\n",
    "hover = HoverTool()\n",
    "\n",
    "p = figure(plot_width = 600, plot_height = 400, x_axis_label = 'Acutal', y_axis_label = 'Prediction')\n",
    "\n",
    "p.circle('x', 'y', size=20, color = 'navy', alpha = 0.5, source = test_source, legend = 'Test Data')\n",
    "p.line([0,max(y_test)],[0, max(y_test)], legend = 'line of perfect fit')\n",
    "hover.tooltips = [\n",
    "    (\"index\", '$index'),\n",
    "    (\"(x,y)\", \"($x, $y)\"),\n",
    "    (\"desc\", \"@desc\"),\n",
    "]\n",
    "\n",
    "p.tools.append(hover)\n",
    "p.legend.location = 'top_left'\n",
    "p.legend.click_policy=\"hide\"\n",
    "p.toolbar.logo = None\n",
    "p.toolbar_location = None\n",
    "show(p)\n",
    "print(\"Test data R2 score: {}\".format(r2_score(y_test, y_pred_multi)))\n"
   ]
  },
  {
   "cell_type": "markdown",
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    "# Results\n",
    "\n",
    "We can see with our combined model we have achieved an R2 value of 57%. This is a modest improvement on our first pass of 49% so we can be happy with our work. Particularly, by using a multi-layer randomforest we can get much better granularity at the lower end of the function. This area may not have a huge impact on the overall R2 score, as it tends to get \"swamped\" by the data at the high end, but it can be extremely helpful to know what drives cycling uptake at the low end. "
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