Enron_Project.ipynb

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    "# Identify Fraud - ENRON SCANDAL\n",
    "\n",
    "[Summary link - Wikipedia](https://en.wikipedia.org/wiki/Enron_scandal)\n",
    "\n",
    ">The Enron scandal was a financial scandal that eventually led to the bankruptcy of the Enron Corporation, an American energy company based in Houston, Texas, and the de facto dissolution of Arthur Andersen, which was one of the five largest audit and accountancy partnerships in the world. In addition to being the largest bankruptcy reorganization in American history at that time, Enron was cited as the biggest audit failure.\n",
    "\n",
    ">Enron was formed in 1985 by Kenneth Lay after merging Houston Natural Gas and InterNorth. Several years later, when Jeffrey Skilling was hired, he developed a staff of executives that – by the use of accounting loopholes, special purpose entities, and poor financial reporting – were able to hide billions of dollars in debt from failed deals and projects. Chief Financial Officer Andrew Fastow and other executives not only misled Enron's Board of Directors and Audit Committee on high-risk accounting practices, but also pressured Arthur Andersen to ignore the issues.\n",
    "\n",
    ">Enron shareholders filed a 40 billion dollar lawsuit after the company's stock price, which achieved a high of US90.75 per share in mid-2000, plummeted to less than 1 dollar by the end of November 2001. The U.S. Securities and Exchange Commission (SEC) began an investigation, and rival Houston competitor Dynegy offered to purchase the company at a very low price. The deal failed, and on December 2, 2001, Enron filed for bankruptcy under Chapter 11 of the United States Bankruptcy Code. Enron's 63.4 billion dollars in assets made it the largest corporate bankruptcy in U.S. history until WorldCom's bankruptcy the next year.\n",
    "\n",
    ">Many executives at Enron were indicted for a variety of charges and some were later sentenced to prison. Enron's auditor, Arthur Andersen, was found guilty in a United States District Court of illegally destroying documents relevant to the SEC investigation which voided its license to audit public companies, effectively closing the business. By the time the ruling was overturned at the U.S. Supreme Court, the company had lost the majority of its customers and had ceased operating. Enron employees and shareholders received limited returns in lawsuits, despite losing billions in pensions and stock prices. As a consequence of the scandal, new regulations and legislation were enacted to expand the accuracy of financial reporting for public companies. One piece of legislation, the Sarbanes–Oxley Act, increased penalties for destroying, altering, or fabricating records in federal investigations or for attempting to defraud shareholders. The act also increased the accountability of auditing firms to remain unbiased and independent of their clients."
   ]
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   "source": [
    "<img src='pics/Enron-Infographic.png'>"
   ]
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    "<a id='top'></a>\n",
    "\n",
    "Table of Contents\n",
    "<br><br>\n",
    "[Project Goal](#Goal) | \n",
    "<br>\n",
    "[Dataset Questions](#Questions) | \n",
    "<br>\n",
    "[Dataset Information](#Info) | \n",
    "<br>\n",
    "[Feature Statistics](#Stats) |\n",
    "<br>\n",
    "[Explore Features](#Features) |\n",
    "<br>\n",
    "- [Salary](#Salary)\n",
    "- [Bonus](#Bonus)\n",
    "- [Total Payments](#Total Payments)\n",
    "- [Exercised Stock Options](#Stock Options)\n",
    "- [Total Stock Value](#Total Stock Value)\n",
    "- [Total Bonus and Exercised Stock Options](#Total BE)\n",
    "- [Total Payments and Stock Value in Millions](#Total Millions)\n",
    "- [Shared Receipt with POI](#Shared Receipt)\n",
    "- [To Messages](#To Messages)\n",
    "- [From Messages](#From Messages)\n",
    "- [Fraction to POI](#FTP)\n",
    "- [Fraction from POI](#FFP)\n",
    "\n",
    "<br>\n",
    "[Outliers](#Outliers) | \n",
    "<br>\n",
    "[Transform, Select, and Scale](#TSS) | \n",
    "<br>\n",
    "[Algorithm Selection](#Algorithm) | \n",
    "<br>\n",
    "[Evaluation Metrics](#Metrics) | \n",
    "<br>\n",
    "[Performance Test](#Test) |\n",
    "<br>\n",
    "[Parameter Tuning](#Tuning) | \n",
    "<br>\n",
    "[Final Analysis](#Analysis) |\n",
    "<br>\n",
    "[Validating Our Analysis](#Validating) | \n",
    "<br>\n",
    "[Final Thoughts](#Thoughts) | \n",
    "<br>\n",
    "____________________________________________________________________________"
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   "source": [
    "<a id='Goal'></a>\n",
    "\n",
    "## Project Goal\n",
    "\n",
    "The goal of this project is to use the Enron dataset to train our machine learning algorithm to detect the possiblity of fraud (identify person's of interest.)  Since we know our persons of interest (POIs) in our dataset, we will be able to use supervised learning algorithms in constructing our POI identifier.  This will be done by picking the features within our dataset that separate our POIs from our non-POIs best.  \n",
    "<br>\n",
    "We will start out our analysis by answering some questions about our data.  Then, we will explore our features further by visualizing any correlations/outliers.  Next, we will transform/scale our features and select those that will be most useful in our POI identifier, engineering new features and adding them to the dataset if provided to be useful for our analysis.  We will identify at least two algorithms that may be best suited for our particular set of data and test them, tuning our parameters until optimal performance is reached.  In our final analysis, the algorithm we have fit will be validated using our training/testing data.  Using performance metrics to evaluate our results, any problems will be addressed and motifications made.  In our final thoughts, the performance of our final algorithm will be discussed. \n",
    "<br>\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Import pickle and sklearn to get started.\n",
    "Load the data as enron_dict\"\"\"\n",
    "%matplotlib inline\n",
    "%pylab inline\n",
    "import pickle\n",
    "import sklearn\n",
    "import pprint\n",
    "import csv\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"final_project_dataset.pkl\", \"rb\") as data_file:\n",
    "    enron_dict = pickle.load(data_file)"
   ]
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   "cell_type": "code",
   "execution_count": 3,
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <td>201955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>477</td>\n",
       "      <td>267102</td>\n",
       "      <td>239671</td>\n",
       "      <td>80818</td>\n",
       "      <td>231330</td>\n",
       "      <td>213999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>216582</td>\n",
       "      <td>...</td>\n",
       "      <td>259996</td>\n",
       "      <td>63744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>510364</td>\n",
       "      <td>317543</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158403</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <td>1407</td>\n",
       "      <td>NaN</td>\n",
       "      <td>465</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2639</td>\n",
       "      <td>5521</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>337</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to_messages</th>\n",
       "      <td>2902</td>\n",
       "      <td>NaN</td>\n",
       "      <td>566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7315</td>\n",
       "      <td>7991</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_payments</th>\n",
       "      <td>4484442</td>\n",
       "      <td>182466</td>\n",
       "      <td>916197</td>\n",
       "      <td>5634343</td>\n",
       "      <td>827696</td>\n",
       "      <td>860136</td>\n",
       "      <td>969068</td>\n",
       "      <td>5501630</td>\n",
       "      <td>102500</td>\n",
       "      <td>228474</td>\n",
       "      <td>...</td>\n",
       "      <td>1034395</td>\n",
       "      <td>762135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4677574</td>\n",
       "      <td>1934359</td>\n",
       "      <td>84992</td>\n",
       "      <td>189583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360300</td>\n",
       "      <td>55097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_stock_value</th>\n",
       "      <td>1729541</td>\n",
       "      <td>257817</td>\n",
       "      <td>5243487</td>\n",
       "      <td>10623258</td>\n",
       "      <td>63014</td>\n",
       "      <td>1599641</td>\n",
       "      <td>126027</td>\n",
       "      <td>1110705</td>\n",
       "      <td>-44093</td>\n",
       "      <td>2493616</td>\n",
       "      <td>...</td>\n",
       "      <td>2056427</td>\n",
       "      <td>384930</td>\n",
       "      <td>98718</td>\n",
       "      <td>6079137</td>\n",
       "      <td>15144123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139130</td>\n",
       "      <td>11884758</td>\n",
       "      <td>192758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 146 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   ALLEN PHILLIP K BADUM JAMES P  \\\n",
       "bonus                                      4175000           NaN   \n",
       "deferral_payments                          2869717        178980   \n",
       "deferred_income                           -3081055           NaN   \n",
       "director_fees                                  NaN           NaN   \n",
       "email_address              phillip.allen@enron.com           NaN   \n",
       "exercised_stock_options                    1729541        257817   \n",
       "expenses                                     13868          3486   \n",
       "from_messages                                 2195           NaN   \n",
       "from_poi_to_this_person                         47           NaN   \n",
       "from_this_person_to_poi                         65           NaN   \n",
       "loan_advances                                  NaN           NaN   \n",
       "long_term_incentive                         304805           NaN   \n",
       "other                                          152           NaN   \n",
       "poi                                          False         False   \n",
       "restricted_stock                            126027           NaN   \n",
       "restricted_stock_deferred                  -126027           NaN   \n",
       "salary                                      201955           NaN   \n",
       "shared_receipt_with_poi                       1407           NaN   \n",
       "to_messages                                   2902           NaN   \n",
       "total_payments                             4484442        182466   \n",
       "total_stock_value                          1729541        257817   \n",
       "\n",
       "                                   BANNANTINE JAMES M BAXTER JOHN C  \\\n",
       "bonus                                             NaN       1200000   \n",
       "deferral_payments                                 NaN       1295738   \n",
       "deferred_income                                 -5104      -1386055   \n",
       "director_fees                                     NaN           NaN   \n",
       "email_address              james.bannantine@enron.com           NaN   \n",
       "exercised_stock_options                       4046157       6680544   \n",
       "expenses                                        56301         11200   \n",
       "from_messages                                      29           NaN   \n",
       "from_poi_to_this_person                            39           NaN   \n",
       "from_this_person_to_poi                             0           NaN   \n",
       "loan_advances                                     NaN           NaN   \n",
       "long_term_incentive                               NaN       1586055   \n",
       "other                                          864523       2660303   \n",
       "poi                                             False         False   \n",
       "restricted_stock                              1757552       3942714   \n",
       "restricted_stock_deferred                     -560222           NaN   \n",
       "salary                                            477        267102   \n",
       "shared_receipt_with_poi                           465           NaN   \n",
       "to_messages                                       566           NaN   \n",
       "total_payments                                 916197       5634343   \n",
       "total_stock_value                             5243487      10623258   \n",
       "\n",
       "                                BAY FRANKLIN R BAZELIDES PHILIP J  \\\n",
       "bonus                                   400000                NaN   \n",
       "deferral_payments                       260455             684694   \n",
       "deferred_income                        -201641                NaN   \n",
       "director_fees                              NaN                NaN   \n",
       "email_address              frank.bay@enron.com                NaN   \n",
       "exercised_stock_options                    NaN            1599641   \n",
       "expenses                                129142                NaN   \n",
       "from_messages                              NaN                NaN   \n",
       "from_poi_to_this_person                    NaN                NaN   \n",
       "from_this_person_to_poi                    NaN                NaN   \n",
       "loan_advances                              NaN                NaN   \n",
       "long_term_incentive                        NaN              93750   \n",
       "other                                       69                874   \n",
       "poi                                      False              False   \n",
       "restricted_stock                        145796                NaN   \n",
       "restricted_stock_deferred               -82782                NaN   \n",
       "salary                                  239671              80818   \n",
       "shared_receipt_with_poi                    NaN                NaN   \n",
       "to_messages                                NaN                NaN   \n",
       "total_payments                          827696             860136   \n",
       "total_stock_value                        63014            1599641   \n",
       "\n",
       "                                   BECK SALLY W      BELDEN TIMOTHY N  \\\n",
       "bonus                                    700000               5249999   \n",
       "deferral_payments                           NaN               2144013   \n",
       "deferred_income                             NaN              -2334434   \n",
       "director_fees                               NaN                   NaN   \n",
       "email_address              sally.beck@enron.com  tim.belden@enron.com   \n",
       "exercised_stock_options                     NaN                953136   \n",
       "expenses                                  37172                 17355   \n",
       "from_messages                              4343                   484   \n",
       "from_poi_to_this_person                     144                   228   \n",
       "from_this_person_to_poi                     386                   108   \n",
       "loan_advances                               NaN                   NaN   \n",
       "long_term_incentive                         NaN                   NaN   \n",
       "other                                       566                210698   \n",
       "poi                                       False                  True   \n",
       "restricted_stock                         126027                157569   \n",
       "restricted_stock_deferred                   NaN                   NaN   \n",
       "salary                                   231330                213999   \n",
       "shared_receipt_with_poi                    2639                  5521   \n",
       "to_messages                                7315                  7991   \n",
       "total_payments                           969068               5501630   \n",
       "total_stock_value                        126027               1110705   \n",
       "\n",
       "                          BELFER ROBERT            BERBERIAN DAVID    ...     \\\n",
       "bonus                               NaN                        NaN    ...      \n",
       "deferral_payments               -102500                        NaN    ...      \n",
       "deferred_income                     NaN                        NaN    ...      \n",
       "director_fees                      3285                        NaN    ...      \n",
       "email_address                       NaN  david.berberian@enron.com    ...      \n",
       "exercised_stock_options            3285                    1624396    ...      \n",
       "expenses                            NaN                      11892    ...      \n",
       "from_messages                       NaN                        NaN    ...      \n",
       "from_poi_to_this_person             NaN                        NaN    ...      \n",
       "from_this_person_to_poi             NaN                        NaN    ...      \n",
       "loan_advances                       NaN                        NaN    ...      \n",
       "long_term_incentive                 NaN                        NaN    ...      \n",
       "other                               NaN                        NaN    ...      \n",
       "poi                               False                      False    ...      \n",
       "restricted_stock                    NaN                     869220    ...      \n",
       "restricted_stock_deferred         44093                        NaN    ...      \n",
       "salary                              NaN                     216582    ...      \n",
       "shared_receipt_with_poi             NaN                        NaN    ...      \n",
       "to_messages                         NaN                        NaN    ...      \n",
       "total_payments                   102500                     228474    ...      \n",
       "total_stock_value                -44093                    2493616    ...      \n",
       "\n",
       "                                     WASAFF GEORGE       WESTFAHL RICHARD K  \\\n",
       "bonus                                       325000                      NaN   \n",
       "deferral_payments                           831299                      NaN   \n",
       "deferred_income                            -583325                   -10800   \n",
       "director_fees                                  NaN                      NaN   \n",
       "email_address              george.wasaff@enron.com  dick.westfahl@enron.com   \n",
       "exercised_stock_options                    1668260                      NaN   \n",
       "expenses                                       NaN                    51870   \n",
       "from_messages                                   30                      NaN   \n",
       "from_poi_to_this_person                         22                      NaN   \n",
       "from_this_person_to_poi                          7                      NaN   \n",
       "loan_advances                                  NaN                      NaN   \n",
       "long_term_incentive                         200000                   256191   \n",
       "other                                         1425                   401130   \n",
       "poi                                          False                    False   \n",
       "restricted_stock                            388167                   384930   \n",
       "restricted_stock_deferred                      NaN                      NaN   \n",
       "salary                                      259996                    63744   \n",
       "shared_receipt_with_poi                        337                      NaN   \n",
       "to_messages                                    400                      NaN   \n",
       "total_payments                             1034395                   762135   \n",
       "total_stock_value                          2056427                   384930   \n",
       "\n",
       "                          WHALEY DAVID A      WHALLEY LAWRENCE G  \\\n",
       "bonus                                NaN                 3000000   \n",
       "deferral_payments                    NaN                     NaN   \n",
       "deferred_income                      NaN                     NaN   \n",
       "director_fees                        NaN                     NaN   \n",
       "email_address                        NaN  greg.whalley@enron.com   \n",
       "exercised_stock_options            98718                 3282960   \n",
       "expenses                             NaN                   57838   \n",
       "from_messages                        NaN                     556   \n",
       "from_poi_to_this_person              NaN                     186   \n",
       "from_this_person_to_poi              NaN                      24   \n",
       "loan_advances                        NaN                     NaN   \n",
       "long_term_incentive                  NaN                  808346   \n",
       "other                                NaN                  301026   \n",
       "poi                                False                   False   \n",
       "restricted_stock                     NaN                 2796177   \n",
       "restricted_stock_deferred            NaN                     NaN   \n",
       "salary                               NaN                  510364   \n",
       "shared_receipt_with_poi              NaN                    3920   \n",
       "to_messages                          NaN                    6019   \n",
       "total_payments                       NaN                 4677574   \n",
       "total_stock_value                  98718                 6079137   \n",
       "\n",
       "                                WHITE JR THOMAS E WINOKUR JR. HERBERT S  \\\n",
       "bonus                                      450000                   NaN   \n",
       "deferral_payments                             NaN                   NaN   \n",
       "deferred_income                               NaN                -25000   \n",
       "director_fees                                 NaN                108579   \n",
       "email_address              thomas.white@enron.com                   NaN   \n",
       "exercised_stock_options                   1297049                   NaN   \n",
       "expenses                                    81353                  1413   \n",
       "from_messages                                 NaN                   NaN   \n",
       "from_poi_to_this_person                       NaN                   NaN   \n",
       "from_this_person_to_poi                       NaN                   NaN   \n",
       "loan_advances                                 NaN                   NaN   \n",
       "long_term_incentive                           NaN                   NaN   \n",
       "other                                     1085463                   NaN   \n",
       "poi                                         False                 False   \n",
       "restricted_stock                         13847074                   NaN   \n",
       "restricted_stock_deferred                     NaN                   NaN   \n",
       "salary                                     317543                   NaN   \n",
       "shared_receipt_with_poi                       NaN                   NaN   \n",
       "to_messages                                   NaN                   NaN   \n",
       "total_payments                            1934359                 84992   \n",
       "total_stock_value                        15144123                   NaN   \n",
       "\n",
       "                                     WODRASKA JOHN WROBEL BRUCE  \\\n",
       "bonus                                          NaN          NaN   \n",
       "deferral_payments                              NaN          NaN   \n",
       "deferred_income                                NaN          NaN   \n",
       "director_fees                                  NaN          NaN   \n",
       "email_address              john.wodraska@enron.com          NaN   \n",
       "exercised_stock_options                        NaN       139130   \n",
       "expenses                                       NaN          NaN   \n",
       "from_messages                                  NaN          NaN   \n",
       "from_poi_to_this_person                        NaN          NaN   \n",
       "from_this_person_to_poi                        NaN          NaN   \n",
       "loan_advances                                  NaN          NaN   \n",
       "long_term_incentive                            NaN          NaN   \n",
       "other                                       189583          NaN   \n",
       "poi                                          False        False   \n",
       "restricted_stock                               NaN          NaN   \n",
       "restricted_stock_deferred                      NaN          NaN   \n",
       "salary                                         NaN          NaN   \n",
       "shared_receipt_with_poi                        NaN          NaN   \n",
       "to_messages                                    NaN          NaN   \n",
       "total_payments                              189583          NaN   \n",
       "total_stock_value                              NaN       139130   \n",
       "\n",
       "                                   YEAGER F SCOTT YEAP SOON  \n",
       "bonus                                         NaN       NaN  \n",
       "deferral_payments                             NaN       NaN  \n",
       "deferred_income                               NaN       NaN  \n",
       "director_fees                                 NaN       NaN  \n",
       "email_address              scott.yeager@enron.com       NaN  \n",
       "exercised_stock_options                   8308552    192758  \n",
       "expenses                                    53947     55097  \n",
       "from_messages                                 NaN       NaN  \n",
       "from_poi_to_this_person                       NaN       NaN  \n",
       "from_this_person_to_poi                       NaN       NaN  \n",
       "loan_advances                                 NaN       NaN  \n",
       "long_term_incentive                           NaN       NaN  \n",
       "other                                      147950       NaN  \n",
       "poi                                          True     False  \n",
       "restricted_stock                          3576206       NaN  \n",
       "restricted_stock_deferred                     NaN       NaN  \n",
       "salary                                     158403       NaN  \n",
       "shared_receipt_with_poi                       NaN       NaN  \n",
       "to_messages                                   NaN       NaN  \n",
       "total_payments                             360300     55097  \n",
       "total_stock_value                        11884758    192758  \n",
       "\n",
       "[21 rows x 146 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(enron_dict)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Questions'></a>\n",
    "\n",
    "## Dataset Questions\n",
    "\n",
    "After getting our data dictionary loaded, we can start exploring our data.  We'll answer the following questions:\n",
    "<br>\n",
    "1. How many people do we have in our dataset?\n",
    "2. What are their names?\n",
    "3. What information do we have about these people?\n",
    "4. Who are the POIs in our dataset?\n",
    "5. Who are the highest earners?  Are they POIs?\n",
    "6. Whos stock options had the highest value (max exercised_stock_options)?\n",
    "7. Are there any features we can ignore due to missing data?\n",
    "8. What is the mean salary for non-POIs and POIs?\n",
    "9. What features might be useful for training our algorithm?\n",
    "10. Are there any features we may need to scale?\n",
    "\n",
    "[Top](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of People in Dataset:  146\n"
     ]
    }
   ],
   "source": [
    "print ('Number of People in Dataset: ', len(enron_dict) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretty = pprint.PrettyPrinter()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorted list of Enron employees by last name\n",
      "['ALLEN PHILLIP K',\n",
      " 'BADUM JAMES P',\n",
      " 'BANNANTINE JAMES M',\n",
      " 'BAXTER JOHN C',\n",
      " 'BAY FRANKLIN R',\n",
      " 'BAZELIDES PHILIP J',\n",
      " 'BECK SALLY W',\n",
      " 'BELDEN TIMOTHY N',\n",
      " 'BELFER ROBERT',\n",
      " 'BERBERIAN DAVID',\n",
      " 'BERGSIEKER RICHARD P',\n",
      " 'BHATNAGAR SANJAY',\n",
      " 'BIBI PHILIPPE A',\n",
      " 'BLACHMAN JEREMY M',\n",
      " 'BLAKE JR. NORMAN P',\n",
      " 'BOWEN JR RAYMOND M',\n",
      " 'BROWN MICHAEL',\n",
      " 'BUCHANAN HAROLD G',\n",
      " 'BUTTS ROBERT H',\n",
      " 'BUY RICHARD B',\n",
      " 'CALGER CHRISTOPHER F',\n",
      " 'CARTER REBECCA C',\n",
      " 'CAUSEY RICHARD A',\n",
      " 'CHAN RONNIE',\n",
      " 'CHRISTODOULOU DIOMEDES',\n",
      " 'CLINE KENNETH W',\n",
      " 'COLWELL WESLEY',\n",
      " 'CORDES WILLIAM R',\n",
      " 'COX DAVID',\n",
      " 'CUMBERLAND MICHAEL S',\n",
      " 'DEFFNER JOSEPH M',\n",
      " 'DELAINEY DAVID W',\n",
      " 'DERRICK JR. JAMES V',\n",
      " 'DETMERING TIMOTHY J',\n",
      " 'DIETRICH JANET R',\n",
      " 'DIMICHELE RICHARD G',\n",
      " 'DODSON KEITH',\n",
      " 'DONAHUE JR JEFFREY M',\n",
      " 'DUNCAN JOHN H',\n",
      " 'DURAN WILLIAM D',\n",
      " 'ECHOLS JOHN B',\n",
      " 'ELLIOTT STEVEN',\n",
      " 'FALLON JAMES B',\n",
      " 'FASTOW ANDREW S',\n",
      " 'FITZGERALD JAY L',\n",
      " 'FOWLER PEGGY',\n",
      " 'FOY JOE',\n",
      " 'FREVERT MARK A',\n",
      " 'FUGH JOHN L',\n",
      " 'GAHN ROBERT S',\n",
      " 'GARLAND C KEVIN',\n",
      " 'GATHMANN WILLIAM D',\n",
      " 'GIBBS DANA R',\n",
      " 'GILLIS JOHN',\n",
      " 'GLISAN JR BEN F',\n",
      " 'GOLD JOSEPH',\n",
      " 'GRAMM WENDY L',\n",
      " 'GRAY RODNEY',\n",
      " 'HAEDICKE MARK E',\n",
      " 'HANNON KEVIN P',\n",
      " 'HAUG DAVID L',\n",
      " 'HAYES ROBERT E',\n",
      " 'HAYSLETT RODERICK J',\n",
      " 'HERMANN ROBERT J',\n",
      " 'HICKERSON GARY J',\n",
      " 'HIRKO JOSEPH',\n",
      " 'HORTON STANLEY C',\n",
      " 'HUGHES JAMES A',\n",
      " 'HUMPHREY GENE E',\n",
      " 'IZZO LAWRENCE L',\n",
      " 'JACKSON CHARLENE R',\n",
      " 'JAEDICKE ROBERT',\n",
      " 'KAMINSKI WINCENTY J',\n",
      " 'KEAN STEVEN J',\n",
      " 'KISHKILL JOSEPH G',\n",
      " 'KITCHEN LOUISE',\n",
      " 'KOENIG MARK E',\n",
      " 'KOPPER MICHAEL J',\n",
      " 'LAVORATO JOHN J',\n",
      " 'LAY KENNETH L',\n",
      " 'LEFF DANIEL P',\n",
      " 'LEMAISTRE CHARLES',\n",
      " 'LEWIS RICHARD',\n",
      " 'LINDHOLM TOD A',\n",
      " 'LOCKHART EUGENE E',\n",
      " 'LOWRY CHARLES P',\n",
      " 'MARTIN AMANDA K',\n",
      " 'MCCARTY DANNY J',\n",
      " 'MCCLELLAN GEORGE',\n",
      " 'MCCONNELL MICHAEL S',\n",
      " 'MCDONALD REBECCA',\n",
      " 'MCMAHON JEFFREY',\n",
      " 'MENDELSOHN JOHN',\n",
      " 'METTS MARK',\n",
      " 'MEYER JEROME J',\n",
      " 'MEYER ROCKFORD G',\n",
      " 'MORAN MICHAEL P',\n",
      " 'MORDAUNT KRISTINA M',\n",
      " 'MULLER MARK S',\n",
      " 'MURRAY JULIA H',\n",
      " 'NOLES JAMES L',\n",
      " 'OLSON CINDY K',\n",
      " 'OVERDYKE JR JERE C',\n",
      " 'PAI LOU L',\n",
      " 'PEREIRA PAULO V. FERRAZ',\n",
      " 'PICKERING MARK R',\n",
      " 'PIPER GREGORY F',\n",
      " 'PIRO JIM',\n",
      " 'POWERS WILLIAM',\n",
      " 'PRENTICE JAMES',\n",
      " 'REDMOND BRIAN L',\n",
      " 'REYNOLDS LAWRENCE',\n",
      " 'RICE KENNETH D',\n",
      " 'RIEKER PAULA H',\n",
      " 'SAVAGE FRANK',\n",
      " 'SCRIMSHAW MATTHEW',\n",
      " 'SHANKMAN JEFFREY A',\n",
      " 'SHAPIRO RICHARD S',\n",
      " 'SHARP VICTORIA T',\n",
      " 'SHELBY REX',\n",
      " 'SHERRICK JEFFREY B',\n",
      " 'SHERRIFF JOHN R',\n",
      " 'SKILLING JEFFREY K',\n",
      " 'STABLER FRANK',\n",
      " 'SULLIVAN-SHAKLOVITZ COLLEEN',\n",
      " 'SUNDE MARTIN',\n",
      " 'TAYLOR MITCHELL S',\n",
      " 'THE TRAVEL AGENCY IN THE PARK',\n",
      " 'THORN TERENCE H',\n",
      " 'TILNEY ELIZABETH A',\n",
      " 'TOTAL',\n",
      " 'UMANOFF ADAM S',\n",
      " 'URQUHART JOHN A',\n",
      " 'WAKEHAM JOHN',\n",
      " 'WALLS JR ROBERT H',\n",
      " 'WALTERS GARETH W',\n",
      " 'WASAFF GEORGE',\n",
      " 'WESTFAHL RICHARD K',\n",
      " 'WHALEY DAVID A',\n",
      " 'WHALLEY LAWRENCE G',\n",
      " 'WHITE JR THOMAS E',\n",
      " 'WINOKUR JR. HERBERT S',\n",
      " 'WODRASKA JOHN',\n",
      " 'WROBEL BRUCE',\n",
      " 'YEAGER F SCOTT',\n",
      " 'YEAP SOON']\n"
     ]
    }
   ],
   "source": [
    "#sort names of Enron employees in dataset by first letter of last name\n",
    "names = sorted(enron_dict.keys())  \n",
    "\n",
    "print ('Sorted list of Enron employees by last name')\n",
    "pretty.pprint(names) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example Value Dictionary of Features\n",
      "{'bonus': 4175000,\n",
      " 'deferral_payments': 2869717,\n",
      " 'deferred_income': -3081055,\n",
      " 'director_fees': 'NaN',\n",
      " 'email_address': 'phillip.allen@enron.com',\n",
      " 'exercised_stock_options': 1729541,\n",
      " 'expenses': 13868,\n",
      " 'from_messages': 2195,\n",
      " 'from_poi_to_this_person': 47,\n",
      " 'from_this_person_to_poi': 65,\n",
      " 'loan_advances': 'NaN',\n",
      " 'long_term_incentive': 304805,\n",
      " 'other': 152,\n",
      " 'poi': False,\n",
      " 'restricted_stock': 126027,\n",
      " 'restricted_stock_deferred': -126027,\n",
      " 'salary': 201955,\n",
      " 'shared_receipt_with_poi': 1407,\n",
      " 'to_messages': 2902,\n",
      " 'total_payments': 4484442,\n",
      " 'total_stock_value': 1729541}\n"
     ]
    }
   ],
   "source": [
    "print ('Example Value Dictionary of Features')\n",
    "pretty.pprint(enron_dict['ALLEN PHILLIP K']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we go any further, let's transform our dictionary into a pandas dataframe to explore further."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['deferred_income',\n",
       " 'expenses',\n",
       " 'bonus',\n",
       " 'director_fees',\n",
       " 'salary',\n",
       " 'email_address',\n",
       " 'total_payments',\n",
       " 'restricted_stock',\n",
       " 'loan_advances',\n",
       " 'to_messages',\n",
       " 'other',\n",
       " 'from_this_person_to_poi',\n",
       " 'deferral_payments',\n",
       " 'long_term_incentive',\n",
       " 'restricted_stock_deferred',\n",
       " 'from_messages',\n",
       " 'exercised_stock_options',\n",
       " 'shared_receipt_with_poi',\n",
       " 'total_stock_value',\n",
       " 'poi',\n",
       " 'from_poi_to_this_person']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enron_dict['METTS MARK'].keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bonus</th>\n",
       "      <th>deferral_payments</th>\n",
       "      <th>deferred_income</th>\n",
       "      <th>director_fees</th>\n",
       "      <th>email_address</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>expenses</th>\n",
       "      <th>from_messages</th>\n",
       "      <th>from_poi_to_this_person</th>\n",
       "      <th>from_this_person_to_poi</th>\n",
       "      <th>...</th>\n",
       "      <th>long_term_incentive</th>\n",
       "      <th>other</th>\n",
       "      <th>poi</th>\n",
       "      <th>restricted_stock</th>\n",
       "      <th>restricted_stock_deferred</th>\n",
       "      <th>salary</th>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <th>to_messages</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>total_stock_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALLEN PHILLIP K</th>\n",
       "      <td>4175000</td>\n",
       "      <td>2869717</td>\n",
       "      <td>-3081055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>phillip.allen@enron.com</td>\n",
       "      <td>1729541</td>\n",
       "      <td>13868</td>\n",
       "      <td>2195</td>\n",
       "      <td>47</td>\n",
       "      <td>65</td>\n",
       "      <td>...</td>\n",
       "      <td>304805</td>\n",
       "      <td>152</td>\n",
       "      <td>False</td>\n",
       "      <td>126027</td>\n",
       "      <td>-126027</td>\n",
       "      <td>201955</td>\n",
       "      <td>1407</td>\n",
       "      <td>2902</td>\n",
       "      <td>4484442</td>\n",
       "      <td>1729541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BADUM JAMES P</th>\n",
       "      <td>NaN</td>\n",
       "      <td>178980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>257817</td>\n",
       "      <td>3486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182466</td>\n",
       "      <td>257817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BANNANTINE JAMES M</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5104</td>\n",
       "      <td>NaN</td>\n",
       "      <td>james.bannantine@enron.com</td>\n",
       "      <td>4046157</td>\n",
       "      <td>56301</td>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>864523</td>\n",
       "      <td>False</td>\n",
       "      <td>1757552</td>\n",
       "      <td>-560222</td>\n",
       "      <td>477</td>\n",
       "      <td>465</td>\n",
       "      <td>566</td>\n",
       "      <td>916197</td>\n",
       "      <td>5243487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAXTER JOHN C</th>\n",
       "      <td>1200000</td>\n",
       "      <td>1295738</td>\n",
       "      <td>-1386055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6680544</td>\n",
       "      <td>11200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1586055</td>\n",
       "      <td>2660303</td>\n",
       "      <td>False</td>\n",
       "      <td>3942714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>267102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5634343</td>\n",
       "      <td>10623258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAY FRANKLIN R</th>\n",
       "      <td>400000</td>\n",
       "      <td>260455</td>\n",
       "      <td>-201641</td>\n",
       "      <td>NaN</td>\n",
       "      <td>frank.bay@enron.com</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129142</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69</td>\n",
       "      <td>False</td>\n",
       "      <td>145796</td>\n",
       "      <td>-82782</td>\n",
       "      <td>239671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>827696</td>\n",
       "      <td>63014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      bonus deferral_payments deferred_income director_fees  \\\n",
       "ALLEN PHILLIP K     4175000           2869717        -3081055           NaN   \n",
       "BADUM JAMES P           NaN            178980             NaN           NaN   \n",
       "BANNANTINE JAMES M      NaN               NaN           -5104           NaN   \n",
       "BAXTER JOHN C       1200000           1295738        -1386055           NaN   \n",
       "BAY FRANKLIN R       400000            260455         -201641           NaN   \n",
       "\n",
       "                                 email_address exercised_stock_options  \\\n",
       "ALLEN PHILLIP K        phillip.allen@enron.com                 1729541   \n",
       "BADUM JAMES P                              NaN                  257817   \n",
       "BANNANTINE JAMES M  james.bannantine@enron.com                 4046157   \n",
       "BAXTER JOHN C                              NaN                 6680544   \n",
       "BAY FRANKLIN R             frank.bay@enron.com                     NaN   \n",
       "\n",
       "                   expenses from_messages from_poi_to_this_person  \\\n",
       "ALLEN PHILLIP K       13868          2195                      47   \n",
       "BADUM JAMES P          3486           NaN                     NaN   \n",
       "BANNANTINE JAMES M    56301            29                      39   \n",
       "BAXTER JOHN C         11200           NaN                     NaN   \n",
       "BAY FRANKLIN R       129142           NaN                     NaN   \n",
       "\n",
       "                   from_this_person_to_poi        ...         \\\n",
       "ALLEN PHILLIP K                         65        ...          \n",
       "BADUM JAMES P                          NaN        ...          \n",
       "BANNANTINE JAMES M                       0        ...          \n",
       "BAXTER JOHN C                          NaN        ...          \n",
       "BAY FRANKLIN R                         NaN        ...          \n",
       "\n",
       "                   long_term_incentive    other    poi restricted_stock  \\\n",
       "ALLEN PHILLIP K                 304805      152  False           126027   \n",
       "BADUM JAMES P                      NaN      NaN  False              NaN   \n",
       "BANNANTINE JAMES M                 NaN   864523  False          1757552   \n",
       "BAXTER JOHN C                  1586055  2660303  False          3942714   \n",
       "BAY FRANKLIN R                     NaN       69  False           145796   \n",
       "\n",
       "                   restricted_stock_deferred  salary shared_receipt_with_poi  \\\n",
       "ALLEN PHILLIP K                      -126027  201955                    1407   \n",
       "BADUM JAMES P                            NaN     NaN                     NaN   \n",
       "BANNANTINE JAMES M                   -560222     477                     465   \n",
       "BAXTER JOHN C                            NaN  267102                     NaN   \n",
       "BAY FRANKLIN R                        -82782  239671                     NaN   \n",
       "\n",
       "                   to_messages total_payments total_stock_value  \n",
       "ALLEN PHILLIP K           2902        4484442           1729541  \n",
       "BADUM JAMES P              NaN         182466            257817  \n",
       "BANNANTINE JAMES M         566         916197           5243487  \n",
       "BAXTER JOHN C              NaN        5634343          10623258  \n",
       "BAY FRANKLIN R             NaN         827696             63014  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Write Enron Dictionary to CSV File for Possible Future Use and Easily Read into Dataframe\"\"\"\n",
    "\n",
    "df1 = df.T\n",
    "df1.index.names = ['name']\n",
    "pd.DataFrame.to_csv(df1, 'enron1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['BELFER ROBERT', 'FUGH JOHN L', 'MCDONALD REBECCA', 'WAKEHAM JOHN', 'WESTFAHL RICHARD K', 'KOENIG MARK E', 'CHAN RONNIE', 'LOWRY CHARLES P', 'CARTER REBECCA C', 'PRENTICE JAMES', 'LEMAISTRE CHARLES', 'WODRASKA JOHN', 'MARTIN AMANDA K', 'PIPER GREGORY F', 'PIRO JIM', 'ALLEN PHILLIP K', 'BROWN MICHAEL', 'GLISAN JR BEN F', 'GATHMANN WILLIAM D', 'MCMAHON JEFFREY', 'METTS MARK', 'KAMINSKI WINCENTY J', 'HAYSLETT RODERICK J', 'SHERRIFF JOHN R', 'HICKERSON GARY J', 'FOWLER PEGGY', 'LOCKHART EUGENE E', 'BERBERIAN DAVID', 'LEFF DANIEL P', 'KISHKILL JOSEPH G', 'SCRIMSHAW MATTHEW', 'STABLER FRANK', 'LINDHOLM TOD A', 'BANNANTINE JAMES M', 'WALLS JR ROBERT H', 'SULLIVAN-SHAKLOVITZ COLLEEN', 'BIBI PHILIPPE A', 'BOWEN JR RAYMOND M', 'MULLER MARK S', 'DONAHUE JR JEFFREY M', 'YEAP SOON', 'UMANOFF ADAM S', 'BHATNAGAR SANJAY', 'THORN TERENCE H', 'SHARP VICTORIA T', 'HORTON STANLEY C', 'ECHOLS JOHN B', 'CORDES WILLIAM R', 'IZZO LAWRENCE L', 'WHALLEY LAWRENCE G', 'FALLON JAMES B', 'GILLIS JOHN', 'OVERDYKE JR JERE C', 'BERGSIEKER RICHARD P', 'WASAFF GEORGE', 'DELAINEY DAVID W', 'BUY RICHARD B', 'WROBEL BRUCE', 'NOLES JAMES L', 'SKILLING JEFFREY K', 'URQUHART JOHN A', 'TOTAL', 'HANNON KEVIN P', 'LAY KENNETH L', 'SHELBY REX', 'DODSON KEITH', 'CUMBERLAND MICHAEL S', 'GAHN ROBERT S', 'MORAN MICHAEL P', 'HAYES ROBERT E', 'FOY JOE', 'BAXTER JOHN C', 'FITZGERALD JAY L', 'BLACHMAN JEREMY M', 'HIRKO JOSEPH', 'PEREIRA PAULO V. FERRAZ', 'REDMOND BRIAN L', 'DEFFNER JOSEPH M', 'POWERS WILLIAM', 'WALTERS GARETH W', 'PAI LOU L', 'PICKERING MARK R', 'GARLAND C KEVIN', 'CAUSEY RICHARD A', 'TAYLOR MITCHELL S', 'ELLIOTT STEVEN', 'THE TRAVEL AGENCY IN THE PARK', 'KEAN STEVEN J', 'MCCLELLAN GEORGE', 'COX DAVID', 'CHRISTODOULOU DIOMEDES', 'OLSON CINDY K', 'GRAY RODNEY', 'MENDELSOHN JOHN', 'SHERRICK JEFFREY B', 'MEYER JEROME J', 'HERMANN ROBERT J', 'DURAN WILLIAM D', 'MORDAUNT KRISTINA M', 'DIETRICH JANET R', 'BUTTS ROBERT H', 'BLAKE JR. NORMAN P', 'MEYER ROCKFORD G', 'GRAMM WENDY L', 'BELDEN TIMOTHY N', 'BAY FRANKLIN R', 'COLWELL WESLEY', 'MCCONNELL MICHAEL S', 'FASTOW ANDREW S', 'HAEDICKE MARK E', 'HUMPHREY GENE E', 'JACKSON CHARLENE R', 'DUNCAN JOHN H', 'LEWIS RICHARD', 'HAUG DAVID L', 'DERRICK JR. JAMES V', 'LAVORATO JOHN J', 'SHAPIRO RICHARD S', 'SAVAGE FRANK', 'WINOKUR JR. HERBERT S', 'SHANKMAN JEFFREY A', 'CALGER CHRISTOPHER F', 'BECK SALLY W', 'GOLD JOSEPH', 'BADUM JAMES P', 'WHALEY DAVID A', 'TILNEY ELIZABETH A', 'GIBBS DANA R', 'KITCHEN LOUISE', 'MCCARTY DANNY J', 'BAZELIDES PHILIP J', 'FREVERT MARK A', 'KOPPER MICHAEL J', 'RICE KENNETH D', 'CLINE KENNETH W', 'DETMERING TIMOTHY J', 'JAEDICKE ROBERT', 'REYNOLDS LAWRENCE', 'SUNDE MARTIN', 'DIMICHELE RICHARD G', 'YEAGER F SCOTT', 'RIEKER PAULA H', 'HUGHES JAMES A', 'MURRAY JULIA H', 'BUCHANAN HAROLD G', 'WHITE JR THOMAS E'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enron_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fieldnames = ['name'] + list(enron_dict['METTS MARK'].keys())\n",
    "\n",
    "with open('enron.csv', 'w') as csvfile:\n",
    "    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "\n",
    "    writer.writeheader()\n",
    "    for name in enron_dict.keys():\n",
    "        if name != 'TOTAL':\n",
    "            n = {'name': name}\n",
    "            n.update(enron_dict[name])\n",
    "            writer.writerow(n)      \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read csv into pandas dataframe \n",
    "enron = pd.read_csv('enron.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#added/combined feature, total bonus and exercised_stock_options\n",
    "enron['total_be'] = enron['bonus'].fillna(0.0) + enron['exercised_stock_options'].fillna(0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#added feature, fraction of e-mails to and from poi\n",
    "enron['fraction_to_poi'] = enron['from_this_person_to_poi'].fillna(0.0)/enron['from_messages'].fillna(0.0)\n",
    "enron['fraction_from_poi'] = enron['from_poi_to_this_person'].fillna(0.0)/enron['to_messages'].fillna(0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#added feature, scaled total compensation\n",
    "enron['total_millions'] = (enron['total_payments'].fillna(0.0) + enron['total_stock_value'].fillna(0.0))/1000000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Info'></a>\n",
    "\n",
    "## Dataset Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 145 entries, 0 to 144\n",
      "Data columns (total 26 columns):\n",
      "name                         145 non-null object\n",
      "deferred_income              48 non-null float64\n",
      "expenses                     94 non-null float64\n",
      "bonus                        81 non-null float64\n",
      "director_fees                16 non-null float64\n",
      "salary                       94 non-null float64\n",
      "email_address                111 non-null object\n",
      "total_payments               124 non-null float64\n",
      "restricted_stock             109 non-null float64\n",
      "loan_advances                3 non-null float64\n",
      "to_messages                  86 non-null float64\n",
      "other                        92 non-null float64\n",
      "from_this_person_to_poi      86 non-null float64\n",
      "deferral_payments            38 non-null float64\n",
      "long_term_incentive          65 non-null float64\n",
      "restricted_stock_deferred    17 non-null float64\n",
      "from_messages                86 non-null float64\n",
      "exercised_stock_options      101 non-null float64\n",
      "shared_receipt_with_poi      86 non-null float64\n",
      "total_stock_value            125 non-null float64\n",
      "poi                          145 non-null bool\n",
      "from_poi_to_this_person      86 non-null float64\n",
      "total_be                     145 non-null float64\n",
      "fraction_to_poi              86 non-null float64\n",
      "fraction_from_poi            86 non-null float64\n",
      "total_millions               145 non-null float64\n",
      "dtypes: bool(1), float64(23), object(2)\n",
      "memory usage: 28.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#data information/types\n",
    "\n",
    "enron.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "Just by looking at our dataset information above, we can quickly point out a few ways to narrow down our feature selection.  Some of our features have lots of <b>missing data</b>, so those may be ones that we can remove.  Features like \"restricted_stock_deferred\", \"loan_advances\", and \"director_fees\" may be some that we can take out altogether.  There are also a few features that seem to be giving us the same information, like \"shared_receipt_with_poi\",\"to_messages\", \"from_messages\", \"from_this_person_to_poi\", and \"from_poi_to_this_person\" all tell us about the person's e-mail behavior and all have the same data count, 86.  We may be able to narrow those features down to just one or two, or <b>create a new feature</b> from them (see feature added above.)\n",
    "<br><br>\n",
    "Features that may be most useful, since we're dealing with corporate fraud, are those features that tell us about the money.  Let's follow the money!  Features that will give us that money trail will be \"salary\", \"total_payments\", \"exercised_stock_options\", \"bonus\", \"restricted_stock\", and \"total_stock_value\".\n",
    "<br><br>\n",
    "For now, let's continue to explore our dataset before making our final selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Stats'></a>\n",
    "\n",
    "## Feature Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 18 POI in our Dataset as you can see by our \"True\" count\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False    127\n",
       "True      18\n",
       "Name: poi, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of POI in dataset\n",
    "print ('There are 18 POI in our Dataset as you can see by our \"True\" count')\n",
    "enron['poi'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "      <th>bonus</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>total_be</th>\n",
       "      <th>total_millions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>6.500000e+01</td>\n",
       "      <td>8.900000e+01</td>\n",
       "      <td>1.070000e+02</td>\n",
       "      <td>1.060000e+02</td>\n",
       "      <td>1.270000e+02</td>\n",
       "      <td>127.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.621515e+05</td>\n",
       "      <td>9.868249e+05</td>\n",
       "      <td>1.947752e+06</td>\n",
       "      <td>2.374085e+06</td>\n",
       "      <td>1.725091e+06</td>\n",
       "      <td>1.870028e+06</td>\n",
       "      <td>3.440052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.392317e+05</td>\n",
       "      <td>1.173880e+06</td>\n",
       "      <td>2.547068e+06</td>\n",
       "      <td>3.535017e+06</td>\n",
       "      <td>2.618288e+06</td>\n",
       "      <td>2.693997e+06</td>\n",
       "      <td>4.839977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.770000e+02</td>\n",
       "      <td>7.000000e+04</td>\n",
       "      <td>3.285000e+03</td>\n",
       "      <td>-4.409300e+04</td>\n",
       "      <td>1.480000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.061210e+05</td>\n",
       "      <td>4.000000e+05</td>\n",
       "      <td>4.365150e+05</td>\n",
       "      <td>4.246845e+05</td>\n",
       "      <td>3.304798e+05</td>\n",
       "      <td>2.213790e+05</td>\n",
       "      <td>0.507935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.516540e+05</td>\n",
       "      <td>7.000000e+05</td>\n",
       "      <td>1.030329e+06</td>\n",
       "      <td>1.030329e+06</td>\n",
       "      <td>1.056092e+06</td>\n",
       "      <td>8.862310e+05</td>\n",
       "      <td>1.884748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.885890e+05</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>2.165172e+06</td>\n",
       "      <td>2.307584e+06</td>\n",
       "      <td>2.006025e+06</td>\n",
       "      <td>2.250522e+06</td>\n",
       "      <td>4.317325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.060932e+06</td>\n",
       "      <td>8.000000e+06</td>\n",
       "      <td>1.536417e+07</td>\n",
       "      <td>2.381793e+07</td>\n",
       "      <td>1.725253e+07</td>\n",
       "      <td>1.636417e+07</td>\n",
       "      <td>31.874715</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             salary         bonus  exercised_stock_options  total_stock_value  \\\n",
       "count  7.700000e+01  6.500000e+01             8.900000e+01       1.070000e+02   \n",
       "mean   2.621515e+05  9.868249e+05             1.947752e+06       2.374085e+06   \n",
       "std    1.392317e+05  1.173880e+06             2.547068e+06       3.535017e+06   \n",
       "min    4.770000e+02  7.000000e+04             3.285000e+03      -4.409300e+04   \n",
       "25%    2.061210e+05  4.000000e+05             4.365150e+05       4.246845e+05   \n",
       "50%    2.516540e+05  7.000000e+05             1.030329e+06       1.030329e+06   \n",
       "75%    2.885890e+05  1.000000e+06             2.165172e+06       2.307584e+06   \n",
       "max    1.060932e+06  8.000000e+06             1.536417e+07       2.381793e+07   \n",
       "\n",
       "       total_payments      total_be  total_millions  \n",
       "count    1.060000e+02  1.270000e+02      127.000000  \n",
       "mean     1.725091e+06  1.870028e+06        3.440052  \n",
       "std      2.618288e+06  2.693997e+06        4.839977  \n",
       "min      1.480000e+02  0.000000e+00        0.000000  \n",
       "25%      3.304798e+05  2.213790e+05        0.507935  \n",
       "50%      1.056092e+06  8.862310e+05        1.884748  \n",
       "75%      2.006025e+06  2.250522e+06        4.317325  \n",
       "max      1.725253e+07  1.636417e+07       31.874715  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set a baseline by extracting non-POIs and printing stats\n",
    "\n",
    "non_poi = enron[enron.poi.isin([False])]\n",
    "\n",
    "non_poi_money = non_poi[['salary','bonus','exercised_stock_options','total_stock_value',\\\n",
    "                         'total_payments','total_be','total_millions']].describe()\n",
    "non_poi_money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <th>to_messages</th>\n",
       "      <th>from_messages</th>\n",
       "      <th>fraction_from_poi</th>\n",
       "      <th>fraction_to_poi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1058.527778</td>\n",
       "      <td>2007.111111</td>\n",
       "      <td>668.763889</td>\n",
       "      <td>0.036107</td>\n",
       "      <td>0.152669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1132.503757</td>\n",
       "      <td>2693.165955</td>\n",
       "      <td>1978.997801</td>\n",
       "      <td>0.041929</td>\n",
       "      <td>0.206057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>191.500000</td>\n",
       "      <td>513.750000</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>0.007760</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>594.000000</td>\n",
       "      <td>944.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.022741</td>\n",
       "      <td>0.053776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1635.500000</td>\n",
       "      <td>2590.750000</td>\n",
       "      <td>216.500000</td>\n",
       "      <td>0.050705</td>\n",
       "      <td>0.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4527.000000</td>\n",
       "      <td>15149.000000</td>\n",
       "      <td>14368.000000</td>\n",
       "      <td>0.217341</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       shared_receipt_with_poi   to_messages  from_messages  \\\n",
       "count                72.000000     72.000000      72.000000   \n",
       "mean               1058.527778   2007.111111     668.763889   \n",
       "std                1132.503757   2693.165955    1978.997801   \n",
       "min                   2.000000     57.000000      12.000000   \n",
       "25%                 191.500000    513.750000      20.500000   \n",
       "50%                 594.000000    944.000000      41.000000   \n",
       "75%                1635.500000   2590.750000     216.500000   \n",
       "max                4527.000000  15149.000000   14368.000000   \n",
       "\n",
       "       fraction_from_poi  fraction_to_poi  \n",
       "count          72.000000        72.000000  \n",
       "mean            0.036107         0.152669  \n",
       "std             0.041929         0.206057  \n",
       "min             0.000000         0.000000  \n",
       "25%             0.007760         0.000000  \n",
       "50%             0.022741         0.053776  \n",
       "75%             0.050705         0.225000  \n",
       "max             0.217341         1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_poi_email_behavior = non_poi[['shared_receipt_with_poi','to_messages',\\\n",
    "                                  'from_messages','fraction_from_poi','fraction_to_poi']].describe()\n",
    "non_poi_email_behavior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I thought it was interesting to see someone with 100% of their e-mails going to persons of interest.  Below, I printed out some features associated with this person.  After a little research, I found that Gene Humphrey was one of the first employees of Enron.  So, it makes sense that all of his e-mails were to persons of interest who had been with the company from the beginning.  Those were the only people he worked with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>salary</th>\n",
       "      <th>total_be</th>\n",
       "      <th>restricted_stock</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>to_messages</th>\n",
       "      <th>poi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>SHERRICK JEFFREY B</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1426469.0</td>\n",
       "      <td>405999.0</td>\n",
       "      <td>1832468.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>HUMPHREY GENE E</td>\n",
       "      <td>130724.0</td>\n",
       "      <td>2282768.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2282768.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name    salary   total_be  restricted_stock  \\\n",
       "93   SHERRICK JEFFREY B       NaN  1426469.0          405999.0   \n",
       "109     HUMPHREY GENE E  130724.0  2282768.0               NaN   \n",
       "\n",
       "     total_stock_value  to_messages    poi  \n",
       "93           1832468.0        613.0  False  \n",
       "109          2282768.0        128.0  False  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enron[(enron['fraction_to_poi']>0.7)][['name','salary','total_be',\\\n",
    "                                       'restricted_stock','total_stock_value','to_messages','poi']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "      <th>bonus</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>total_be</th>\n",
       "      <th>total_millions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.700000e+01</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.834449e+05</td>\n",
       "      <td>2.075000e+06</td>\n",
       "      <td>1.046379e+07</td>\n",
       "      <td>9.165671e+06</td>\n",
       "      <td>7.913590e+06</td>\n",
       "      <td>8.820307e+06</td>\n",
       "      <td>17.079261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.783597e+05</td>\n",
       "      <td>2.047437e+06</td>\n",
       "      <td>1.238259e+07</td>\n",
       "      <td>1.384117e+07</td>\n",
       "      <td>2.396549e+07</td>\n",
       "      <td>1.222914e+07</td>\n",
       "      <td>35.289434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.584030e+05</td>\n",
       "      <td>2.000000e+05</td>\n",
       "      <td>3.847280e+05</td>\n",
       "      <td>1.260270e+05</td>\n",
       "      <td>9.109300e+04</td>\n",
       "      <td>8.000000e+05</td>\n",
       "      <td>1.765324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.401890e+05</td>\n",
       "      <td>7.750000e+05</td>\n",
       "      <td>1.456581e+06</td>\n",
       "      <td>1.016450e+06</td>\n",
       "      <td>1.142396e+06</td>\n",
       "      <td>1.262500e+06</td>\n",
       "      <td>3.140359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.786010e+05</td>\n",
       "      <td>1.275000e+06</td>\n",
       "      <td>3.914557e+06</td>\n",
       "      <td>2.206836e+06</td>\n",
       "      <td>1.754028e+06</td>\n",
       "      <td>2.079817e+06</td>\n",
       "      <td>4.434161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.151890e+05</td>\n",
       "      <td>2.062500e+06</td>\n",
       "      <td>1.938604e+07</td>\n",
       "      <td>1.051133e+07</td>\n",
       "      <td>2.665345e+06</td>\n",
       "      <td>7.990914e+06</td>\n",
       "      <td>11.274354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.111258e+06</td>\n",
       "      <td>7.000000e+06</td>\n",
       "      <td>3.434838e+07</td>\n",
       "      <td>4.911008e+07</td>\n",
       "      <td>1.035598e+08</td>\n",
       "      <td>4.134838e+07</td>\n",
       "      <td>152.669871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             salary         bonus  exercised_stock_options  total_stock_value  \\\n",
       "count  1.700000e+01  1.600000e+01             1.200000e+01       1.800000e+01   \n",
       "mean   3.834449e+05  2.075000e+06             1.046379e+07       9.165671e+06   \n",
       "std    2.783597e+05  2.047437e+06             1.238259e+07       1.384117e+07   \n",
       "min    1.584030e+05  2.000000e+05             3.847280e+05       1.260270e+05   \n",
       "25%    2.401890e+05  7.750000e+05             1.456581e+06       1.016450e+06   \n",
       "50%    2.786010e+05  1.275000e+06             3.914557e+06       2.206836e+06   \n",
       "75%    4.151890e+05  2.062500e+06             1.938604e+07       1.051133e+07   \n",
       "max    1.111258e+06  7.000000e+06             3.434838e+07       4.911008e+07   \n",
       "\n",
       "       total_payments      total_be  total_millions  \n",
       "count    1.800000e+01  1.800000e+01       18.000000  \n",
       "mean     7.913590e+06  8.820307e+06       17.079261  \n",
       "std      2.396549e+07  1.222914e+07       35.289434  \n",
       "min      9.109300e+04  8.000000e+05        1.765324  \n",
       "25%      1.142396e+06  1.262500e+06        3.140359  \n",
       "50%      1.754028e+06  2.079817e+06        4.434161  \n",
       "75%      2.665345e+06  7.990914e+06       11.274354  \n",
       "max      1.035598e+08  4.134838e+07      152.669871  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#POI stats\n",
    "\n",
    "poi_info = enron[enron.poi.isin([True])]\n",
    "poi_money = poi_info[['salary','bonus','exercised_stock_options','total_stock_value',\\\n",
    "                      'total_payments','total_be','total_millions']].describe()\n",
    "poi_money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <th>to_messages</th>\n",
       "      <th>from_messages</th>\n",
       "      <th>fraction_from_poi</th>\n",
       "      <th>fraction_to_poi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1783.000000</td>\n",
       "      <td>2417.142857</td>\n",
       "      <td>300.357143</td>\n",
       "      <td>0.047507</td>\n",
       "      <td>0.345470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1264.996625</td>\n",
       "      <td>1961.858101</td>\n",
       "      <td>805.844574</td>\n",
       "      <td>0.032085</td>\n",
       "      <td>0.156894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>91.000000</td>\n",
       "      <td>225.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.021339</td>\n",
       "      <td>0.173611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1059.250000</td>\n",
       "      <td>1115.750000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.026900</td>\n",
       "      <td>0.228580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1589.000000</td>\n",
       "      <td>1875.000000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>0.030639</td>\n",
       "      <td>0.276389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2165.250000</td>\n",
       "      <td>2969.250000</td>\n",
       "      <td>101.500000</td>\n",
       "      <td>0.059118</td>\n",
       "      <td>0.427083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5521.000000</td>\n",
       "      <td>7991.000000</td>\n",
       "      <td>3069.000000</td>\n",
       "      <td>0.136519</td>\n",
       "      <td>0.656250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       shared_receipt_with_poi  to_messages  from_messages  fraction_from_poi  \\\n",
       "count                14.000000    14.000000      14.000000          14.000000   \n",
       "mean               1783.000000  2417.142857     300.357143           0.047507   \n",
       "std                1264.996625  1961.858101     805.844574           0.032085   \n",
       "min                  91.000000   225.000000      16.000000           0.021339   \n",
       "25%                1059.250000  1115.750000      33.000000           0.026900   \n",
       "50%                1589.000000  1875.000000      44.500000           0.030639   \n",
       "75%                2165.250000  2969.250000     101.500000           0.059118   \n",
       "max                5521.000000  7991.000000    3069.000000           0.136519   \n",
       "\n",
       "       fraction_to_poi  \n",
       "count        14.000000  \n",
       "mean          0.345470  \n",
       "std           0.156894  \n",
       "min           0.173611  \n",
       "25%           0.228580  \n",
       "50%           0.276389  \n",
       "75%           0.427083  \n",
       "max           0.656250  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poi_email_behavior = poi_info[['shared_receipt_with_poi','to_messages', \\\n",
    "                               'from_messages','fraction_from_poi','fraction_to_poi']].describe()\n",
    "poi_email_behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "      <th>bonus</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>total_be</th>\n",
       "      <th>total_millions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>-60.000000</td>\n",
       "      <td>-4.900000e+01</td>\n",
       "      <td>-7.700000e+01</td>\n",
       "      <td>-8.900000e+01</td>\n",
       "      <td>-8.800000e+01</td>\n",
       "      <td>-1.090000e+02</td>\n",
       "      <td>-109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>121293.375859</td>\n",
       "      <td>1.088175e+06</td>\n",
       "      <td>8.516041e+06</td>\n",
       "      <td>6.791586e+06</td>\n",
       "      <td>6.188499e+06</td>\n",
       "      <td>6.950279e+06</td>\n",
       "      <td>13.639208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>139128.027285</td>\n",
       "      <td>8.735576e+05</td>\n",
       "      <td>9.835520e+06</td>\n",
       "      <td>1.030615e+07</td>\n",
       "      <td>2.134720e+07</td>\n",
       "      <td>9.535142e+06</td>\n",
       "      <td>30.449457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>157926.000000</td>\n",
       "      <td>1.300000e+05</td>\n",
       "      <td>3.814430e+05</td>\n",
       "      <td>1.701200e+05</td>\n",
       "      <td>9.094500e+04</td>\n",
       "      <td>8.000000e+05</td>\n",
       "      <td>1.765324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34068.000000</td>\n",
       "      <td>3.750000e+05</td>\n",
       "      <td>1.020066e+06</td>\n",
       "      <td>5.917658e+05</td>\n",
       "      <td>8.119162e+05</td>\n",
       "      <td>1.041121e+06</td>\n",
       "      <td>2.632424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26947.000000</td>\n",
       "      <td>5.750000e+05</td>\n",
       "      <td>2.884228e+06</td>\n",
       "      <td>1.176506e+06</td>\n",
       "      <td>6.979350e+05</td>\n",
       "      <td>1.193586e+06</td>\n",
       "      <td>2.549413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>126600.000000</td>\n",
       "      <td>1.062500e+06</td>\n",
       "      <td>1.722087e+07</td>\n",
       "      <td>8.203751e+06</td>\n",
       "      <td>6.593195e+05</td>\n",
       "      <td>5.740393e+06</td>\n",
       "      <td>6.957028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50326.000000</td>\n",
       "      <td>-1.000000e+06</td>\n",
       "      <td>1.898422e+07</td>\n",
       "      <td>2.529215e+07</td>\n",
       "      <td>8.630726e+07</td>\n",
       "      <td>2.498422e+07</td>\n",
       "      <td>120.795156</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              salary         bonus  exercised_stock_options  \\\n",
       "count     -60.000000 -4.900000e+01            -7.700000e+01   \n",
       "mean   121293.375859  1.088175e+06             8.516041e+06   \n",
       "std    139128.027285  8.735576e+05             9.835520e+06   \n",
       "min    157926.000000  1.300000e+05             3.814430e+05   \n",
       "25%     34068.000000  3.750000e+05             1.020066e+06   \n",
       "50%     26947.000000  5.750000e+05             2.884228e+06   \n",
       "75%    126600.000000  1.062500e+06             1.722087e+07   \n",
       "max     50326.000000 -1.000000e+06             1.898422e+07   \n",
       "\n",
       "       total_stock_value  total_payments      total_be  total_millions  \n",
       "count      -8.900000e+01   -8.800000e+01 -1.090000e+02     -109.000000  \n",
       "mean        6.791586e+06    6.188499e+06  6.950279e+06       13.639208  \n",
       "std         1.030615e+07    2.134720e+07  9.535142e+06       30.449457  \n",
       "min         1.701200e+05    9.094500e+04  8.000000e+05        1.765324  \n",
       "25%         5.917658e+05    8.119162e+05  1.041121e+06        2.632424  \n",
       "50%         1.176506e+06    6.979350e+05  1.193586e+06        2.549413  \n",
       "75%         8.203751e+06    6.593195e+05  5.740393e+06        6.957028  \n",
       "max         2.529215e+07    8.630726e+07  2.498422e+07      120.795156  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#difference in non-poi compensation and poi compensation\n",
    "\n",
    "difference_in_money = poi_money - non_poi_money\n",
    "difference_in_money"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the table above that money matters!  The mean difference is especially telling.  And, although upper management tends to have greater compensation, you can't help but be shocked by the tremendous gap seen here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <th>to_messages</th>\n",
       "      <th>from_messages</th>\n",
       "      <th>fraction_from_poi</th>\n",
       "      <th>fraction_to_poi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>-58.000000</td>\n",
       "      <td>-58.000000</td>\n",
       "      <td>-58.000000</td>\n",
       "      <td>-58.000000</td>\n",
       "      <td>-58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>724.472222</td>\n",
       "      <td>410.031746</td>\n",
       "      <td>-368.406746</td>\n",
       "      <td>0.011399</td>\n",
       "      <td>0.192800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>132.492868</td>\n",
       "      <td>-731.307854</td>\n",
       "      <td>-1173.153226</td>\n",
       "      <td>-0.009844</td>\n",
       "      <td>-0.049163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.021339</td>\n",
       "      <td>0.173611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>867.750000</td>\n",
       "      <td>602.000000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>0.019140</td>\n",
       "      <td>0.228580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>995.000000</td>\n",
       "      <td>931.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.007898</td>\n",
       "      <td>0.222613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>529.750000</td>\n",
       "      <td>378.500000</td>\n",
       "      <td>-115.000000</td>\n",
       "      <td>0.008413</td>\n",
       "      <td>0.202083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>994.000000</td>\n",
       "      <td>-7158.000000</td>\n",
       "      <td>-11299.000000</td>\n",
       "      <td>-0.080822</td>\n",
       "      <td>-0.343750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       shared_receipt_with_poi  to_messages  from_messages  fraction_from_poi  \\\n",
       "count               -58.000000   -58.000000     -58.000000         -58.000000   \n",
       "mean                724.472222   410.031746    -368.406746           0.011399   \n",
       "std                 132.492868  -731.307854   -1173.153226          -0.009844   \n",
       "min                  89.000000   168.000000       4.000000           0.021339   \n",
       "25%                 867.750000   602.000000      12.500000           0.019140   \n",
       "50%                 995.000000   931.000000       3.500000           0.007898   \n",
       "75%                 529.750000   378.500000    -115.000000           0.008413   \n",
       "max                 994.000000 -7158.000000  -11299.000000          -0.080822   \n",
       "\n",
       "       fraction_to_poi  \n",
       "count       -58.000000  \n",
       "mean          0.192800  \n",
       "std          -0.049163  \n",
       "min           0.173611  \n",
       "25%           0.228580  \n",
       "50%           0.222613  \n",
       "75%           0.202083  \n",
       "max          -0.343750  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#difference in non-poi email behavior and poi behavior\n",
    "\n",
    "difference_in_email = poi_email_behavior - non_poi_email_behavior\n",
    "difference_in_email"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My original email behavior table was a bit less telling than the money table, but I was able to scale features to reflect e-mail behavior more accurately.  The updated tables can be seen above with the fraction of emails sent to and from POIs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>salary</th>\n",
       "      <th>bonus</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>total_be</th>\n",
       "      <th>total_millions</th>\n",
       "      <th>from_messages</th>\n",
       "      <th>fraction_to_poi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>LAY KENNETH L</td>\n",
       "      <td>1072321.0</td>\n",
       "      <td>7000000.0</td>\n",
       "      <td>34348384.0</td>\n",
       "      <td>41348384.0</td>\n",
       "      <td>152.669871</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>SKILLING JEFFREY K</td>\n",
       "      <td>1111258.0</td>\n",
       "      <td>5600000.0</td>\n",
       "      <td>19250000.0</td>\n",
       "      <td>24850000.0</td>\n",
       "      <td>34.776388</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>HIRKO JOSEPH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30766064.0</td>\n",
       "      <td>30766064.0</td>\n",
       "      <td>30.857157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>RICE KENNETH D</td>\n",
       "      <td>420636.0</td>\n",
       "      <td>1750000.0</td>\n",
       "      <td>19794175.0</td>\n",
       "      <td>21544175.0</td>\n",
       "      <td>23.047589</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>YEAGER F SCOTT</td>\n",
       "      <td>158403.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8308552.0</td>\n",
       "      <td>8308552.0</td>\n",
       "      <td>12.245058</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>DELAINEY DAVID W</td>\n",
       "      <td>365163.0</td>\n",
       "      <td>3000000.0</td>\n",
       "      <td>2291113.0</td>\n",
       "      <td>5291113.0</td>\n",
       "      <td>8.362240</td>\n",
       "      <td>3069.0</td>\n",
       "      <td>0.198436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>HANNON KEVIN P</td>\n",
       "      <td>243293.0</td>\n",
       "      <td>1500000.0</td>\n",
       "      <td>5538001.0</td>\n",
       "      <td>7038001.0</td>\n",
       "      <td>6.679747</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.656250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>BELDEN TIMOTHY N</td>\n",
       "      <td>213999.0</td>\n",
       "      <td>5249999.0</td>\n",
       "      <td>953136.0</td>\n",
       "      <td>6203135.0</td>\n",
       "      <td>6.612335</td>\n",
       "      <td>484.0</td>\n",
       "      <td>0.223140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>SHELBY REX</td>\n",
       "      <td>211844.0</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>1624396.0</td>\n",
       "      <td>1824396.0</td>\n",
       "      <td>4.497501</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.358974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>CAUSEY RICHARD A</td>\n",
       "      <td>415189.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>4.370821</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0.244898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>FASTOW ANDREW S</td>\n",
       "      <td>440698.0</td>\n",
       "      <td>1300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300000.0</td>\n",
       "      <td>4.218495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>KOPPER MICHAEL J</td>\n",
       "      <td>224305.0</td>\n",
       "      <td>800000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800000.0</td>\n",
       "      <td>3.637644</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KOENIG MARK E</td>\n",
       "      <td>309946.0</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>671737.0</td>\n",
       "      <td>1371737.0</td>\n",
       "      <td>3.507476</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.245902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>RIEKER PAULA H</td>\n",
       "      <td>249201.0</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>1635238.0</td>\n",
       "      <td>2335238.0</td>\n",
       "      <td>3.017987</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0.585366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>BOWEN JR RAYMOND M</td>\n",
       "      <td>278601.0</td>\n",
       "      <td>1350000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1350000.0</td>\n",
       "      <td>2.921644</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>COLWELL WESLEY</td>\n",
       "      <td>288542.0</td>\n",
       "      <td>1200000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200000.0</td>\n",
       "      <td>2.188586</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0.275000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GLISAN JR BEN F</td>\n",
       "      <td>274975.0</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>384728.0</td>\n",
       "      <td>984728.0</td>\n",
       "      <td>2.050830</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>CALGER CHRISTOPHER F</td>\n",
       "      <td>240189.0</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>1.765324</td>\n",
       "      <td>144.0</td>\n",
       "      <td>0.173611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name     salary      bonus  exercised_stock_options  \\\n",
       "62          LAY KENNETH L  1072321.0  7000000.0               34348384.0   \n",
       "59     SKILLING JEFFREY K  1111258.0  5600000.0               19250000.0   \n",
       "73           HIRKO JOSEPH        NaN        NaN               30766064.0   \n",
       "132        RICE KENNETH D   420636.0  1750000.0               19794175.0   \n",
       "139        YEAGER F SCOTT   158403.0        NaN                8308552.0   \n",
       "55       DELAINEY DAVID W   365163.0  3000000.0                2291113.0   \n",
       "61         HANNON KEVIN P   243293.0  1500000.0                5538001.0   \n",
       "103      BELDEN TIMOTHY N   213999.0  5249999.0                 953136.0   \n",
       "63             SHELBY REX   211844.0   200000.0                1624396.0   \n",
       "82       CAUSEY RICHARD A   415189.0  1000000.0                      NaN   \n",
       "107       FASTOW ANDREW S   440698.0  1300000.0                      NaN   \n",
       "131      KOPPER MICHAEL J   224305.0   800000.0                      NaN   \n",
       "5           KOENIG MARK E   309946.0   700000.0                 671737.0   \n",
       "140        RIEKER PAULA H   249201.0   700000.0                1635238.0   \n",
       "37     BOWEN JR RAYMOND M   278601.0  1350000.0                      NaN   \n",
       "105        COLWELL WESLEY   288542.0  1200000.0                      NaN   \n",
       "17        GLISAN JR BEN F   274975.0   600000.0                 384728.0   \n",
       "120  CALGER CHRISTOPHER F   240189.0  1250000.0                      NaN   \n",
       "\n",
       "       total_be  total_millions  from_messages  fraction_to_poi  \n",
       "62   41348384.0      152.669871           36.0         0.444444  \n",
       "59   24850000.0       34.776388          108.0         0.277778  \n",
       "73   30766064.0       30.857157            NaN              NaN  \n",
       "132  21544175.0       23.047589           18.0         0.222222  \n",
       "139   8308552.0       12.245058            NaN              NaN  \n",
       "55    5291113.0        8.362240         3069.0         0.198436  \n",
       "61    7038001.0        6.679747           32.0         0.656250  \n",
       "103   6203135.0        6.612335          484.0         0.223140  \n",
       "63    1824396.0        4.497501           39.0         0.358974  \n",
       "82    1000000.0        4.370821           49.0         0.244898  \n",
       "107   1300000.0        4.218495            NaN              NaN  \n",
       "131    800000.0        3.637644            NaN              NaN  \n",
       "5     1371737.0        3.507476           61.0         0.245902  \n",
       "140   2335238.0        3.017987           82.0         0.585366  \n",
       "37    1350000.0        2.921644           27.0         0.555556  \n",
       "105   1200000.0        2.188586           40.0         0.275000  \n",
       "17     984728.0        2.050830           16.0         0.375000  \n",
       "120   1250000.0        1.765324          144.0         0.173611  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#poi name, salary, bonus, stock options, total bonus and options, from messages, \n",
    "# and fraction to poi, ordered by total descending\n",
    "\n",
    "poi_info[['name','salary','bonus','exercised_stock_options','total_be','total_millions',\\\n",
    "          'from_messages','fraction_to_poi']].sort_values('total_millions',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although we don't have the salary and bonus data for Joseph Hirko, his exercised stock options is second to Kenneth Lay.  Since \"exercised_stock_options\" seems to be a key indicator of a POI when salary/bonus is unavailable, that is definitely a feature we'll want to include in our final feature selection.  These features may be even more robust when taking the total of bonus and options.  In fact, let's add the feature, total_be, to our dataset and maybe it will come in handy.  *I went back and added this feature to the top of my code in order to include it in the info and analysis.*\n",
    "<br>\n",
    "Also, it was interesting to see that POIs don't have as many \"from_messages\" as non-POIs.  David Delainey is the only one that has well over 500 emails from him.  This may be a telling behavior of POIs, as they may prefer to talk face-to-face with others.\n",
    "<br>\n",
    "[Top](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Features'></a>\n",
    "\n",
    "## EDA - Explore Features\n",
    "\n",
    "In this section, we'll visualize some of our features in order to explore them further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Salary'></a>\n",
    "\n",
    "### Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poi\n",
       "False    262151.506494\n",
       "True     383444.882353\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_salary = enron.groupby('poi').mean()['salary']\n",
    "average_salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d4330b1d30>"
      ]
     },
     "execution_count": 28,
     "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": [
    "sns.boxplot(x='poi',y='salary',data=enron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Bonus'></a>\n",
    "\n",
    "### Bonus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poi\n",
       "False    9.868249e+05\n",
       "True     2.075000e+06\n",
       "Name: bonus, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_bonus = enron.groupby('poi').mean()['bonus']\n",
    "average_bonus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d4333fdcf8>"
      ]
     },
     "execution_count": 30,
     "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": [
    "sns.boxplot(x='poi',y='bonus',data=enron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow!  An 8 million dollar bonus seems a bit much, but you can see the difference between John and Ken in other ways.  Below, you can see the difference between all other financial features is significant.  And, bonuses among POIs are still higher on average than non-POIs.  So, despite our non-POI outlier, this feature may still be useful in training our algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>salary</th>\n",
       "      <th>bonus</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>restricted_stock</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>poi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>LAY KENNETH L</td>\n",
       "      <td>1072321.0</td>\n",
       "      <td>7000000.0</td>\n",
       "      <td>34348384.0</td>\n",
       "      <td>14761694.0</td>\n",
       "      <td>49110078.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>LAVORATO JOHN J</td>\n",
       "      <td>339288.0</td>\n",
       "      <td>8000000.0</td>\n",
       "      <td>4158995.0</td>\n",
       "      <td>1008149.0</td>\n",
       "      <td>5167144.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                name     salary      bonus  exercised_stock_options  \\\n",
       "62     LAY KENNETH L  1072321.0  7000000.0               34348384.0   \n",
       "115  LAVORATO JOHN J   339288.0  8000000.0                4158995.0   \n",
       "\n",
       "     restricted_stock  total_stock_value    poi  \n",
       "62         14761694.0         49110078.0   True  \n",
       "115         1008149.0          5167144.0  False  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enron[(enron['bonus']>6000000)][['name','salary','bonus','exercised_stock_options','restricted_stock','total_stock_value','poi']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Total Payments'></a>\n",
    "\n",
    "### Total Payments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poi\n",
       "False    1.725091e+06\n",
       "True     7.913590e+06\n",
       "Name: total_payments, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_total_payments = enron.groupby('poi').mean()['total_payments']\n",
    "average_total_payments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d43341cc18>"
      ]
     },
     "execution_count": 33,
     "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": [
    "sns.boxplot(x='poi',y='total_payments',data=enron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow!  That last boxplot has an outlier that is obviously pulling the mean waaaaaaaay up.  Who is that?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>poi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>LAY KENNETH L</td>\n",
       "      <td>103559793.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name  total_payments   poi\n",
       "62  LAY KENNETH L     103559793.0  True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enron[(enron['total_payments']>40000000)][['name','total_payments','poi']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d4334a5a90>"
      ]
     },
     "execution_count": 35,
     "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": [
    "#take Ken Lay out of the poi boxplot\n",
    "\n",
    "kl_not_in = enron[(enron['total_payments']<40000000)]\n",
    "\n",
    "sns.boxplot(x='poi',y='total_payments',data=kl_not_in)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, at least now we can see the boxplots more clearly.  There's not much of a difference between POI and non-POI here when we take Ken Lay out, so total_payments probably won't be a feature we'll use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Stock Options'></a>\n",
    "\n",
    "### Exercised Stock Options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poi\n",
       "False    1.947752e+06\n",
       "True     1.046379e+07\n",
       "Name: exercised_stock_options, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_optionsvalue = enron.groupby('poi').mean()['exercised_stock_options']\n",
    "average_optionsvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d43341c0b8>"
      ]
     },
     "execution_count": 37,
     "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": [
    "sns.boxplot(x='poi',y='exercised_stock_options',data=enron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercised stock options definitely looks to be higher among POIs, so this will definitely be a feature to include in our list of features for our algorithm.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Total BE'></a>\n",
    "\n",
    "### Total Bonus and Exercised Stock Options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poi\n",
       "False    1.870028e+06\n",
       "True     8.820307e+06\n",
       "Name: total_be, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_total_sbe = enron.groupby('poi').mean()['total_be']\n",
    "average_total_sbe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d4335280b8>"
      ]
     },
     "execution_count": 39,
     "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": [
    "sns.boxplot(x='poi',y='total_be',data=enron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total Bonus and Exercised Stock Options might be useful, but it might also just add to the noise.  So, maybe we won't use this one.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Total Stock Value'></a>\n",
    "\n",
    "### Total Stock Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poi\n",
       "False    2.374085e+06\n",
       "True     9.165671e+06\n",
       "Name: total_stock_value, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_stockvalue = enron.groupby('poi').mean()['total_stock_value']\n",
    "average_stockvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d434539e48>"
      ]
     },
     "execution_count": 41,
     "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": [
    "sns.boxplot(x='poi',y='total_stock_value',data=enron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total stock value for POIs on average is much higher than non-POIs.  This feature is another good option for our POI identifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Total Millions'></a>\n",
    "\n",
    "### Total Payments and Stock Value in Millions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poi\n",
       "False     3.440052\n",
       "True     17.079261\n",
       "Name: total_millions, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_total_comp = enron.groupby('poi').mean()['total_millions']\n",
    "average_total_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d434586da0>"
      ]
     },
     "execution_count": 43,
     "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": [
    "sns.boxplot(x='poi',y='total_millions',data= enron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try that one again without Ken Lay..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d4345cb4e0>"
      ]
     },
     "execution_count": 44,
     "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": [
    "sns.boxplot(x='poi',y='total_millions',data= kl_not_in)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmmmm, maybe we didn't need to add this feature.  We can look closer by using lmplot and pairplot later on in our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Shared Receipt'></a>\n",
    "\n",
    "### Shared Receipt with POI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poi\n",
       "False    1058.527778\n",
       "True     1783.000000\n",
       "Name: shared_receipt_with_poi, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_shared_receipt = enron.groupby('poi').mean()['shared_receipt_with_poi']\n",
    "average_shared_receipt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d43458e550>"
      ]
     },
     "execution_count": 46,
     "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": [
    "sns.boxplot(x='poi',y='shared_receipt_with_poi',data= enron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='To Messages'></a>\n",
    "\n",
    "### To Messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poi\n",
       "False    2007.111111\n",
       "True     2417.142857\n",
       "Name: to_messages, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_to = enron.groupby('poi').mean()['to_messages']\n",
    "average_to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d434668048>"
      ]
     },
     "execution_count": 48,
     "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": [
    "sns.boxplot(x='poi',y='to_messages',data= enron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='From Messages'></a>\n",
    "\n",
    "### From Messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poi\n",
       "False    668.763889\n",
       "True     300.357143\n",
       "Name: from_messages, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_from = enron.groupby('poi').mean()['from_messages']\n",
    "average_from"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d4330a5ba8>"
      ]
     },
     "execution_count": 50,
     "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": [
    "sns.boxplot(x='poi',y='from_messages',data= enron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='FTP'></a>\n",
    "\n",
    "### Fraction to POI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poi\n",
       "False    0.152669\n",
       "True     0.345470\n",
       "Name: fraction_to_poi, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_fraction_to = enron.groupby('poi').mean()['fraction_to_poi']\n",
    "average_fraction_to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d4346e1828>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEKCAYAAAAW8vJGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAFddJREFUeJzt3X+U5XV93/Hna2cFFxWMw9bEBVzsblS0RnGCRnuMCcvpQFtoq1E4VSZq5CQV2Na09UdSk5jWtKSpBzZYpf4azIlWjalbzrIErLTWqmH5ISsodYqr7GJ0MxJBWcTZffePexfvd5jduSz7ne9l5/k4Z87c7/d+7p3X7t6zr/l8f6aqkCRpvxVdB5AkjRaLQZLUYDFIkhosBklSg8UgSWqwGCRJDRaDJKnBYpAkNVgMkqSGlV0HOBTHH398rV27tusYkvSYcuONN/51Va1ebNxjshjWrl3Ltm3buo4hSY8pSb45zDg3JUmSGiwGSVKDxSBJarAYJEkNFsMyNzs7y8UXX8zs7GzXUSSNiFaLIckHk3w3yVcO8HySXJZkJsmtSU5tM48ebnp6mu3bt3PllVd2HUXSiGh7xvBhYPIgz58JrO9/XQD855bzaMDs7Cxbt26lqti6dauzBklAy8VQVf8L+N5BhpwDXFk9XwSenORn2sykn5ienmbfvn0A7N2711mDJKD7fQxrgLsGlnf21z1MkguSbEuybffu3UsS7kh33XXXMTc3B8Dc3BzXXnttx4kkjYKuiyELrKuFBlbVFVU1UVUTq1cveka3hrBhwwZWruyd/L5y5UrOOOOMjhNJGgVdF8NO4MSB5ROAuzvKsuxMTU2xYkXvIzA2Nsb555/fcSJJo6DrYtgMnN8/OunFwPer6tsdZ1o2xsfHmZycJAmTk5OMj493HUnSCGj1InpJPgq8HDg+yU7gd4DHAVTVe4EtwFnADHA/8Lo28+jhpqam2LFjh7MFSQ9J1YKb9EfaxMREeXVVSXpkktxYVROLjet6U5IkacRYDJKkBotBktRgMUiSGiwGSVKDxSBJarAYJEkNFoMkqcFikCQ1WAySpAaLQZLUYDFIkhosBklSg8UgSWqwGCRJDRaDJKnBYpAkNVgMkqQGi0GS1GAxSJIaLAZJUoPFIElqsBgkSQ0WgySpwWKQJDVYDJKkBotBktRgMUiSGiwGSVKDxSBJami9GJJMJrkjyUySty7w/ElJPpvk5iS3Jjmr7UySpANrtRiSjAGXA2cCpwDnJTll3rDfBj5eVS8AzgXe02YmSdLBtT1jOA2Yqao7q+pB4GPAOfPGFHBs//FxwN0tZ5IkHcTKlt9/DXDXwPJO4EXzxvwu8BdJLgKeAGxoOZMk6SDanjFkgXU1b/k84MNVdQJwFvCRJA/LleSCJNuSbNu9e3cLUSVJ0H4x7AROHFg+gYdvKnoD8HGAqvoC8Hjg+PlvVFVXVNVEVU2sXr26pbiSpLaL4QZgfZKTkxxFb+fy5nljvgWcDpDk2fSKwSmBJHWk1WKoqjngQuAa4Kv0jj66Lck7k5zdH/abwBuTfBn4KPCrVTV/c5MkaYm0vfOZqtoCbJm37h0Dj28HXtp2DknScDzzWZLUYDFIkhosBklSg8UgSWqwGCRJDRaDJKnBYpAkNVgMy9zs7CwXX3wxs7OzXUeRNCIshmVuenqa7du3c+WVV3YdRdKIsBiWsdnZWbZu3UpVsXXrVmcNkgCLYVmbnp5m3759AOzdu9dZgyTAYljWrrvuOubm5gCYm5vj2muv7TiRpFFgMSxjGzZsYOXK3nUUV65cyRlnnNFxIkmjwGJYxqamplixovcRGBsb4/zzz+84kaRRYDEsY+Pj40xOTpKEyclJxsfHu44kaQS0fj8GjbapqSl27NjhbEHSQyyGZW58fJzLLrus6xiSRoibkiRJDRaDJKnBYpAkNRx0H0OSf11VlyTZBNT856vq4taSSZI6sdjO56/2v29rO4gkaTQctBiq6r/3v08DJHlSb7F+sATZJHVk06ZNzMzMdJph165dAKxZs6bTHADr1q3joosu6jrGkhlqH0OS5ya5GfgKcHuSG5M8p91okpazPXv2sGfPnq5jLEvDnsdwBfDmqvosQJKXA/8FeElLuSR1aBR+O964cSMAl156acdJlp9hj0p6wv5SAKiq64EntJJIktSpYWcMdyb5N8BH+suvAb7RTiRJUpeGnTG8HlgNfAr48/7j17UVSpLUnaGKoaru6Z+z8EvAy6pqY1Xd0240LYXZ2Vkuvvhib+sp6SHDHpX080m2A18Gtif5cpIXthtNS2F6eprt27d7W09JDxl2U9IHgH9WVWurai3wJuBDraXSkpidnWXr1q1UFVu3bnXWIAkYvhjuq6rP7V+oqv8N3DfMC5NMJrkjyUyStx5gzKuS3J7ktiR/OmQmPUrT09Ps27cPgL179zprkAQMXwx/meR9SV6e5BeTvAe4PsmpSU490IuSjAGXA2cCpwDnJTll3pj1wNuAl1bVc4B/fkh/Ej1i1113HXNzcwDMzc1x7bXXdpxI0igY9nDV5/e//8689S+hd3G9Xz7A604DZqrqToAkHwPOAW4fGPNG4PL9O7Or6rtDZtKjtGHDBrZs2cLc3BwrV67kjDPO6DqSpBEwVDFU1S8d7PkkU/uvpzTPGuCugeWdwIvmjfnZ/nt8HhgDfreqti7wMy4ALgA46aSThomtRUxNTXH11VcDsGLFCm/vKQk4fPdj2HiA9Vlg3fzLd68E1gMvB84D3p/kyQ97UdUVVTVRVROrV69+NFnVNz4+/tAFyp72tKcxPj7ecSJJo+BwFcNCBQC9GcKJA8snAHcvMObTVfXjqvoGcAe9olDLZmdnufvu3j/H3Xff7VFJkoDDVwwPu4lP3w3A+iQnJzkKOBfYPG/Mf6N34hxJjqe3aenOw5RLBzF4VNK+ffs8KkkS0PKMoarmgAuBa+jd9OfjVXVbkncmObs/7BpgNsntwGeBf1VV/uq6BDwqSdJChj0qaTGfP9ATVbUF2DJv3TsGHhfw5v6XlpBHJUlayLCXxDguybuTbOt//VGS4/Y/X1UXthdRbZmammLFit5HYGxszKOSJAHDb0r6IHAv8Kr+1714SYzHvPHxcSYnJ0nC5OSkRyVJAobflPS3q+oVA8u/l+SWNgJpaU1NTbFjxw5nC5IeMuyMYU+Sv7t/IclLAW/GegQYHx/nsssuc7Yg6SHDzhh+HbhyYL/CPcBUO5EkSV0athjuraqfS3IsQFXdm+TkFnMd8TZt2sTMzEzXMdi1axfAQ2dAd2XdunUjcQN6ScNvSvoz6BVCVd3bX/fJdiJpKe3Zs4c9e9wqKOknDjpjSPIs4DnAcUn+ycBTxwKPbzPYkW5UfjveuLF3matLL7204ySSRsVim5KeCfwD4MnAPxxYfx+9y2VLko4wBy2Gqvo08Okkv1BVXzjQuCRvq6o/OOzpJElLbqh9DAcrhb5fOQxZJEkjoO3LbkuSHmPavuy2JOkxxhmDJKnhcBXDJw7T+0iSOjbUmc9JVtM7PHXt4Guq6vX97+9qI5wkaekNe0mMTwOfA64D9rYXR5LUtWGL4ZiqekurSSRJI2HYfQxXJTmr1SSSpJEwbDFspFcODyS5r/9176KvkiQ95gy1KamqntR2EEnSaBh2HwNJzgZe1l+8vqquaieSJKlLQ21KSvLv6W1Our3/tbG/TpJ0hBl2xnAW8Pyq2geQZBq4GXhrW8EkSd14JGc+P3ng8XEHHCVJekwbdsbwB8DNST5L77pILwPe1loqSVJnhj0q6aNJrgd+nl4xvKWq/qrNYJKkbix6z+eq+lqSU/urdva/Py3J06rqpnbjScvLpk2bmJmZ6TrGSNj/97D/vuTL3bp165bsXvGLzRjeDFwA/NECzxXwy4c9kbSMzczM8PXbbuakJ3pJsqN+3NsF+qNvbus4Sfe+9YOxJf15i93z+YL+wzOr6oHB55I8vrVU0jJ20hP38vZTvbCAfuJdNx27pD9v2KOS/s+Q6x4myWSSO5LMJDng4a1JXpmkkkwMmUmS1ILF9jH8NLAGWJXkBfzkTm3HAscs9uZJxoDLgTPo7Z+4Icnmqrp93rgnARcDX3rEfwJJ0mG12D6Gvwf8KnACvf0M+4vhXuDtQ7z/acBMVd0JkORjwDn0zp4e9PvAJcC/HCq1JKk1i+1jmAamk7yiqv7sEN5/DXDXwPJO4EWDA/ozkROr6qokFoMkdWzYfQwvTPLQmc9JfirJvx3idVlgXQ28zwrg3cBvLvpGyQVJtiXZtnv37mEyS5IOwbDFcGZV/c3+haq6h971kxazEzhxYPkE4O6B5ScBzwWuT7IDeDGweaEd0FV1RVVNVNXE6tWrh4wtSXqkhi2GsSRH719Isgo4+iDj97sBWJ/k5CRHAecCm/c/WVXfr6rjq2ptVa0FvgicXVUeuCxJHRn2Wkl/AnwmyYfobQp6PTC92Iuqai7JhcA1wBjwwaq6Lck7gW1Vtfng7yBJWmrDXivpkiTbgdPp7Tf4/aq6ZsjXbgG2zFv3jgOMffkw7ylJas/Qd3CrqquBq1vMIkkaAcPewe3FSW5I8oMkDybZm8Rz9iXpCDTszuc/Bs4Dvg6sAn4N2NRWKElSdx7JpqSZJGNVtRf4UJKhrpUkSXpsGbYY7u8fbnpLkkuAbwNPaC+WJKkrw25Kem1/7IXAD+mdtPaKtkJJkrqz6Iyhf4XUf1dVrwEeAH6v9VSSpM4sOmPo71NY3d+UJEk6wg27j2EH8Pkkm+ltSgKgqv5TG6EkSd056IwhyUf6D18NXNUf/6SBL0nSEWaxGcMLkzwd+BaetyBJy8JixfBeYCtwMjB4xdPQu5jeM1rKJUnqyEE3JVXVZVX1bOBDVfWMga+Tq8pSkKQj0FDnMVTVb7QdRJI0GoY9wU2StExYDJKkBotBktRgMUiSGoa+7Lak9u3atYsf3jfGu246tusoGiHfvG+MJ+zatWQ/zxmDJKnBGYM0QtasWcOP5r7N20/1zrn6iXfddCxHr1mzZD/PGYMkqcFikCQ1WAySpAaLQZLUYDFIkhosBklSg8UgSWpYlucxbNq0iZmZma5jjIT9fw8bN27sOMloWLduHRdddFGnGb71A898BvjO/b3fW596zL6Ok3TvWz8YY/0S/rxlWQwzMzPc8pWvsveYp3QdpXMrHiwAbrzzOx0n6d7Y/d/rOgLr1q3rOsLIeLD/S8vRT/fvZD1L+9lYlsUAsPeYp7DnWWd1HUMjZNXXtnQdofPZyijZP4u99NJLO06y/LS+jyHJZJI7kswkeesCz785ye1Jbk3ymSRPbzuTJOnAWi2GJGPA5cCZwCnAeUlOmTfsZmCiqp4HfBK4pM1MkqSDa3vGcBowU1V3VtWDwMeAcwYHVNVnq+r+/uIXgRNaziRJOoi2i2ENcNfA8s7+ugN5A3D1Qk8kuSDJtiTbdu/efRgjSpIGtV0MWWBdLTgweQ0wAfzhQs9X1RVVNVFVE6tXrz6MESVJg9o+KmkncOLA8gnA3fMHJdkA/Bbwi1X1o5YzSZIOou0Zww3A+iQnJzkKOBfYPDggyQuA9wFnV9V3W84jSVpEq8VQVXPAhcA1wFeBj1fVbUnemeTs/rA/BJ4IfCLJLUk2H+DtJElLoPUT3KpqC7Bl3rp3DDze0HYGSY/MKFw2ZpQu1zIKl0pZSsv2zGdJo23VqlVdR1i2LAZJD7OcfjvWw3nZbUlSg8UgSWqwGCRJDRaDJKnBYpAkNVgMkqQGi0GS1GAxSJIaLAZJUoPFIElqsBgkSQ0WgySpwWKQJDVYDJKkBotBktRgMUiSGiwGSVKDxSBJarAYJEkNFoMkqWFl1wG6sGvXLsbu/z6rvral6ygaIWP3z7Jr11zXMaTOOWOQJDUsyxnDmjVr+KsfrWTPs87qOopGyKqvbWHNmqd2HUPqnDMGSVKDxSBJarAYJEkNFoMkqcFikCQ1tF4MSSaT3JFkJslbF3j+6CT/tf/8l5KsbTuTJOnAWi2GJGPA5cCZwCnAeUlOmTfsDcA9VbUOeDfwH9rMJEk6uLZnDKcBM1V1Z1U9CHwMOGfemHOA6f7jTwKnJ0nLuSRJB9D2CW5rgLsGlncCLzrQmKqaS/J9YBz46zaDjd3/vU4vibHigXvJvh939vNHTa14HPsef2ynGcbu/x7gCW5S28Ww0G/+dQhjSHIBcAHASSed9KhCrVu37lG9/nDYtWuOPXv2dB1jZKxatWoEzjp+6kh8NqSutV0MO4ETB5ZPAO4+wJidSVYCxwHfm/9GVXUFcAXAxMTEw4rjkbjooosezcsl6YjW9j6GG4D1SU5OchRwLrB53pjNwFT/8SuB/1FVj+o/fknSoWt1xtDfZ3AhcA0wBnywqm5L8k5gW1VtBj4AfCTJDL2ZwrltZpIkHVzrV1etqi3Alnnr3jHw+AHgV9rOIUkajmc+S5IaLAZJUoPFIElqsBgkSQ0WgySpIY/FUwaS7Aa+2XWOI8jxtHwJEukQ+dk8vJ5eVasXG/SYLAYdXkm2VdVE1zmk+fxsdsNNSZKkBotBktRgMQj6FyeURpCfzQ64j0GS1OCMQZLU0PpF9LT0kuwFtg+s+kdVteMAY9cCV1XVc9tPJkGSceAz/cWfBvYCu/vLp/VvA6wOWQxHpj1V9fyuQ0gLqapZ4PkASX4X+EFV/cfBMf37vqeq9i19QrkpaZlIsjbJ55Lc1P96yQJjnpPkL5PckuTWJOv7618zsP59ScaW/k+gI12SdUm+kuS9wE3AiUn+ZuD5c5O8v//4qUk+lWRb/7P54q5yH4kshiPTqv5/4rck+fP+uu8CZ1TVqcCrgcsWeN2vA5f2ZxsT9G63+uz++Jf21+8F/mn7fwQtU6cAH6iqFwC7DjLuMuCS/slvrwLevxThlgs3JR2ZFtqU9Djgj5Ps/8/9Zxd43ReA30pyAvCpqvp6ktOBFwI39Gb3rKJXMlIb/l9V3TDEuA3AM/ufSYCfSrKqqva0F235sBiWj38BfAf4OXozxQfmD6iqP03yJeDvA9ck+TUgwHRVvW0pw2rZ+uHA4330Pn/7PX7gcXBHdWvclLR8HAd8u78z77X07sHdkOQZwJ1VdRmwGXgevaNHXpnkb/XHPCXJ05cutpar/mf1niTrk6wA/vHA09cBb9q/0J8J6zCxGJaP9wBTSb5IbzPSDxcY82rgK0luAZ4FXFlVtwO/DfxFkluBa4GfWaLM0luArfR+Qdk5sP5NwEv7B0ncDryxi3BHKs98liQ1OGOQJDVYDJKkBotBktRgMUiSGiwGSVKDxSAtgSTvT3JK1zmkYXi4qiSpwRmDdAj6V6v9WpLp/klWn0xyTJLTk9ycZHuSDyY5uj/++iQTXeeWhmExSIfumcAVVfU84F7gzcCHgVdX1d+hdy2y3+gunnRoLAbp0N1VVZ/vP/4T4HTgG1X1f/vrpoGXdZJMehQsBunQuYNORySLQTp0JyX5hf7j8+hd8XNtknX9da8F/mcnyaRHwWKQDt1X6V2x9lbgKcC7gdcBn0iynd79BN7bYT7pkHi4qnQIkqwFrqqq53YcRTrsnDFIkhqcMUiSGpwxSJIaLAZJUoPFIElqsBgkSQ0WgySpwWKQJDX8f6SF5RCxbNCEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='poi',y='fraction_to_poi',data= enron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fraction_to_poi looks like a good feature to add to our list, since most of the poi distribution is in the upper range of the non-poi distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='FFP'></a>\n",
    "\n",
    "### Fraction from POI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "poi\n",
       "False    0.036107\n",
       "True     0.047507\n",
       "Name: fraction_from_poi, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_fraction_from = enron.groupby('poi').mean()['fraction_from_poi']\n",
    "average_fraction_from"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d434717be0>"
      ]
     },
     "execution_count": 54,
     "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": [
    "sns.boxplot(x='poi',y='fraction_from_poi',data= enron)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "<b>Pairplot Analysis</b>\n",
    "\n",
    "Now, let's take a look at some of our features in the following pairplot.  Maybe it will help us make our final decisions for our features list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmo4cob\\AppData\\Local\\conda\\conda\\envs\\tf\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:448: RuntimeWarning: invalid value encountered in greater\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "C:\\Users\\jmo4cob\\AppData\\Local\\conda\\conda\\envs\\tf\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:448: RuntimeWarning: invalid value encountered in less\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n"
     ]
    },
    {
     "data": {
      "image/png": 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L3R39oIgijLOyUFswX4jCw88KJSqWyBKzO/0tIK1VIBploAsWCxBKPJyRitqC+UIUHn5WKFGxAJGYw+mFWiVvtQVEdbEFxOpgAUKJiTNSUVswX4jCw88KJaK4KpFdLhdKS0ulDiOmHC5PcI2PlsgEAWqlHFYHB5YRERERUeKSvADZunUrXnzxRVgsFkycOBF33HEHli9fLnVYMWMLdMEKg1olh50FCBERERElMMkLkLfffht33303ioqKcMUVV+DLL7/Ehg0bpA4rZhxOD9StLEIYoFHJ2QWLiIiIiBKa5AWIKIoYOHAgtm3bhmuvvRZ6vR6i2NqIiM7D4fRArQpvKI5apWALCBERERElNMkLEJlMhk8//RTffvstrrnmGnz99dcQBEHqsGLG7vRA3coihAEalTy4cCERERERUSKSvAB56qmnsHbtWjzxxBMwGo1488038eyzz0odVkyIogi7ywtVmF2w1Co5bGwBISIiIqIEJvk0vCNHjsSyZcuCt1evXi1dMDHm9vjg84nhjwFRsgWEiIiIiBKb5AXIo48+2uT2t956K8aRxF6gmFAqwuyCpVbA7mIBQkRERESJS/ICZMKECcF/u91ubNmyBUOHDpUwotgJFCBhd8FSyuF0eeH1iZDLus44GSIiIiLqPCQvQCZNmtTo9n333SdRNLEVLEDCbQG5OFuW3emBXquMWlxE0bBl+xnk98tArlEf3FZqsuDAiSpMKOglYWTUWTDHKJEwX6krk3wQekOiKKKiokLqMGLC4fQCAJSK8AehA4DNzrVAKPHk98vAq6t2o9RkAeC/0L66ajfy+2VIHBl1FswxSiTMV+rKJG8BaTgG5OjRo7j66qtbvM+mTZvw5ptvwuPxYObMmZg+fXqTx3311Vd44YUX8MUXX0Qs3kgKjOcIdwxIsADhQHRKQLlGPZ6cdiVeXbUbU8cPxOqiI3hy2pUhv/4RdQRzjBIJ85W6MskLkPpjQARBwL333ouxY8c2e3x5eTlee+01fPTRR1CpVJg6dSpGjx6N/v37hxxXWVmJP//5z1GLOxICXbDUyvDehkAXLBtXQ6cElWvUY+r4gfjjO9sx/6ECXmgp4phjlEiYr9RVSd4Fa9KkSbj11ltxww034Fe/+hWGDx+Ourq6Zo/ftm0bCgoKkJqaCp1OhwkTJmDz5s2Njps3bx5mz54dzdA7zBEchB7+QoQAuBYIJaxSkwWri45g/kMFWF10JNj1gChSmGOUSJiv1FVJ3gKyevVqLFy4EG63/1d9URQhCAIOHz7c5PEVFRUwGo3B21lZWdi/f3/IMStWrMDll1+O4cOHt/jcZrMZZrM5ZFtZWVl7/ox2aWsBEuiCZeEYkC5H6lyNhED/5kAXg9zMpJDb1DlImavMMWoLqc+rzFfqyiQvQN555x3885//xJAhQ8I63ufzQRAuTUEbKFgCjh49iqKiIixbtqzVE8ny5cuxePHi9gUeAbZgF6wwFyK8WIBY7a6oxUTxSepcjYQDJ6pCLqyB/s8HTlTxYtuJSJmrzDFqC6nPq8xX6sokL0AyMzPDLj4AICcnB7t27QreNplMyMrKCt7evHkzTCYT7rrrLrjdblRUVGDatGlYtWpVo8eaOXNmo2mAy8rKmh3UHmkOlxeCAMjlMohi68erL44BsbIFpMuROlcjoalpJXONel5oOxkpc5U5Rm0h9XmV+UpdmeQFyNixY7Fq1SrceOONUKvVwe2pqalNHj9mzBi8/vrrqK6uhlarRVFREV588cXg/jlz5mDOnDkAgHPnzmHGjBlNFh8AkJKSgpSUlAj+NW3jcHqgUSkgQICI1isQlUIGQQCsHAPS5Uidq0ThYq5SomCuEklH8gJkyZIlcLlceOGFF4LbWhoDkp2djSeeeAIzZsyA2+3G5MmTMWzYMMyaNQtz5sxBfn5+rELvMLvTA61aHlbxIS89AOVPm/CUwYyK8l8CCL/ViIiIiIgoXkhegDQcQB6OwsJCFBYWhmxbunRpo+N69OgRt2uAAP4CRK1SoLX6Q1ZxBOpv/xtINkKEHMPLNsJysC/0Q34Zm0CJiIiIiCJE8ml4fT4fli5divvuuw/33nsvFi9eDI+na3Qxcri80CjlLdcfXjfUO94Dko3wjpqO9323oFyeA9Onb8Nra366YiIiIiKieCR5AfLXv/4V27dvx8yZM/HAAw9gz549cb+AYKTYnR5o1C3PgKU4/jVkthpg6M1w+wCFUoGv5NdAdDlQs+3DGEVKRERERBQZknfB+uabb/Dhhx9CqVQCAH71q1/h9ttvlziq2LA7PUjWKZs/wOeF8shWiNkD4NIZAa8XGoWAUk8qNH3yUbe7COnX3gOZShu7oImIiIiIOkDyFhBRFIPFBwCoVKqQ252Z3emBpoU1QOQ//wSZ/QLQ6yr4vF4AgEYpwOYSkTRgFES3E5aD38UqXCIiIiKiDpO8ABk0aBAWLlyIkpISnD17FgsXLsSAAQOkDismHIFB6M1QnPwOoiYF7pTuwW1qpQwOlw/KzJ5QGIyo27M1FqESEREREUWE5AXI/PnzYTabMWXKFEyePBk1NTV47rnnpA4rJhwuD1TKZt4Clx3ysp8g5F0Br9cX3KxRCrC7/au/a/sMg/Pn4/CYq2IUMRERERFRx0hegFRVVeHkyZOoq6uDxWJBeXk57Ha71GFFnc8nwu70QtVMFyz5zwcg+LzwZvQL2a5WCHB7RXi8IjQ9LwcAWI9sj3q8RO21ZfsZlJosIdtKTRZs2X5Gooios2BuUWfG/KbOTPIC5LnnnsPkyZOxb98+7N27F+PGjcO8efOkDivqnG7/mI7mChDFud0QtQZ4kzJDtmuUAgDA7vJBYciEwmCEtZgFCMWv/H4ZeHXV7uCFtKLairrqSoztr4bHUgNR9Lfw8cJKAeF+8WqYW6UmC15dtRv5/TKafWxR9MFjqYG71hSSf0RSaCnXW8tv5jIlMskLELPZjLvvvhtKpRJKpRL33XcfKisrpQ4r6hxO/1onTXbB8nkhLzsModvl8F4cfB5QvwABAHWPgXCcOwKfyxHdgInaKdeox5PTrsSrq3bj8KlKKMyl0G5dhPK3H0fpsqfhqihBRbW11S+O1HWEW1jUz61dh8vx6qrdeHLalcg16pt8XFH0wVVRgtJlT+Ps4keD+ccvbiSVlnK9pfxmLlOik7wAycvLw759+4K3i4uLkZeXJ2FEsWEPFCCKxi0gsqpTEDwO+NIavw5qhb8AsQUKkJy+gM8Lx9nDUYyWqGNyjXpMHT8QXpsZlo//Ck+tCQDgqTWhfN0i1FVVtfjFkbqWthQWgdz64zvbMXX8wBZzyGutRfm6RY3yz2utjdrfQtSS1nK9ufxmLlOik2wdkMLCQgCA1WrFtGnTMHDgQMhkMhQXF6Nfv36t3DvxXSpAGteA8vLDECHAm5LTaF+gBSRQgKiMPQGZHLYTe6DrNyKKERO1X6nJgtVFR/DH6QNQfvGCGeCpNSEnTYWkTBYfdEn9L17zHypotrAI5Nb8hwqwuugIcjOTmm8B8XqCX9gCPLUmiF5PxOMnCldLud5cfjOXKdFJVoB0lZmumuNw+btWKZosQIqB9J7wypSAL7Q5NViAOP33FxQqqIw9YT99IMoRE7VPoEvBk9OuhFrjhsJgDLlwKgxGlNW4oBUtbAGhoHAKi/q5lWvUIzczqcXWEkGuaDL/BLnka/JSF9ZcrreU31la5jIlNsm6YF199dUt/tfZBVpA1A0HobsdkFWdBDL7QvQ17supvliw2ByX9qly+sJtKoHXZo5ewETtdODEpe5V8iQDsqfMhcJgBOC/YGZPmYvkjNB+0NS11f/iNXJwdrCLSsP8qJ9bwKXuLAdOND01eXP5J08yRPcPImpGS7neUn4zlynRsVSWSKAAUTYYAyI3HYUg+uBN7dHk/QItIFbXpQJEndMXln1fwH76APSXXxOliInaZ0JBr+C/BUEGVVYedJMXQK8SoFCpIE8yIEuQBS+sbAWhlr541c+P+rkVkGvUN5tDgfzLvf9liF4PBLkC8iQDBEHy4ZDURbWU663lN3OZEhkzVSKOYAtI6FsgLzsMUa6ENzm7yfsFCxDnpdmxlBm5EJRq2E7siVK0RB1Tf6pJQZAhMycH1d4kfP6TOXjBzDXqm7zgUufXcCrSQB7Un3Y3UvkhCDIo9GlQGoxQ6NP4hY0kNaGgV6OCOdxclyqXuT4JRQLPvBIJDkJXNWgBKS+GkNkXPrHp+ylkgFwArM5LLSCCTA5VVm84zhyMWrxEHVFrcWDR8p0hU00uWr4TtRZOH03MD6J41rDgyO+XgUXLd2LtZ0cAhLf+DlFDLEAkYnc1MQ2v0wKZuRRiRm+IYtMViCAI0KoEWB2h40NU2b3gqa2Ap64majETtdfY4d0BAVi0fCd2HS7HouU7AeHidurymB9E8avhWiUAAAH4dm9pWOvvEDWFBYhE7A4P5HIBcvmlt0BeeQIA4EtpuvtVgFYlC2kBAQBVlr+51l5yKMKREnVcrlGPuTNGweXx4Y/vbIfL48PcGaN4wSIAzA+ieNbUWiVzZ4zCjFsvD2v9HaKmsACRiM3pgU6tAOo1dMgqj0OUKeBNMrZ4X61SaFSAKNO7QVAo4Sj5KRrhEhERURfVcEFEACFTB3MGQ2orFiASsdk90KoVEOtVIHLTcQjpPZsd/xGgVQkhg9AB/zgQZWZPOEq4IjrFn0CffpVShvkPFUCllIX0+aeujflBFN/qr1Wy4tNDWLR8Z6vTZBO1hAWIRGxON7QaJYJDPTwuyGrOQEzv1ez4jwCtsnEXLMA/JZ+78hy8DmsUIiZqv2/2lgICMHfGKIwcnI25M0YBwsXt1OUxP4jiV8O1Sq4Z5h+zFdDa+jtETWEBIhGbwwNtvRmwZNWnIfi88KXktHpfrUqAzdVUAdILgAjnueJIhkrUYanJ6pA+/YE+/6nJaokjo3jA/CCKXw3XKrln3ADMnTEqpODgNOrUVlyIUCI2hxsG/aWLq7zyOADA18z6H/XpVALsLhE+UYRMuPQzhCqzByDIYD9zELr+V0U+aKJ2auuCcdS1MD+I4hc/nxQNCdsCsmnTJtxyyy0YP348Vq5c2Wj/Z599hjvuuAO33347HnvsMdTW1koQZfP8LSCX6j+Z6ThEQzf4ZMpW76tVyiACsDXohiUoVFBm5MLBmbAoDrV38SouetX5Res9Zu5QPItmfjL3Kd4lZAFSXl6O1157DatWrcL69euxZs0aHD9+PLjfYrFgwYIFWLJkCTZu3IiBAwfi9ddflzDixmwODzSBLlg+H+RVJyBk9ILP17hrVUNalb/Vw+JoohuWMQ/OslPweVwRjZeooxrOJR/u4lXtvR8ljmi9x8wdimfRzE/mPsW7hOyCtW3bNhQUFCA1NRUAMGHCBGzevBmzZ88GALjdbsyfPx/Z2f7uTAMHDsSmTZsaPY7ZbIbZbA7ZVlZWFuXoAVEUYXe6oVb7CxBZ7XkIbgd8qT3Cur9W6S9A/DNhhbaYqLJ6wXp4G5ylx6DNGxLRuEk6UuVqJNWfS37q+IFYXXQkrMWr2ns/kkZ7cjVa7zFzh1oi9Xk1mvnJ3Kd4l5AFSEVFBYzGS2tlZGVlYf/+/cHbaWlpGDduHADA4XBgyZIluO+++xo9zvLly7F48eLoB9yAy+ODxysGW0BkbRj/AfgXIgTQ7ExYAOA4c4gFSCciVa5GWv255Oc/VBD2xbC996PYa2+uRus9Zu5Qc+LhvBrN/GTuUzxLyALE5/NBqDf4WhTFkNsBdXV1ePzxxzFo0CBMmjSp0f6ZM2c22l5WVobp06dHPuh6bA43AECl9BcgctNxiLpUeBVaIJwuWBdbQOrs3kb7ZGodFIYs2EsOIg1TIhg1SUmqXI20+nPJry46gtzMpLAuiu29HwCIog9eay1ErweCXAF5kgGCkJC9TxNCe3O1otqKNIUTq58ejbIaJyqqrchKT+pwPB3JHerc4uG8Gon8bO4cx9yneJaQBUhOTg527doVvG0ymZCVlRVyTEVFBR588EEUFBTgmWeeafJxUlJSkJKSEtVYm2J3eAAAaoUcEEXIKo9DMPYJa/wH4J8FCwDMTRQggL8VxH76J4g+LwSZvMljKLGvPszpAAAgAElEQVRIlauRVH8u+VyjHrmZSSG3I30/wH9hdlWUoHzdInhqTVAYjMieMheqrDwWIVHSnlz9bMdpjMh2Q7nlryivNUFpMEJ+23/gs2NK3DS6d7tj6UjuUOcn9Xm11GTB80u+x+OTh2PEwKxgfk6fOAgVNfawprVt7hxXqzQy9ymuJeQVeMyYMfj+++9RXV0Nu92OoqIiXHvttcH9Xq8Xjz76KG6++WY8++yzTbaOSMkaaAFRySHYqiCzX4AvLbzxHwCgU/vfNrPd0+R+VVYviG4HXOWc7YLiR8O55MNdvKq99wMAr7U2eGEGAE+tCeXrFsFrja9Z8bq6/B5qWD/+a8j7ZP34r8jv0bF1QDqSO0TRduBEFR6fPBwrNxej1GRBrlGP6RMH4Y0P9oU9WLy5c5zMVcfcp7iWkC0g2dnZeOKJJzBjxgy43W5MnjwZw4YNw6xZszBnzhyUlZXh0KFD8Hq92LJlCwBg6NCheOmllySO3M92sQVEpZBBZvKP/xCTW1+AMEAhE6BVCqi1Nd1i4l+QELCfPQR1t74djJYoMto7l3xH5qAXvZ7ghTnAU2uC6G26eCdppOsVONvE+9RN37FLFNcvoHgWyM/sdF3IYPEXHv5F+F1MmznHGbRyKA2hj8Hcp3iSkAUIABQWFqKwsDBk29KlSwEA+fn5KC6O39XAAwWIWqWA/OwJiEoNvBoD4A2vCxYA6NUy1DbTBUueZIA8KRWOMweBq2+LSMxEkbBl+xnk98sIuQiWmiwoKTNjZB9txMdpCHIFFAZjyAVaYTBCkCfsqa9TqrV7m3yfau1eZBokDIwoBjoyWLy1c1ypyYIDJ6okW6WcY/CoOcwCCdid/i5YaqUcctMxCJl94POJbXqMJI0As63pAgTwjwNxnCuGKLbtcYmiqam56T/4/CiGZzpRuuxpnF38KEqXPQ1XRQlEMfyCvDnyJAOyp8yFwuCfNS/QP1qexG+18USmTUbWXb8PeZ+y7vo9ZNpkiSMjir6Gg8UbLiDYkqbOcca7noI8ySD52h+B8SnROLdT4uPPgBKwXhy7oRXtkJl/hthzWJsLBb1ahiprSwVIL9hP7Yen5mco03M7FC9RpDQ1N/0LM4bAtPLZRn2Yc+9/GQp9WoeeTxBkUGXlIff+l/kLXBzT+Byo+WYdMsY9AJlGD5/Dgppv1sEwbhaAjs+ERRSvOjpRQsNznNsnw9ItZzBmuC7qa3+01rrR3PiUSJzbKfGxAJFAnc2/SrnW7B8k7mvD+I8AvVqGU5XN92NXBsaBlBxiAUJxpWF3A6XMF9VxGoIg48Uuzvk8LtiP7YT92M6Q7cnX3y9NQEQx0tJECeEWDvXPcUoAY4Z7or72RzgzDHIMHrWEBYgE6mwuJGkUkFeegCiTw5eUAbSxRVKvlsHq9MHrEyGXNZ7lS5GSCZlaB8eZg0i54qYIRU7UcQ27G1w+YwjHaXRxapUSCoMR8qRUpI6Z5G8FcTug0qikDo0oqtozUUJLLQ+xWvsjnNYNjsGjljALJFBndSNJq/SvgJ7WE17I0NYKRK++tBhhalLjt1EQBCiz8uA4G7+D8anraaq7wTtbjmLWXU/B9OGfQ35JC/RhlnIAJcWGF0DW3U8DHjcqPvrPS3kw+SmI+lR2mSO6qKWWh58rbcHza7dMHS7PUcBiqYGtxgltalpEP0fhtG4Exqc0jJVj8AhgASKJOrsLqVoBsurTQP+xEMNcgLC+JI3/RHLB1nQBAgDq7N4wny2G+0I5lKnZHQmZqENeXbUbKUlKJOvUweJjz5EKfPLdKQzIS8W+SjWGTX8JFosNer0OqtS0kIspdW4XrB6ka5QoW/ty6C+qH/w5rP7izc2uFljzoLl9LGxJCoF8PXCiKpibpSYLVm89iuuv6hGyCGHDXG2p5eHACXOw+HBVlKDy4nGOKCzAGk7rBsfgUUuYBRKos7rQT1EOweeFmNazXY+hv7gYYW0LM2GpcwcAAGzHdjV7DFEsXH9VD2z+/gy+2l0CANhzpAIvvfcDzpvqMHZ4dxTk50KXlgGPJg3PrziIH4tNXLW3CzHolPBZze3uL97U7GqB2X9a2kckhUBOZqVp8eqq3dhzpAKvrtqNYf0zsHDZD8hK0wJoOldbanmYUNALuUZ9TBZgDXeGwcD4FKXBCIU+sq0wlNiYCRKw2NzoJZ6DKFPAq29fy0TKxRaQqrrmL86KlAzIk9NhPfZju56DKFJGDMzChILe+LnShqcWf4sX/7EDxjQNHv71MHyztxRbtvsnZKg/QH3c6LxGv1oHjqtvy/YzjaatbO5Yik82qwNeW23wy0xAuP3F68+ututweWg3vxb2xRLzlAICOblyczHGjc7DwmU/oHtWEjZ+cxLP3H81Vm4uxq7D5Vi0YidGXZ4TkquBlof6Ap+TimorKsvK4HM7oz74u37rRs/ZbyH3/pcj2sJCnR8zRQJmmwvd3CVAZh940XgAeTgMOhkEAOVmV4vHqbsPgLPkEHxuZ7uehyhSbr2mN5J1KlywOOH2+DDpuv54b9NBfLf/fPAXvsAAytlThuOdDT9hz5GK4PbmfrXmL9yJT61RwbzvSxhvfSx0LZDJfwi7v3j94nXq+IEhX9pa2hcrzFOqL5CTi9ftw53X98cXu87B5fYhO10XzFWX24dfXhE6i2VTLQ/62/4D582AwlwK2wcL4K440+5ivi3YukEdwTEgMeb1iYCjDqnacojGEe0a/wEACpmAFK0MJnPLv2houg+ArXg77KcPIOmyke16LqKO2LL9DHYeKsPAXmmwOdwQBEAUgdfX7YNSIcNzvx0NAFiz9Sh2HirD9ImDUFFjxzP3X42Fy37ArDuGomhHSbO/Wje1tgi7biWOLdvPYFgPFVKvvg0XfvgYGeMegFxngEyXglqPAl/vOAug9XEcLc3+E6uZgVrCPKWAQHerzd+fCf7Ycse1fbH5+zN4fsk2KORydDfqoVLKUF5tw+qtRzF13ADkGvXBlofM6S/B7XSiss6DY3VyJMtrIHzxV3hqTbiw7X9hvPUxmD7575gO/m5pLBbHW1FDLFdjzGJzYYDyZwCAL7V7hx4rTdd6AaLK6gVBoYLt6M4WjyOKllqLA/uPV2LFp4eRkqRC4di+wX2CACz53/1YtGInBEHE9ImDsHJzMfL7ZaCixo7JN1yG19ftC/5q3VyXlXj4hZvaJ79fBnxuF6q/fB8pw6+HTKOH11YL08b/gstmR63FEewrH2g92HOkAs8v+T6k5SzQtWrk4OzgF/1Sk6XFfbHGPCUAkMmAv6/Zi8F90rF1RwnuvmkANn5zEj2yklBebcd5kwUP3TEUw/pn4qX3fsCw/hkh+bzu82N4fsVBWOUpqPGo8adlu5Cbrgl2u3KWHkP1V6uQMe4B9HzsjQ53jwq3+yBb+agtWIDEmMXuxgDFz/DINfDq0jv0WGlJclRaWi5ABLkCqpy+sJ3Y3ebV1okiRfT5c6+q1oGPvz0Z3O71iqiosQEi0Ld7KlZuLg7+KpyVpsUHXxzDv00ZjtVFR4IDNZu6mDX8hVuKL5fUPgdOVMHmEuG1XkD5B6/g5/efR/kHr8BrvQC7Bxg7vDtWbi7G9ImD8Oqq3diy/TQWLvsBj08eHvwC39Jibi3tizXmKQFAtww9MgwarP/6BLoZk7Bm61Gkp2jwc5UNGQY1UvVqvLPhJ/x4uBzGNA2qzQ48Oe1KLFq+Ey+8uwPf7i0Nzg64uugI/m3KcJRWO0K6XTlLj6Fq63twQ4nPfzJDEGTtHnMUbmERL+OtKDGwAImxOosTA5VlcBj6wOttX/ergDSdDDVWr79bVws0PQfBW1cNZ+mxDj0fUXuIogCtRoHh/dLx2mNX4p9Pj8aauaPwxuwrcVkPA3QaJW4d2yfkV+FSkwUrNxfjmfuvRtGOkuBAzekTBzW6mMXTL9zUdvn9MvD+V6Uw3Pk0su95Bt1+8wKy73kGhjufxq6T9pABu4ZkFRav24eH7hiKEQOzACD4PjfMi1yjHhMKegVnBmpqX0siPWiceUoB94wbgIW/uwapSSp89eM5AIBCLkN2mg63jOmD39w8COdNFljsHjz862H4dm8pPv72FFweH86bLJhx6+UA/F25pk8chKIdJZDrDEi5/f+FjA1Juf3/4a1PTyG/XwZKTRY8v+T74AxbAeHkdDiFhSj64LHUwKiy47lpg7CmqJitfNQiFiAxZis/gwy5Ba70Pv6O8B2QppPDJwLVrbSCaHoOBmQKWA583aHnI2qPY2drcN0V3TBv2kD00LlQ+c8FKFvyb8AnL2HenT3QK0cfHHj+1kf7g32Gn5x2JUYMzAoO1HzojqGoqLE3evx4+oWb2s7f2qWBWvCiavNS/Pz+86javNR/u9aGLdvPINeox/jRefjhYDkyDBp88u2pkO5V0ejiEenuJMxTCtiy/QwOnaqC1em/djvdXtRanMjvb8RlPdPw9kcHoFLKAIj4sbgcLo8Pmy62HAdaz7652Ary5Y/nMH3iIAzolQaNVoOMibPQ7TcvIGPiLEChBESgtNKKV1ftxuOTh2Pl5uJgTldUW2G/UI3rBmjgsdRAFJv/UbSl7oOBxRFLlz2Ns4sfRe2a57FgSk9s23eeBTY1iwVIjPlO/QifCIgZfVs/uBXpSf63r7TG3eJxMpUGmh4DYDm8LaLT8BGFY/zonrj3aj28lWdR/uFfQuamr93wF8y4oSc0ajk++e4UHp88PPglL7g418UuK1t3lDT55a+9v3BTfCg1WXD/jd1R8eErIblR8eErmHlDd2SlabHnSAWWbvgJ/zZlOFJ0Kri9Przw7g4sWrEzal08It2dhHlKAXIZ8F9r9yIlSYlMgwYA4HB58dXus/j7mj1we30QfcBtY/0D0z1eX3BQem5mEp6cdiV2HioDAEwdNwArNxfDfqEGFWv+hPI1C/3dGNcshPmjl/HghLxg0TBiYFYwpw+fqoTCXArlloUoffMxlC57Gq6KkmaLkJa6Dza17kjVR6/goQm92MpHzWIBEmPqsv047TEiSafp8GPlpvonMTtW5mj1WG3fK+CzmWHlYHSKsWytF5Uf/QUypabJuelz0tRQKuS4Zlj34AXywIkqdlnpIg6cqIJSEJvMDZVMxNGSC3jjg3145v6rMb6gN+bOHAVRBM6bLBjaNzOqXTw4aJyiYeM3J2FIUsJi82Dq+IHISdcBAMxWF6pqHcgwaPDonfl4f3MxUpNV6N0tBc8/OBpzZ4zCq6t2A0DwPBkolC0WW5OfIYfdGVI0BHLaazPD8vFfw1qssLVzcXOLIyplPrbyUbNYgMSQ+0IFkmylOILeENDxAeFalQyZejlOlLdegKhzL4M8KRXmnZ90+HmJ2kKn8l+MfA5Lk3PTl5jsGNo3E/eMGwDg0q/C7LLSNeT3ywAEWZO5AZkM500WvPDwL4JjPgBApZShcGxfHC2piWpBykHjkccFGYFrhnWH2wtMLOiFxev24aar85CSpLp0gAj8s+gICsf2hU6jxG8Lh4QsqhkoPCYU9Aq+bmqNusnPUJ3DF2w1Cay6vrroCPpk68JerLC1c3FLiyOylY+awwIkhqxHdgAAzin7QoxAAQIA3dMUOGVqeTFCABBkMugGjILj7GE4So9H5LmJwuH2+b9cBuamb7iA1sqvzuOnE5Uhfe23bD/DLitdxIETVaix+2C8bXZIbhhvmw1TnTe4/gFw6ZfYuTNG4eFJ+VFtFWMLXHRwqlb/IPSHbh+Cjd+exA1X9cDKzcUwW12QywCZAFSbHZDLBdw2tk+w1aP+ZAv1z4H5/TKwaPlOvFtUAsMdvw/9DN31FFIyM4MFy/SJg/DGB/vw5LQrmy1Y2rNYYVOLI8Zi3RFKbCxAYsh2ZAdMQgagS41Q+QH0SFWg2upFVV3rYzt0l42EoNai5ut/RujZiVq3aVcltLc8Ca/1gn9u+omz0ON3r0N22zy8vKEU5yoseKBwSPDXua72ZYSA/1p/DC5VcsgAWpcqGX/76GjIcbFsFWMLXHRwqlb/oqsbvzmJ6RMG4YsfzwW/DwztlwmDXg25HAAEfLvvfFh5V2dz4djZCyj1GOAa/zSS7vsbDPe8iH2VamSlJwULlooaO154+BfINepbLRrqt1QFisY9RyqC2wPn6S3bz+DnShtUWXnIvf9l9Jz9FjKnv4Q9FSqujE4tYnbEiMdcBce5I/jJnYdUrRCxxx3czd9s+/Vhc6vHylQa6C+/BvaTe2E7uTdiMUSCz2mD+0IFfG6n1KFQhB08VY03P6+E+fo/AL/6HU450/Ds/xzD717/EaYLdlx3pf8XwPGj84K/znWlLyNdXX6/DOw/UY0/rTuN0840VPv0OOVMw5/WncaxcxdCvnjFslWMLXDR09XH1hw7WwObw43v9peid7cUAIBOJce+Y5WYc88IPP/gL9C7WwoMev9Y0Zby7sCJKsy5ZwRECPjD4u9wwatBqV2N/1iyF3k5Kc3GUH9F9cxZixstVli/pSrQerJw2Q8QIIYUjYHjfq60QaFPg8mlxfMrDrb43EQAC5CYqTvwNSD68K2lN5K1kXvZM5Pl6JupwOcHzXB5Ls1eccHmgdvTuJ0laVAB5CmZqPz0bfhcjac0jSXR50Xdvi9w7p3/h9P/eR/OvvE7nP7Lb1C6Yh4sh77jwomdxAO3DcGPR0x4+h8H8LeNpwEA/357b7z823z0yknGLWP6YPzoPLy+bh8evXNYl/sy0tXlGvWYcctgAGjUMjx94mB+4e+EuvrYmluv6YMLdS7YHB64PT6k6lWwuby4/Zd9sbroCLLTdZj329Fh5f6Egl7ITtdBpZChu1GPdzb8hPc+Phhs6aivflEhij7YL9TAYrFBoVRBnmQIabFo2FK1cnMxZt0xFK+v2xdSNLJFi9qLBUgMiKIIy/4vIMu5DJW+ZKRoIvuyXzdQh8o6LxZ8WIq/bS7HY++dwcPvnMGj/ziNbUdDT+yCXAnD6EJ4zCZUbFrc4rzf0eSpq0Hp/zwH08dvQPR5kXL1bUj95d1IHn4DPHVVqPjfV1G6/Fk4fz4hSXwUOUdLLkCjUmBQXir+UNgNKV++AueqJ5Hy5SuYPc6It/93H95efwD/NmU4VnxyCGs/OyJ1yBRjaclKPHV7aG48dXs3FJ+u6nJfTjs7jq3xd4V69M58VJsdOG+ywOrwYMYtg7HjYBmmTxzUptcjOC5q5ig8dMdQnDdZ4HI3fV0PFAsffH4Utp9Po3Lls7Cs+HdUrny2ySl467dUjRudh6IdJU0WjV29RYvaJ2ELkE2bNuGWW27B+PHjsXLlykb7Dx8+jDvvvBMTJkzAs88+C49HuvUv7Kf2w139M+w5wwEAyerIvuwDc1S4e6QetTYPis/b0TtTiV9fmYxMvRx/31KO3aetIcers3sjecR42Iq3o/LTtyH6vBGNpzWO88dw/h9/gKv8NNJuuA8ZE2chacAoaHsNgX7oL5F56+9gGDMJ7upSnH/vKVR9voJdsxJYarIaednJmHlTT9g/fTVk2kf7p6/i7mu64eZf9MbQfpmAAHy7t7RLfRnp6rZsP4OBWUrYPgnNDdsnr+LBcXkcc9HJcGyNv9WiqtaJ9BR/F6v0ZA2uGZaLFx7+BSpq7G16PVZvPYrpEwf5/32xVUkQRPxj08Emj8816jHzxh6o+uiVVqfgDbRUzZ4yHO9s+AnTJw5qsmjs6i1a1D5tn+4gDpSXl+O1117DRx99BJVKhalTp2L06NHo379/8Jjf//73+NOf/oQrrrgCzzzzDNauXYtp06ZJEm/tDx9DlmTAWXkegMrgAoKRNKKXBiN6aSDgUjeGK3uq8NbXtVhcVIG/3NsTGcn+t7u41I7Vh/IwxHcFxu79DBdKSyAf+wC2nfGi+HQ1hvTNxN03Xga5PPJxOs4exs///BPkumQYb3vcP+Ctwa8ugiCDru8V0PQYhLq9n6F2+wbYjv4A422P+1d1p4SS3y8D7//fIaTp5HA2Me1jul6BKwZkBWc3AhCctYU6v6w0LWSivcncSFKAXbA6mabez8AUs11FqcmC7/afh06twPyHCrDik0NYtGIn5s4YFXx9wn09po4bgEUrdgIiMHem//ypVMhRUW0Ljt9o+NwKh7PVKXjrt1QdOFGFZ+6/Gis3FyM7XdeoaKzf7So3M4ndsCgsCdkCsm3bNhQUFCA1NRU6nQ4TJkzA5s2bg/vPnz8Ph8OBK664AgBw5513huyPJWf5adhP7IZ+yLU4W+2BXAak66L3stfvQ61SCLj36mS4PCL+a0s5XB4fPvvJjD9+VIpyswcnU0djjX0s3OUn4fnwWbi+WwnbhRqs2lKMNz/aH/HYHOeK8fPqP0GRnI7Mmx9pdYo+mUoDw9W3If2m++FzO1G64jmYPn0b7pqyiMdG0fPN3lIo5DL4BHmT0z7W2rwhTfcc6Nu1VNTYYXGKTeZGOLP7ESWab/edDxYMIwdn+wsH8eL2Nso16nHNsO6AAJRWWoM/5MydOapRK0qgqNDrda1OwVu/pWpCQa+QRWIDz8v1mqgjErIFpKKiAkbjpQ9PVlYW9u/f3+x+o9GI8vLyRo9jNpthNofOHlVWFtkvtzX/Wg2ZWgddvxE4e6QCOQYlBKHxYMtoMSbLMWlEEtbstOCRd8/A6vRhWE8Npo7SQyYATs8oHD5/GXrW7MJ1NT/hOpzAsd4jsHyHHdeN6IH8/pkRicNx/qi/5SMpDRnjH4CgUIZ9X3VOH2Te8igs+79C3b7PUbdnK7R9r4AmbzDUWb0h0+ggKNXAxQF0glwBRUomZKqOrzYfL2KRq9GSmqzGnHtG4H+3n8ODd/4h2PSvMBiRPukP+OCrKnQ36rHi00PIzUzir2YJrq25OqGgF/65uRi3/vr3uLD+L8HcyLjzD9i4344BA6MdMXVVUp1XDXoN5s4cFfKlvamCIVz3jBuAfj0M+OM72zH/oYKQx60vUCxoU3XInjIX5esWBT9vDdftCLelii1a1F4JWYD4fD4IwqWpbEVRDLnd2v6A5cuXY/HixVGL037mIGxHdyJldCEEuRznql3omaaMWfERMCJPgySVDAdK3eiVocTIXpdWXFUrBAzqlQb0GgeXfSSUJTtxWekPmJ+6Cwc/PI5Bjz0GZXJ6h57fcdbf8iHXpSBz/AMQFKrW79SATKlGylUTkDR4DOwndsN+5iDsJ/e0eB+5Pg3aPsOhGzASSZeNhCAPv+iJN9HO1WgKrNb7yxE98O6/zuGeSfORpZXB6hLxpw+O4Z5xAzF1vA7f7C1l030n0J5cve6qHlj2+VHMvOcFOB1O6PU6aFPTcG9uQjbSU4KQ6rwa6S/tDcdgNPdDTv3nDazbIXo9EOSKRrNgEUVbQhYgOTk52LVrV/C2yWRCVlZWyH6T6VL/xsrKypD9ATNnzsSkSZNCtpWVlWH69OkdjlH0uFG5eQkUBiN0/a+Cy+NDRa0HI3trO/zY7TEgR4UBOS1/8fdp0+AcOB6ynqNQe+h7DDfvRckbjyFtdCFSx0yCTK1r8/PazxxE2ZqFkOvTkDn+txCUbS8+6pPrkqHPvw76/OvgcznhqauC6HEBPi8giv5ZPHxeeK1muC+Uw3b0B1gOfAV5cjoMVxci5aoJkCnVHYpBCtHM1VgIFCGTbxyA7IsXxq+3n8E94waiosaOEQOzcM+4AfjlFbkc/5Hg2pqr9fuaG4x6lJoseH4FC1GKvkQ/rwKhn5+2jMEQBBkU+rQYRkoUKiELkDFjxuD1119HdXU1tFotioqK8OKLLwb3d+/eHWq1Gj/++COuuuoqbNiwAddee22jx0lJSUFKSnQWy6n6YgXcleeQMfFhCIKAkxUOiAByDPKoPF8k+XRp0I64GX8vuhy3Kfbjsm0fwbz3c6Rfew+SR9wEQdb4bzhVWovSSisu75OOtGR/1yfz3s9RtXkpFKlZyBj3WwiKyKabTKWGKiO3xWPEq2+Fs+wUrIe/R/Xny1G7YxPSrrsHycOuD/k7Sk0WnCo1o3/PVGSnt73QirZo5mqsNPzVj033nVNbc7WlPuTMBYqmznBe5eeHElVCFiDZ2dl44oknMGPGDLjdbkyePBnDhg3DrFmzMGfOHOTn5+M///M/MW/ePFgsFgwZMgQzZsyIWXzmPZ/BvPNTJF9xE1TGHoAoYvdpG2QC0Cs9MV5yhVzAkP7ZWLx/DBZOGIu0s/9C5eYlqN31KTJumAFt/yshCAJEUcQ7G3/Cxn+dBAAkaRR4YvJg9P25CHV7tkKTNwSpY+6MePERLkEmhya3PzS5/eGqKIF572eo/ORN1O7YhPTrfwPdZSOx/usTWP7JIXh9IhRyGR7+9VDcPKaPJPESdTUsRInaj58fSlSJ8W24CYWFhSgsLAzZtnTp0uC/Bw0ahA8++CDWYaF212ZUFb0LTe9hSBp6LXBxNe89p224LEcNtTx2A9A76uo+GnxZbMM/D6nx7B33wVV6DHV7tqJs7UIoM3sgedj1+ORMMj7ZWYubruqBfKMHpbv+Bc3Hq2CWOS+O2RgLAdIsdtiQKisPGeMegOPcEVj2fobydYtgSemDz88OwpWDhmJMfjds2X4G//3hfshkMs7ERERERBQFCVuAxBuPuQpVny+H9dB30Pa9AikFv4ZwsdQ4Xu7AmUoX7rwqJWGKD8A/QP3GwTps2mfFtmNWjB04EOpu/WA/8xPsJ/ai+ov/wS8A/CIdwCn/f70BnJL3xLt1w/AbfT7y46T4CBAEAdqeg6Dpfhl2/+t7JJd8gycNp+BRlMPtuwnTxg/E+1uK8cYHe6FWyvCrq3pKHTIRERFRp8ICpJ1EUYTXUgPHuSOwHvoW1mO7AFGEoeB2aPtfhcCcWzanD+9+VYkUrQxX5nVsALYUftFPg/1nnXjzMxMUMgGj+yfB0y0fH57ujj0XzuPWXnW4spsPgAyi1gCfIRcpHiUcX9fglU0/46HrjfjlID1kTaMV0Z0AACAASURBVMxCJhWXx4eNP9Zi7f5uGNnzXkzrdhyK0zugOLcban0WHswZiq0uLf6x5ltU145E4bX9oFRwdhAiIiKiSGAB0oDX6wXQeC5w0V4H7w8fQbRdAFw2iPY6wGn179Tooeg/GrK8fFjlSlgvzsC1/aQL6/c5YXWKuHekBlbzhZj+LZHy6yEi1uwDXv2/cqgVgMsLQASuH5iJnnkZKPfVa+Wo9b8mU/KB9QcFvLG1Aiu/M6FnmhxpOgFymQCZAMgFoP6y7fVbhsQmttXfHrxd7yARof8PHFz/Lj4RqHOIOF7hhdUl4qo8JcYNEGASL4NsSG/oLOehrjkJ9cl/odDnQaEBcGxbjz3f6eBTaqHUJSE9PQXy/Jsgy2i6ZSQnJweKGI13aS5XicLBXKVEwVylRBHLXE10gig2/FrXte3atavZKfi6deuGfr3zIAiAQhCgkMmgVvi/VDvs9uCJq77+/S+DXKGGxyvC403kVX0FaDRqiD4PIIoQZAp4fD543O4W76XRaCD63PB5PBDhlX4AjCBAEOSQy5UQIYPT5Wx0iEKphEKhhE8EFGotvF4P3F43fD4fjh8/hnJTFYqPHGny4T///HP06NEj2n8FgJZzlag1zFVKFMxVShSxzNVExwKkAYfDgZ9++glGoxFyefSmzA3MNb5y5Urk5ORE7XniUWf+22P560fDXI331zWe44vn2IDoxCdlrjYl3t+D+hhrdDQXa7zlaqQk0nsDJF68QOxjZgtI+PgqNaDRaDBy5MiYPV9OTk6XrZa78t8eCc3lary/rvEcXzzHBsR/fM1py3k1kf5GxhodUsYa6+8AQGK9N0DixQskZsydHUfWEhERERFRzLAAISIiIiKimGEBQkREREREMSNfsGDBAqmD6KrUajVGjx4NtVotdSgx15X/9miK99c1nuOL59iA+I8vEhLpb2Ss0ZFIsUZCov29iRYvkJgxdwWcBYuIiIiIiGKGXbCIiIiIiChmWIAQEREREVHMsAAhIiIiIqKYYQFCREREREQxwwKEiIiIiIhihgUIERERERHFDAsQIiIiIiKKGYXUAcQzi8WCqVOn4q233kKPHj2aPObw4cOYO3du8HZ1dTUMBgM+/vjjWIVJRERERJQwWIA0Y9++fZg3bx5Onz7d4nGDBw/Ghg0bAAB2ux1TpkwBF5cnIiIiImoau2A1Y+3atZg/fz6ysrKC29avX49JkybhjjvuwDPPPAOn0xlyn7fffhujRo3CyJEjYx0uEREREVFCYAHSjJdeeimkkDh27BjWrl2L1atXY8OGDcjIyMC7774b3F9XV4e1a9di9uzZUoRLRERERJQQ2AUrTDt27MCZM2dw9913AwDcbjcuv/zy4P6NGzfipptuQkZGhlQhEhERERHFPRYgYfJ6vbj55psxb948AIDVaoXX6w3u/+yzz/DII49IFR4RERERUUJgF6wwjR49Glu3bkVVVRVEUcSCBQuwfPlyAIAoijh48CBGjBghcZRERERERPGNLSBhGjRoEGbPno2ZM2fC5/Nh8ODBePjhhwH4p95VKpVQq9USR0lEREREFN8EURRFqYMgIiIiIqKugV2wiIiIiIgoZliANODxeHDu3Dl4PB6pQyFqEXOVEgVzlRIFc5UoNliANFBWVoYbb7wRZWVlUodC1CLmKiUK5iolCuYqUWywACEiIiIiophhAUJERERERDHDAoSIiIiIiGKGBQgREREREcUMCxAiihmr3Q0uPURERNS1sQChTkcUffBYauCuNcFjqYEo+qQOiQCcN1kwdd6n+OjL41KHQnGIn1ui+MDPIsWCQuoAiCJJFH1wVZSgfN0ieGpNUBiMyJ4yF6qsPAgC620pVVTbAABf7zmHu264TOJoKJ7wc0sUH/hZpFhhNlGn4rXWBk+cAOCpNaF83SJ4rbUSR0Z25/9n787jo6ru/4+/7sxkDwkhJIGwqcimIoILQVtXBGSpIsqqgIWvC4o/l6qIKKK1UL4WW0Vb/bohghStggsCitWyi6CIWhBBNiNJCFnIPsv9/RFmyCQhhJBk5mbez8eDB2SW5DPk3HPv557POad8Yy+PRyVY4k/HrUhw0LEojcUSCcihQ4dYtWoVAP/7v//LuHHj2L59e4CjkmBkul2+jtPLlZeF6dautoFWUlb+O3ArAZFKdNyKBAcdi9JYLJGATJkyhf3797N+/XpWr17Ntddeyx//+MdAhyVByLA7cMQn+T3miE/CsKvaMNCKS8pPYJqELpXpuBUJDjoWpbFYIgHJzc1l/Pjx/Oc//2Hw4MFcf/31FBcXBzosCUL2mHhSbpzi60C99av2mPgARyYlZW4ADMMIcCQSbHTcigQHHYvSWCyR0jqdTpxOJ6tXr2bWrFkUFxdTVFQU6LAkCBmGjfDk9qSOn4npdmHYHdhj4jV5Lgg43VpJRaqn41YkOOhYlMZiiQTkqquuok+fPnTr1o1zzjmHwYMHM3jw4ECHJUHKMGw4YhMCHYZU4nKVJyBut0qwpCodtyLBQceiNAZLJCB33303w4cPJyUlBYCnn36arl27BjgqETkZrqMjIGUud4AjERERkUCyRAJSXFzM+vXrycvL801gXb9+PbfcckuAIxOR2nIeHQHx/i0iIiKhyRIJyIMPPsgvv/xC586dNYFVxKK8c0CUgIiIiIQ2SyQgO3bsYNmyZTgclghXRKrh0giIiIiIYJFleFu1ahXoEETkFHlHQFxuj/YCERERCWGWGFLo3LkzY8eO5be//S2RkZG+xzUHRMQ6XBVGPspcHiLC7AGMRkRERALFEglIYWEhHTp0YN++fYEORUTqqGLpldPpVgIiIiISoiyRgMycOROAX375BZfLRYcOHQIckYicLLfnWNlVmeaBiIiIhCxLJCB79+5l0qRJZGZm4vF4SEhI4MUXX6Rjx441vu9vf/sbK1aswDAMbrjhBpVsiQSQp8K8jzKn9gIREREJVZaYhP7EE08wceJENm3axObNm7njjjuYMWNGje/58ssv2bBhA++//z7/+te/mD9/Prt3726kiEWkMk+FHdC1EpaIiEjossQISHZ2NkOHDvV9PWzYMF5//fUa33PRRRfxxhtv4HA4yMjIwO12Ex0d7fea/Px88vPz/R47ePBgvcUtUl+aQlutWIJVqhGQJqsptFUJDWqrIoFjiQTE7XaTm5tL8+bNATh8+HCt3hcWFsazzz7Lq6++yoABA0hJSfF7ft68ecydO7fe4xWpb02hrVYswXJpBKTJagptVUKD2qpI4BimBRbk/+c//8mrr77KNddcg2EYLFu2jHHjxjF69Ohavb+4uJjbb7+dgQMHMmLECN/jx7v7MWbMGFatWkXbtm3r9XOI1FVTaKsPPPsfdu7Pxe0x+dMdl9D9zJaBDkkaQFNoqxIa1FZFAscSIyAjRoygQ4cOrF69Go/Hw/Tp07n44otrfM+uXbsoKyujW7duREVF0a9fP3bs2OH3mri4OOLi4hoydJF60RTaqttjEh5mp7jURZlLJVhNVVNoqxIa1FZFAieoJ6Hv2rULgO+//55mzZoxcOBABg8eTHx8PN9//32N7z1w4ADTpk2jrKyMsrIyVq1axfnnn98YYYtINTymSURYeZdTVqYEREREJFQF9QjI7NmzefHFF5k8eXKV5wzDYNWqVcd972WXXca3337Lddddh91up1+/fgwaNKghwxWRGrjd5SMgoH1AREREQllQJyAvvvgiAAsXLqRVq1Z+z+3cufOE7588eXK1yYuINL7yERBvAqIREBERkVAV1CVYubm55Obmcuutt5KXl0dubi55eXkcOnRIiYWIxbjdJuHh5QmI06kREBERkVAV1CMg999/P2vXrgWgd+/evscdDgf9+/cPVFgiUgcez7EREKdbCYiIiEioCuoE5JVXXgHg4YcfZubMmQGORkROhds0CT86Cd2pjQhFRERCVlAnIF4zZ85k586drF27FpvNxmWXXUaHDh0CHZaInASP20O4Q3NAREREQl1QzwHxWrRoEWPHjuWHH37g22+/ZdSoUSxbtizQYYnISfDuAwLg1CpYIiIiIcsSIyCvvfYaS5YsISUlBYD09HRuvfVWBg4cGODIRKS2PKaJ3WZgtxlKQEREREKYJUZAYmNjfckHQGpqKuHh4QGMSEROltttYhgGdrtN+4CIiIiEMEuMgFxyySVMnz6dMWPGYLfbWbp0KaeddppvN/Szzz47wBGKyIm4PSY2m0GYXSMgIiIiocwSCciHH34IwOrVq/0enzx58gl3RBeR4OArwbLbcCkBERERCVmWSEA+++yzQIcgIqeovAQLHHYbTq2CJSIiErIskYAUFRUxe/Zs/vOf/+Byubjkkkt45JFHiI2NDXRoIlJLHrO8BMthN7QRoYiISAizxCT0mTNnUlZWxvPPP88LL7yAYRg8+eSTgQ5LRGrJNE08HhPb0UnomgMiIiISuiwxArJ161bef/9939d//OMfGTRoUAAjEpGT4fGYANgMA4fN0BwQERGREGaJERC3243Hc+yCxePxYLfbAxiRiJwMj1megBg2NAIiIiIS4iwxAtKnTx/uueceRo0aBcBbb71F7969AxyViNSW211hBMRu0xwQERGREGaJBGTKlCn8/e9/Z86cObjdbn77298yadKkQIclIrXk9pZgHZ2EXlyqVbBERERClSUSEIfDweTJk5k8eXKV52666SbefPPNAEQlIrXlLcGy+Zbh1QiIiIhIqLLEHJCaFBQUBDoEETkBbwmWoRIsERGRkGf5BMQwjECHICIn4D66iITNZmC3axUsERGRUGb5BEREgp93ETtNQhcRERElICLS4LwjIAblk9A1AiIiIhK6lICISIPzTUI/ug+ISyMgIiIiIcvyCYh59MJGRIKX3z4gNhsut6ljV0REJERZIgH59NNP/b7Ozs7mtttuA2DBggWBCElEToLH490JvbwEC9AoiIiISIiyRAIyc+ZMNm7cCMDKlSsZMmQI7du3ByAmJiaQoYlILfjtA+Io73a0F4iIiEhossRGhC+99BJ33HEH3bp144cffuCvf/0rF110UaDDEpFaqrgPiN1enoCUOT1ERwYyKhEREQkES4yAdOzYkeeff55NmzYxe/ZsJR8iFuNbBctm4LCVl2BpBERERCQ0BfUISM+ePf02GiwrK+Pmm28mLCwMwzDYsmVLje+fO3cuH3/8MQCXXXYZDz74YIPGKyLVq7wPCIDT7Q5gRCIiIhIoQZ2AfPjhh3V+77p161izZg3vvfcehmEwceJEPvnkE66++up6jDC4mKYHd2EeptuFYXdgj4nHMCwxyCVNnG8ndANfCZZGQKQi9V8idaNjR6woqFtomzZtaNOmDUeOHGHGjBm0adOGgoICJk2aRGlpaY3vTUpKYsqUKYSHhxMWFkbHjh1JT09vpMgbn2l6KMvcR/rrD7N/7u2kv/4wZZn7ME1d5EnguT3efUCOrYKlBES81H+J1I2OHbGqoB4B8Xr88ceZMGECAF26dGHy5MlMnz6d+fPnH/c9nTp18v17z549fPzxx7z11lt+r8nPzyc/P9/vsYMHD9Zj5I3HXZhHxtuzcOVlAeDKyyLj7Vmkjp+JIzYhwNHVju7iHJ/V26p3Gd6KJVjaDb1pqktbbQr9l1iP1ftVqPnYAXQ+laBliQSkuLjYr3Sqb9++PP/887V6786dO7ntttt48MEHOe200/yemzdvHnPnzq3PUAPGdLt8HZCXKy8L0+0KUEQnx3sXx9uROuKTSLlxCuHJ7dVpYv22WnEExK4RkCatLm3V6v2XWJPV+1Wo4dgpK+HXhTN0PpWgZYkExDAMtm/fTteuXQHYtWsXNtuJD6LNmzdz9913M3XqVAYNGlTl+XHjxjF06FC/xw4ePMiYMWPqJ/BGZNgdOOKT/DoiR3wSht0Sv2LdAT0Bq7dV7z4gBgZhmgPSpNWtrRrV9l9gHP8tIqfI6v0qHP/cX3Y4XedTCWqWuDr9f//v/3HzzTfTuXNnAHbv3s3TTz9d43t+/fVX7rzzTp555hn69OlT7Wvi4uKIi4ur93gDwR4TT8qNU6qMINhj4gMdWq3oDmjNrN5WvfuA2G0GHl8ColWwmqI6tVWbjaRBk8j66AVf/5U0aBLU4kaTSF1ZvV+F45z7b3iQQx+/5Pc6nU8l2FgiAbniiitYvnw5W7ZswW6306NHDxITE2t8zyuvvEJpaSmzZs3yPTZy5EhGjRrV0OEGhGHYCE9uT+r4mZas+bT6CI7UzDsHxKgwB6TMqQREyhmGQe6mZSRefQu2yFg8JQXkblpG0sDbAh2aSFCr7tyPYcNdmOv3Op1PJdhYpjVu3LiR1atX43Q6yc/P57rrrqvx9dOmTWPatGmNFF1wMAybZYdXrT6CIzXzTUK3ge1oWU2pSrDkKHtMPC0uG6njX6QOKp/7TdOj86kEPUskIK+88grvv/8+Q4cOxTRNXnvtNdLT05k0aVKgQ5N6YvURHKmZbx8Qm+HbXNSpERA5Sse/SP3R8SRWYIkEZMmSJbz11lvExsYCcMMNNzB8+HAlIE2MlUdwpGbuCsvw2m1aBUuq0vEvUn90PEmws0w67E0+AJo1a4bDYYncSUTwL8HyzQFRAiIiIhKSLJGAtGnThnnz5uF0OnE6nbz++uukpqYGOiwRqSV3hUnodq2CJSIiEtIskYDMmDGDTz/9lPPOO4/zzjuPlStXMn369ECHJSK15FeCpY0IRUREQpol6pgyMjKYP38+xcXFeDweYmJiWLdunUZBRCzCU2EndO88EI2AiIiIhKagTkB++OEHTNPkoYce4i9/+Qvm0d2UXS4Xjz/+OCtXrgxwhCJSGxVHQKB8HohGQEREREJTUCcgb731FmvXriUzM5O77rrL97jD4eDqq68OYGQicjI8VRIQQwmIiIhIiArqBOTJJ58E4JlnnuHee+8NcDQiUlcV9wEBsNttlDmVgIiIiIQiS0xCv+222/jmm2+A8lGRqVOnkp6eHuCoGseKDXtJzyrweyw9q4AVG/YGKCKRk+fxrYJV/rVKsEREREKXJRKQqVOnsmrVKr799ltefvllWrduzaOPPhrosBpF946JzFm4xZeEpGcVMGfhFrp3TAxwZCK150tAbBVKsNxKQEREREKRJRKQ/fv3c//99/Pvf/+boUOHMnnyZHJzcwMdVqNITYrlvtG9mLNwC1/9N4M5C7dw3+hepCbFnvjNIkHC7TExjGNzQOx2Gy6tgiUiIhKSLJGAuFwuANasWUNaWhput5uioqIAR9V4UpNiGdmvCzNe3sDIfl2UfIjluD2mb/4HgMOmSegiIiKhyhIJSM+ePRk4cCAlJSX06tWL8ePHc/HFFwc6rEaTnlXAopU7mD4xjUUrd1SZEyIS7DweE7vNgPJKLBwOm0qwREREQlRQr4Ll9eijj/L111/TpUsXbDYbEyZM4NJLLwXgyy+/5KKLLgpwhA3HO+fDW3aV2jJGZVhiOW6P6Su/gvJJ6C6NgIiIiIQkS4yA2O12LrjgApo1awbA5Zdfjs1WHvrMmTMDGVqD27Yr2y/Z8M4J2bYrO8CRidSe2+MpHwE5OgRiVwmWiIhIyLLECEhNvLujN1X90zpUeSw1KVajH2IpnqNzQLxHq8OuEiwREZFQZYkRkJoYFco6RCQ4VZmErn1AREREQpblExARCX6eSgmI3W5oDoiIiEiIUgIiIg2uuknoKsESEREJTZZPQJr6HBCRpsDjMXHYj3U3Do2AiIiIhCzLJyC9e/cOdAgicgK+OSBH7xfY7TZcGgEREREJSUG9CtbDDz9c4/MzZ8484WtEJPCOLcNbzmG34XKbmKaphSRERERCTFCPgHTq1IlOnTpx5MgRduzYQZcuXTjrrLPYs2cPbrc70OGJSC253VWX4QU0CiIiIhKCgnoE5Pe//z0An3zyCQsWLCAqKgqA4cOHM3bs2ECGJiInwe0xsftNQi//t9PlIcxhD1RYIiIiEgBBPQLilZ2dTXh4uO9rwzDIyckJYEQicjLcbg92e8VleMu7Hu0FIiIiEnqCegTEq0+fPkycOJHBgwdjmiZLly7lyiuvDHRYIlJLVTYiPPrvMqcSEBERkVBjiRGQRx99lMsvv5xPPvmETz/9lGuuuYYHH3ww0GEFrRUb9pKeVeD3WHpWASs27A1QRBLq3B6zyiR0AKfmcgnqs0Tqm44pCXaWSEAcDgf9+/dn1KhRzJ07l8svvxybrXahFxQUMHjwYA4cONDAUQaP7h0TmbNwi6/zSc8qYM7CLXTvmBjgyCRUle+EfuyYVQmWVKQ+S6R+6ZiSYGeJBOTzzz9n5MiRzJgxg8OHDzNo0CA+/fTTE75v69atjBo1ij179jR8kEEkNSmW+0b3Ys7CLXz13wzmLNzCfaN7kZoUG+jQJES5PR4qDID4TUIXUZ8lUr90TEmws8QckOeff57Fixdz6623kpyczMKFC3nooYfo27dvje9bvHgx06dPP265Vn5+Pvn5+X6PHTx4sN7iDqTUpFhG9uvCjJc3MH1imjodi7N6W60yB8S7DK8SkCanrm1VfZY0Nqv3qyeiY0qCmSUSELfbTXJysu/rbt261WrzsqeeeqrG5+fNm8fcuXNPOb5glJ5VwKKVO5g+MY1FK3eQ2jJGnY+FWb2tut0m9golWA6VYDVZdW2r6rOksVm9Xz0RHVMSzCyRgERFRZGenu5LOr766isiIiJO+fuOGzeOoUOH+j128OBBxowZc8rf+1St2LCX7h0T/TqL9KwCtu3Kpn9ahxrf66319A63praM0fCrxQVzW62N8hGQY1+rBKvpqktbranP2rYru859oUhNAtmvnso5vjZ0HSDBzhIJyB/+8Ad+//vfk5WVxYgRI9izZw/PPffcKX/fuLg44uLi6iHC+uedQObtLCp2JieybVe2XyfjrQXdtitbHY9FBXNbrQ232+O3EeGxSehaBaupqUtbranPOpW+UKQmgexXG7pd6zpAgp0lEpCePXuyePFivv76azweDz169KBFixaBDqtBVZxANrJfFxat3FHrOxfV3T1JTYpVpyMB4660Cpa3BEv7gAicuM+qa18oEqxO5RxfG7oOkGBniVWwiouL2b17N5dddhkHDx7k6aefJj09PdBhNbjUpFg6t09gxssbGNmvi6/j0FreYjXHK8Eq0wiI1ELFybQV+0IRK1O7llBmiQTk4YcfZtWqVWzbto2XX36Z1q1b8+ijj9b6/Z999hlt27ZtwAgbRnpWAd/tOkSbpFje+OgH0rMKtJa3WJLH4/GbhO4twSp1KgGRE6s8mbbyBmsiVqR2LaHMEgnI/v37uf/++/nss88YOnQokydPJjc3N9BhNShvojFl3IU8NqE3GPDEKxuZ9cYmlR+I5VRZhtemSeji73g7N//zkx99tfEXdEvxla3oYk2srOKcj1Np19rxXKzKEgmIy+UCYM2aNaSlpeF2uykqKgpwVA2r4gSy1KRYxg48i1+yCjjnjJZKPsRy3O5KJViOo5PQNQIiRx1v52bDMI87mVbEqmqaJH4ytOO5WJUlJqGfd955DBw4ELvdTq9evRg3bhwXX3xxoMNqUBUnkFU3TKskRKzE7TGxGVU3InS6NQIi5U5mUq4m04rV1dck8YaezC7SUCwxAvLYY4/xxBNPsHDhQmw2GxMmTGDatGmBDqtRVBymzc4rYcyArlXudmioVYJd5RIs+9F/l2kERCrQpFyRk6fjRqwoqBOQ9evXA7Bq1SoOHz7M+vXrWblyJWVlZXzyyScBjq5xVBym7d4xkQXLtzNmQFe27crWUKtYhsfj8UtADMPAbjNwahleqUCTckVOno4bsaKgLsH66KOP6NOnD/Pnz6/ynGEY9OvXLwBRNa6Kw7SVh1q1q6lYReUSLICwMBslGgGRo7Rzs8jJ03EjVhXUCcgf//hHAObPn8/+/ftp164dBQUF7Nu3j7POOivA0QVGxaHW6RPT1MFI0HN7TEwTvxEQgHCHndIyJSBSTjs3i5w8HTdiVUFdguX15ptvMmnSJABycnKYPHkyb7/9doCjCgwNtYrVeDzlZVaV8g/Cw2yUlLkCEJEEo/5pHapcMKUmxVY7WVdEyum4EauyRAKyaNEi3nrrLQDatWvHkiVLeOONNwIcVeOrbt3wd1b9SFFONs68LFwFOZimauoluLjdJqAREKmZaXpwFeSoLxNpQnRcy/EEdQmWl9vtJjb2WIbfrFkzjEr15KGg8lBr65bR/M9lCWQteARXXhaO+CRSbpxCeHJ7DMMSuaWEALenPAGpuBM6QJjDTokSEKH8IqUscx8Zb886bl+2YsNeundM9Lvbm55VwLZd2brbK5YTCu25Nse1hC5LtIAzzjiDp59+mv3797N//37+9re/cdpppwU6rHpxMruYVh5qdRfmkfWvP+PKywLAlZdFxtuzcBfmNWzQIifBm4BUPt+Eh9m0DK8A5X2Z9yIFqu/LtOGaNCWh0J5rc1xL6LJEAjJjxgz27t3Lddddxw033MCePXt4/PHHAx1WvTiVTsh0u3wHtpcrLwvTrbp6CR5ut3cOSDUlWEpABCgqKqm2LysqKvF9XXEVwK/+m6GVfsSyvDcYK7bnWW9s4sKzWjWp9qxrFKmJJRKQFStW8Nxzz7F582Y2btzIM888Q4sWLQIdVr2o6aR6otERw+7AEZ/k97wjPgnDbonKOgkRTld5AmKvPAckzKY5IAKAIyy82r7MNBx+o8HacE2agtwjpcx6YxOArz0XlTgDHFX90zWK1MQSCYh3AnpTUTmxSE2K5ere7aucVE80OmKPiSflxim+A9xbX2mPiW/kT2RdJ1MCJ3XjPDoC4rBXMwdEIyACfHuglBZDH/Try1oMfZDnP9ztNxp8sqsA6viWYOV0uXny1Q28vPQ7UlpEkZNfSn5hWY3vsVp71jWK1MQSCcjpMz4M2QAAIABJREFUp5/OtGnTWLJkCStXrvT9sarKicXXOzJ5eel33HVjD7+T6olKDgzDRnhye1LHz6TdXf8gdfxMTe46SaFQhxtox0ZA/NtleJiNMo2ACNC+VRyvrs4lYuh0IkbPwf67R5m59Beuufh0X39X3SqAFY/d6uj4lmD02/NSwYSM7CJ+Odo2kxOi2LYrq0m1Z12jSE0sMQ6Wm5tLbm4ue/cey/KtvBN6xcSiX+/2/N/S75g6/iJ6dkmme8eW/ruanmDjQcOw4YhNCNAnsb7Ku8svWrlDdeX1zHW8Eqyjc0A8HrPKEr0SWlKTYvltz7bcNfdL0s5pxedbfuHmAV3p2SXZ95q6bLim41uCUWpSLOd3S+GjtT8DcDi/lEd/35uUFtFNrj3rGkWOxxIJyPz58wMdQr2rmFhMvrEHPbsk+5blq3hS/XpHJv9491tfyUFqy5gG6WxCYUnA49Hu8g3LNwJi908yoiLLu5/CEifNosMbPS4JHos/3cGab9K54cpOvLl8O+ee2ZJFn+4gv6iMidd2B8rv/la+OPPepKmJjm8JNos/3cHm7RkkxkWRkVNEXHQ4Ly35livOb8fwvl1qPPeqPUtTYYlxsKysLG699Vb69+9PdnY2EyZMICsr68RvDGIVa5lXbtxHelaBb3gVypfc/XpHJn96/UvuvKFHrUsO6spqQ7v1SbvLNyynu7zMqvIoR3REeQJy5AR1z9L0maZBcamLxat+5K4be3A4rxhMgy+2HCA9q+CU+iMd3xJs8gvLyDxcTJjD4K4be5BfVEZGdhH5hWUnbOtqz9JUWCIBmTFjBn379iUiIoK4uDi6du3KI488Euiw6iw9q4DHXlrPmAFd/RKLjMNFXHhWK9+cj6cXbOa2od19ZQipSbGMGdCVRZ/8WO8xheoSl3WpK5eTc2wExL+7iT46AnKkqIytP2aRk19S5b0SGgzDpKTMRXJCNInxUYSF2WkRF4FhGHy45mdff1l5hPZEk291fEswiosJJzmhvJ3v/fUIiXGRYBjs2JvDrHmbGDOgK9t2ZQP+7VztWZoSSyQgv/zyC8OHD8dmsxEWFsYDDzzAr7/+Guiw6mzbrmzuvKEHC5ZvJz2rwJdYPP/OVn57XqpveHXcwG4sX7/Xb1RiwfLtjLy6c4PE1bplNI+O7krryJLyv1tGN8jPCSY11ZVL/fAmII5Kk9CjIsIAWL/tV6a9uI7Z879q9NgkOPy4L5fY6DCGXtaRGS9vYPAlpxMWZiO5RTQfrNnNjVd18vWXUPsR2uqO7wdvPp9wdwHOvCxcBTmYpqfBP59IRfGxkTw6IY2xA8/igzW7sdttjOjbme17cygqdfHaB9/TvWNilXZeuT23bhnNE2PPJtY8orZcidVWDAtFlpgDYhgGHs+xA6ugoMDva6vx1nWmtIhmzsItdG6fwHe7DvHErX0AWLRyB3fd2IPFq3aSdk4rZs3bxNhBZ/kmnEH5wVWfczNM00PJwb3kHd1Z3R2fRPiwh4hs1aFJr1hxvBrbpj7y05hcvmV4K5VgHR0B+eyr/QB8tzub4lIXURGW6JaknqzYsJeUFjH8d89h/vHeNkb378I/3vuWiDA7hUVOhvzmDD7ZuI8xA7qe9OTbyse3aXqId2aR8c4s8vOyfMuCNsbKPKE8z078fb87G7sNlq/fy/SJabzy/ncsXLGdNkmxZOUWEeaIJv1QIW989AO/OS/V12YqthPT9FCWuY9DR3ca97blrzPDad8qLuTbmbes3NtPVBw9kuBgiSvLfv368Yc//IEjR46waNEixo0bxzXXXBPosE6ZdzLZB2t2U+bykHG4iDkLtzBmQFc+2biPO2/owbc7D1FU6vLtEQL47ojUZ4ZfnJtD1tHkA8p3K836158pzs2p8X26yyAncqISrJwjpb7H9mccabzAJCh075jI6m8OUFRchmmaLFyxA48HCopduDwmg39zOveN7sWC5dvpV81+SRWdqD9yF+aRcfSCDcr7uYy3Z+EuzGuUzxmq8+zE37lnJvLs4m9omxKL3WbgdLnxmPBLVgHXpJ1GQZGTGS9voMzl4Tc92lT7PY7Xlru1CquyzP9jL60PuXYWqmXlVmKJBOT222/n0ksvpXv37qxbt44RI0Zw1113BTqsU5Z5uJBmtmIWPHQRf7j+TN77904uPCuF2fO/4vyuyXRvE8b0UZ25b2hHzuuUyKx5m5j1xrH60OSEKB57aX2VE1peQclJJwUuZ5mvI/M9lpeFy1nzBGGdVOVEjrcTemSFkY5eXco3qtqT3vAXghJctu3KprjEyVUXtOONP1zE21Mv4o0HLqLfhe1wu03fiOSYAV1ZvGpnjZNvK/dHiz/dwdS/ryU5IQoA0+2qtp8z3a4G/5y6IBIvtwfuHn4eX2w5wMx5X3I4r4SUFlGckdqMpat3U+Z007xZBOEOGxmHi6o9dxcVlVTblouKihnQpwP3/+0Lnnp1A398bSNp57TyjQJ4v5dpenAV5NRbKWJ9f7/6UHHFsOPdtJDAsUQCsnv3bq677jr++te/8uyzzzJ8+HC++OKLQIdVZys27GXHnmwc+elEfTKLQ/93F+ErZ3J3/yS++uEgXTs0p28nG+mvP0ze63cT+9ls7u6XzOmtm5FfWMprH3xPckIUC5Zv584belQ5of2mR5uTTgqioyN9u5V6OeKTiI6OrPGz6KQqJ+KbA1KpBMtmGISHlXdBF56Vgt1msFcjICGne8dELj+/LRN/G0/O4sdJ/8dd5C5+nP+5NJ7Bl5wGHJv/9sStfWqcfFu5P1rzTTpREQ5e++B70rMKcHps1fZzhr1xyv50QSReZ52eyDV9TqO41I3bNLn6og5k5ZYvxFFU6uLmAV25ZcjZ/On1L30JdEWOsPBq23J+iYe//+tbDMNgw/cZxEWHM/Di0/2uA7zlW+mvP8z+ubeT/vrDlGXuq3PScLLfr7EqJ7RiWHCzRAJy00038dFHHwHgcrmYOXMm06dPD3BUdde9YyKUHqHgw7/4DZ8WfTSH3/drzw2XtKJ42Ry/54qXzeHuaztyOL+UlMRoFizfzpgBXcnMKa5yQjteUrBtV7bvIK+YnKzYsJfDZeEkDH3Q16F560ntMfEn/Dx1PakG4x0TqX/H2wkd4NrfdqR7x5a0T2lGQlwEGdlFjR2eBFhqUixjL29N5rv/69fnZb77vwy/OAlnXhbh7gIevPn8Wi0WUbE/GjvoLB79fW8w4IlXNvLc+7uIGXx/nfq5+qALIgHIKyjhsZfWsXLjPs49syUeD7y5fDtlTg8pLaJIaRHN/I+389qH3zN1/EVk5hRX+R5RzRNIGvYQjvgkIlI7kTJiKimjHiMyzE6ntvEcKXLSvFkE+UVlfLfrkN/NwZMtRaycMKzYsJevd2TWubSxMSontGJY8LNEAjJv3jz+8Y9/MG3aNIYPH052djbvv/9+oMOqs9SkWDokRVU7fNo6IYIWMY5qn7OZbtq0jGHDdwe58KwUFizfTnJCVLUntOqSAu9Bn5wQxZyFW/h6R6bv69nzN1MclULq+Jm0u+sfpI6fWeuJmXU5qdb3HRgJXt5J6JVLsAB6dklmVL8uGIaNFs0iyTysBCTU3P2XfxNmM6svjSo4zP65t1P0zuPEO7P8+ofUpNhqJ9VW7o8Axg48i1+yCljz7a9kmgl16udOlS6IymneICz9YjcZh4uJjQ7jsp5t8HaNpU4353Rsye3Xn0tuQSllzvL2/v3ubBZ/6n9u/fVQEWv3GdivnU6L/reSvfz/+OUfkzGWPcX0YW3566RenHN6C4Zf1Znn3t5Kv97tfQn8yZYibvrhIE++usH385MTonjy1Y2s+eZA+XvLqi/hNt2uan+3jVE5oRUug58lEpBOnToxYcIElixZQlZWFrfffjtxcXGBDuuUFJSZ1Q6fZh9x0SwmotrnSt0GB7IKaZscyz8/+ZHe57Ti+Xe2VntCqy4p8B6A3smcf3r9S67u3Z4Fy7dz3+heJLeIwRGbQFh8Eo7YhFonH3U5qQZyMqg0rmMlWBXaU1kxRv5BjNwDGLm/QGkBCc0iyMxRAhJqIsLtuMzqS6PcReX9gbd/yM7IrPbitWK/57uYaRnDRWenMGveJl55/zvaJMXSJimWv/9rG5nFYSfVz9UHXRCV07xBKClzApCdV8Jzb2/FYx577vPN+3nmrc1ccX5bikqc/O2fX3PF+W1Z8006s97Y5Gvns+Zt4l+f76KwqJSsd2f7LyDz7mxamZlMvCyBb37M5Kaj86e8/+eG3XFSpYiDLjmdrJwSnnx1A1/9N4OXlnyLARzKLeal97Zx4FBJtd/P6bEd93fb0OWI/dM6VPmex7tpIYFhiQTknnvu4cUXX+Ttt99mypQpjBs3jtdff/2E7/vggw8YOHAg/fr1Y8GCBQ0f6EnILgkjauB9fqUAsYPvJyHKw5HPXiNp0CS/55IG34VpGIQ7bBzILGDgxafx1oodjLy6c5UT2upv0o+bFHgP+ufe3sr1V5zJ3Le3+g7+utyZqutJNZCTQaVxOV1uEh2FOHZ+RsR/5hK19AFi3ruH6I+nE73iSaJXPEHMkvsZkfFXJoZ9wK8fv0LRz1sx3c5Ahy6NYHS/rhS6DFKGPeDf5w2aRO6693yvc+VlERtuVJlkPmveJq44vy1zFm5h9TfpjBnQlTVbf2HOwi10apeA0+2huNTFYxN689iE8nKsWfM2NfrIgy6IymneINwxrEe1j/fs3BK3B44UOflqewYlZS4cdhspLaKZMu5CMOGRv6/lnme+wOn2EGa30SYxsvqKibBICj/8C7f0bQ8YPHFrHx57aT1f78jEHhNPyo1TqlxjuEuLqq1C6NklmUduuYiMw8XMeHkDGYeLmfb73ky4tjsfrNnNv9Zn+MrBvN8vYeiDvLxi73F/typHFEssuB8VFcV7773H3r17GThwIJ07d+bRRx9l/Pjxx31PRkYGzzzzDO+++y7h4eGMHDmS3r17c+aZZzZe4MeRnlXA/y39nuF9zyTmigdJjg8nI68MpzuMsA+fwpWXhacwl8Srb8EeHY8tMoasD58n8vI7ABjym9NZtm4Pk244F3elviI1KZbmzSJqTAoWrdzB5Bt78H9Lv+OuG3uwaOUOUlvG1Gnd7Lruo+G9A1Ox42zMyaDS8EzTpGjHRs7+8W1+E7cHtoDZLAmjVRfMmBaYYTGYNhuGxw1lRRzOPAS/7qPom5UUb1mGER5FdMfziD37UqLP7IlhDwv0R5IGkJlTTJeWEeR/vYZWIx4Bmw3D5uDQJ69Rmr7T9zpHfBKO8HDfxevIfl1Y8006GOV7Kt03uhez5m3i35v3E+6wMWXchWzblc3lvdrx2wp7KUwZeyFrtv7Ctl3ZIXXRG0wq3v2ePjEt5H4PG78/SLMoB0eK/W+4ff3jIeJiwikoKuNIoZPeZ7fimx+zWLbuZyZe251LeqTy5vLtAPTsnMzg35xOsaug2nOpp6QAV14W7ZMi+Tmn/P/8zht68Pw7W3ni1j4kxyaQOOB/sIVF4ikp4PC/38RdmEvq+Jk4YhOqxJzSIpq46HCy80uIiw4H8CUQb3z0A3//zMaEEU9QWlKKLSyMJ/65gxH9uh43+ah4rZHaMiYkE9FQZ4mrvREjRtC3b18cDgeLFi3ipptu4oUXXqjxPevWrSMtLY3mzZsD0L9/f5YvXx4Uy/dWHDX45yc7ePPVbYTZDZ67owelRzuR0vSdZLwzG4DWNz2BuzCX/CI311x8Gkv/s5vJN/Zg+fq91SYHx0sKAN8+IwuWb2fq+It8k9m9B3/Fk3ttN/uqC+8dmIxKmyg11mRQaVjFe7Zx+N8LKE3fSURYcz519uLKvmk4w2LwuN3Vvic/wsWzP57N/X1a0KPZIUp//YniPd9R+N/12KLjiD37NzTrfjnhrc7AMKrOJxFr6p/WgaJDv3Jk4/sc2Vg+ty8itRMtLh+NM3NPlf4hNdbmf/F69OJlZL8ulLk8/JJV4Luora7vSk2KZXjfLo39MaWCyne/U1vGhNSFZ3RkWJXkwyu/sIzEuAgiI8LIPFzE7dd358X3tuHxmHyw5mdaxkcSEe7gu92HGPyb00lMSSZm2EO+fby8o4eHP1/oS9r7p6UA5SMZT9zahzkLtzBjTGcy/vmnKj+/uiqE9KwCnnx1AwXFTkb378I7q3by5KsbefT3venZJZnUljE88cpGbpq1nsk39mDlxp8Z2a/rcX+3NVVOhFI7CHWWSEBmz57N66+/zh/+8AdatWrF7Nmz+dOf/sS//vWv474nMzOTpKRjNYnJycl8++23fq/Jz88nPz/f77GDBw/Wb/DV8CYI6VkFrN2aTnS4naIyNzlFbuKqu5PhLCF28P288MF+ftiTy00DurLy6M7AJ3PAeg/6igd/Sotov8f7p3VolDtThmEjPLk9qeNnYrpdGHYH9pj4Jr3r+qkIVFs9WaUHf+bwvxdQvPtr7LEtSLhsFO/80JztRSX8xhaJeZzkA6BFTPnv/tcC6N21E5FtOhHXqx+lB3dT/PM28resJH/TMsKS2tGs++XEnHUxYfHJjfXRpJZOtq1++uVeTk8wCKvQ95Wm7yR30zJajJyBgUlEZISvf6h88Xrf6F6+PqtNUmzIXtRaRTDd/Q5Uv1pUcqy8NCUhiowKq1wZBuQXOfl/I3uR0iKaOQu30Kd7az5Y8zPNm0Xw1B2XAPj2Bbtl8Nms/jqH/7npKRyeMsoOp3P484W4C3OrvannHX36OaOw2uuN6qoQlq37maycEh655SJ6dkmmoMjF8vU/s3l7Bj27lPfB4WE2rji/Hf+39Dumjr/Il5hU97uta+WENC2WSEBKSkr8Sqcuu+wy5syZU+N7PB6P311S0zSr3DWdN28ec+fOrd9ga8nbCZ/RNp49v+YT7rAx79P9PDTkPoqOLsHriE8i6foH+bU4jJc+/JmMnBKaxYSxc3+ub6SipvKoyrwHfcWDvOJB7y27aqw7U4Zhq3aoV6oKZFutDWduBjlfLKLgu/9gi4whvve1RJ3RA8Nup2TrQSLCTpxYRobZiA43yMg7dnI2bHYiUzsRmdoJzwXXULL/vxT/vJXDn83n8GfziUjtREy3i4nplqZkJEicbFt9f/VuIhw2Hr72ft/S5I74JCIuGsaMhdtp3bKZr5+r7uJ11rxNON0e2iTFEh5mI7VljF//qIua4BJMd78D1a/+sPswAF3bN2f7vlwiwmyUOj0YBpgmxEaF8doH3zNl3IUM6NOBuW9v5dwzW7J972EyDhfRs0syU8ZdyOpv0vn35gOMvLozUc1jMU0PRngkidfeS0GZSXhycpWbet5z/ITfnUXsYP9j7nhVCPmFTl/yAfA/153DBd2S+ffmA75jcsrY8pLHK85vy4Ll20lpEa2RDamRJRIQh8NBXl6eL4HYvXv3CUswWrVqxVdffeX7Oisri+Rk/wuUcePGMXToUL/HDh48yJgxY+op8uPbtiubMQO68vw7W7nygnbs2HeYg4eL+PMHv3LL1Q8SH20np9DNvGUZ9Dk3lZ/T84kIs/OHMReQmVPcIAd2MN2ZEn+BbKs1cRfmkbPmHfK3rMSw2WjWsx/RXS7E5gj3vabE6SHCYWDW8H28EmPsfglIRbaIKKLP7EX0mb1wHcmh9MB2ivd9z+FV8zi8ah5hLdsSdVp3ok7rTkRqZxzNlNwGwsm21UGXnMEbH/3AzKXpjOv7IClH58Q9t3Q/Ow/kcu+o832vrXzxCoABbZJi+f2QswH8ykl14RN8gunud6D61dZJMVx5Wjs+XLObLh0S2LE3h56dW/LDnhyGX9mJhSu306tLMsvW/cyKDXuZPjGNnl2S+XpHJn96/UvfCMOIqzv7fV/vTT0HUN0WwpXP8ZmHo3D2n0qbxAgc4eHHrUKo7kZnzy7J9OySzIoNxyaae3+H3soK72M6BqU6lkhA7rjjDm666SYOHTrEfffdx9q1a3niiSdqfM/FF1/Mc889x+HDh4mKimLlypU8+eSTfq+Ji4sL2HK+/dM6sGLDXu68oQcLlm/n8l7tyS8sY+P36Ty58L9cdWF7Tmsdx+70fDp3SOSZey9nzdZfyMwp9hvJaKx1s9WBBFYg22p1PKXF5G38gNyNSzGdZcSedQkxZ12CLaLqjr0lZeUJSG0kxNg4mO/C7TF5b1MO638qZHDPeK44y/+zO5ol4OjWh5hufXAV5FJ6YAdlGT9z5JtV5H/1MQD22AQiWp1BWMs2hDVvhSOhFWEJKdibtfBLkKR+nWxb7Z/WgU+/3Mv2vTn892Brzjn7DLam72X7vlzOaBPn1/9UvnjdtiubKWMv9OufKpaTqt+SmgSqXx3Tvyt/ev1LHpuQRmZOMb3PbsX8j//L3cPPo+9FHejUPoHn39nK6alxvmQDyi/6p46/iH9vPuB77GRUPscnt4jB5Tb54sds3zyRkxVMCaVYiyUSkCuuuIIzzjiDtWvX4vF4uPPOO+nYsWON70lJSeHee+9l7NixOJ1ObrjhBs4999xGirh2vEmIt0NYsWEvgy65GCjvKPpe1IGzTk/0nYAbeuKkOhI5EU9ZCfmbl5O7YSmeonyizjyfZj2uwB4VB8cZ4ygo9dA2oXZdTYsYO98eKOOvH2ewcVchzSJtvLQqi+7tomnZrPrv4YhtjqNrb2K69sZ0u3EeTseVm4EzJwNn9i8U//xtlSV9jfAo7DHxR/80xx4dhz2qGbboOOzRzbBHxfn92wiP1MT3BmQC/29E+cUX4Pt7+Ya9NS5Tqz5LrOjfmw/4JRYrNuzl7uHn8e1P5ed972TxbbuyqyQa3pGHutDxIsHEEgkIQIcOHejQ4eTWSx8yZAhDhgxpoIjqR8UOoeK/K45CqHOQQHMX5ZP/9afkffkBnqJ8Ijt0p1mPK3HEJ5YXLddQYJVf7CYmpXYjDknN7ABs3FXIDRfEcWZLO39ensOqbXmMuPjEG5UZdjvhSe0IT2rne8zExFNciLsgB3dhHp6SwvI/pYW4i4/gzNpPSUkBnuICqGYNfADsDuxR5QmJLTquvMwhPhlH82TCmifjaJ6CIy4Rw2av1ecUf0/ffWmVx/pe1MGXiIg0JZVLmrzn/ortXed+aeosk4CISOMyTQ+lB3aQ//UnFP6wDtPtJLLDOcSeeyVhzZPKL9bNmmd2uNwmRaUeoiNqN3rQvU0Ev+S66Nwqkm4pDkygU0oYq344QlyMg4O5Tm7+TSIOe9Xv9+HXuUQ4DK7u7j+J0sDAHhWLPSoWKiQmVHqViYlZVoqnrBhPWRFmSTGe0iI8zmI8peV/zNJC3CWFFGen4y5Y7ff5DXsYYYmphLVsS3hSe8JbtiUsqR1hCa2UmIiIiFSgBEREfDwlhZQc2EHhzk0U/bgJd0EORngUMWf/hugzzy9fIcX0HH+koJLsAhcm5XM7aiPcYfC7HuV3/byX9n06RjFvXT6vfXEIgIQYO9dd4D/B/Ju9RbyxunyjzdSEcM5uW3UuSs1MDMAIj8AWHgE0P/E7TA/uoiO4C3PxFObiOpKDKzeTkv3bKfxh7bEX2h2EJ6YS1rIdYYltCE9sQ1hiG8ISU7GFRZxknCIiItanBEQkxJimiVlahKswF+fhX3EeTsd56BdK03+kLHM/YGKERRDZ4Ryi2p9FWKszsNmOJhC1TDy8DhwuAyAhqu77u3RtFcbgc2MId9j44ddSlnyVQ99z4oiNLB9VcHtM5q/JJiHajtPtYeHabP44vE2Dz9kwDBuOmHgcMfGAf6mQx+XElX8Id/4hXPnZuPKyyhO7H9ZRsVzNEZ9EWItUHHEtccS1xB7XovzfzRKxRTXDHhWjHeBFRKTJUQJSifvoRmnBuMmbBL9WrVrhcDTOYXW8tmqWFuL++mMoLcB0OcFdBm4nuMowSwqh5Ah4Km0IGBGDPbEtYedcAfEpENcSpwlO04Ts7DrHuHFHCeF2iLUVkZd7cslLRd2P7ima4DDZ8avJk+/u58wkOwawL8fN/mw3N10YyaEjZSzfXsqMd/YxNi2SZpGB3NjSATGtyv+0BgNwmCYU50NRHhTnYRbmUJqfTcnB3ZjFBVQ7l8YRDhExGOGRYA8HexjYHWB3lCcnNkf57mVw7G8Amx1b10uxJbSuNrpgaKsitaG2KlbRmG3V6gzTPEERd4j56quvAr63gljXqlWraNu2baP8rJraanJyMmeefjoY5dekNgNsgGEY2G0GdsPAbgOHzcB29KK1pKQEt8tV73Ge2bkLbo8dT+Wkp44cYWHY8OBxlfoes9nDMGzhlJaVEhkZidtVQnZmBjm5ufXyMxtaWFgY4RHHyrE8ponbY+I2weMp/7r8D3gwwQQP/vP/vR25efRfBgaHsrPZ+dNP1f7MYGmrIieitipW0Zht1eqUgFRSUlLCd999R1JSEnZ7w00c9W52tGDBAlq1atVgPycYNeXP3ph3Pyq31WD/fw3m+II5NmiY+ALZVqsT7L+DihRrwzherMHWVuuLlX43YL14ofFj1ghI7el/qZLIyEguuOCCRvt5rVq1CtlsOZQ/e304XlsN9v/XYI4vmGOD4I/veE6mX7XSZ1SsDSOQsTb2NQBY63cD1osXrBlzUxfIAmkREREREQkxSkBERERERJq4jIwM7r///kCHASgBERERERFp8lJSUvjLX/4S6DAAsD/++OOPBzqIUBUREUHv3r2JiAi9zchC+bM3pGD/fw3m+II5Ngj++OqDlT6jYm0YVoq1Pljt81otXrBmzCfr3Xff5cUXX+TVV1/l5ZdfpnXr1kRGRnLnnXfy2muv8fnnn3PJJZeQnZ3N+PHjGTFiRKBD1ipYIiIiIiJW5U1A3n33XfLz8xkxYgSdOnVi2LBhDBw4kAULFvD9998zadIk7r77bt59991Ah6wSLBERERERK7v66quJiYmhdevWdOrUiU2bNnHNNdcAcO2117Jp06YAR+hPCYiIiIgVdZmOAAAgAElEQVSIiIVV3H/ENE08Ho/f1253/WwGXF+UgIiIiIiIWNgXX3xBWVkZ6enp7Nmzh7S0ND7++GMA3n//fXr27BngCP1pI0IREREREQuLiopi1KhRlJaW8sQTT9ChQwemTp3KCy+8QMuWLfnzn/+M0+kMdJg+moQuIiIiImJR7777Ljt37uShhx4KdCi1phIsERERERFpNBoBERERERGRRqMREBERERERaTRKQEREREREpNEoARERERERkUajBERERERERBqNEhAREREREWk02ohQRERERKQJO3DgAAMGDKBjx45+j//jH/+gdevWVV7/3HPPATB58uQGiUcJiIiIiIhIgK3YsJfuHRNJTYr1PZaeVcC2Xdn0T+twyt8/OTmZpUuXnvL3qQ8qwRIRERERCbDuHROZs3AL6VkFQHnyMWfhFrp3TGywn/njjz9y8803M2zYMK644greeustv+edTicPPPAA1113Hddddx2LFy8G4NChQ0yaNInrr7+eYcOGsW7dupP6uRoBEREREREJsNSkWO4b3Ys5C7cwsl8XFq3cwX2je/mNiJyKzMxMrr32Wt/XQ4YMISMjg0mTJtGnTx/279/P7373O0aNGuV7zddff01eXh5LliwhIyODv/zlLwwfPpynnnqKYcOGcdVVV5GZmcno0aNZsmQJsbG1i1UJiIiIiIhIEEhNimVkvy7MeHkD0yem1VvyAdWXYLndblavXs2LL77Ijz/+SFFRkd/znTp14ueff2bChAlceumlPPjggwCsW7eO3bt38+yzzwLgcrnYv38/3bp1q1UsSkAqcblcHDx4kFatWuFw6L9HgpfaqliF2qpYhdqqBFp6VgGLVu5g+sQ0Fq3cQWrLmHpNQiq75557iIuL44orrmDgwIF8+OGHfs8nJCTw0UcfsXbtWr744guGDh3KRx99hMfjYd68eTRv3hwoH11JTKx9qVhA54AUFBQwePBgDhw4AMDDDz9Mv379uPbaa7n22mv55JNPgPIsa8iQIfTr149nnnnG9/7//ve/XH/99fTv359HHnkEl8sFQHp6OmPGjGHAgAHccccdFBYW1jqmgwcPctVVV3Hw4MF6/KQi9U9tVaxCbVWsQm1VAsk75+O+0b24oFuKrxzLOyekIaxdu5a7776bvn378p///AcoHxXxWrVqFQ888ACXX34506ZNIzo6ml9//ZW0tDQWLlwIwE8//cSQIUMoLi6u9c8NWAKydetWRo0axZ49e3yPfffdd7z55pssXbqUpUuXcvXVV1NSUsLUqVN54YUXWLZsGd999x1ffPEFAA888ACPPfYYK1aswDRN38SYGTNmMHr0aJYvX84555zDCy+8EIiPKCIiIiJSK9t2ZfvN+fDOCdm2K7vBfubkyZMZPXo0AwcOZPPmzbRp08Y3MABw6aWXEhkZyaBBg7jxxhv53e9+R5cuXZg2bRpbt25lyJAh3HvvvcyePbvW8z8ggCVYixcvZvr06b5asuLiYtLT05k6dSoZGRlcffXV3HXXXXz77bd06NCBdu3aAeUTZpYvX86ZZ55JSUkJ5513HgDXX389zz77LDfeeCObNm3i+eef9z1+00038cADDwTmg4qIiIiInEB1S+2mJsXWSwlW27Zt+eyzz6o8fsstt3DLLbdUebzi/h9//vOfqzyfkpLCiy++WOd4ApaAPPXUU35fHzp0iLS0NKZPn06zZs247bbbeOedd4iOjiYpKcn3uuTkZDIyMsjMzPR7PCkpiYyMDHJycoiNjfXVbnofr05+fj75+fl+j2nYVYKR2qpYhdqqWIXaqkjgBM0Mq3bt2vlGLQBuvvlmlixZQv/+/TEMw/e4aZoYhoHH46n2ce/fFVX+2mvevHnMnTu3nj+JSP1TWxWrUFsVq1BbFQmcoElAduzYwZ49e+jfvz9QnlA4HA5atWpFVlaW73VZWVkkJydXefzQoUMkJyfTokULjhw5gtvtxm63+15fnXHjxjF06FC/xw4ePMiYMWMa4BOK1J3aqljFqbRV0+0Emx3D0B650vDUr4oETtAkIKZp8qc//Ym0tDSio6P55z//ydChQ+nRowc///wze/fupW3btnz44YcMGzaMNm3aEBERwebNmzn//PNZunQpl156KWFhYVxwwQUsW7aMIUOGsGTJEi699NJqf2ZcXBxxcXGN/EmrWrFhL907JvrV+KVnFbBtV3a19YASeoKlrdaV2njoOJW2+vOskTTrcSVJg++s56hEqgpkv6o+UUJd0Nxm6tq1K7feeiujRo1i0KBBdOvWjcGDBxMREcGsWbOYPHkyAwcO5IwzzmDAgAEAPP3008ycOZMBAwZQVFTE2LFjAZg+fTqLFy9m4MCBfPXVV9xzzz2B/Ggn1L1jot8ya95l2Lp3rP16yiLBTG1cTsQ0TQCObK06SVKkqVGfKKEu4CMgFWfkjxkzptqhzz59+vD+++9Xebxr16688847VR5v06YN8+fPr99AG5B3mbU5C7cwsl8XFq3c4bcMm4jVqY3LCbldgY5ApNGoT5RQF/AERMqlJsUysl8XZry8gekT09QJSZOjNi41MV1lR/9V/aIhIk2N+kRpTDNmzGDLli04nU727dtHx44dARg7dizDhg1r9HiUgASJ9KwCFq3cwfSJaSxauYPUljHqjKRJURuXmnhczvJ/2HVaktCgPlEa0/Tp0wE4cOAAY8eOZenSpQGNJ2jmgIQyb+3nfaN7cUG3FN+wrLc2VMTq1MblhNzlCYihBERCgPpEOR7T9OAqyMGZl4WrIAfT9DToz3vuueeYMGECAwcOZOHChdx8881s3LgRKE9WrrzySqB8tdlJkyZx/fXXM2zYMNatW3dKP1c9fRDYtivbr/bTWxu6bVe27oZIk6A2LifiHQEx7PYARyLS8NQnSnVM00NZ5j4y3p6FKy8LR3wSKTdOITy5fYMuT15WVsayZcsA+Pjjj6t9zVNPPcWwYcO46qqryMzMZPTo0SxZsoTY2Lq1VyUgQaC6JfdSk2LVCUmToTYuJ+QdAbEpAZGmT32iVMddmOdLPgBceVlkvD2L1PEzccQmNNjPPffcc0/4mnXr1rF7926effbZ8thcLvbv30+3bt3q9DOVgIiISMCZbnf5P7QJoYiEKNPt8iUfXq68LMwGXiUwMjLSP46jy6K7XMd+rsfjYd68eTRv3hyAzMxMEhPrvmy0enoREQkCZqADEBEJKMPuwBGf5PeYIz6pUefGJSQk8NNPPwHw6aef+h5PS0tj4cKFAPz0008MGTKE4uLiOv8cJSAiIhJ4phIQEQlt9ph4Um6c4ktCvHNA7DHxjRbDxIkTWbhwIUOHDqWkpMT3+LRp09i6dStDhgzh3nvvZfbs2XWe/wEqwRIRkSDgSz+UiIhIiDIMG+HJ7UkdPxPT7cKwO7DHxNfrBPS2bdv6bQI+efJkv+fPPfdc34R0gLvuuguAlJQUXnzxxXqLQwmIiIgEni/xUAIiIqHLMGwNOuE8WKgES0REgoASDxGRUKEEREREgoYqsEREmj4lICIiEngqwRIRCRlKQEREJAgo8RARCRVKQEREJPB8AyBKREREmjolICIiEgSUeIiIhAolICIiEnCmRj5EREKGEhAREQk8JSAiIiFDCYiIiASBowmIEhERkSZPCYiIiASeluEVEQkZSkBERERERKTRKAEREZHA846AaABERKTJUwIiIiIBZ6ISLBGRUHHKCciuXbt4++23MU2Te+65h759+7Jhw4b6iE1EREKF8g4RkZBxygnI9OnTiYiI4PPPPycjI4OnnnqKZ555pj5iExGRkFGegWg/EBGRpu+UE5DS0lJ+97vfsWbNGq655hp69+6N0+msj9hERCRUKPEQEQkZp5yAlJWVcejQIT7//HMuvvhiDh06RGlpaX3EJiIiIiIiTcwpJyAjRozgiiuu4Pzzz+fMM8/khhtuYNy4cfURm4jI/2fvzsObKtO/gX9PkiZtmqZr0tIClUXKjBYEWYo6ICMCOuAIgmKr4ALOOKIj/AQZRRAVQWRgVBznBWcUFQYBlcVhoO6DYllEoSCLgiyltE33Jk3SJOe8f4SEpgu0TZo0yfdzXb0gJ8nJnXLznNx5NgoX3AeEiChsKLw9QXZ2NiZNmgSZzFnLfPTRR4iPj/c6MCIiCifcCZ2IKFx43QNiMpnwwgsvYMqUKaisrMTy5cthMpl8ERsREYUL1h1ERGHD6wLkhRdeQExMDMrKyqBSqWA0GjFv3jxfxEZERGFCksRAh0BERH7idQFy5MgRzJgxAwqFAlFRUVi6dCmOHDnii9iIiIiIiCjEeF2AuOZ+uDgcjkbHiIiILkniHBAionDh9ST0gQMH4uWXX4bFYsHOnTuxZs0aDB482BexERFR2GDhQUQULrzuqnjiiSegVqsRExOD5cuXIyMjA7Nnz/ZFbEREFC5YfxARhQ2vC5CIiAg88sgj2LBhAz788EPMmDEDKpWqRc81Go0YM2YMCgoKAAC7du3C2LFjMXLkSCxfvtz9uCNHjmD8+PEYNWoUnn76adjtdgBAYWEhcnJyMHr0aDz88MPu1beqq6vx0EMP4ZZbbkFOTg4MBoO3b5OIiNoVh2AREYULrwuQ3/72t7jpppvcPyNGjMDYsWMxc+ZMlJSUNPu8AwcO4O6778apU6cAABaLBU899RT+/ve/Y9u2bTh06BC++uorAMCsWbMwb9487NixA5IkYf369QCABQsWIDs7G9u3b8fVV1+Nv//97wCAv/3tbxgwYAD++9//YuLEiVi4cKG3b5OIiNqRxC4QIqKw4fUckBEjRsBkMiEnJwcymQwbN26EyWRCRkYG5s2bh3/84x9NPm/9+vWYP3++e7jWwYMHkZ6eji5dugAAxo4di+3bt6Nnz56wWCy45pprAADjx4/Hq6++iokTJ2Lv3r14/fXX3cfvuecezJo1C19++SXWrFkDABgzZgyee+452Gw2REREeMRQXV2N6upqj2NFRUXe/kqIfI65SsGizbnq6gBhIUJ+wnaVKHC8LkD27duHDz/80H177ty5mDBhAhYtWoQPPvig2ec17JUoKSmBTqdz39br9SguLm50XKfTobi4GBUVFdBoNFAoFB7HG55LoVBAo9GgvLwcycnJHq+5evVqrFixoo3vnMh/mKsULNqeqyw8yL/YrhIFjtcFiMlkgtFohEajAeCc12GxWFp9HlEUIQiC+7YkSRAEodnjrj/ra3i7/nOaWhp4ypQpGDdunMexoqIi5OTktDp+ovbEXKVg0eZc5TK85GdsV4kCx+sC5I477sCdd96J0aNHQ5Ik5ObmYuLEiXj33XfRvXv3Fp8nJSXFY7K4wWCAXq9vdLy0tBR6vR4JCQmoqamBw+GAXC53Px5w9p6UlpYiJSUFdrsdJpMJcXFxjV5Tq9VCq9V68e6J/IO5SsGCuUrBgrlKFDheT0J/6KGH8Je//AU1NTWwWCx45plncN9996Ffv36tmvzdt29f/PLLLzh9+jQcDgc+/vhjDB06FGlpaVCpVPjuu+8AAJs3b8bQoUMRERGBAQMGYNu2bQCATZs2YejQoQCAYcOGYdOmTQCAbdu2YcCAAY3mfxARUQfCng8iorDhdQ8IAGRmZqJnz56QJAkOhwPffPMNrr/++ladQ6VSYfHixXj00UdhtVoxbNgwjB49GgCwdOlSzJ07F0ajEVdddRUmT54MAJg/fz7mzJmDN954A506dcKyZcsAAH/+858xZ84c/O53v0NMTAyWLl3qi7dJRETthQUIEVHY8LoAeeWVV7By5UrnyRQK1NXVoWfPnti6dWuLnv/555+7/z5kyBBs2bKl0WN69+6NjRs3NjqelpaGd999t9HxuLi4ZlffIiKijoerXxERhQ+vh2Bt3rwZX3zxBUaNGoUdO3Zg0aJF6Nmzpy9iIyKicMFJ6EREYcPrAiQhIQF6vR7du3fH0aNHcfvtt+P48eO+iI2IiMKG1OBPIiIKVV4XIAqFAmfOnEH37t2xb98+2O12WK1WX8RGREThgnUHEVHY8LoA+cMf/oBnnnkGN954I3Jzc3HjjTciKyvLF7EREVHYkDz+ICKi0OX1JPThw4dj+PDhAJzzQU6fPo2MjAyvAyMiojAicQgWEVG48LoH5MSJE9iwYQMkScJf/vIXTJ8+Hbt37/ZFbEREREREFGK8LkDmz58PlUqFL7/8EsXFxVi4cCGWL1/ui9iIiChMSFz9iogobHhdgFitVtx22234+uuvccstt2Dw4MGw2Wy+iI2IiMIGCxAionDhdQFSV1eH0tJSfPnll7juuutQWlrKVbCIiKh1WH8QEYUNrwuQu+66C8OHD8e1116Lnj17YsKECZgyZYovYiMiorDBCoSIKFx4vQpWdnY2Jk2aBJnMWct89NFHiI+P9zowIiIKI5wDQkQUNnyyCtYHH3wASZLw+OOPY+LEicjLy/NFbEREFC5YgBARhQ2ugkVERB0ACxAionDBVbCIiCjg2AFCRBQ+uAoWERF1AKxAiIjCBVfBIiKiwGMXCBFR2OAqWERERERE5Dde94CYTCa88MILmDJlCiorK7F8+XKYTCZfxEZERGGDPSBEROHC6wLkhRdeQExMDMrKyqBSqWA0GjFv3jxfxEZEROGCQ7CIiMKG1wXIkSNHMGPGDCgUCkRFRWHp0qU4cuSIL2IjIiIiIqIQ43UB4pr74eJwOBodo7bbkXcahQajx7FCgxE78k4HKCKitmM+U3Mk9oBQGGPbSOHG60ph4MCBePnll2GxWLBz5048+uijGDx4sC9iIwCZPRKxbO1+d8NUaDBi2dr9yOyRGODIiFqP+UzNYwFC4YttI4UbrwuQJ554Amq1GjExMVi+fDkyMjIwe/ZsX8RGAFJ1GszM7o9la/dj35FiLFu7HzOz+yNVpwl0aEStxnymZrH+oDDGtpHCjdfL8EZEROCRRx7BI4884ot4qAmpOg0mjczAgjfzMH9qFhskCmrMZ2qSJAY6AqKAYttI4cTrAmT37t1YuXIlqqqqPI5v3LjR21PTBYUGI9blHsP8qVlYl3sMqUnRbJgoaDGf6XIkSYIgCIEOg8iv2DZSOPG6AJk7dy7uvfdedO3a1RfxUAOucaCurtjUpGh2zVLQYj5TszwmoUsAWIBQ+GDbSOHG6wIkMTERkydP9kUs1IT8E2UeDZBrnGj+iTI2ShR0mM/UvHoFiCSx/qCwwraRwo3XBchvf/tbrFmzBr/5zW+gUFw8XWpqqrenJgCjstIbHUvVadggUVBiPlNzPDtAOCOdwgvbRgo3XhcgFRUVWLZsGaKiotzHBEHA/v37vT01ERGFjQY9IEREFLK8LkC++OILfP3110hKSvJFPEREFI7qFR2SJHIEFhFRCPN6H5DExEQkJCT4IhYiIiIiIgpxXveA9OrVC9nZ2Rg+fDiUSqX7+P333+/tqYmIKFzUH3bFPUGIiEKa1wWIxWJBt27dcOrUKR+EQ0RE4YlzQIiIwoXXBciiRYuavW/mzJlYtmyZty9BREShjqtgERGFDa/ngFzKL7/80p6nJyKiECGh/iR0FiBERKHM6x6Q9nDvvfeivLzcva/Ic889hzNnzuCNN96A3W7HlClTkJOTAwDYtWsXFi1aBKvViltuuQUzZswAABw5cgRPP/00TCYTBgwYgAULFnjsU0JERB2IxCFYREThol17QNpCkiScOnUKmzdvdv+kpKRg+fLlWLt2LTZt2oT3338fP//8MywWC5566in8/e9/x7Zt23Do0CF89dVXAIBZs2Zh3rx52LFjByRJwvr16wP8zoiIqFmchE5EFDY6XJfAyZMnAQAPPPAAKisrceeddyI6OhpZWVmIi4sDAIwaNQrbt2/HoEGDkJ6eji5dugAAxo4di+3bt6Nnz56wWCy45pprAADjx4/Hq6++iuzsbI/Xqq6uRnV1tcexoqKi9n6LRK3GXKVg0fZcZa8H+RfbVaLA6XAFSHV1NYYMGYJnnnkGNpsNkydPxi233AKdTud+jF6vx8GDB1FSUtLoeHFxcaPjOp0OxcXFjV5r9erVWLFiRfu+ISIfYK5SsGhzrjbYiJCovbFdJQqcdi1A2jKRsF+/fujXr5/79oQJE7Bo0SI8/PDDHucVBAGiKEIQhBYfb2jKlCkYN26cx7GioiL3/BKijoK5SsHCJ7nKOSDkB2xXiQLHJwWIwWBAVVWVx7GePXti+fLlrT7Xvn37YLPZMGTIEADO4iEtLQ0Gg8Hj9fR6PVJSUlp0vLS0FHq9vtFrabVaaLXaVsdI5G/MVQoWbc9VTkIn/2K7ShQ4Xk9CX7RoEYYPH4577rnH/XPvvfcCALp169bq89XU1GDJkiWwWq0wGo346KOP8PLLL+Pbb79FeXk5zGYzcnNzMXToUPTt2xe//PILTp8+DYfDgY8//hhDhw5FWloaVCoVvvvuOwDA5s2bMXToUG/fqt/syDuNQoPR41ihwYgdeacDFBERUfvy7DFnAULhidd/Chde94B88skn2LlzJ+Lj430RD4YPH44DBw7g9ttvhyiKyM7OxrXXXosZM2Zg8uTJsNlsmDBhAvr06QMAWLx4MR599FFYrVYMGzYMo0ePBgAsXboUc+fOhdFoxFVXXYXJkyf7JD5/yOyRiGVr92Nmdn+k6jQoNBjdt4mIQh3ngFC44vWfwoXXBcgVV1zh8y7Mxx9/HI8//rjHsbFjx2Ls2LGNHjtkyBBs2bKl0fHevXtj48aNPo3LX1J1GszM7o9la/dj0sgMrMs95m6MiIhCkscyvIELgyiQeP2ncOF1AXLvvffinnvuweDBgz02+ps+fbq3pw5rqToNJo3MwII38zB/ahYbHyIKcdwHhAjg9Z/Cg9cFyMqVK6HRaFBTU+OLeOiCQoMR63KPYf7ULKzLPYbUpGg2QkQUujymgLALhMIXr/8UDrwuQMxmM/7973/7Iha6oP6Yz1SdBqlJ0R63iYhCT/19QFiAUHji9Z/ChderYHXr1g1Hjx71RSx0Qf6JMo/GxjUmNP9EWYAjIyJqJ1wFi4jXfwobXveAnD9/HhMmTEBaWhqUSqX7+NatW709ddgalZXe6FiqTsNvP4goZHn0erAHhMIUr/8ULrwuQGbOnOmLOIiIKKxxEjoRUbjwegjWoEGDoFKpsGfPHnzzzTfuY0RERC3GEVhERGHD6wJk06ZNeOyxx1BVVQWTyYT/+7//w/r1630RGxERhY36k9DZA0JEFMq8HoL19ttvY8OGDdDr9QCAadOm4cEHH8Sdd97pdXDkX5IkwmGqguSwQ5ArII+OhSB4XaMSeWCeUZMkDsEiag22pRTMvC5ARFF0Fx8AkJycDJmM/wGCjSSJqCs5g+INi2GvMkARq0PyxDlQ6ruyQSOfYZ5RS0giCxCiS2FbSsHO6yyNi4vDp59+6r796aefIjY21tvTkp85TFXuhgwA7FUGFG9YDIepKsCRUShhnlGz6veAiPbAxUEUBNiWUrDzugCZN28eXnrpJQwbNgw33ngjFi9ejLlz5/oiNqpnR95pFBqMHscKDUbsyDvt1WNdJIfd3ZC52KsMkBz8IEC+sWztflgtVuYZNUmqPwfE4QhgJET+0fBavSPvNL4/VuJxrW7u2s1rNgU7rwuQU6dOYfv27fjXv/6FlStXYvv27cjIyPBFbFRPZo9ELFu7391YuXZLzeyR6NVjXQS5AopYnccxRawOgtzrUXodWluKNWqb4dd2xumS2kvmGX/3Yaz+ylciCxAKfQ2v1fr4KLz49h7o46MANL52179ehes1m0KH1wXI8uXLIZfL0aNHD/Tq1QsKBZO/Pbh2Q122dj/2HSnGsrX7PXZLbetjXeTRsUieOMfdoLnGk8qjQ3s4XVuKNWqbfhl6yNWxiLp1pkee6e54EvLoWP7uw97FCkTkt7gUBhpeq9dsP4qn7huENduPNnntrn+9kkfHQnfHk2F3zabQ4XW10KtXL7zxxhsYMGAA1Gq1+/hVV13l7ampgVSdBpNGZmDBm3mYPzXrkgVFax4LAIIgg1LfFan3LQqrFTXqXwAmjczAutxjly3WqO0yrkhEbl41UofPRufESChVKqzacRrX9VXzdx/uOAeEwlDDa3W/DD0cotTktbvh9WrXgQpMzVmICJkYNtdsCh1tLkD+9re/4fHHH8eOHTtw4MABbNiwwX2fIAj47LPPfBIgXVRoMGJd7jHMn5qFdbnHkJoU3eyHtdY81kUQZFBo4tsj9A6ttcUatd33x0qwavNhjB/eEwve+xFP3TcI1/VN4++ePHAcO4WLhtdquUy45LW74fVKHc8eYwpObS5APv74Y9x9993o0aMH3n33XUiSBEEQfBkb1eManuL6hjg1KbrZoVWteay3duSdRmaPRI/zFhqMyD9RhlFZ6T59rfbSlmKNWm/VpkPYkXcKT98/CP0y9OidnoAX/rUb0ZEK/u7JoweEk9ApHLz/yXF8c/Ac5kweiFSdBnKZgIVv7cGorCsw4FfJTV67eb2iUNHmAuT666/HjTfeCAAYMmSI+7irEDly5IjXwdFF+SfKPBohV1ds/omyRo1Pax7rLdeYVNfr1S9+goE/i7VwV1Jhgi4uEskJF4dqSgDSO2mbvdhS+JA4BIvCjCBIHosv/HS2Arq4SMRqIgA0vnbzekWhpM0FyIIFC7BgwQLk5ORgzZo1voyJvNRUz0OqTtMuDVSwz6HwZ7EWznbkncb9Y5zzwly58vqGH3DrdVdg6u8zAfB3T/V7QFiAUOiL1UTi/rFXudvEPYeL8dC4PiipMLsfU//azesVhRKvZyux+PCPjrxaU/0xqZNGZgRVQzgqK71RvKk6TdAMHwsWrvwF4M4VlVKBW6/r5vE4/u7DmQRcmEDLIVgUDjJ7JGLN9qO4eXBXLHgzDzcP7oo12482e13n9YpCCZdLCBJtWVq3tdq6J0bDMakNz5XnZmMAACAASURBVEHhzZU/M7P7Y/E7e/Hm5kNIjlfDWsdvuakeCRBkcudfOQSLwkCqToOc0b3x5uZDyB6VgTc3H0LO6N7u6zr3qaJQxgIkiLR3T0Nbelnqj0kd8Ktkd5HEIoRcXHlVXF6LOpuIcwYjFAoZHrurH3OF6pEAubMAgSgGNhQiPyg0GLFm+1FM+/3VWLvjGKb9/mqs2X7U3SZ25JEPRN5iARJE2runoS29LJcak0oEXMyJV9//HnaHA2k6DZQKGZIT1MwVukiSIMidk285B4TCQf6JMuSM7o3c3Wcwf2oWcnefQc7o3u420R8jH4gChQVIkPBXT0Nre1k4JpVaSqVUoLjcjKm/vxpzpgx0zwlhrhAASKIDgiLC/XeiUOeaA1L/ut5wDkgwz7EkuhQWIB1Mc2M+131y3C89DZzPQe3hX1sPQxDgzisAyBndG+s+OR7gyKjDkER3DwiX4aVw4BpBkH+iDIUGo8d13TXXg9dkClUsQDqY5sZ8Trq5V7v3NHA+B7WHQoMRJRW1iJDLkJoU7Z6M/tbWw5h0cy9OtCQAgCSKEOTOleG5ChaFuvqb+Lqu+98fK0H+iTL3bX18FK/JFLJYgHQw9cd8rvwoH4vf2dtoF9T2+mDG+RzUHvJPlGHO5IHuYVcff/0LbHYHbrgm1X3xXbx6L9Z/6uwZ4UTLMCVJgCA4l+JlDwiFOFeR8f6FXuCc0b3x/D93o7LGgsXv7MXAX6egpMLc5JwQXpMpFLR5I0JqP6k6DXp1jcfWr08irV6vx/fHSvD6xgN47qEhl3h22/lzA0MKH/XzyjWWOU2nwQ190y4+SAC+/qEQ3dPi3JtZ1r/ouhQajMg/UcZ5I6FIEp3Fh0zOHhAKea4J6G99fBhffHcWyggZbr3+Cry3/SiS46Pwm2tSAcDdA+LCazKFCvaAdECFBiMOnSxFmk4DQQAWr96LHXmn8OLbe/DIhL5sfCgo1R/LrIyQYfHqve6VXeZMHojJv/u1x0RLLkEZXiRRxInzJthFgZPQKeS5JqD/7vpuOGcwotpYh227TiFRq0KlsQ75J0q56hWFNBYgHYzrQ9acyQMx78HBiJDLUF1bhxUbDmDq769Gvwx9oEMkarWG84vmTB6IOrvoLjgANJpoySUow4skihAhwOIQIDnskBw2VO76CJLdFujQiHyu/iaEY27ohvJqC2x2EdPv7Idpt1+NFRsO4ObBXdneUchiAdLB1J+HkarTYMwN3VBWZcGgq5Lxye4z7Tb5jBOBqT01nF+084dCCIKEsTd0xzvbfsTi1XuRM7o3yqosHhMtuQRl+HA4HJAAOCADRAeq93+C8i/eQ+XurT5/LfOpfIg2q8/PS9RSrk0Ir+vTCR9//QsStCqk6TT45+Z8bPrqBIZf2wUbPvuJE84pZLEA6WDq76vx/bESrNp8CI9O7IuqmjrkjO7dbitgBGK4iySJsBsrYKsywG6sgCRx9+NQNSorHZ2S1LAbK2ApL8a16SpcmRaHMTd0w/V90mBziHhr62H3qjD1l6LkEpThwWF3QJIEiJJzCJZ0oUAQLTU+fR17dRnOr3kWhq2v+fS8RK3hmgNy/EwlIhQyREVGYMJve8JksaOkohbDr+2M5x4a0qJrPq+lFIw4Cb2DKjQY8frGA3jqvkHol6HH1T2SsGztfveKGL7+Jrj+cJdJIzPcE4Hb6xtnSRJRV3IGxRsWw15lgCJWh+SJc6DUd4UgsC4ONQ3/vZWxOjx8x5NY9dlxXNc3Dd8cOOdeFQu4OPG8/rCr1KRoDsMKYQ6HAyJkcEAGs6UOKpmzHZBE336YkhzOIV2Ws0d9el6i1nB96Xdj/y7o1TUOb209jNXbjkATqcCMu/ujpMKMfhl695cxzbV5vJZSsGJ2dgBNDX/6+sA53Dwo3T3nw1UglFSY220FIH8Od3GYqtwNJgDYqwwo3rAYDlNVu70mBU7B6cJG/96GD17CmAFJWPBmHq7ukYQ7Rzjngrj+P9QftlX/NpegDE2i6IAIwVmAmK3OJXkBSA7fLsnrmlMiWs0+PS9RSy1bux/bdv2Cmdn9cdfNvdAvQ48b+qaissaKfhnJ6Jehd1/nL7ffV1Nta/GGxSg4XeiX90LUViHdA7J161a88cYbsNvtmDJlCnJycgIdkod/bz+KYb+OwaB0BUrKSrHkvz8iMU6Nr/YXYMhVybjnxk6wlJ2HCDkkuQLKulr07arC+XPnER8lgwAJoiwCkbHx7m86LrVMqWvjI9dGR/U/2GX2SMTOHwqx98ci93CX1KTo9usBcdjdDaaLvcoAyVYHW5UBglwBeXRsm77BkSQRDlMVJIfdq/OQb4ybvQUrHu4La5UBqtQrEXfdOMgiNRAtRlg1ERAAfJtfiDE3dEOqTgO5DJj12k68/Ohv3Dnq0RPSipxs71xgrvmOwyFCAiBKMpjNdRBkcucdPl4RS7LXtct5iVrqf9+fhatjr7i8FharHT/8VApBAA6dLMXyf++HyWyDOjLCvQmx6/oNAGeKqtFTJ0OUAkjSRqA8Os7jemqvMqBzvAp1VaWAaIcIGZSxCVi0+jvo46Mx7far3W2X1WJFudGOzump7rarYbu27xczuqZo3W3vjrzT0MdHeXwhWlJugrW6AjptBBRKpbst5NLp1JyQLUCKi4uxfPlyfPjhh1AqlZg0aRIGDx6Mnj17Bjo07Mg7jX69knBbHyXKPnweFVUGRMXqcP+tM7FujxEvPtAXuggzit9/1t2lqhszHWL+V4i7dhQkmwUlH6242N064UkgKgYi5BBNJmRdEYEqgwFHimzIynSuJV5oMKLKaHEP43L9uWb7UeSM7o3Fq/cCAjBn8kC/DHcR5AooYnUejaYiVoe6sgIUv/9im7uR2R3d8aiUckiCHFFXDkTcwFth+M/f6+XubNw8sAsqjFZUl5VCWVeJTtEO/Pmuvu7hgP/48GCj5adbclFz5UL5V+ug7TsccnUsJJsV8tgkyGTeN33MNd+SHK4eEAEWa137vc6FAkSC1G6vQXQpjgvFx5b/nQQAdyZKElBtsuLzfWcRIRfwpwl98fQb36BH5zj87vpuWPzOXlyRHIOHb0qEYeNLqL7Q7ujHPwHRYoIgV0C0GFF79ihEUxVKPlzqbpv0d8xCWpIam3eehD5ehZG9FDB88JL7fuuEJ/GzMRpX9Uhq1K5ljH0Cq/5bgJsHX4GSCjP08VF4/l+7MWpwV5QWqaBWyaGx1MDy8RIUXnhO/LjZMEclY+NnxzF1VLrHF4u5u8+6vwR18XWhUn+n+fZ6DfJOyF4ld+3ahaysLMTFxUGtVmPUqFHYvn17oMMC4Bz7WVNWhrIPl3h0m5q3LcO0mzshvq4QxR943mf4eAXism6Do7YKho9XeHa3bnwJjooioOIcxI9fQNk/H0XV+88gM9ECSRLd3yDf0DcNM7P7Y832oxg5uCtefHsPbh7cFWu2H8UN16S6iw+g/XdclUfHInniHChidQDgLrIqd264+L7aMCSLQ7s6npl390OiWkLiiPvcxQfgyt0luH9EGh7+bSLUny5G9dt/RswXS9A3qQ4P3ubcF2TiTVdizfajrV4gwWGqQvlX6xA38FaUffIWCt95GufXPAtbyVmfTNJkrvmW6HBAkmQQZDLY6mz1hl4Jvn0dW/sVN0QtkZIYBcBZeDQsg8uqnIsvXNUjEVt2nkSkUo5zBiP+30f5sNkduG2Qzl04AIA8Og5SnQWl297A+ffmoeyTt6C9+jeo+HqjR9tU8sHLmDBEh0cm9MWVOrnHOexVBpRsfAm9EsQm27WarUsxZkASnv/XbgiQsHLTQWR0jcOEfmrUbnwWUslJlDb4PFPx0RLUVVdg2rB4lK55GmdX/BGFb/8FdSVn0K9XUrsvesN9pDq+kC1ASkpKoNPp3Lf1ej2Ki4s9HlNdXY2CggKPn6KionaPLVWnQVqiqskhSKitgiwisun7ZLJm71PEJDQqTMo+XILq0tJGw1cmjczAaxsOYPzwnlix4QAmjczAnSMaz/m43NhTbwiCDEp9V6Tetwhdpv8Dne59HuVfvAdr4U8e76u147+bHdrl43Hk/haoXPWF/ulRMKxfCEBs8t9GKZNg3ras0Qd5pb0W86dm4ZPdZ9y9dq3ZD0Ry2KHtO7yJouclnxQJoZpr3mprrooX9gFRKOSw2eyQ3IWCb3sq3EOwKOwFql1d9dRIxMdENHt/tEoBQ4UFkIBnHszCg7ddjXMGIyRJQJI2wqPdibtuXBNfSi6Btu9wj3PaqwxQyoERg9KRmtD05wjRVAnJVtfkfQkaBSBJeG3DARSXmzHnzt4w/eevsFcZIIvUNPkcnVbRqNAp3rAYCcq6dt/jiftIdXwhOwRLFEUIwsVvziRJ8rgNAKtXr8aKFSv8HRoAQKFUNjkEyVFb5f57w/sgihBtlqbvk5r+cGc0mj0mlLuWNX10Yl+s2nwI0yf2bff5Hs0RBBkUmnhnrMYKOEyVHvcrYnUQ5K1L0eaGdrX2PB1NIHPVW+4P6g5H07krkzWZu2mJKvRISHYPBxw5uCsWvJmH+VOzWpSrglwBuTq23YqEUM01b7U1VyVRBAQ55HI5RKsdjgvL8Pp8FSxXASJxCFa4C1S7+v2xElTWNL3BpgDAZLXDZDBi/tQsABc3af3HBwdRUGaBtl6709yHf7k61uOYIlYHCHJ8f6wEKocFmmY+f8i1iU22awqlEtpoFcqqLdCqlVAIDvdjRIuxyeeIEJptf1N18e5Fb1raprdW/YV12us1qO1CtgckJSUFBsPFxDcYDNDrPXcRnzJlCj777DOPnzVr1vglvvI6JaLH/J/HEKSk8bNQfeALVO76CLrf/anx8KS8LZCrY6EbM93zvrGPwl5d5j7moojVQaOJcu+f4OqCzBndG7m7z+Cp+wZ5fLscyD0WmhqSlTxxDuTRsZd5Zvucp6MJZK56q7DcWTRX5m2Bfvwsj38b/R2zUFLjaDJ3FUolgIs7Bq//7KdW7Qcij46FXBPX5Ll9USSEaq55q625KokOyGQyKORyyCDCZKx13uHw7U7o7AEhl0C1q6++/32T/Xpy4WJ/n0Im4B8fHMDid/ZiZnZ/yGUCKo1WbNlj8Pjs4PpSsj5FrA4ytbZRW7t2ZxEWvrUHcnUs9BOe9Pwc8bs/ofrAFxAUykbtWvy42Vj6wU+oqa1D9qgM1NTW4VRJrfsxTX1mSZ44B5Arm21//bHHE/eR6thC9qu66667Dq+99hrKy8sRFRWF3NxcPP/88x6P0Wq10Gq1fo/NVQhc1S0BQ0c/hUi5hCqzA5/uNWLsgPEwb1uG8i/XInH0NEQkpMIOOSpqHZCuHoPzFhk0ai063fOccz17QYayT1dDNFVCN2a6uytWEatD4vjZUCclubshB/46xT2vw9UVmZyg9ljeNFDfENQfkuXNikK+Ok9HE6hc9YXXt57ErDEzYd62DACQMmkuBLkCNlHAqk/O4UxxAZ4cOxO1F4ZhKWJ10N72hPuDvGvH4OceGtKqBRIEQQZ5bBKSJzyJ4o0XJ1v6qkgI1VzzVltzVRJFCDIZFAoJckFErbEWKlzct8NX3EO72AMS9gLVrpZWWZyvr1YgKV6Nk+eqAQAOCYiLjkClyYZOumhUVFsQoZChuLwWr288gKfvH4TkBDW+/8WALjfPQWq8CuVmCTFjn0DN1osTzpMnzIZDEQld9nMQJAdsooBimwo7dh/F9X1TEROtwpbvrbg9+1mIpko4aqtQuXcb4n5zF+TqGMjVMUjKWQijsRYajRqfHCzHT2cqoY+PwrB+nWGsteO9L85hzu9noWrzy7AW/oTKvduQnP0sHBJwvrwOVRGJ0GmjoLvjSY/J7skT56C8Tolla79r1z2eGq2eyH2kOpyQLUCSk5MxY8YMTJ48GTabDRMmTECfPn0CHRYAeBQAUxd+glFZ6bi+Typ+/LQA1/e9CpG3Pg0IIqrkChScl/D1gQKk6TQ4X2qE0WzDwF+nYOTgLjBXVkCUHIi/+QEIAORKFVLvW4TaWgskQYH8IhuyUmUeE8obLmNa/3ag/1PWH5LVEc5DviFCwJKt53H/zU9CGS1HsRH4LL8Um3eegMMBJCdE4Z1vq5Ezbj5i5RJskCE2We/+IF///wuARvl8KTKZAsrk9HYrEphrviNJEuRyGSIUgAwSamvNzgLE7ts5NSJ7QKgDSEmMwqqnRuKJV/+HP991Dd7aegjVtXa89OhQ/PhLGbbnncbc+wfj6wPn8MV3Be4vYAC4lyffdWGRGLmgwHU5C2G31cFsA05ZlNj435/Qs3M8fnNNKr4+cA6xmkj89c/DkH+iDPknynBDv86IiFfDoVRBrk1ErL4nDhRYkdXJ2TbuPGZEZo9EqOM1OHHuNOY+MNj9heW026/G98dKsO1wOW6e8Cw0SsFj6d0o0Yjvj5diVFY6IlMat7/f7z7b5ja9pby5bpB/CJLEr4HqKygowE033YTPPvsMnTt3DnQ4RM1irlKwaEmu/vDi/SiWd0K3OAcMxaWQYlORavwRUd2vQae7nwEAmK12vPj2HowecgWu75PaplgqvvkQFV+uAQQZuj+1oc3viUIT21Ui/wjvsQJERNQhKKQ6iHIlIJMhUiHBUuucAyLWXdyxfO2Oo/jhuAFvbT0Mh9jy784sdXZ8tvcMKmoskOzOye2QRPD7NyKiwGABQkREAaeUbJDkSkCuQpRgA+ouFCBWZwFyoqASW/53Amk6DYrLa7H70PkWnbfWYsNjS7/E39Z9j7e2HoZorXXf15L5JYdPlsFS5+NhYK0onoiIQlHIzgEhIqLgYDZboBAcgEIJKUIGlWhBFJz7JFRXVqGmtg6vbfgB2mgVHhqXiVfXfY9/5x7DkMxOEEUJGz//CaVVFuz9sQgDfpUMdWQElAoZbh6cjq/2F+B8mQk90mKx84dC3Dnw4nLfktUMKJTNxvXD8RI88/++xW8HdMGMu/v75L3mnyjF4tV7cf+YqzBiUFefnDPYnDMYsfmrE+iWFouRg7pCLud3oUThhgVIAw6HAwCCZpM36lhSUlKgUPjnvxVzlbzRkXL19Y0/4gFJgFoTBbPDCC1EdFI490SKslVh7aIXkSEBmd3igL3HkZ1kxcnzJmxengeHQ0JllRWRMmB0VASqD9RBFARYIOG/eQJEScIDKUqkaFX4sbwa5uNHIb/wuu9/8DkqVKkQRQmOCz+i6OyhcEgSjp+tAQB8se8s6ixmRETIECEXIJcLEOrt0C41WFRVklw/EkTXnyJQa7Vj/08VsNslvPHhARw4VgC5THD+XOaclyK0Yrf4ps7b0pFo9bfSahhr/ffsfKwAQXA+x+G4+LsVBGDvsXKYLA6IooT/fXcKD/++B2RC8++hI+Uq0aX4M1eDHSehN7Bv3z7k5OQEOgwKUv6cuMhcJW90pFyNiorCDcNuQkbXTqitrkakWg2zPAoxMufeIFU2AYAIhwOou7AqljoqCg5bLSTRDpksArKIKNjtNkAQoJArIIkOQABEuxUyhQpmSx2UygjIISJSNEMll/DT+TIYDKXNBy4IUCg1gDwCDnPlhQ/X3lwyBedKQErnKjx2S+WF04XqZViA8715FheCIINcFQNBJofDZsaP+ftRXV3d7Fk6Uq4SXQoXL2g5FiANWCwWHDp0CDqdDnK5/PJPaKOioiLk5ORgzZo1SElJabfX6YhC+b3789uPhrna0X+vHTm+jhwb0D7xBTJXm9LR/w3qY6zto7lYO1qu+kow/dsAwRcv4P+Y2QPScvwtNRAZGYkBAwb47fVSUlLCtloO5/fuC83lakf/vXbk+DpybEDHj685rWlXg+k9Mtb2EchY/f0ZAAiufxsg+OIFgjPmUMeZX0RERERE5DcsQIiIiIiIyG9YgBARERERkd/In3322WcDHUS4UqlUGDx4MFQqVaBD8btwfu/tqaP/XjtyfB05NqDjx+cLwfQeGWv7CKZYfSHY3m+wxQsEZ8zhgKtgERERERGR33AIFhERERER+Q0LECIiIiIi8hsWIERERERE5DcsQIiIiIiIyG9YgBARERERkd+wACEiIiIiIr9hAUJERERERH7DAoSIiIiIiPyGBQgREREREfkNCxAiIiIiIvIbFiBEREREROQ3LECIiIiIiMhvWIAQEREREZHfsAAhIiIiIiK/YQFCRERERER+wwKEiIiIiIj8hgUIERERERH5DQuQBux2OwoKCmC32wMdCtElMVcpWDBXKVgwV4n8gwVIA0VFRbjppptQVFQU6FCILom5SsGCuUrBgrlK5B8sQIiIiIiIyG9YgBARERERkd+wACEiIiIiIr9hAUJERERERH7DAoSIiIgoQKw2BxyiFOgwiPwqaAsQo9GIMWPGoKCgoNF9R44cwfjx4zFq1Cg8/fTTXE6PqIPYkXcahQajx7FCgxE78k4HKCLqCJgXFK5qLTY8/NJnmP7y5zhRUBnocIj8JigLkAMHDuDuu+/GqVOnmrx/1qxZmDdvHnbs2AFJkrB+/Xr/BkhETcrskYhla/e7P2wWGoxYtnY/MnskBjgyCiTmBYWr9Z8eh6HCjMoaK/7fR/mBDofIbxSBDqAt1q9fj/nz52P27NmN7jt37hwsFguuueYaAMD48ePx6quvIjs7u9Fjq6urUV1d7XGMa39TRxQquZqq02Bmdn8sW7sfk0ZmYF3uMczM7o9UnSbQoZGPtCVXmRcUCIFuV+0OEf/55hcMyeyEGHUEcnefQXF5LZIT1H6LgShQgrIAWbhwYbP3lZSUQKfTuW/rdDoUFxc3+djVq1djxYoVPo+PyNdCKVdTdRpMGpmBBW/mYf7ULH7IDDFtzVXmBflboNvVn89WwlLnwK+7JSBBG4nc3WfwxXdnMenmjIDFROQvQVmAXIooihAEwX1bkiSP2/VNmTIF48aN8zhWVFSEnJycdo2RqLVCKVcLDUasyz2G+VOzsC73GFKTovlhM4S0NVeZF+RvgW5X80+UAgA66zVQRSjQWafB90dLWIBQWAi5AiQlJQUGg8F9u7S0FHq9vsnHarVaaLVaf4VG1Gahkquusf2u4TWpSdEetyn4tSVXmRcUCIFuVw+dLEOaToMoZQRESULn5Bh8f7wEDlGCXNb0F6dEoSIoJ6FfSlpaGlQqFb777jsAwObNmzF06NAAR0VEAJB/oszjQ6Vr7H/+ibIAR0aBxLygcCNJEo6eKseVXWIhSs4leLska2Ctc+BscU2AoyNqfyFTgEybNg35+c4VJJYuXYpFixZh9OjRqK2txeTJkwMcHREBwKis9EbfaKfqNBiVlR6giKgjYF5QuCmrsqDWYkdyYrT7WBd9DADg6KnyQIVF5DdBPQTr888/d/991apV7r/37t0bGzduDERIRERERJfk6uVI1Ea6jyXGRkKtUuDoqXKMHnJFgCIj8o+Q6QEhIiIiCgZnS1wFSJT7mCAISE5U43QRh2BR6GMBQkRERORHBcVGREcqoI7yHIiij1fjnMEI6cK8EKJQxQKEiIiIyI/OltQ45z01qDP08WqYrXaUV1sCExiRn7AAISIiIvKjghIjUhLVDesP6OOdQ7IKio3+D4rIj1iAEBEREfmJxWpHZY0VibGRje7TxasBAKeKqv0dFpFfsQAhIiIi8pOSiloAQGy0qtF9MeoIRKrkOH2eBQiFNhYgRERERH5SUmEGAMREKxvdJwgCdLFROF9q8ndYRH7FAoSIiIjITy7VAwIA8dpIFF94DFGoYgFCRERE5Ccl5bWQywVo1BFN3p+gjURZpQUOh+jnyIj8hwUIERERkZ8YKsxIio1q9v4EbSRESYKh0uzHqIj8iwUIERERkZ8UV9QiMTYSze01mKB1ro5VXMZhWBS6WIAQERER+YmhotZdZDTFdd85Q42/QiLyOxYgRERERH5gd4ioqLEiLqbpCegAoI1WQi4TuBIWhTQWIERERER+UF5tgSQ59/tojkwmIC5GhfPlHIJFoUsR6ACIiIiIwkF5tQUAEB3VfAECOIdhlbShABFtVlR+8wEcpirEZv0eysTUNsVJ1N5YgBARERH5QVmVswDRRDXehLC+BG0kDp0sa9W5JUlC8cYlMJ/8AZDJYTq2G52n/hUKbWKb4yVqL0E5BGvr1q249dZbMXLkSKxZs6bR/YcPH8Ydd9yB2267DX/4wx9QXV0dgCiJiIiILiq/UIBoLzEEC3AWICazDUazrcXnNh3ZBfPJHxB3w0ToxjwCyV6H0h1vehUvUXsJugKkuLgYy5cvx9q1a7Fp0ya8//77+Pnnnz0es3DhQjz22GPYsmULunXrhn/+858BipaIiIjIqbzaArlcQFTk5QsQACgua/lE9Kq8LVAkpCLyikwotImI7j0Etcf3wFp8ypuQidpF0A3B2rVrF7KyshAXFwcAGDVqFLZv347p06e7HyOKIkwm539as9mM2NjYJs9VXV3dqHekqKionSInajvmKgUL5ioFi0DkalmVGfEaFQQAzWwDAuBiAXK+1IQeneMue15r8SlYz/+MuOsnQLhw5ujeWTD++DWq9++A7pY/+CB6It8JugKkpKQEOp3OfVuv1+PgwYMej5kzZw4eeOABvPjii4iKisL69eubPNfq1auxYsWKdo2XyBeYqxQsmKsULAKRq+XVFsRrIyFeqvoAEK91LtNbWGps0XlNR78FBAGqzhnuYzJVFCK7/BrGQzuRdPMDEBSX7nUh8qegK0BEUYQgCO7bkiR53LZYLHj66afx9ttvo0+fPnjrrbfw5JNPYuXKlY3ONWXKFIwbN87jWFFREXJyctrvDRC1AXOVggVzlYJFIHK1vNqC5AT1ZR8XqVQgOlKB8y3cDb32+D6oUq+ETKlC/S3Wo67IhOXUQZhPH4K6R782x03kawEpQFatWoVp06bh+eef9ygeXObOndvsc1NSUrBv3z73bYPBAL1e7759/PhxqFQq9OnTBwBwO5TIZgAAIABJREFU11134ZVXXmnyXFqtFlqttq1vg8hvmKsULJirFCwCkatlVRZc2SW+RY+N10aiqAVzQBymKtSVnEJs1u89ig8AUKV0A+QRMB3fwwKEOpSATEKPiYkBAMTHxyMuLq7Rz6Vcd911+Pbbb1FeXg6z2Yzc3FwMHTrUfX96ejqKiopw8uRJAMBnn32GzMzM9nszRERERJdhqbOj1mK/7ApYLvExKhgqzJc/79mjAICIJvb8EBQRUKV0R+1P30GSLjPui8iPAtIDMmnSJADA9OnTYTKZcPjwYdjtdvTp0wcajeaSz01OTsaMGTMwefJk2Gw2TJgwAX369MG0adPw2GOPITMzE4sWLcLjjz8OSZKQmJiIF1980R9vi4iIiKhJVcY6AID6MpsQusRrI3HkVDlEUYJM1ni0iIul4Cggj4AiLrnJ+yM790LV7mOwGc5Cqe/a+sCJ2kFA54AcPHgQf/rTn5CUlASHw4Hi4mL84x//QP/+/S/5vLFjx2Ls2LEex1atWuX++7BhwzBs2LB2iZmIiIiotSprnHuAqCNb9tErPkYFu0NCRY0FibFRzT7OUnAUquT0Joe0A4AqrRcAwPTTXhYg1GEEdB+Ql156CUuXLsWmTZuwdetWvPLKK1i8eHEgQyIiIiLyucoaKwBArWrpEKwLe4GUNz8RXbRZYT1/Ekr9Fc0+Rq7WQhGrg/lUfsuDJWpnAS1ATCYTsrKy3LeHDBkCs/ny4x2JKPjsyDuNQoPnkpKFBiN25J0OUETUUTA3KBxUGp0FiKalQ7BinEvxFpU2PxG9rugkINoRoetyyXMp9emwnvsJkuhoYbRE7SugBYggCDh37pz7dkFBAeRyeQAjIqL2ktkjEcvW7nd/0Cw0GLFs7X5k9kgMcGQUaMwNCgeuHpDoFhYgcRd6QM5fYiUsS8ExAIAiodMlz6VMvgKSzYK6ol9a9NpE7S2gc0AeeeQR3HXXXRgyZAgA4JtvvsH8+fMDGRIRtZNUnQYzs/tj2dr9mDQyA+tyj2Fmdn+k6i698ASFPuYGhYPKGiuiIxWQy2QQW7AiVYRChhi1EkWX2AukruQ05JoEyJVRgCQ2+zilPh0AYD59CKrUnq0PnsjHAlqAjBgxAt27d0deXh4kScIf//hH9OjRI5AhEVE7StVpMGlkBha8mYf5U7P4AZPcmBsU6iqMVmijlZDQ8uVw42NUKKm4dAESkZh6yeIDcM4DkcckwHz6EOKG3N7i1ydqLwEdggUAZ8+excmTJ3HmzBmUlpYGOhwiakeFBiPW5R7D/KlZWJd7rNG4fwpfzA0KdVVGK7TRqoZ7BV5SvLb5vUAkhx11pecQcZnhVy5K/RWwFhzjfiDUIQS0AHnttdewePFixMTEIDIyEvPmzcM777wTyJCIqJ24xvXPzO6PAb9Kdg+54QdNYm5QOKissSImumXzP1ziYyJRVmWBw9G4h8NWVgiIdihi9S06l1LXGaK1Frby862Kgag9BHQI1pYtW/Dhhx+6d0Z/4IEHMGnSJEyePDmQYRFRO8g/UeYxrt817j//RBmH24Q55gaFg8oaK3p2iWvVc+JjVBAlCWVVFugT1B731ZU4V4lTaJNadK6IpM4AAMu5Y1A2sWs6kT8FtACJi4tDdHS0+7ZWq4Varb7EM4goWI3KSm90LFWn4QdMYm5QyLPZRRjNNsS0cAUsl3jtxb1AGhYg1pJTgEwOuTahRedSaHUQFEpYC44BfYa3Kg4iXwtoAXLttdfiT3/6E+666y7I5XJs2bIFqampyM3NBQCMHDkykOEFBUkS4TBVQXLYIcgVkEfHQhACPrWHqFWYx8QcoFBWZWzdErwurr1AzpeZkNnTs6ejruQMIhJSnTugt2BehyCTISIxDdbCn1oVA1F7CGgBcvjwYQDAv/71L4/j7777LgRBYAFyGZIkoq7kDIo3LIa9ygBFrA7JE+dAqe/KCzcFDeYxMQco1F3cBb11H7tiNSoIaHozwjrDGag69WhR8eESkZQG05FdEG1WyCJUrYqFyJcCWoC8++67zd63aNEiP0YSnBymKvcFGwDsVQYUb1iM1PsWQaGJD3B0RC3DPCbmAIU61y7oUZGt6wFRyGXQRitRVO65FK9os8JRXQpF76xWnU+Z1BkmUURd8S+I7Ny7Vc8l8qUO+9XSnj17Ah1Chyc57O4Ltou9ygDJYQ9QREStxzwm5gCFOlcPiKaVQ7AA5zyQ4gYFiL2iGAAgj27dpPaIxAsT0c8ebXUcRL7UYQsQrlN9eYJcAUWszuOYIlYHQR7Qji2iVmEeE3OAQp2rB6QtBUhibCSKyjyHYNkqnEvpytWxrTqXXB0DmToWlnPHWx0HkS912AJEEIRAh9DhyaNjkTxxjvvC7Ro3LY9uXYNEFEjMY2IOUKirrLFCpZQjQiFv9XOTYqNQbapDrcXmPmarKAIAyGNa1wMCOIdhWc//3OrnEfkSv17qwHbknUZmj0SPpSgLDUbknyjDqKx0CIIMSn1XpN63iCvHUId2uVxmHoe33N1n0a+XziMHyuuU+H732SaX6CUKNpU1VsRGKyGh9aM7EmOdS/EWlprQs7Oz4LCVF0EWFeOcSN7KESMRSWmwnDkMh6mKRT4FTNBe4bdu3Ypbb70VI0eOxJo1axrdf/LkSdx777247bbb8OCDD6KqqioAUXons0eix27Art2CM3skuh8jCDIoNPGIiNVBoYnnhzbqkC6Xy8zj8JbZIxFL3v0OJeYIRMTqUGKOwJJ3v/No64iCWaXRAm20qrW1AgAgKS4KAHCuxOg+Zq847+wxbMMJle4NCTkMiwKnw17lLzUHpLi4GMuXL8fatWuxadMmvP/++/j55589nvvwww9j2rRp2LJlC371q19h5cqV/gjbp1y7AS9bux/7jhRj2dr9HrsFEwUL5jJdCvODQl1ljRXaaGWbnptwYTPCgpIa9zFb+fkW74DeUERCJ0CQwVLAiegUOAEfgnXu3Dns2bMHdrsdgwYNQnq6s7v9L3/5S7PP2bVrF7KyshAX5+yKHDVqFLZv347p06cDcO4volarMXToUADAH//4R1RXVzc6T3V1daPjRUVFPnlf3nJtyqVT2vFMdm+8sPYIJo3szQtymOrIudpSqToNHrzt13DUVmNBTi+oIm2QJJG9HSGmrbnaKUmNZ7J7w2g045ns3tAmqS/7HCJv+LNdrTRakd5J26bnKiPkiNUocc7gnIgu2utgry6D+soBbTqfoFBCEZ8M6zluSEiBE9ACZOfOnZg1axb69+8PURSxZMkSLFq0CCNGjMDgwYObfV5JSQl0uosrpuj1ehw8eNB9+8yZM0hKSsJTTz2FI0eOoHv37njmmWcanWf16tVYsWKFb99UKzU1Nr6k3IQoczEqPlri3pTr2Ymz8c//nUNqUjSLkDDUEXLVGzvyTuOKFA2ShQoYv/grii/kdfy42dCkXsEiJIS0JVfz8guRmWhB1YfONs8Rq4Ni/Gzkl0UiKzO1nSKlcOevdtUhSqg21bVpBSyXpNgoFJY6h2DZK0sASJBrWj8B3UWZ1BnmU/n8EogCJqAFyCuvvIL33nsPPXv2BAD89NNPmDVrFkaMGHHJ54mi6LFKliRJHrftdjv27NmD9957D5mZmfjb3/6GxYsXY/HixR7nmTJlCsaNG+dxrKioCDk5Od6+tRZzjY13DTcoNBhhriyHZccSj025yj5cgqk5CzHvHQ5NCEcdIVe9oY+PgqO2CsbP/+qR1xUfLUEUN5sLKW3J1V+lRKDs/ecatXm/uuv5do2Vwpu/2tVqkxWSBER7UYAkxkbi8C/lAJzDrwBA1so9QOqLSOqM2uN7YSstgFLXtc3nIWqrgBYgNpvNXXwAwJVXXgmHw3HZ56WkpGDfvn3u2waDAXq93n1bp9MhPT0dmZmZAIAxY8bgsccea3QerVYLrbZtXaK+Un/s86SRGViXewwv3JuBwiY25YqQiZiZ3R/5J8pYgISZjpCr3iipMOOGnmoUc7O5kNeWXBWkpjciFCTmBrUff7Wrrk0Io1u5C3p9ibFRMJltqKmtg+PCErwKTUKbz+eeiF5wjAUIBURA+90iIyORn5/vvp2fn4+oqKjLPu+6667Dt99+i/LycpjNZuTm5rrnewBAv379UF5ejqNHnROsPv/8c1x11VW+fwM+kqrTYNLIDCx4Mw+TRmZAoVQ2uylXqk6DUVnpkCQRdmMFbFUG2I0VkCQxQNETXd6orHSoIlXcbI6apFZHNpkbanVkgCIi8h1XARIV2fa2zrUSVqHBCHtFEWSqaMhUbf//IY9JhKCM4o7oFDABvfLPmjULf/zjH5Geng5BEHDy5Em88sorl31ecnIyZsyYgcmTJ8Nms2HChAno06cPpk2bhsceewyZmZl4/fXXMXfuXJjNZqSkpGDJkiV+eEdtU2gwYl3uMcyfmoV1ucfQ9d5rkTxxDoo3LHbPAam/KZckiagrOdPofqW+K8dyUodVXqeEZsz/wfjxX5vMawpfMnUM9HfMRskHF+e96e+YDZk6JtChEXnNvQu6Vz0gzmLjnMEIbcV5KOLatgSviyAIzg0JCzkRnQIjoAXItddei//85z84cOAARFHENddcA5msZR+gx44di7Fjx3ocW7Vqlfvvffv2xcaNG30ab3tw7YfgmteRmhSNJe9+h9n3XtvsxmwOU5W7+ACcQxWKNyxGagvH0l9uUzgiX3v/k+P45uA5zL1/EFLvWwR7XR0KSi3YeUrCiGQWzeHOcN4A8Zv1SLz5fsgiNRAtRlTsXA/Z9VOQnNYp0OERecU9BEvd9gIkQRsJQQDOFtegZ/l5nwybikjqDOPBLyFaTJBFRnt9PqLWCOiVf/z48YiLi8OwYcMwfPhwxMfHB82kWl/JP1HmMancNSfk++OlzW7MJjmaHi/d0rH0LdngkMiXBEECJMDukKDQxKPcEY1Xt5xE+YULM4W3SLkE8097UbxxCc6/Nw/FG5fA/NNeRMrb/g0vUUdRWWOFQi6DKkLe5nMo5DLEx0TifEk17FWlkMd4f71W6roAkGAp/PmyjyXytYD0gEyZMgX5+fmwWCzo37+/+7goiu6J4+GiqR6HVJ3mkpPMBbkCilidRxHSmrH0TU1858pa1J7uHJGBG/qmeeTcnCkDmXMEAIjWRKGqiTYtWnP5OYFEHV2l0YpYTds2IawvKTYSRsN5QBKh8GIFLJeIxDQAAiwFR6Hu3tfr8xG1RkAKkNdffx2VlZV46qmnsGjRoovBKBTu/T1qa2uhVnMjqqbIo2MvOUekJepPfJ8/NYsfBKndMeeoOb5o04g6qkqjcxd0L6ZsAACSE9SoOPojEA2v9gBxkSmdiz9wR3QKhIAUIBqNBhqNBu+8806zj8nJycFHH33kx6iChyDIoNR3bXaOSEs0nPjODQ6pvTHnqDmCIENVhA62UU8hJV6Jooo6VEUkQs9FNSgEVNY4CxBvJSdGQ0AVAEDmo72TIpI6w1pwtNF+akTtrcO27pK3XxUEiR15p91zMVxcczKaOr4j7zQA5wW7uTkil1N/4vuAXyW7h2M1fD0iX1n41m48/888j5x7/p95WPjW7kCHRh3Awrd2Y/7KbxEVl4DopBRExSVg/spvmR8UEiprrIhRe1+ApCSqkSSvgShXQabyzfBEpa4zRGute3NDIn/psAVIuFTizU0IH35tZ59PFHcVO/Unvte/nX+izCfviaghs8WOkgozistrAQDF5bUoqTDDbOFGc8T8oNAlihKqjFbEeLEClos+Xg2dvAamiHj46hNSRFIXAM4NCYn8qcMWIOGi/oTwfUeK3T0T/TL0TR73ZsiKq9hxLcFbv6hxbXBI1B7+NKEv9AlRWPjWHvw79+j/Z+++46Oo88ePv2ZLNslueiX0GqSDlNBEVBI6qHgiaEAEvbPgnXci4HEong09y8mdP1C/ggpHsSHIUVT0pPcOASkBEtJ7skm2fH5/xKwJSUjZzRbyeT4ePB7szs7Me2bfmZ3PzGc+b17+eB/hwT48Pkk++CjJ/JBuXgVGExarQO9jfwNEo1YRqcknw+q4+jiagFAUrY6SK6cdtkxJqgvZAHED11dCrzgkb3Xv27MeRzdqJKkuosIMLJgRg8FHy6otCRh8tCyYIR9El8rI/JBuVrm/FiHU21GE0MZqIVAp4KrRcQP0KIoKbWgL+SC65HSyAeJiW/YkcjghrdLDuYcT0mzdpSq+74hnNBzdqJGkukrNKiKvqBSAvKJSW3cbqWkrP9ZVyo/CEr7decm1gUmSA5QXIfT1tn/MH6UoCxVWrhj1ZBc6rnuiLqINpswkLEV5DlumJNXGbRsgTWUI3vAgH15Zvo+pIzvT95YIpo7szCvL96FW0SgPijdGo0aSanM4IY2XP95HRLAPC2fGEPFrd5vDCWmuDk1yse7tQ3jpoz38/f/2EhHsw1P39QRFYfPuizI/JI/3WwPE/jsgqvyyv4d0qx/nUx1XxNUrvA0AxZdPOWyZklQblwzDW85oNLJ582Zyc3MrjXr18MMPs3LlShdG5jxp2UbmT+/Pys1nsFgFq7cmMH96f7YfvFpthfTj5zMbfNei4uhXUWEGokL1shuW5BTf7rxIWJC3rVtNVKiel/5vD9/uvEjv6HBXhye5UFSYgdBAH1KzjUwc1oGtey+zYMYAln19TOaH5PGyC4oBMDjgGRCloKwBkmX1IyHZSN92eruXCaANiQK1lqKLx9B3jnHIMiWpNi5tgMyZM4ekpCQ6derUZEa9ul75g98Wq7AVaOsdHV7tj25tFdJrU3H0q/Ll2duocUdCWLEU5ja4RorkeP26RDKjfddKubdgRozHj7wmc80xhvRqwZjBbTCoSnhxaid03lpenDWQw2czXB2aJNklJ78ElUrBW6exuxChqiAdofFC7+/HuZRixwQIKGoNXmEt5R0Qyalc2gBJSEhg06ZNaDQuDcPlnFWgrbpRruxt1LgbIayUpl2uUlHZK7yVPDF0oZsx92SuOU7vTqFo8pIp2PgPUivsy9gBrVwdmiTZpbwIoYKCwL4WiFKQhmIIpZWi5VBiCWaLQKN2zMVbr4g2FBz9AUtRHmpff4csU5JuxKW/kpGRka5cvVuQRQEdy1KYazshBDDnppO67jUshbkujky62chcc4zk9ALyMzMp2PgPuS+lm05OQQkBei+HFFdW5aeBPoR2YVpKzILzaY57DkQX2RYA46XjDlumJN2IS289dOrUifj4eIYOHYq3t7ft/YcfftiFUTlXU+kW5SzCYradxJQz56YjLLKgmeRYMtcc4/j5TIZ18iZZ7kvpJpRbUIKf3svOex+A1YpSmIGI7Ey70LLnSY5fLiK6mXctM9aNNqQ5ipc3Rb8cxNBlsEOWKUk34tIGSGFhIa1bt+by5cuuDMOlbsauKa6kqDVoAsIqnRhqAsJQ1E27m5/keDLXHCMupjXmgmy5L6WbUk5+CR1aBNq9HMWYhWK1YPUJQK9T0SxAzfHLRUwaEOyAKEFRqdE1a4/xwhGEEE32uVzJeVx6dH/11VcBSEpKwmw207p13Spxb9iwgffffx+z2cy0adOYOnVqtZ/78ccfWbRoET/88IPDYpbcm1ofQMR9c6v0y1frA1wdmnSTkbnmOHJfSjcjIQQ5+SUYfB0wAlZ+WeNc6MqqoHeK8OLnc0YKSyzodWq7lw+gi+pIceJJStMS0UW0ccgyJakmLm2AJCYm8vjjj5OWlobVaiUoKIilS5fSvn37GudJTU3l7bff5ssvv8TLy4vJkyczYMAAOnToUOlzGRkZvP766429CZKbURQVXuGtiJr+qhyZSGpUMtccR+5L6WZkLDFTarZi8PGye1mqX4fgtfqUNcq7RHnx01kjhy8VMSTaz+7lA+ialZ1HFf1yUDZApEbn0qP7okWLmDlzJvv37+fgwYP84Q9/4MUXX7zhPLt27SImJobAwEB8fX2Ji4tj8+bNVT7317/+lSeffLKxQpfcmKKo0BiC0AaEoTEEyZMYqdHIXHMcuS+lm01W3q81QBxwB0RVkI5Qa7Fqy4o0twzW4OetYvc5xw1Yo/b1QxPcjMKEvQ5bpiTVxKV3QDIzM7n77rttr++9916WL19+w3nS0tIICwuzvQ4PD+fYsWOVPvPJJ5/QpUsXevbsecNl5eXlkZeXV+m9lJSUOkYvSc4jc1XyFDJXJU/R2Lla3gDRO6AKulKQBoZQ22haKkWha5QXhxKNFJVY8HVQNyyfVl3IP/K9rSukJDUWlzZALBYLOTk5BAaWPaCVlZVV6zxWq7XSw1HXPyx19uxZtm7dyvLly2s9kKxYsYIlS5Y0MPrGIQubSdVxx1ytD5nXTUdDc1XmiORsjX1czcorGybXEVXQVflpKH4hlYbzvbW1jj0XitlxtoDY7o55Xsq7dVfyj3xPwendBMaMd8gyJak6Lm2APPjgg9x///2MGjUKRVHYtGkT06ZNu+E8kZGRHDhwwPY6PT2d8PDfqoZv3ryZ9PR07r33XkwmE2lpaUyZMoVVq1ZVWda0adMq3YGBsqsfNT3U3thkYTOpJu6Wq/Uh87ppaUiu7jmeTM/QEtK/eN2WI2H3PsfRDB0x3aMaO2SpiWrs42pWbtkdED+9nQ0QYUUpSEdEdKz0dosgDZH+ar47kceIbv4OGblK4xeCJrgZBad2yAaI1Khc2gC5//77ad26NT///DNWq5WFCxcyaNCgG84zaNAg3nvvPbKysvDx8WHr1q289NJLtumzZ89m9uzZAFy9epX4+PhqGx8A/v7++Pu7ruLn9Vf8UFTVFjaLmv4qGkOQy+KUXM/VuWqPigX7dFEdCRx0N6K0GHNeFhr/YNkIuck0JFd7tNCRs+0TQkY8jMrbgLW4gJyf19BjxKxGilKSGv+4mpVXjM5LjZdGjdWOQiCKMQfFasbqU3k4X0VRiGnvzdeHCzl51Ui3lr52RlzGp1VX8o98hyk7BW2QLBgtNQ6XNEDOnz9P+/btOXnyJH5+fowePdo27eTJk3Tt2rXGeSMiIvjTn/5EfHw8JpOJSZMm0aNHD2bNmsXs2bPp3r27MzbBbtVeFZ40B7U+sNJY+LIYl+Tpygv26aI6Enz7FNK//be8EyJVotUoBPYbXSk3wsY8jkYjaxFInis7r5ggg87uIoS2IXi9qzaWbm3tzfenjXyxP9txDZB2Pck/+gN5B7cQcteNe6VIUkO5pAGyePFili5dylNPPVVlmqIofP/99zecf9y4cYwbN67Sex988EGVz7Vo0cJta4BUvCoMv97t+HwxISNnkbrmFdvnyotxJacXcPx8ZrWFCyXJneUaLWgCwggcdLftBBPkHT7pN6ZSExnX5Ub6t/8m9IEXsb/3vCS5RmZeMYF+OoSdLRDbELzeVYfb1aoVhnb0ZtPxIs6lFNMx0v7K6Gpff7xbdib/6PcEDZuMSquze5mSdD2XNECWLl0KwKpVq4iMrHx779y5c64IyenKrwpXZM5NRxPUzFYRuPwKcVapF2+tOsgzU/q4KFpJajirlx+GsX9GrbZUm/PyDp8EotrcwO5rx5LkOll5xbSMsL9Gh1KQhlBpEF56sFirTB/QzocfE4x8sS+bueOb2b0+AN+O/Si+fIrC07vw6zHcIcuUpIpc0u8hJyeHnJwcHn30UXJzc8nJySE3N5eMjIxq74rcjBS1psoQd5qAMKxqHVHTXyXqD//GFDefC0UGFn9a1viICjO4KFpJarjwYD1m/yjMPkHV5ryidumjaJI7UGmrzQ1U8v6H5JmEEGTnFROgd0ARwvw0MITU+ByJTqMwuIMPhy4VcT612O71AXhFtkUTGEH2js8RVotDlilJFbmkAfLnP/+ZmJgYzp49y4ABA4iJiWHAgAEMGzbshs9/3EzU+gAi7ptr+9HVBIQRcs8cvAOC0BiC8A6OINusY86SnUyOjZaND8mjhQfruZKvxmf0M5VyPuK+uaj1jhk+UvJcx66WEHbvc5VyI+ze5zh2tcTFkUlSwxhLzBSXWvB3RAOkIA3FEIawVr37UW5wB298vRT+s6v2cgZ1oSgKfj3vwJydQv7R7Q5ZpiRV5JJLjx999BEA8+bN49VXX3VFCC6nKCq8wlsROvVlCgqK0Hnr+Oj7q0y6s4ioMAPJ6QWs3prAwpkxrN6aQFSoXjZCJI+VnF7AR9+c4pHxXfCKm0/zEB0aLy9Z60ECIKZ7FAmXMrHcMYeoYG+Ss4oxlvgT0z3E1aFJUoNk5jqoCrrVipKfhgjvcMOPeWtV3B7tw6bjRZxOMnJLcx/71gvoWkSjDW1J9v9Wo79lIGpvvd3LlKRyLv3lf/rpp3nhhRcAuHDhAo8//jgZGRmuDMmprmUU8bdPTmL2DiIgLJxJd3birVWHOJyQxlurDvHMlD70vSWCZ6b04a1Vh0hOL3B1yJJUb8npBbZ8vqVtKD6Bwfz10wTSjFrZ+JAAOJyQxl+X7qZEbSAwMooStYG/Lt3N4YQ0V4cmSQ2Snf9rA8TOKuhKURaK1YzwDa71szHtfPDzVrFqV2algoUNXrei4N93JJbCXDL+u9Qhy5Skci799Z87dy7t2rUDoHnz5vTv35958+a5MiSnOn4+s9KzHVFhBp6Z0oftB69W+/7x85muDFeSGqSmPJf5LJXbfvAq86f3p3d0WVHZ3tHhzJ/en+0Hr7o4MklqmPIihL523gFR5acCIHxqr1fipVG4o7MPCddKOHbZaNd6bcsMbYFfz+EUntpJ3v5vHbJMSQIXN0Cys7OJj48HQKfTMX36dNLT02uZ6+YRF9O6SreqqDADXdvZ1+1gy57EKndLktML2LIn0a7lSlJDlOd5xbyMCjMQF9O6Tnkp81mSJE+TlfdrFXQfO++A/NoAsXrX7Vm5fm28CfRV8R8H3QUB0HcZgq7lLWRu+5icXV/JOyGSQ7i0AWKxWEhNTbW9zsjIkIkNdG8fUqnLVXke1+WWAAAgAElEQVQXlu7tQxDCirkgG1NuOuaCbISo+lDajeZ3Z3XZNslzNTQv7clnmVOeYfitLdh7PImS7HRM2SmUZKez93gSw29t4erQJKlBsvJKbFXQ7aHKT0VovRGautX30KgVhkf7ciG9lFNJjhkRS1GpCBp6H96tu5G1/TNSVr1IScpFhyxbarpcOv7l9OnTmThxIkOHDkVRFHbt2sWcOXNcGZJbKO+i8taqQ0yOjWb11gSemdKHZqG+VaunV1NJuqb53fkh9morw8sq2TeVhuZlQ+eTOeU5enYM5hb/fFJXLrB9V9PvfRaviNr7vUuSO8rKKybIz/4Cfqr8VPALx1qPi7N9WuvYerKQDQez6drC/ofRARSVmsAhkyhq1o78g1tJ+ugv+LTtSeCgu/Fu3Q1FURyyHqnpcOmv8KRJk/j444/p0qUL3bp146OPPqpS4bypigozMDk2mhc/3GMbhrfa6unrXsNSmFun+d1ZfbZN8lzHz2cSO6BVpbysS3eqhuSzzCnPUZqbRdoXb1T6rtK+eIPSXMcMKSpJzpaVV0yAQVdj7Y66UvJTUfxuPATv9bRqhZj23hxKNJKcXWpfABVjURT0HW4lfOIf8esTR0nqRa6tfIHkFfMxXjrusPVITYPLLwNGRkYSFxfHnXfeiY+PDzt37nR1SC63ZU8ihxPSKg3DezghjaKi4lorSZefzF0/jK+7j6BVU2V4WSX75rFlTyKnLmay7OsTPHVfT1ZvTeC7fYk8//5OcvJvXO+hIfksc8ozbNmTiCIs1X5XiuwyJ3morLxiAg123gExl6IqyqrTCFjXi2nng1oFGw87/oKLSueDocsgwifMxn/AeMy5GVxb+QIZ2z6Wx1epzlzaBevdd99l2bJlZYFoNJSWltKhQwc2bNjgyrBcLjzIh8+/T2DBlFvw9YLoh7qRnlWAt5c3moCwSj/UmoAwTFYVWn7rGz91ZGfbsKdRYQaiQvWVXruj8srw12+brJJ982gTaaClv4UHBoVSUCIYGdOSf649QnigD0N7RdU4X8VhfOuTzzKnPEP39iGYLAXVfldCdpWTPJAQgqy8YrrZOaCMbQQs36B6z+vnraJ3Kx3/O53P1MHB6HX2PYtSHUWtRd/xVnzb9iDvyHfk7duIOTeDiLv/JI+zUq1cenRfv34927dvJy4uji1btvDqq6/SocONi+3cbKob4Sczt5A546LIXbOAtC//gSYvGc3mV0j/6i3Cxj5ZpVrwh1sSOXA61XZSlpZt9LhhT6urDC+rZN88hLASocrG8MNiij77E15bX6VnmIlbO4XRtnnADRsSDR3GV+aUZ4gKM3DymomIe5+t/F3d+yzHkhzXfUSSnKWo2ExJqQU/X/uqoCsFZXVwrN5+DZo/pp0PpRbBz2fy7YqjNopGS0DfUfj3HUlRwh7Sv/23HFBIqpVLm6jBwcGEh4fTrl07zpw5w8SJE/nggw9cGZLTlY/wU36ClZxeQNsgNbnrX8ecm07IiIdJ37gEc2465tx0srZ/RsjIWXiFtEDRllWSHtTTlxc/3MPCmTFlV4irOZmr6X13UV4ZPmr6qwiLGUWtkVWybyKWwlzyvnmzUh9/46a3mHTHHErVN87LuJjWVd6rSz7LnPIcvVt5k7l1JSEjHkblbcBaXEDWz+voFfuIq0OTpHrLyC2rweFnbw2QvBQAhM4fGnA+3zxQTVSAhu9P5DOyZ6BdsdSFvvNArKUlFBzbjnfLLvj3vqvR1yl5Lpf+Ems0Gi5fvky7du04cOAAZrOZkpIb9wW/2VQc4WfZV8d5bcV+mofobCdqKm9DpW4JJcnnSF3zCkaTFY0hiGsZRba+8buOJlGUnekxQ45eP0QqgMYQhDYgDI0hSJ4o3kSExYxaH0jEpDk0e3AREZPmoNYHEqzX8M81hyvdBXRk3Q9FUZU1OtQahMWMpTDX7f8umiJhMWMtzKn0ntrHDwVBXto1jzieSVK59OxfGyB6O++A5KchfAKxqhp2rVhRFPq11ZGYWcqFNOecWxm63YZXs/ZkbvkQU3aKU9YpeSaXnuH9/ve/Z8GCBdx+++1s27aN22+/nZiYGFeG5BTXn2AdP59J/y4RbNhxgVKzlRKLytYVwVpcYPt/OU1AGBqtV6W+8bd2DmPWsCAyVj7PlSW/J3n5PErTLrvtj3b5EKnJy+d5RLySfQpMEDz8QTK3fcy1z/5G5raPCR7+IEazglqlsONoku2zjqxjI/PM/a3ZdhazoqmSH/59R5Kx9f/I+OBJ+b1JHiU9uwgAf719D6Gr8lNQ/MOx1mMErOv1aqlDq4Ztx50z+p+iUhE4cCIoCplb/88p65Q8k0sbIGazmRUrVuDr68vXX3/Nhx9+yEsvvVSneTds2MDo0aOJjY1l5cqVVaZ/9913TJgwgfHjx/P444+Tm+s+Q29ef4KlVsFnW87w0MjOeGlUvLv+F/RjnkETEEbOrq+qfe7DJzCoUt94S2Eu6V+87jFDjsohUpsWb43K1pUQyr7v9I1LCNBriY1pTYDhtyJbFe8KVny2qSFdCGWeuT9FEeQWlFbJj7Qv3sC/53Dba/m9SZ4iPceIWqXYVwVdCFT5KQhDCNjxPIWPl4ruzXXsPFtAsck5DXi1rz+GHrdT9MtBis4ddMo6Jc/j0gbI22+/bfu/j48PnTt3rlMxm9TUVN5++21WrVrF119/zZo1a/jll19s0wsKCnjhhRdYtmwZ33zzDdHR0bz33nuNsg0NUfEEa+ueSyz96jizf9eLfadSGTOkLUfOZfDaN9cIuH8RfmOehqDmBD3wEropbxFw/yLydeFs3XuFuJjWtpMyRww56syq0XKI1KZFo1ir/b59NYJgP12V5zwcVcdG5pn7+91d0fhoqfZ7UnkbKr12xPfmzOOc1DSlZRkJ9vfGntp8SlE2iqkY9KF2x9OvrTfFJsHuc84bjl8fPQC1XwiZ2z+Tf2NStVzaAOnUqRPvv/8++/fv5+TJk7Z/tdm1axcxMTEEBgbi6+tLXFwcmzdvtk03mUwsXLiQiIgIAKKjo7l27VqjbUdDlJ9gvbfuKLMmdOOu/q2ZHBvNknVHCfb3pmOrYF5adYbz2Wqe+/AIz398gmvF3ixYcZK/f7yvSneU8iFHK6rPkKPO7qpib7ySZ6np+7bmJNO/uaVKnjmqjo3MM89QalVVnx/FBZVe2/u9yS55kjOk5xQREuBtVxFCVW5Zt1RrA2qAXK9NiIYwPzXfHc+ze1l1pag1GLoPw5R+maKE/U5br+Q5XNIAeeeddwDYsmUL69at47nnnuOpp57iqaeeYvbs2bXOn5aWRljYbz9W4eHhpKam2l4HBQUxYsQIAIqLi1m2bBl33VV1NIa8vDyuXr1a6V9KinMemqp4grV172UOJ6TxybenaB5mwFenYeyQtjwzpQ8fbzyJyWSB8ispCtWOhmHvkKPO7qoih0itH1fmqiOo9QGE3DOnclfCMY+T8/M6cr5+o1KeVXy2qe8tEba7hQ1phMg8c76G5GpijsBn9DOVvqfwe54l7+h222tHfG+yS55UUWMdV9Oyy+6A2MPWAPGpfw2Q6ymKQr82Os6llnA103lDW/u06YbaL4Ssn9fIYXmlKlxyGXDjxo088MADtG/fnk8//RQhRJ26XpWzWq2VPl/T/Pn5+TzxxBN07tyZu+++u8r0FStWsGTJkoZthB2uL6ymVim8snwfcTGtGT2oLYBt+uAezVEUQbvmgbahdltFGFCV5mPKNVYaWtSeIUed3VVFDpFaP67KVUe5llHE8i3p/OXBRVhy08uGWf1xFSXJ5wAq5dmN6n7UtyuWzDPnq2+uJqcX8M7qo8T2b8nw0c8T6adBUasotqoo7vsgIbfFozf4OOR7k13ypIoa47hqsVjJyjUS1CXCruWocpMQvoFY1VqwWOyOq09rb7acLGLbiVweHhZW+wwOoKjUGLoNJXf31xRfOo5P2x5OWa/kGVzSABk8eDC33347AAMHDrS9X96QOH369A3nj4yM5MCBA7bX6enphIeHV/pMWloajzzyCDExMcyfP7/a5UybNq1KwyQlJYWpU6fWZ3Pq7foTrLRsI/On9yct22h7r/yE6/4RnWwNlvKhdrsOCyL9i9fJy023XRn0Cm+FoqjQGBp2tcQVVaPtibepcVWuOsrx85lMH9sVsyghY8N7N8yzhtb9qInMM+eqb64eP5/Jm7OHcupCBgZVMRlr/oH512Nb5L3PcTRdR0ykY74/VxznJPfVGMfVzNxirAICDPaNgKXkJqMENLNrBKyKDDoVXaO8+N+ZAqYODsFL45yLMD5tupF/eBs5+zbIBohUiUsuA7744oucPn2aPn36cPr0adu/M2fO1Nr4ABg0aBC7d+8mKysLo9HI1q1bue2222zTLRYLv//97xk1ahTPP/98jXdX/P39adGiRaV/kZGRDtvOmlR8eLz8de/o8EonXlFhBuJiWlfpjjIzrnWjjHbVVLuqOLLmRGNyVa46Snluf7glkbB7n6syqlt5nrnjvpfqp765Wp4bbYNVFG78R+WR0r54nb5tfRocy/V/32p9QJX8awrHOal6jXFcvZZZCECgnx01QKwWVHkpCL8Iu0bAul6/Nt4UlljZd77QYcusjaLW4tvhVoy/HJZ1QaRKXHrZp7rhc+siIiKCP/3pT8THx2MymZg0aRI9evRg1qxZzJ49m5SUFE6dOoXFYmHLli0AdOvWjZdfftmR4TvF9XdLtKrqRxOytwtBU+2qUl0l+vLXkuNs2ZNITn4Jk+7shHeoL1HTX6WkuISrGcXsvAx3NVPJfd+Erd52lsdio0h18LHt+r/vaxlFfP5TNjOnvoxWZW0yxznJea5llJ3cVxxavL6UgjQUqxmr3v4H0CtqH64lWK9i2/FchkT7OXTZN+LbqR8FJ3eQu+9bQuMecdp6Jffmsfedx40bx7hx4yq998EHHwDQvXt3zpw544qwHGrLnkS6tw+pdLfE9OtoMY3RhaApdlWpOCTy5NhoVm9NaHDNCalmOfkl7DyWxNBeUSiKijSjltc+OUL39mEkJGYR6O8j930TtWVPIsNvbUFKdjFaBx/bavr79g2SOSY1jmsZhWjUKvx9vaobL6ZOVDlXAcc8gF5puYpCvzZlz4JczSylRYh9ldrrSu3rj3frbuQf207w7VNQ6Rp+V1O6ecjLPm6suorQ1XVhkV0I7OOomhNSzYb2igIBr32ynwOnU3ntk/0gYMzgNnLfN3Hd24ewcvMZzFo9hrF/dvixTf59S850LbOQ8CCf30aubABV9mWESoPV1/EXBPu39Uarhm8O5Th82Teij+6PKDWSf+Inp65Xcl+yAeLGqqsIPenOTnhHtiZq+qu0fPL/ETX9VdsD6FLDOKrmhFSzqDADc6f1o9Rk5cUP91BqsjJ3Wj8Aue+buPLj3AfrT3IgRUPe8DmEzlrisGOb/PuWnOlaRiFhQb52PbqhzkqEwCi76ojURK9T0be1NzsS8skudN7ob9rQFmiCIsk7uFkOySsBsgHitsofnqx49W7EgFYcP59p6yqlDQhDYwiSjQ87OLLmhFS96h70N1usfLvzktz3ElD2rFu/LhG8t+4Yx5NKWfSfcxxPMrF17xW7liv/viVnEkKQkllIWKAdNUCEFVV2IkpQC4SDRsC63pCOPlissOmI8+rfKIqCvlM/TOlXKElKcNp6Jfclz1zdVHn3q8MJaazemsCT9/Xkw/Unym7tNhJPGRHKkW5Uc0JyjNyCYl76aA8v/d9evDQqnryvJ1l5Rr7bn8jUkZ3lvpdQq2DlljM8OLIzB0+nMXJga15Zvq/W411txyz59y05U05+CcWlFoID7HgAPT8NxVSM8G+8UQ5DDGq6Nvdi2/Fcikoap5FTHe823VG0OnIP/Ndp65Tcl2yAuKmoMANTR3bmleX7iB3Qim17LzN/en9Wbj7TaFfvqnvm5K1Vh+jePqRR1ucOrh8SGX4bAllyjCE9m1NYbCY1q5CxQ9ry7Y6LRATrCfbTce5KdqXPyn3f9CSnF7B621lm/64X+0+lEjugFUu/Os5jd3cnLdt4w3lrO2bJv2/Jma6k5QMQ7NfwBogq6xIAFn3j/u7eHu1LUangm0PZtX/YQVRaHT5te1J4eg+WojynrVdyT7IB4sbSso3MmtCN99YdZXJsNL2jwxv16l11z5zIUYkke0WFGRgzuB3B/j68t+4opWYrC2YMYMEjMXYNVSndHI6fz2TRowO5q39rJsdG8966o8ya0A2LtfqilBXJY5bkTi6nlDVAQgIa3lNBnZWIUGuxegc6KqxqtQjS0Kuljo2HcknLMzXquiry7dQXrGbyj/7gtHVK7kk2QNxQebeC7u1D2Lr3MgtnxvDJt6dY+11Co1+9kyPGSI1haK8oNOrKhxt5JVqC3+5SVHxYfOvey3W+89pYx6ym2CVVss/llHx8vTUYfLUNXoYqKxGCWjisAvqNjOzmi6LA//su3WkPhmsDI9CGtybv0BaEcF73L8n9yAaIG+rePoTXPtnPayv2l13NC9WDAjuOJDf6w5NyxBjJ0ZLTC5j//k4UBRbOjMFLq+K1Ffs5nJAmT+YktuxJ5HBCWqWHxaeO7Mzflu2u0/GnsY5ZTbFLqmSfy6n5NA8z0OACIOZSVNmXILiVUxoEgb5qRnX35cRVI1/sc15XLH3Hvphz0jBePOa0dUruRzZA3FBUmIHBPZqTX1TKifMZvLXqEIN7NOfhcV3ZcTTJdtLm6KtxcsQYqTH8fCQZH50GY4kZtUphcI/mGEvMvLvmMOFBPrYrzbIx0jR1bx/Cvz4/ahuQIDm9gJWbz/DEpJ627qY13Y1Ys+1srcesht7JkN27pPoQQnA5JZ+oUH3DCxBmXkCxWhCBzR0a240MaOvNra11rN2bzVf7s6tt+JgtgrPXivnhZB7fHs5hR0I+GfkNH8LXu1UXVN568g5stid0ycN5bCX0m939IzoR5OfFe+uO8tR9PenWPtRWvG3utH6VGguOcqMRY+SPrtRQgX46FswYQGpWEa8s38ekOzqSlV/M0J7NWbn5DFNHdnZ4LkueIyrMQOyAVny84SQWq7BVKwdsD6GX340oPz6VH//6d42o9ZhV07x1ybeK3bsWzoyRx0GpRjkFJeQXlRIe7NvgZajTzyIUBYtfBDipd5KiKNzd24BFwH92Z7HvQiGDOxrQe6tIzjZxKb2EM9eKKTFVbZj0buPL1EHBtArV1W+dag0+7XtTeGon5rxMNP7yrmJTJBsgbmTLnkRyC4oZ0rPs6sfWvZeJ6RbJv784xm29W5Td1lXgxPkM1n5/jkWPDnToD2J1/fGjwgzyR1ey246jSQzp2ZyB3aP4bPMZht/agp8OXWVo7+Z8vOEkQ3pFyTxr4oqKzbYTfYDXVuxnSK8o4LeGxd+W7ea+Ozuybe/lSg2KLXsSbcev649ZFe9kTI6NtjVw6pJv13fvigrVNzhPt+xJpHv7kErzJ6cXcPx8pnwW6iZwIamspkZYoB0PoKedhaCWWNEAFgdFVjuNWuF3fQ10Cvfip7NGPtlRdudRrYKoQC2D2vvSLkxLuJ8abw3kGK2culbKjnNGnlt9lfihoYzqGVCvdfp27EvhyZ3kHd5G8LDJjbFZkpuTDRA3UP7DlJNfwo+HrrD9YFnxrVs7R7DnxDWEEGw/eIUn7+uJAra7Io48YZM/jlJj2X8qhUvXctl+8CoIQaBBx/aDV9GoFbYfvErzMANDejaX+daE5RWWkpFbRIi/Nx+uP0GRsRQvLw15haWVGhdd2oawZN1Rnvz1+FfXuxkNuZNRcdlRYQaiQvV2dcOy506M5P7OXclBUSAypIF3QCwmVJkXUToMwmp1XuOjnEpR6NNaR5/WOgpLBKUWQZCvChVVb8ZEaNVE+PvQv42OLw8V8PFPGaTkmJg2NASVSqnT+jSGIHRRHcg/vI2gIZNQ1PJ0tKmRz4C4gfIfprzCUqDsxzg108iOo0lk5RZj8NESEeTLim9Ps+zrEzx1X0+27r1s69fsiNFa5AOXUmMZM7gt2XklpGQWUlxqIbegBCjrVxxk8MJLo+LUxUz+tmy3zLcmKDm9gE27L6H38SK3sISk9ALyi0xk5BjZtOsi3duHsGbbWV76vz1cupZrK8q6ZlsCf1u2u04NgoY8qO7oIobymZKb27nLOTQL0eOlUTdoflXGeRSrGWuA857/qIlepxDkW3Z6eKOeYHqdiqkxfgzt4MN/j+ayZFsa1no8PO/bqR+WwhyKzh2wM2LJE8kGiBv47YctHZPJirHYjMliJTO3BIHCXx7sS5d2weQXlRJo0NGtfSjPTOnDayv2s/a7hCqNh7XfJfDaiv2VTuZqa5A09MdRDlUp1aZ3dDi3tAnGYhFk5BaX9ST89SJZdkEpJrOVf649wuQRneTJWBN0/HwmD4yIJq+wFKu17OTFIgQWqyDAoCM5o5AfD10mPbuYh8d2JS6mDZPu6Mhnm88won8rosIMlQYyuP4iynf7Em0NlfqMsNUYRQzlMOc3JyEE565k0ybKH2sDn0DXJB1FqLVY/Jo5NrhGplIUxvTUM7KrLzsSCvhwe0adR/DSRXVE5RsgK6M3UbIB4iaiwgzEj+5CarYRy69/vAoQGlBWqO3YLxmEBHhTajbz85Fkyj8ghFKl8bDjSHLZzL+q692Mhvw4yjsnUl3ce0dH28gwarWCWqWg/jVHr6YX8GBcZyzXXWqTjdumIS6mNUIIAvVa28mbEKDXqenRIZQXP9xDx5bBPP9wf1ZuPsOWPZdY9/05YrpFsm3fZVvNpLdWHSI8yIe3Vh2yDevbo0MI//78mK1xW90IW84khzm/OWXmFpOdX0LL8AY2KIVAnXQUJSIaC3XrwuRubu/sy/BoH747kcenOzLr1AhRVCr0nfpSnHiCktRLjR+k5FZkA8QNlI+D/8mmU4QE6CivzaOoFEwWCy9/vI8HR3bGX++F3kfL9oNXeO2T/cyN78f9IzoBlRsP8WO6MDe+X73vZjTkx1F2K5Bqk5xewJufHUABfHRqLBZR9u/X3ye9j5bth65UuWOXk18iG7dNRG6BicISC95eZd1XvLQqCkssHDidypP39WT38WQOnE6jX5cIlqw7SqBBx8Nju/LEpJ78bdluAJ6Z0oeVm88QO6AVryzfx4gBrdi8O5EFjwxg8+7ESsen3tHhTn/WSA5zXuZmvLBw4nwGAFGhDfvdU+VcRVWUiYiIdlpBwMYQ29WXwe192Hg4l7V761ZXxLdjPxStjpwd6xo5OsndeGwDZMOGDYwePZrY2FhWrlxZZfrp06e55557iIuL4/nnn8dsbviY1Y0tPMiHV5bvo13zAKxWQbC/Dq1GRZDBi5z8Ujq3CWL1trPMje/HI+O7k5ReQLd2oVUeGK/YeADqdTfDnh9H2a1AupFNuy5SYDQR7K9jYPey7gXlP7HtovwJMnhRaDTx2if7SU4vsOXi0F5RsnHbRPjrvVCrFIpLLXRqGUipyYpapYCA3IIS5k/vz+bdF1m15QwhAd74emtIzSqqdDej/Dj03rqj3DO8A0vWHWVybDS9o8Pd4vjk6GdKPNXNeNf86LkM9D5awoMa9gC6OulI2fC7QS0cHJlzKYrCmJ6+9G/jzRf7svlyf+2NEJXOB99O/Sk8s5fSzCQnRCm5C48cdiA1NZW3336bL7/8Ei8vLyZPnsyAAQPo0KGD7TPPPvssf//73+nVqxfz589n7dq1TJkyxYVRV1U+7C6UdUPYtjcRL40aqyLo0jaY1MwiNGqFo+cyGDekHacuZvLJptP06BDK2cvZHE5IIy3bWGV0lahQPa+t2A+/Vp6uy/CR9tQAceRQldLN59udF1GrFAqLzfxw4CqKUtbFBiAxJY/uHULJvlJCUbGJt/9ziNSsIjq1CrLlkKzDcHOb+fI2UrOKAPD11nD2Sg6RwT6kZBnJKzKx42gyQ3o2J8hPR2q2EW8vDWOHtOWV5fuYP70/vaPDgd+OQ0/d15MP1p/gyft6snprAmqV4hbHJznMeRl7hkV2R0IIjv6Szi1tghpWgFBY0STug9D2mBUtti4QHkqlKEzso8ciBKt3Z6FRwfhbg244j/6WgRQl7CH7p/8Qcc9fnBSp5GoeeQdk165dxMTEEBgYiK+vL3FxcWze/FtFzaSkJIqLi+nVqxcA99xzT6Xp7qJ7+xB2HElmx9Fkbu0cgaJSkVNYikqt4vY+LcjINVJistKzYyj7T6Xw7pojFBSVMumOjkwd2ZlXlu8jPMinSuMBAAUG92he57sZDX3gUnYrkGozdkg7TBZBcWnZ0JIVexhYrHDkbAYDuzXDWGLhTGI2+UUmxgxuC8g+801BxWNMUbEZX28NKVlG23sPj+3Kz0eS8fXR2mqEvLfuKLMmdLMVKiw/Dk0d2Zmtey8zf3p/tu29zMiBrXll+T6mjuwsj09u5Ga6a34to5D0bCMdWgY2aH5VWgKqgjRo1Rth9ezGRzmVonDvrQZ6tdLx2c4s1h+svsJ6ObW3Hv0tgyg8vZvipHNOjFRyJY+8A5KWlkZYWJjtdXh4OMeOHatxelhYGKmpqVWWk5eXR15eXqX3UlJSGiHi6kWFGZg7rR+vrdjPvz4/SkmpGY1aIbeglJWbz6AoCiEBXpy6mIVGpaAAwQHetmrB86f3Jy3bWKWRcPx8JnPj+zmlormsnu4crs5VezwyvhuXruVx5Gx6pfe1agWTRaBRw3f7r6BWgUatIjLEl4hgX4fXYZCco765et+dncgrLOXrn84DZY2Qck/f34uIYF9Wbj7D3Ph+AHhpVDQPM7Bxx0XmTit7r/w4VPF4FBHsy+ptZ23HSZDHJ3fhLnfNHXFc3XmsbFCYdlH1K8RXTvvL/xBeesxBbcDi/PofjUWlKNx3qwEErNyZRXqemYeHhZZ1rayGvstgis4dJPO7j4mK/zuK4kaOkAMAACAASURBVJHXx6V68MgGiNVqRVF+S2IhRKXXtU0vt2LFCpYsWdK4wdYiKszA2CFteW/dUQCmxEWzaksCGbnFPDSyM+1aBPLih3swAWOHtEVBsXVHKe96cD1n3uqX3Qqcwx1y1R5/uKcHT77xAybLb1fBTBZB/y4R7DtVdnHAYoUpcR0Z1rsFb606RP+uEbJx64Eakqstwv3o1DKQs1dybO8F+unIzC2xNSoA3lp1yNbo2HE0ydYgrVgFvVx5vlxPHp9cy50uLDjiuLrjSDIdWgTg7+tV7yF4laJs1ElHUDoOxXwTNT7KqVUKv+tnIMBHxdbjeVzLMfFUbDiB+qqnniqtDr9ed5C75xvyD3+Hf59YF0QsOZNHNkAiIyM5cOC3wjXp6emEh4dXmp6e/tvV1oyMjErTy02bNo2777670nspKSlMnTq1EaKuXnJ6Aau2JhAR7AsCPv/+HBHBPoDCxh0X8PJSo9Wo8Pf1YuueRMKDfVx+1UhyPnfIVXu8/Z9DlRof5fadSkWnVVFishKo9+KLH36hc+vgGhsa8uTR/TUkV/MKSyo1PgBy8kv48dAVFswYYKv1UfEk9Xd3RTOkZ3PZIPUw7nTX3N7j6pXUfC4k53LfnR0bVP9De2IDKAqWqB71n9lDqBSFUd31hPup+fpIIX9ZdYXf3xlO33b6Kp/1ad8H46XjZH6/At8OfdD4h7ogYslZPPIe16BBg9i9ezdZWVkYjUa2bt3KbbfdZpvevHlzdDodBw8eBGD9+vWVppfz9/enRYsWlf5FRkY6bTuS0wt4bcV+/PVePDGpJ1qtCgFoNSomj+hEgdFEapaRx+/twdOTeyOAtCwjapUi+zI3Ma7OVXt89M0JziSWjYbi56vh+jvwJSYrE4e1JyjAm0CDFy9/vI/UrCKnD5MqOUZ9c3Xd92f5ZNNp2+u7+rW0/f9qWgGbdl0EGqcwoOR87vQ92ntc/ebnC2g1Krq0Da73upWcJDSXdqF0GIJJ5VXv+T3NrW28efKOAAw6FYs3prB4wzXSck2VPqMoCgEDxoMQpH7xBsJsqmFp0s3AIxsgERER/OlPfyI+Pp6JEycyduxYevTowaxZszh+/DgAb775Jq+++iojR46kqKiI+Ph4F0dd1fHzmQzpFcXc+H6kZRu5vU9LFswYwO19WnHsl0y6tA3hzr4tsVghLdvIghkDGDWoDdsPXm2yQzhKnuf7/Vfw1amJ6RZJl7ahvDBrIL06hqJSyvrzD+weSW5BKXPj+xEb05q4mLIcl5qG8voPGjUsenQgT0/uQ/zoW2zT8wrlSYjkfrLzi/lh/2UGdW+Gt7aenUmsFnQHV4LGG3PLPh5d+6M+Ivw1PD48gNHd9Ry7YuTpTy/z0Y/pZBf+9tyXxi+YgIETKUn+hfTNy5rMvmmKFCG/3UquXr3KnXfeyffff0+LFp49Jrd0c5O5KnkKmauSp6hrrr79n0P8dOgqcx7qi59v/e5gaI9+gdeZrYh+kyk2RNkbskfKLbKwPcHI/ovFqFQKI7r7M6ZXAGH+WgDyj22n4NiP+N86kpC4R+RD6Tchj3wGRJIkSZIkyRV2H0/mhwNXGDu4Lf6+XnWv/yEE2tP/xevMVmg/iNKAljfVyFf1EeCrZmJvA7d19OGHBCObj+ay+WguMR0MjOzpT6duwxAWM3kHN2POyyBs3FOofW78jJDVKkhMySMhMZuk9ALSs42YzFYUBYL9vYkI9iW6dRCdWgXhpVU7aUulmsgGiCRJkiRJUh3sO5XCmysP0aFFAAN7RNa98VGch+7IOjSJ+xCtb8XUdjDWJtr4qCjYoGbSrQbuusWHvRdL2HOhkF3nCogM0DKoYx/6dfSB899zdenTBN0+Bb/ut6Goy+6SFJea+eVKDqcvZXH6UhanLmZRaCzrsqnVqAgN9MFLo8JqFZy8mElBUdk0jVpFdOsgenUKo3enMDq0DKpxeGCp8cgGiCRJkiRJ0g2YzBbeW3uE7Qev0raZPw+OugXlRo/RCoFSnIcq6xLq5GNoLu8Hqxm6xmJq1hOLbHxUEuirJq6rL7d38ubkNRNHrpTw1cEcvhTNaK0dyf3WA1i+/TdXNi3nglcnzpREkpDnQ7pZjxUVzUL13No5nDbN/GkWqifQoAN+K3yrAMYSM5dT80lMyeNCUi6rtpxh5eYzGHy09OwURnSrINpFBdC2eQD++qrd6oQQ5BWW4q/3qra0g1Q/sgFynfKDgqcUeZPcS2RkJBqNc/6sZK5K9pC5KnkKd8jV1OxiDp1JZXjvSHp3DCQ3O6vGZWgK02l2cClqczEAQq3F1KwbxZE9MAotIlMOHnMj7f2hfVcojtZwNRdSCyLZVDiG0JKrdBdn6VRygq4cAX8QKAi1FqHxJi38QUp89ZQW5ZFWVP2yg3wgqK0vvdr6UmIK52pGMVfTjZw8n8HOo8m2z3lpVBh8Nfj5aFAUMFsEGbklFJda+d3tLRneu/o6bM7MVU8nH0K/zoEDBzymtoLkfpz5kK3MVckeMlclT+EuudqqVWvCm7en1FT7yGyREWG0Dg9AA6gUKC0pkXc97KQAikpBo9Gi1mgQgFmAVSgIICU7nytJDbvIoVIU9Pqy2iRCWBBWM8JqQQhrhfUrKGoNppIiDuzbU+1y5EAbdScbINcpLi7mxIkThIWFoVY33kNK5cWOVq5c6TH1HBzlZt52Z179uD5X3X2/unN87hwbNE58rszV6rj7d1CRjLVx1BSru+Wqo3jSdwOeFy84P2Z5B6Tu5F66jre3N3379nXa+iIjI5tsa7kpb7sj1JSr7r5f3Tk+d44N3D++mtTnuOpJ2yhjbRyujNXZ5wDgWd8NeF684Jkx3+zkwMqSJEmSJEmSJDmNbIBIkiRJkiRJkuQ0sgEiSZIkSZIkSZLTqF944YUXXB1EU6XT6RgwYAA6nc7VoThdU972xuTu+9Wd43Pn2MD943MET9pGGWvj8KRYHcHTttfT4gXPjLkpkKNgSZIkSZIkSZLkNLILliRJkiRJkiRJTiMbIJIkSZIkSZIkOY1sgDjBhg0bGD16NLGxsaxcubLK9NOnT3PPPfcQFxfH888/j9lsdkGUjaO2bV+yZAnDhw9nwoQJTJgwodrPSDU7ePAgkyZNYsKECUybNo2kpKQqn0lKSqJ37962ffzII480akzunu9LlixhzJgxjBkzhsWLF1c73ZU5+dBDDzFmzBjb+o8ePVpp+q5duxg3bhyxsbG8/fbbTo2tMdWWN85S3f6vKbaavovGzPGCggLGjh3L1atXGxRDcnIyU6dOZeTIkfzhD3+gsLAQgLy8PB599FFGjRrF1KlTSU9Pd3is8+bNIzY21rZvt23b5tBtcFfufkysjif+dl+fbxW54z5u8oTUqFJSUsTw4cNFdna2KCwsFOPGjRPnzp2r9JkxY8aIw4cPCyGEmDdvnli5cqUrQnW4umz7Y489Jg4dOuSiCD3f8OHDxenTp4UQQqxbt078/ve/r/KZzZs3iwULFjglHnfP9507d4r7779flJSUiNLSUhEfHy+2bt1a6TOuzEmr1SqGDBkiTCZTtdONRqMYNmyYuHz5sjCZTGLGjBnixx9/dHKUjleXvHGG6vZ/TbHd6LtorBw/cuSIGDt2rOjatau4cuVKg2J49NFHxcaNG4UQQixZskQsXrxYCCHEiy++KJYuXSqEEOKrr74STz/9tENjFUKIsWPHitTU1Eqfc+Q2uCN3PyZWxxN/u6vLt4rcbR9LQsg7II1s165dxMTEEBgYiK+vL3FxcWzevNk2PSkpieLiYnr16gXAPffcU2m6J6tt2wFOnDjB0qVLGTduHIsWLaKkpMRF0Xqe0tJSnn76aTp37gxAdHQ0165dq/K548ePc/bsWSZMmEB8fDwJCQmNFpO753tYWBhz587Fy8sLrVZL+/btSU5OrvQZV+bkhQsXAJgxYwbjx4/ns88+qzT92LFjtG7dmpYtW6LRaBg3btxNcbyoy7HCGarb/zXFVtN30Zg5vnbtWhYuXEh4eDhQcz7UFIPJZGL//v3ExcVVie3HH39k3LhxAIwdO5b//e9/mEwmh8VqNBpJTk5m/vz5jBs3jn/+859YrVaHboM7cvdjYnU88bf7+nyryB33sSS7YDW6tLQ0wsLCbK/Dw8NJTU2tcXpYWFil6Z6stm0vLCzklltu4dlnn+Wrr74iLy+Pf//7364I1SN5eXkxYcIEAKxWK0uWLOGuu+6q8jmdTsf48eP56quveOSRR3jiiScoLS1tlJjcPd87duxo+xG6dOkS//3vfxk2bJhtuqtzMi8vj4EDB/Kvf/2L5cuXs3r1anbu3GmbXtv+9VTusl3V7f/k5ORqY6sp5sbM8Zdffpm+ffvaXtc3huzsbAwGAxqNpkpsFefRaDQYDAaysrIcFmtGRgYxMTG88sorrF27lgMHDvD55587dBvckbsfE6vjib/d1+dbRe64jyXZAGl0VqsVRVFsr4UQlV7XNt2T1bZter2eDz74gPbt26PRaJgxYwY//fSTK0J1e//973+57bbbKv2bPn06UHYn5C9/+Qtms5nHHnusyrxPPfUUU6ZMQaVSMWzYMHx9fW1Xeh3NU/L93LlzzJgxgzlz5tCmTRvb+67Oyd69e7N48WL8/PwIDg5m0qRJldbvLvvP0dxlu6rb///85z+rja2mmJ25LfWNobpYaopNCIFK5bhThJYtW/Kvf/2L8PBwfHx8eOihh/jpp58adRvcgaccEyu62X673XEfS7IB0ugiIyMrPcyXnp5e6Rbh9dMzMjKqvYXoiWrb9uTkZD7//HPbayGE7aqWVNmoUaP43//+V+nf8uXLKSwsZObMmZjNZt5//320Wm2VeT/99FOys7NtrxtzP3tCvh88eJDp06fz5z//mbvvvrvSNFfn5IEDB9i9e3eN669t/3oqd9mu6vZ/8+bNq42tppidmeP1jSE4OJj8/HwsFkulz0PZVe6MjAwAzGYzhYWFBAYGOizWhIQEtmzZYntdntuO3AZ35AnHxOvdbL/d7riPJdkAaXSDBg1i9+7dZGVlYTQa2bp1K7fddpttevPmzdHpdBw8eBCA9evXV5ruyWrbdm9vb9544w2uXLmCEIKVK1cyYsQIF0bseZ599llat27NO++8g5eXV7Wf2b9/v+3HYt++fVitVtq1a9co8bh7vl+7do0nnniCN998kzFjxlSZ7uqczM/PZ/HixZSUlFBQUMBXX31Vaf09e/bk4sWLJCYmYrFY2Lhx401xvKgtb5yluv3/xhtvVBtbTd+FM3O8vjFotVr69u3Lpk2bAPj6669tsQ0bNoyvv/4agE2bNtG3b99qL2g0lBCCV155hdzcXEwmE2vWrGHEiBEO3QZ35O7HxOrcbL/d7riPJeQoWM7wzTffiDFjxojY2FixbNkyIYQQM2fOFMeOHRNCCHH69Glx7733iri4OPHMM8+IkpISV4brULVt++bNm23T586de1Nte2M7efKk6NSpkxg9erQYP368GD9+vJg5c6YQQohVq1aJd955RwhRNqLJ9OnTxZgxY8Q999xjGzWrsbhzvr/00kuiV69etv01fvx4sWrVKrfKybfffluMHDlSxMbGiuXLlwshhBg/frxISUkRQgixa9cuMW7cOBEbGytefvllYbVanRpfY6kub1yhuv1fU2w1fReNnePDhw+3jfRT3xiuXr0qHnzwQTFq1CgxY8YMkZOTI4QQIjs7Wzz22GNi9OjR4v777692JCF7Y/3ss8/EqFGjxIgRI8Qbb7xh+4yjtsFdufMxsSae+ttdMd/cfR83dYoQQri6ESRJkiRJkiRJUtMgu2BJkiRJkiRJkuQ0sgEiSZIkSZIkSZLTyAaIJEmSJEmSJElOIxsgkiRJkiRJkiQ5jfsO3CxJTlZQUMDkyZP5f//v/9GiRYtqP3P69Gnmzp1re52VlUVAQAAbN250VpiSJHNVkiRJ8mjyDkgTtHfvXsaOHevqMNzK0aNHeeCBB7h06dINP3fLLbewfv161q9fz+rVqwkICOCFF15wSozu7vnnn2fXrl0OWdZHH31U6eS5vv76179y4sSJBs179epVevfu3eB118eMGTPIysoCYNasWfzyyy+1ziNzte4WLlzIHXfcwdtvv+3qUCSpXhyZuw05ztRXfn4+8fHxDl9ubf7zn/+wbNkyp69Xsp+8AyJJwNq1a1m4cCFz5syxvff111+zYsUKrFYrXbt2ZeHCheh0Otv0pUuX0q9fP/r27euKkN3Oyy+/7OoQbHbt2sX999/v6jBqtXPnTtv/P/jggzrNI3O17tasWcOPP/5IZGSkq0ORpHpxZO425DhTX7m5uRw/frxRln0jDzzwgNPXKTmGbIA0UUVFRcyePZvExET8/f1ZtGgRoaGhvPjii5w5cwZFURg6dCjPPPMMGo2G7t278+ijj7Jz507S0tKYOXMmU6ZM4csvv2TLli0sXboUoNLrAwcO8Nprr2G1WgF47LHHiIuLc+Vm1+j6k+dz586xdu1aVq9ejU6n4x//+AcfffQRjz/+OFB2tWft2rVs2LDBFeHWyQ8//MD777+PyWTC29ub5557jrVr11JUVMS7777LuXPniI+P57PPPqN9+/a8//77bN26FavVSvPmzVm4cCERERE89NBDBAQEcOHCBR544AFGjhzJwoULuXDhAiqVismTJxMfH89DDz3E1KlTueuuu3jppZc4dOgQWq2WFi1a8Oqrr6LX6zl06BBvvvkmRqMRlUrFk08+yfDhwzGZTPz9739n165dhISEEBISgp+f3w23z2w2V7ueZcuWkZaWxl/+8hcWL15MREQEL7zwAklJSQghmDhxIjNnzgRg+/btvPPOO1itVnx9fXnxxRcxGAy2dZw/f55Zs2Yxb968G1b6NZlMvPbaa+zevRu1Wk2PHj2YN28eBoOBO+64gzFjxrBz507y8/N5+OGHmTJlCvPmzQNg2rRpLFu2jKlTp/Luu+/SvXt31qxZw6effopKpSI0NJQFCxbQtm1b5s6di8Fg4N133yUlJYWFCxfy9NNPs3btWoYOHcr27dv56aefGDt2LCtXriQ8PNwjcrUxTJkyBSGE7YpvXFwcCQkJPPPMM7Rp04ZFixaRk5ODoijMmDGDiRMnsnfvXt566y2aNWvGxYsX8fHx4dFHH+XTTz/l4sWLxMbGMn/+/Buud+7cuXh7e3P27FkyMzO54447CAwMZPv27aSnp/P3v/+dgQMHUlpayptvvsn+/fuxWCx06dKFv/71rxgMBlatWsXq1avRarXodDoWLVpEhw4danx/+/btLF26lNLSUrKyspg4cSJ//OMfAVi2bBmff/45er2evn378v333/PDDz80aP2Sc9wodzUaTY3f9eeff87HH3+MSqUiKCiI119/nX/+859Aw44zCQkJpKSkEB0dzeuvv45er68x5nnz5lFcXMyECRP48ssvOXz4MIsXL8ZoNKLVavnjH/9Ya/Xxhx56iC5dunDw4EGys7OZMGECs2fPBuC7775jyZIlWK1W9Ho98+bNo0ePHrz33ntkZ2fzt7/9zUF7X3Ial5ZBlFxiz549onPnzuLgwYNCCCFWr14tJk2aJObMmSNeeuklYbVaRUlJiZgxY4ZYunSpEEKITp06iU8//VQIIcTx48dFt27dRHFxsfjiiy/Eo48+alt2xdfx8fFi48aNQoiyKqQvvPCCMzezQcqrqH766adi0KBBtmrZo0aNEn/+859tn/vss8/EggULXBjpjV28eFGMHTtWZGVlCSGEOHv2rBg8eLAoLCwUsbGx4ssvvxRjxowR69evF0II8dVXX4k//vGPwmQyCSHKcqK8qvqDDz4o5s2bZ1v2E088IV5//XUhhBB5eXn/v717D4qy6gM4/l2BHcLLa+ugOaM2oKJmo6blclsVJS7iOoChjJrSMGUmllpEOqLkBS9RiVpZzaA1UabSaGMqZqQoF3Oi0hwkwxsqoaMjKcgu7PN7/2B4xlUuaoU0nc8MM8t5nj3nPM9zztl99lweiYiIkDNnzsjUqVNl9+7dcuTIEQkLC9OfZrx69Wr58ccf5dq1axISEqI/pfaPP/6QESNGyIULF2TTpk0ybdo0sdlsUlVVJVFRUZKUlNTsMTaVjkj9dWx4Au6UKVMkIyNDz6/VapWdO3fK5cuXZdiwYXL8+HEREcnOzpb4+HgpKyuTIUOGSElJiQQHB0t+fn6L5zs9PV0SEhLEbreLw+GQN954Qy8fQUFBkpycLJqmSXl5uZjNZjlx4oSI1NerK1euOOU5Pz9fgoOD9fCsrCwJDw8XTdMkKSlJJk2aJDabTUaNGiVjx46VV199Vcxms/Tv31+sVquEh4dLZGSkfPvttyLS9svqP6nh/AYFBcn69etFRKS2tlbGjBkj2dnZIlJfDi0WixQVFUlhYaEMGDBALxPx8fH6+b5y5YoMHDhQfyJ9U5KSkiQmJkbsdrtcunRJfHx85NNPPxURkU2bNslzzz0nIiLr1q2TlStX6uX37bfflsWLF0tdXZ0MHDhQKioqRKS+bm7evLnJcE3TZOrUqXL69Gn9eAYMGCBXrlyR3NxcCQ0NlcrKStE0TebPny9BQUH3lb7Suhoru81d6+LiYjGbzXLx4kUREdm4caNe7/9KO2O32yUyMlK2bdvWbH4b2k0RkatXr4qfn5/8/PPPIlL/+TN8+HA5d+5cs3FMnTpVnn/+ebHb7VJZWSmhoaGSk5Mjv//+u/j7++vvz8/Pl4CAALl+/bqsXbtW3nzzzXs9vUoboHpA/qP69evH0KFDAYiKiiIlJYUzZ86wdetWDAYDRqOR2NhYPvnkE1544QUAxowZA8DAgQOx2+1UV1c3m0Z4eDhLliwhJycHf39/5s2b988e1N/I4XAQHh7OwoULAaiqqsLhcOjb9+3bx4wZMx5U9lrU0FMVFxenhxkMBs6dO8e7777LxIkTGT9+POPHjwfqewKOHTvGhAkTANA0jZs3b+rvvXXoTn5+PomJiQB07NjxjknNPj4+uLi4EBMTQ2BgIKGhoQwaNIgDBw5w+fJlZs2a5ZSnkpISCgoKGDduHEajEaPRiNVqpaSkpNljbCqdW1VXV1NUVERGRoae3+joaHJzc3Fzc6Nv37489thjAISEhBASEsL58+ex2+1MmzaN4cOH4+fn1+L5zs3NZe7cubi5uQH1v+TdepyTJ0/GYDDwyCOPYLFYyMvLo1+/fo3GdfDgQcaOHYvJZAIgOjqa5cuXc/78eQAsFgtGoxGDwYCXlxfV1dVERERw4sQJrl27hr+/P76+vgQHBwNtv6y2loYyfObMGWw2GyEhIQB069aNkJAQDh48iNlspkePHnqZ6NWrFx07dsRoNGIymWjfvj2VlZV069at2bSCgoJwc3PD09MTDw8PLBaLHt+1a9cA2L9/P9evX9fnTdXW1tKlSxdcXFwICwsjNjaWUaNGERgYyMiRI5sMNxgMbNiwgf3797Nz505KS0sREW7evMmBAwcICwujU6dOAEyZMoXCwsL7Sl95cBrKbnPXuqCggMDAQLp37w7g1PY35m7bGahvaysrK+86v0ePHqVXr14MHjwYgL59+zJ06FB++OEHevbs2ex7J02ahJubG25uboSFhXHo0CG8vb3x9fXV3+vn54fJZLrveX5K26BuQP6j2rVzXn/AYDDofw00TaOurk7/v2FMecM+IoLBYEBE9H1qa2v117GxsQQFBZGXl8fBgwdZv349e/bscRqb3laZzWYyMjKYOXMmJpOJlJQUevXqxezZsxERjh8/3moTle+Hpmn4+fmxZs0aPay8vJyuXbuyZ88eOnfuTHFxMXa7HaPRiKZp+rA6ALvd7vSB4+Hhob92dXV1KidlZWU8/PDD+v+dOnVix44dFBUVUVhYyJw5c4iPj6d79+707t2brVu36vtWVFRgMpn48ssvnfLv4uLS4jE2lc6UKVOczsOt5bMhrK6uDhcXF6fjEBFKSkr0IVjvvfcer7/+OtnZ2S0OHdQ07Y66c2tdcHV1ddp2e/27Pa7biYheF93d3fVwg8FAz5492bNnD1lZWVy4cIHFixfzzTffUFRURGJiYpsvq62loQw7HA6nawXO57fhS1eDW6/d3bqbODRNY8GCBfqX+6qqKmw2GwBpaWn89ttv5Ofn89FHH7Fjxw7S09MbDV+xYgVRUVEEBwfz5JNPMmHCBPbt24eI4Orq6lT+b61X95O+8mA0lN3q6uomr/Xt7VlNTQ0XLlygd+/ejcZ5r+3M7e1oc1qqY825ta6ICO3atbujfb2X+JS2S62C9R9VUlJCcXExUD/ZbdiwYVgsFj777DNEBLvdzpYtW/D39282HpPJxMmTJ7HZbNTW1pKdna1vi42Npbi4mOjoaJYuXcqff/7J5cuX/9Hj+rv079+fhIQEpk+fTkREBJqm6T1BV69e1cdGt1V+fn7k5eVRWloKwIEDBxg/fjylpaUsX76cjIwMvL29SUtLAyAwMJBt27Zx48YNANLT050mOd8ed1ZWFlA/F2b69OlOKzJ9//33xMXF8cQTTzB79mwiIyP59ddfGTJkCGfPnuXIkSNA/TKxoaGhVFRUYLFY2L59OzabDZvNxq5du1o8xqbSgfovWnV1dXTo0IHBgweTmZmp53f79u34+/szePBgSktLOXnyJADfffed3rNjNBoZNmwYqamppKSktFhuLRYLX3zxBbW1tWiaRmZmJgEBAfr27du3A3Dx4kXy8vL0sdAN+bw9rl27dumr1mRlZdG5c2ceffTRRtPu2rUrEyZMICgoiKSkJHr37s0rr7zCsWPH/hVltbV5e3vj6urK3r17gfqb4Ozs7Bbbur9bYGAgmZmZ2O12NE0jOTmZd955h6tXrzJy5Eg6d+5MXFwcc+bM0a9lY+Fnz57lxo0bzJkzh9GjR3P48GE9zpEjB+QEagAAA/dJREFUR7J3716uX78O1M8RuN/0lQevuWttNpspKCjg0qVLAGzevJm33noL+HvamZa4urricDgQEYYMGcKpU6c4evQoUD+n8siRIwwfPrzFeL7++ms0TaOyspLdu3czevRo/Pz8OHToEGVlZQAUFBRQXl6u97Ao/06qB+Q/ytvbm/Xr11NWVkaXLl1YuXIlHh4eLFu2DKvVSm1tLRaLhRdffLHZeAICAnjqqacIDw/H09MTs9msD5157bXXSE1NZc2aNRgMBhISEpp8ZkFbkZOTo7+OiYkhJibmjn26dOnitKpIW9SnTx+WLFnCvHnz9F9CP/jgA5KTk4mPj8fHx4dFixZhtVrx9/cnJiaGiooKJk6ciMFgoHv37qxcubLRuBctWkRKSgpWqxURYcaMGTz++OP69hEjRpCbm8u4cePw8PDgf//7H0uXLsVkMrF27VpWr16NzWZDRFi9ejU9evQgNjaWc+fOMW7cuLv+EGwqHYCnn36axMREUlJSSEtLY8mSJXz11VfY7XasVivR0dEYDAbS0tJISkrC4XDQoUOHO5a8NJvNREREsGDBgmZXj5k5cyarVq0iMjKSuro6Bg0aRHJysr79/PnzREdHU1NTw8KFC/H29gYgLCyMZ599lnXr1un7BgQEEBcXx/Tp09E0DZPJxIcffnhHr0lOTo6+VPHLL79Mu3bt2LlzJ6dPn6a8vJyFCxf+K8pqa3Nzc+P9999n2bJlrFu3DofDwaxZs/D19eXw4cOtlo+XXnqJVatWERUVhcPhYMCAAfrk35kzZxIXF4e7uzsuLi4sW7YMk8nUaHi/fv0YNWoU4eHhGI1GfHx86NOnD2fPnsVisTBx4kQmTZqEu7s7ffv25aGHHrqv9JUHr6VrnZiYqC+w4enpSWpqKvDX2pm75enpyaBBg4iIiCAzM5P09HSWLl1KTU0NBoOBFStW4OXl1WI8NTU1PPPMM1RVVTF58mR9COzixYtJSEjA4XDg7u7Ohg0bWlyoRGnbDHIv/WqKoijKPRk9erS+6oyitKZjx47x008/6c9n2LhxI7/88ovT0ExFaSsaVlIMCwt70FlRWoHqAVEUpc1KTU1t8lfp+fPn4+vr2yr5uHHjhtPcklu1b9+ezz//vFXyoTx4p06dYu7cuY1u8/LyalNf7r28vPj444/ZsmWL3rPZ0EuoKPdq8uTJVFVVNbotMzPTaQnzxhQWFrJixYpGt5nN5r+cP+XfRfWAKIqiKIqiKIrSatQkdEVRFEVRFEVRWo26AVEURVEURVEUpdWoGxBFURRFURRFUVqNugFRFEVRFEVRFKXVqBsQRVEURVEURVFajboBURRFURRFURSl1fwfjvVSROamxdIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 786.35x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns; sns.set(style=\"ticks\", color_codes=True)\n",
    "\n",
    "g = sns.pairplot(enron, vars=['bonus','exercised_stock_options','from_messages','fraction_to_poi'],\n",
    "                 dropna=True, diag_kind='kde', hue='poi', markers=['x','o'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<a id='Outliers'></a>\n",
    "\n",
    "## Outliers\n",
    "\n",
    "When looking at the stats for poi and non-poi for the first time, I noticed that the non-poi stats were much higher than the poi stats.  That's when I remembered I didn't account for the \"TOTAL\" key.  So, I went back and skipped over that key when writing to my csv.  I figured I'd just pop it out of my dictionary later if I need to.  After doing that, my stats were as expected.  Now, let's see what other outliers we can find.  In the pairplot above, there were two POIs that really stood out.  Let's take a closer look in the following lmplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmo4cob\\AppData\\Local\\conda\\conda\\envs\\tf\\lib\\site-packages\\seaborn\\regression.py:546: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(30.282459722222228, 0.5, 'Salary')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 786.35x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='bonus', y= 'salary', hue='poi', data=enron, palette='Set1',size=10,markers=['x','o'])\n",
    "plt.title('Salary/Bonus for POI and non-POI', fontsize=18)\n",
    "plt.xlabel('Bonus', fontsize=16)\n",
    "plt.ylabel('Salary', fontsize=16)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>salary</th>\n",
       "      <th>bonus</th>\n",
       "      <th>poi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>SKILLING JEFFREY K</td>\n",
       "      <td>1111258.0</td>\n",
       "      <td>5600000.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>LAY KENNETH L</td>\n",
       "      <td>1072321.0</td>\n",
       "      <td>7000000.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>FREVERT MARK A</td>\n",
       "      <td>1060932.0</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name     salary      bonus    poi\n",
       "59   SKILLING JEFFREY K  1111258.0  5600000.0   True\n",
       "62        LAY KENNETH L  1072321.0  7000000.0   True\n",
       "130      FREVERT MARK A  1060932.0  2000000.0  False"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Who are the two outliers in blue with the high salary AND high bonus?  Ken Lay and Jeff Skilling of course!\n",
    "\n",
    "enron[(enron['salary']>1000000)][['name','salary','bonus','poi']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='Ken_Lay.jpg' width=100 height =150>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are two of our persons of interest, so we definitely don't want to take them out of our dataset.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to [Executive Excess 2002](http://d3n8a8pro7vhmx.cloudfront.net/ufe/legacy_url/629/Executive_Excess_2002.pdf?1448073268)\n",
    ">Top executives at 23 companies under investigation for their accounting practices earned far more during the\n",
    "past three years than the average CEO at large companies. CEOs at the firms under investigation earned an\n",
    "average of 62.2 million dollars during 1999-2001, 70 percent more than the average of 36.5 million dollars for all\n",
    "leading executives for that period."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may also be able to find a few datapoints that are just causing noise by checking for lots of missing values in rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name                         LOCKHART EUGENE E\n",
      "deferred_income                            NaN\n",
      "expenses                                   NaN\n",
      "bonus                                      NaN\n",
      "director_fees                              NaN\n",
      "salary                                     NaN\n",
      "email_address                              NaN\n",
      "total_payments                             NaN\n",
      "restricted_stock                           NaN\n",
      "loan_advances                              NaN\n",
      "to_messages                                NaN\n",
      "other                                      NaN\n",
      "from_this_person_to_poi                    NaN\n",
      "deferral_payments                          NaN\n",
      "long_term_incentive                        NaN\n",
      "restricted_stock_deferred                  NaN\n",
      "from_messages                              NaN\n",
      "exercised_stock_options                    NaN\n",
      "shared_receipt_with_poi                    NaN\n",
      "total_stock_value                          NaN\n",
      "poi                                      False\n",
      "from_poi_to_this_person                    NaN\n",
      "total_be                                     0\n",
      "fraction_to_poi                            NaN\n",
      "fraction_from_poi                          NaN\n",
      "total_millions                               0\n",
      "Name: 26, dtype: object\n"
     ]
    }
   ],
   "source": [
    "#check for more than 20 missing values for each datapoint\n",
    "\n",
    "i = 0\n",
    "\n",
    "for row in enron.isnull().sum(axis=1):\n",
    "    if row > 20:\n",
    "        print (enron.iloc[i])\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                           0\n",
       "deferred_income               97\n",
       "expenses                      51\n",
       "bonus                         64\n",
       "director_fees                129\n",
       "salary                        51\n",
       "email_address                 34\n",
       "total_payments                21\n",
       "restricted_stock              36\n",
       "loan_advances                142\n",
       "to_messages                   59\n",
       "other                         53\n",
       "from_this_person_to_poi       59\n",
       "deferral_payments            107\n",
       "long_term_incentive           80\n",
       "restricted_stock_deferred    128\n",
       "from_messages                 59\n",
       "exercised_stock_options       44\n",
       "shared_receipt_with_poi       59\n",
       "total_stock_value             20\n",
       "poi                            0\n",
       "from_poi_to_this_person       59\n",
       "total_be                       0\n",
       "fraction_to_poi               59\n",
       "fraction_from_poi             59\n",
       "total_millions                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for missing values in features\n",
    "\n",
    "enron.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loan advances has 142 missing values!  That's definitely a feature we can remove before we run our tests."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Money and Messages Regression Model</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmo4cob\\AppData\\Local\\conda\\conda\\envs\\tf\\lib\\site-packages\\seaborn\\regression.py:546: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Money & Messages')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 786.35x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='bonus', y='fraction_to_poi', hue='poi', data=enron, palette='Set1',size=10,markers=['x','o'])\n",
    "plt.title('Money & Messages', fontsize=18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "<a id='TSS'></a>\n",
    "\n",
    "## Transform, Select, and Scale\n",
    "\n",
    "Now let's transform, select, and scale our features.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmo4cob\\AppData\\Local\\conda\\conda\\envs\\tf\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import pickle\n",
    "\n",
    "from feature_format import featureFormat, targetFeatureSplit\n",
    "from tester import dump_classifier_and_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['name',\n",
       " 'deferred_income',\n",
       " 'expenses',\n",
       " 'bonus',\n",
       " 'director_fees',\n",
       " 'salary',\n",
       " 'email_address',\n",
       " 'total_payments',\n",
       " 'restricted_stock',\n",
       " 'loan_advances',\n",
       " 'to_messages',\n",
       " 'other',\n",
       " 'from_this_person_to_poi',\n",
       " 'deferral_payments',\n",
       " 'long_term_incentive',\n",
       " 'restricted_stock_deferred',\n",
       " 'from_messages',\n",
       " 'exercised_stock_options',\n",
       " 'shared_receipt_with_poi',\n",
       " 'total_stock_value',\n",
       " 'poi',\n",
       " 'from_poi_to_this_person',\n",
       " 'total_be',\n",
       " 'fraction_to_poi',\n",
       " 'fraction_from_poi',\n",
       " 'total_millions']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enron.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['poi',\n",
       " 'deferred_income',\n",
       " 'expenses',\n",
       " 'bonus',\n",
       " 'director_fees',\n",
       " 'salary',\n",
       " 'email_address',\n",
       " 'total_payments',\n",
       " 'restricted_stock',\n",
       " 'loan_advances',\n",
       " 'to_messages',\n",
       " 'other',\n",
       " 'deferral_payments',\n",
       " 'long_term_incentive',\n",
       " 'restricted_stock_deferred',\n",
       " 'shared_receipt_with_poi',\n",
       " 'total_stock_value',\n",
       " 'from_poi_to_this_person',\n",
       " 'total_be',\n",
       " 'fraction_to_poi',\n",
       " 'fraction_from_poi',\n",
       " 'total_millions']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_full_list = enron.columns.tolist()\n",
    "features_full_list.pop(0) #take out 'name'\n",
    "features_full_list.pop(19) #take out 'email_address'\n",
    "features_full_list.pop(11) #take out 'loan_advances' because of missing values\n",
    "features_full_list.pop(15) #take out 'director_fees' because of missing values\n",
    "features_full_list.pop(14) #take out 'poi' for now and add to beginning of list\n",
    "features_list = ['poi']\n",
    "for n in features_full_list:\n",
    "    features_list.append(n)\n",
    "features_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bonus': 97343619,\n",
       " 'deferral_payments': 32083396,\n",
       " 'deferred_income': -27992891,\n",
       " 'director_fees': 1398517,\n",
       " 'email_address': 'NaN',\n",
       " 'exercised_stock_options': 311764000,\n",
       " 'expenses': 5235198,\n",
       " 'from_messages': 'NaN',\n",
       " 'from_poi_to_this_person': 'NaN',\n",
       " 'from_this_person_to_poi': 'NaN',\n",
       " 'loan_advances': 83925000,\n",
       " 'long_term_incentive': 48521928,\n",
       " 'other': 42667589,\n",
       " 'poi': False,\n",
       " 'restricted_stock': 130322299,\n",
       " 'restricted_stock_deferred': -7576788,\n",
       " 'salary': 26704229,\n",
       " 'shared_receipt_with_poi': 'NaN',\n",
       " 'to_messages': 'NaN',\n",
       " 'total_payments': 309886585,\n",
       " 'total_stock_value': 434509511}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Remove outliers that corrupt the data\n",
    "enron_dict.pop('TOTAL', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bonus': 'NaN',\n",
       " 'deferral_payments': 'NaN',\n",
       " 'deferred_income': 'NaN',\n",
       " 'director_fees': 'NaN',\n",
       " 'email_address': 'NaN',\n",
       " 'exercised_stock_options': 'NaN',\n",
       " 'expenses': 'NaN',\n",
       " 'from_messages': 'NaN',\n",
       " 'from_poi_to_this_person': 'NaN',\n",
       " 'from_this_person_to_poi': 'NaN',\n",
       " 'loan_advances': 'NaN',\n",
       " 'long_term_incentive': 'NaN',\n",
       " 'other': 'NaN',\n",
       " 'poi': False,\n",
       " 'restricted_stock': 'NaN',\n",
       " 'restricted_stock_deferred': 'NaN',\n",
       " 'salary': 'NaN',\n",
       " 'shared_receipt_with_poi': 'NaN',\n",
       " 'to_messages': 'NaN',\n",
       " 'total_payments': 'NaN',\n",
       " 'total_stock_value': 'NaN'}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove datapoints that create noise\n",
    "\n",
    "enron_dict.pop('LOCKHART EUGENE E',0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#take out all 'loan_advances' because of missing values\n",
    "\n",
    "for name in enron_dict:\n",
    "    enron_dict[name].pop('loan_advances',0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create new feature(s)\n",
    "\n",
    "#add fraction of emails from and to poi\n",
    "#idea for this added feature taken from course materials\n",
    "\n",
    "def computeFraction( poi_messages, all_messages ):\n",
    "    \"\"\" given a number messages to/from POI (numerator) \n",
    "        and number of all messages to/from a person (denominator),\n",
    "        return the fraction of messages to/from that person\n",
    "        that are from/to a POI\n",
    "   \"\"\"\n",
    "    fraction = 0.\n",
    "    if poi_messages != 'NaN' and all_messages != 'NaN':\n",
    "        fraction = float(poi_messages)/all_messages\n",
    "\n",
    "\n",
    "    return fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "for name in enron_dict:\n",
    "\n",
    "    data_point = enron_dict[name]\n",
    "\n",
    "    from_poi_to_this_person = data_point[\"from_poi_to_this_person\"]\n",
    "    to_messages = data_point[\"to_messages\"]\n",
    "    fraction_from_poi = computeFraction( from_poi_to_this_person, to_messages )\n",
    "    \n",
    "    enron_dict[name][\"fraction_from_poi\"] = fraction_from_poi\n",
    "  \n",
    "    from_this_person_to_poi = data_point[\"from_this_person_to_poi\"]\n",
    "    from_messages = data_point[\"from_messages\"]\n",
    "    fraction_to_poi = computeFraction( from_this_person_to_poi, from_messages )\n",
    "\n",
    "    enron_dict[name][\"fraction_to_poi\"] = fraction_to_poi\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add total_be to dictionary\n",
    "\n",
    "for name in enron_dict:\n",
    "    data_point = enron_dict[name]\n",
    "    \n",
    "    bonus = data_point['bonus']\n",
    "    if bonus == 'NaN':\n",
    "        bonus = 0.0\n",
    "    options = data_point['exercised_stock_options']\n",
    "    if options == 'NaN':\n",
    "        options = 0.0\n",
    "    total = bonus+options\n",
    "\n",
    "    enron_dict[name]['total_be'] = total\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add total compensation in millions to dataset\n",
    "\n",
    "for name in enron_dict:\n",
    "    data_point = enron_dict[name]\n",
    "    \n",
    "    total_payments = data_point['total_payments']\n",
    "    if total_payments == 'NaN':\n",
    "        total_payments = 0.0\n",
    "    total_stock = data_point['total_stock_value']\n",
    "    if total_stock == 'NaN':\n",
    "        total_stock = 0.0\n",
    "    total = (total_payments + total_stock)/1000000\n",
    "\n",
    "    enron_dict[name]['total_millions'] = total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>SELECT FEATURES</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've selected a few lists that may be useful in training our classifiers.  Each of the features selected may be able to give us some insight into the compensation and behavior of a POI.  The total compensation (total_millions) shows us that, on average, POIs are compensated more highly than non-POIs.  The same holds true for individual payments, like salary and bonus.  And, when it comes to stock behavior, POIs are more active in their exercising of stock options(exercised_stock_options.)  Other features, like from_messages, show a kind of pattern in e-mail behavior.  POIs do not send many messages.  However, the ones they do send are often to other POIs(fraction_to_poi).  These are all features we'll test before making our final feature selection.\n",
    "<br><br>\n",
    "<b>We will start out with our full list as a baseline and test that against our selected lists' metrics in order to find our final feature list for our POI identifier.  Our lists were chosen based on our stats and plots generated in our analysis.  Those features that showed a greater overall difference between POI and non-POI stats were chosen</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['poi', 'deferred_income', 'expenses', 'bonus', 'director_fees', 'salary', 'email_address', 'total_payments', 'restricted_stock', 'loan_advances', 'to_messages', 'other', 'deferral_payments', 'long_term_incentive', 'restricted_stock_deferred', 'shared_receipt_with_poi', 'total_stock_value', 'from_poi_to_this_person', 'total_be', 'fraction_to_poi', 'fraction_from_poi', 'total_millions']\n"
     ]
    }
   ],
   "source": [
    "print (features_list) #list of features available for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final List ['poi', 'bonus', 'exercised_stock_options', 'fraction_to_poi']\n"
     ]
    }
   ],
   "source": [
    "### Select what features to use\n",
    "first_list = ['poi','total_millions','fraction_to_poi','from_messages']\n",
    "second_list = ['poi','total_be','fraction_to_poi','from_messages']\n",
    "third_list = ['poi','salary','bonus','fraction_to_poi','from_messages']\n",
    "fourth_list = ['poi','bonus','exercised_stock_options','fraction_to_poi']\n",
    "features_final_list = fourth_list\n",
    "print (\"Final List\", features_final_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"Algorithm\"></a>\n",
    "\n",
    "## Algorithm Selection\n",
    "\n",
    "<a id='Metrics'></a>\n",
    "\n",
    "### Evaluation Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Evaluation metrics\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "from sklearn.cross_validation import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Classifiers\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def test_list(classifier, feature_list, enron_dict):\n",
    "    \n",
    "    my_dataset = enron_dict\n",
    "    data = featureFormat(my_dataset, feature_list, sort_keys = True) \n",
    "    labels, features = targetFeatureSplit(data) \n",
    "    \n",
    "    X = np.array(features)\n",
    "    y = np.array(labels)\n",
    "    sss = StratifiedShuffleSplit(labels, n_iter=1000, test_size=0.3, random_state=42)      \n",
    "    for train_index, test_index in sss:\n",
    "        features_train, features_test = X[train_index], X[test_index]\n",
    "        labels_train, labels_test = y[train_index], y[test_index]\n",
    "        \n",
    "    clf = classifier\n",
    "    clf.fit(features_train,labels_train)\n",
    "    pred = clf.predict(features_test)\n",
    "    \n",
    "    if classifier == DecisionTreeClassifier():\n",
    "        return {'Accuracy': accuracy_score(labels_test,pred),'Precision': precision_score(labels_test,pred),\n",
    "                'Recall': recall_score(labels_test,pred), 'Feature Importance': clf.feature_importances_}\n",
    "    \n",
    "    return {'Accuracy': accuracy_score(labels_test,pred),'Precision': precision_score(labels_test,pred),\n",
    "            'Recall': recall_score(labels_test,pred)}\n",
    "    \n",
    "    \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Test'></a>\n",
    "\n",
    "### Performance:  Accuracy, Precision, and Recall\n",
    "\n",
    "Below, we will test each list using three different algorithms: Naive Bayes, Decision Tree, and KNearest Neighbors.  \n",
    "- Our accuracy score will show us our ratio of correctly predicted observation to the total observations.  \n",
    "    Accuracy = TP+TN/TP+FP+FN+TN\n",
    "- Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.  \n",
    "    Precision = TP/TP+FP\n",
    "- And, recall is the ratio of correctly predicted positive observations to all observations in the class.  \n",
    "    Recall = TP/TP+FN\n",
    "\n",
    "When trying to identify POIs, we want to see as few falsely identified positives.  We don't want to falsely identify anyone as a POI.  So, I'd say precision is a bit more important here.  Let's see how each list does using each classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['poi', 'total_millions', 'fraction_to_poi', 'from_messages']\n",
      "GaussianNB:  {'Accuracy': 0.9302325581395349, 'Recall': 0.4, 'Precision': 1.0}\n",
      "DecisionTree:  {'Accuracy': 0.7674418604651163, 'Recall': 0.2, 'Precision': 0.14285714285714285}\n",
      "KNeighbors:  {'Accuracy': 0.8837209302325582, 'Recall': 0.0, 'Precision': 0.0}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmo4cob\\AppData\\Local\\conda\\conda\\envs\\tf\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "#first list\n",
    "print (first_list)\n",
    "print ('GaussianNB: ', test_list(GaussianNB(),first_list,enron_dict))\n",
    "print ('DecisionTree: ', test_list(DecisionTreeClassifier(),first_list,enron_dict))\n",
    "print ('KNeighbors: ', test_list(KNeighborsClassifier(),first_list,enron_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['poi', 'total_be', 'fraction_to_poi', 'from_messages']\n",
      "GaussianNB:  {'Accuracy': 0.7948717948717948, 'Recall': 0.3333333333333333, 'Precision': 0.3333333333333333}\n",
      "DecisionTree:  {'Accuracy': 0.7692307692307693, 'Recall': 0.16666666666666666, 'Precision': 0.2}\n",
      "KNeighbors:  {'Accuracy': 0.8205128205128205, 'Recall': 0.3333333333333333, 'Precision': 0.4}\n"
     ]
    }
   ],
   "source": [
    "#second list\n",
    "print (second_list)\n",
    "print ('GaussianNB: ', test_list(GaussianNB(),second_list,enron_dict))\n",
    "print ('DecisionTree: ', test_list(DecisionTreeClassifier(),second_list,enron_dict))\n",
    "print ('KNeighbors: ', test_list(KNeighborsClassifier(),second_list,enron_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['poi', 'salary', 'bonus', 'fraction_to_poi', 'from_messages']\n",
      "GaussianNB:  {'Accuracy': 0.7647058823529411, 'Recall': 0.4, 'Precision': 0.2857142857142857}\n",
      "DecisionTree:  {'Accuracy': 0.7941176470588235, 'Recall': 0.8, 'Precision': 0.4}\n",
      "KNeighbors:  {'Accuracy': 0.8823529411764706, 'Recall': 0.4, 'Precision': 0.6666666666666666}\n"
     ]
    }
   ],
   "source": [
    "#third list\n",
    "print (third_list)\n",
    "print ('GaussianNB: ', test_list(GaussianNB(),third_list,enron_dict))\n",
    "print ('DecisionTree: ', test_list(DecisionTreeClassifier(),third_list,enron_dict))\n",
    "print ('KNeighbors: ', test_list(KNeighborsClassifier(),third_list,enron_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['poi', 'bonus', 'exercised_stock_options', 'fraction_to_poi']\n",
      "GaussianNB:  {'Accuracy': 0.7368421052631579, 'Recall': 0.2, 'Precision': 0.14285714285714285}\n",
      "DecisionTree:  {'Accuracy': 0.7105263157894737, 'Recall': 0.4, 'Precision': 0.2}\n",
      "KNeighbors:  {'Accuracy': 0.9210526315789473, 'Recall': 0.6, 'Precision': 0.75}\n"
     ]
    }
   ],
   "source": [
    "#fourth_list\n",
    "print (fourth_list)\n",
    "print ('GaussianNB: ', test_list(GaussianNB(),fourth_list,enron_dict))\n",
    "print ('DecisionTree: ', test_list(DecisionTreeClassifier(),fourth_list,enron_dict))\n",
    "print ('KNeighbors: ', test_list(KNeighborsClassifier(),fourth_list,enron_dict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With an accuracy score of 92%, a precision score of 75%, and a recall score of 60% ...\n",
    "\n",
    "Our Final List includes <b>'poi', 'bonus', 'exercised stock options', and 'fraction to poi'</b>\n",
    "<br>\n",
    "Our Final Classifier with be <b>KNeighbors</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='Validating'></a>\n",
    "\n",
    "## Validation\n",
    "\n",
    "We've already implemented our validation process, but here we will discuss its importance.  Without validating our classifier using training/testing data, we have no way of measuring its accuracy and reliability.  Training and testing the classifier against the same data will only yield overfitting results.  This is why validation is important.  By using StratifiedShuffleSplit to split our data into training and testing data, we can make sure that our classes are allocated by the same ratio set for training/testing and that each datapoint in the class is randomly selected.  Because of our small dataset, setting the iterations to 1000 will give us more reliable results in the end, as we will have trained and tested on almost all of our datapoints.  The only downside is the run time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Store to my_dataset for easy export below.\n",
    "my_dataset = enron_dict\n",
    "\n",
    "### Extract features and labels from dataset for local testing\n",
    "data = featureFormat(my_dataset, features_final_list, sort_keys = True)\n",
    "labels, features = targetFeatureSplit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9210526315789473\n"
     ]
    }
   ],
   "source": [
    "#Validation using StratifiedShuffleSplit in order to evenly dispurse the classes between training and test data\n",
    "X = np.array(features)\n",
    "y = np.array(labels)\n",
    "sss = StratifiedShuffleSplit(labels, n_iter=1000, test_size=0.3, random_state=42)      \n",
    "for train_index, test_index in sss:\n",
    "    features_train, features_test = X[train_index], X[test_index]\n",
    "    labels_train, labels_test = y[train_index], y[test_index]\n",
    "\n",
    "#check for accuracy\n",
    "    \n",
    "clf = KNeighborsClassifier()\n",
    "clf.fit(features_train, labels_train)\n",
    "\n",
    "print (clf.score(features_test, labels_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "<a id=\"Tuning\"></a>\n",
    "\n",
    "## Tuning\n",
    "\n",
    "Even though we've all but settled on KNeighbors, let's see if tuning the parameters of our Decision Tree Classifier would make a difference.  Tuning parameters can sometimes significantly change our performance metrics outcome.  Parameters can control for overfitting/underfitting, so tuning them can certainly change the metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': None,\n",
       " 'max_leaf_nodes': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_impurity_split': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'presort': False,\n",
       " 'random_state': None,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DecisionTreeClassifier().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Decision Tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "#set min_samples_split to 3 and increase until no longer helpful\n",
    "clf = DecisionTreeClassifier(min_samples_split=9)\n",
    "clf.fit(features_train,labels_train)\n",
    "pred = clf.predict(features_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Accuracy': 0.7631578947368421, 'Recall': 0.4, 'Precision': 0.25}\n"
     ]
    }
   ],
   "source": [
    "\"\"\"  Can we do better?  [0.71052631578947367, 0.20000000000000001, 0.40000000000000002]\"\"\"\n",
    "\n",
    "#print performance metrics\n",
    "print ({'Accuracy': accuracy_score(labels_test,pred),'Precision': precision_score(labels_test,pred),\n",
    "       'Recall': recall_score(labels_test,pred)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scikitplot as skplt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d436f49908>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot confusion matrix\n",
    "skplt.metrics.plot_confusion_matrix(labels_test, pred, normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How about our KNeighbors Classifier?  What are the parameters?  If we changed any of them, would it make a difference?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto',\n",
       " 'leaf_size': 30,\n",
       " 'metric': 'minkowski',\n",
       " 'metric_params': None,\n",
       " 'n_jobs': 1,\n",
       " 'n_neighbors': 5,\n",
       " 'p': 2,\n",
       " 'weights': 'uniform'}"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNeighborsClassifier().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Accuracy': 0.9473684210526315, 'Recall': 0.6, 'Precision': 1.0}\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Can we do better without overfitting?  [0.92105263157894735, 0.75, 0.59999999999999998]\"\"\"\n",
    "\n",
    "#set n_neighbors to 2 and increase until metrics show overfitting\n",
    "clf = KNeighborsClassifier(n_neighbors=6)\n",
    "clf.fit(features_train,labels_train)\n",
    "pred = clf.predict(features_test)\n",
    "\n",
    "print ({'Accuracy': accuracy_score(labels_test,pred),'Precision': precision_score(labels_test,pred),\n",
    "       'Recall': recall_score(labels_test,pred)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "^OVERFIT!  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Can we still do better?  [0.92105263157894735, 0.75, 0.59999999999999998]\"\"\"\n",
    "\n",
    "#set n_neighbors to 2,3, and 4\n",
    "\n",
    "def test_param(n):\n",
    "    clf = KNeighborsClassifier(n_neighbors=n)\n",
    "    clf.fit(features_train,labels_train)\n",
    "    pred = clf.predict(features_test)\n",
    "\n",
    "    return [accuracy_score(labels_test,pred), precision_score(labels_test,pred), recall_score(labels_test,pred)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2:  [0.9210526315789473, 0.75, 0.6]\n",
      "3:  [0.8947368421052632, 0.6, 0.6]\n",
      "4:  [0.9210526315789473, 0.75, 0.6]\n"
     ]
    }
   ],
   "source": [
    "print (\"2: \", test_param(2))\n",
    "print (\"3: \", test_param(3))\n",
    "print (\"4: \", test_param(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using GridSearchCV to find the best parameters for KNeighbors, our estimator gives us the same results.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10]), 'leaf_size': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]), 'algorithm': ('auto', 'ball_tree', 'kd_tree', 'brute')},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "k = np.arange(10)+1\n",
    "leaf = np.arange(30)+1\n",
    "params = {'algorithm': ('auto', 'ball_tree', 'kd_tree', 'brute'),'leaf_size': leaf,'n_neighbors': k}\n",
    "\n",
    "clf_params = GridSearchCV(KNeighborsClassifier(), params, cv=5)\n",
    "clf_params.fit(features_train,labels_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=1, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_params.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see what kind of results we get using the parameters \"suggested\" by GridSearchCV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Accuracy': 0.9210526315789473, 'Recall': 0.6, 'Precision': 0.75}\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(algorithm='auto', leaf_size=1, metric='minkowski',\n",
    "                           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
    "                           weights='uniform')\n",
    "clf.fit(features_train,labels_train)\n",
    "pred = clf.predict(features_test)\n",
    "\n",
    "print ({'Accuracy': accuracy_score(labels_test,pred),'Precision': precision_score(labels_test,pred),\n",
    "       'Recall': recall_score(labels_test,pred)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the same results as our default settings, so that's what we'll use.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Top](#top)\n",
    "\n",
    "<a id=\"Analysis\"></a>\n",
    "\n",
    "### FINAL FEATURES and ALGORITHM SELECTION\n",
    "\n",
    "Our final features, based on our feature analysis and testing, will be:\n",
    "\n",
    "- Bonus\n",
    "- Exercised Stock Options\n",
    "- Fraction to POI\n",
    "\n",
    "The KNeighbors Classifier, even without any parameter tuning, had a higher accuracy score than the Decision Tree Classifier with its min_samples_split tuned to 9.  So, we're going to use KNeighbors in our POI identifier.  KNearest Neighbors will help us zero in on pockets of POIs/non-POIs within our testing data.  The features I selected work well with this particular classifier because 'bonus' and 'exercised stock options' are good for training the algorithm to pick up on POI compensation trends, and 'fraction_to_poi' will help our algorithm pick up on POI e-mail behavior.  I narrowed it down to three features so as not to create noise.  When running our longer lists of features, we saw our precision and recall drop to zero, so cutting down our features to 3 was the strategy moving forward.  As we had already gotten rid of features with lots of missing data, it was easier to narrow them down and test each list against our chosen classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9210526315789473\n",
      "0.75\n",
      "0.6\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier()\n",
    "clf.fit(features_train,labels_train)\n",
    "pred = clf.predict(features_test)\n",
    "\n",
    "print (accuracy_score(labels_test,pred))\n",
    "print (precision_score(labels_test,pred))\n",
    "print (recall_score(labels_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2d4371e0ba8>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/y0X6lbJvvvZKVGdkZWbineAxGu+h1qagoABGKEWW6SsolVT9u2osFAK5v6CgoIAJsbrKT5MVUlMopGYGjoZ01aZN/Zh8l9TVxGWpUqkpFFLzqisoqy7Sl3qbEImonpFIy16ayg2MCZGI9EMiKXtpKjcwJkQi0g+JESDVcOotGH60CBMiEemHRKLllJktRCJqLHjKTESkwk4VIiIVthCJiFTYQiQiUpFKtfQyMyESUaOhpYVYBybfYkIkIv2QSspemsoNjAmRiPSD1xCJiFTYy0xEpKKtU0XKFiIRNRY8ZSYiKqfllBk8ZSaixoItRCIiFQm0dKroLZIqMSESkX6whUhEpCLVMkGspjI9YUIkIv1gC5GISIXXEImIynFyByKiMrx1j4hIhdcQiYjKSKRSSDTcr6ypTF+YEIlIL8rOmKs+La4DZ8xMiESkJxJo7klmQiSixkIikWhpIRo+IzIhEpFeSKAlIdaBJiITIhHphVQqhaCh40TKThUiajR4DZGISEXLNcS60M1s+DYqETUK5Z0qml7VkZiYCF9fXwwcOBDx8fEVyi9evIg33ngD/v7+mDhxIvLy8rRukwmRiPSiJhNiVlYWVq5cic2bNyMhIQHbtm1DamqqWp2FCxciNDQU+/btQ/v27fHFF19o3S4TIhHpRfnA7KpfZfUyMzNx69YttdezrbsTJ07A3d0d1tbWMDc3h4+PD5KSktTqKJVKPHr0CADw5MkTmJqaao2R1xCJSC8kEgkkUu3jEMeMGVOhLCQkBFOnThWXs7OzYWNjIy7b2tri3Llzau8JDw/HuHHjsGjRIpiZmWH79u1aY2RCJCK90HVgdnx8PGQymVqZpaWl2rJSqVTbliAIasuFhYWYPXs2Nm7cCDc3N3z11VcICwvD+vXrNcbIhEhEeqFrQpTJZLC3t9e4LZlMhtOnT4vLcrkctra24vLVq1fRtGlTuLm5AQDefvtt/Pe//9UaI68hEpF+SHR46ah37944efIkcnJy8OTJEyQnJ6NPnz5iuYODAzIzM/Hnn38CAA4fPgxXV1et22ULkYj0oibvZbazs8P06dMRHByMkpISvPnmm3Bzc8OECRMQGhoKV1dXLF68GNOmTYMgCGjZsiUWLVqkdbtMiESkHzU8MNvPzw9+fn5q6+Li4sSfPT094enpWa1tMiESkV5IJZrvZZZwxmwiajR4LzMRURmJlseQ1oFbmZkQiUg/JFqeuscJYomo0WBCpAoGebyM6Kn+aNrEGBeu3cak+ZuR/6hQrc7kUZ6Y9HYfPCkqwZXrmZi2eDse5D3G5uXj0aHtX7crObZuiZ/PpGLktHX6PoxG4/++34+PZkegqLgILq5uWLv+iwp3TVRVJzc3F5PeG4+rVy5DqVRiTNBYzJwVZqAjMTyJVAJounVPQ5m+1Gq3ztGjR+Hn5wcfHx+EhoaioKDgb9VpKJ5vYYF18wMRMGsDOo+IwfVb9xET6q9Wp0+PF/Dhf7zhOzEW7qOWIOnYRayeEwAAGD3rC7iPWgL3UUswJXozcgueYPribYY4lEZBLpdj4rvvYMv2XTh38Qrat++AOZHhOteZP3cO2rSxx2+/X8Cxk78ibt3n+N/Jk4Y4lDqhpqf/qg21lhBzcnIQERGB2NhYHDhwAG3btsXHH39c7ToNibd7J/x28QbSbsoBAOt3/IxRg3uq1en2UjscOXUFt7MfAgD2Hk6Bbx8XmBgbiXVMjI0QFxOEWct34lbWQ/0dQCNz6GAyuvfoiY4vvAAAeG/iZGzdEg9BEHSqs2Llf7FkWdm/58y7d1FcVAQrKyv9H0idoS0ZNuCEeOzYMbi6usLR0REAEBAQgMTERLV/TLrUAYC8vLwK0wFlZmbWVui1xl7WQi2B3c5+CKvmZmje7K9piX69kI6+PV9Eu1YtAADBw9zRtIkJWlo3E+v8Z8SruCvPxb4f1Gf3oJp161YG7O3bistt7O2Rl5eH/Px8nepIJBIYGxvjneBAdO/igtc8++JFZ2e9HkNd0qhbiJmZmWozVshkMhQUFIjzk+laBwC+/vpr9O/fX+1V2RRBdZ1EIqmQ7AFAoVCKPx8/m4aF6/8PW1e8h2Px/w9KpYD7Dx+huEQh1pk6xgtL4g7oJebGTHhmRpVyRkZG1arz1aZvcSvzHh7k5GDRgujaCbY+qMF7mWtLrXWqPDs9T7mnn6ylSx0AGDt2LEaMGKG2LjMzs94lxYzMB+jp6igut7G1Qk7uIzwuLBbXWZg3xc+/XcPXCWXXmlrbWOGj94ciJ7fsj0RnZ3sYG0nx82/X9Bp7Y9S2bTv8+sspcfn27dto0aIFmjVrplOdg8kH8LKLK1q3bg0LCwu89XYAEvbs0usx1CXaepnrwkDEWmshtmrVCtnZ2eJyVlYWrKysYG5uXq06QNlcaPb29mqvZ+dLqw8On/wDr7g6wqldWU/xu2++hu+Onler08rGCslxH4in0f/vXR/sSPprmqPXunfE0V+v6i/oRqz/gIH45dT/kHqt7I/PhvVrMdRvmM51du3YjkUx8yEIAoqKirBr53Z49vXS70HUIVIpIJVKNLwMHWEtJkQPDw+kpKQgPT0dALB161b079+/2nUaEvmDAkyc9y02Lx+Ps7ui8HLH1gj/ZDe6/asd/re1rGfy2o1sfPzVQfy0aSZS9sxB0ybGiPg0QdyGUztb3Lhz31CH0KjY2tpi3YavMPrtN9HF9SVcvHAeS5avwG+nT6NX9y4a6wDAkuUrkJuXix5dXdG7V3d07dYdIaEfGPKQDKo+XEOUCJVd1KohP/74I1asWIGSkhK0a9cOS5cuRUZGBqKiorB3794q61hbW2vd9q1bt9C/f3/cNvWAQmpWW4dANezBr58ZOgSqhtu3b8F3YH8cPnxY66StVSn/XRUGfASYt6y64uP7kByM/kf7+qdqdWB2ZdPvWFtbi8mwqjpE1PBIoOUaYh3oVeGdKkSkH1ryoWD4fMiESET6IZVqfuqeIJVAWWWpfjAhEpFeMCESEaloG4ZYBy4hMiESkX5oHVpTB4bdMCESkZ5oTohCHWgiMiESkV7Ugzv3mBCJSD/Kb9GruoLhMyITIhHpRVkLkQ+ZIiLiKTMRUTltvcx1YXIHJkQi0gu2EImIRNqm+DJ8RmRCJCK90NbLLLCXmYgaC54yExGpsFOFiEiFLUQiIhW2EImIVJgQiYhUJBLNvcxKJkQiaix4DZGISKU+TO5Qaw+qJyJ6WnkLUdOrOhITE+Hr64uBAwciPj6+Qvmff/6JoKAg+Pv7Y/z48cjNzdW6TSZEItILqUSi9aWrrKwsrFy5Eps3b0ZCQgK2bduG1NRUsVwQBEyePBkTJkzAvn378NJLL2H9+vVat1vlKfOCBQs0vjEqKkrn4ImIJFpu3St/Il9mZmaFMktLS1haWorLJ06cgLu7O6ytrQEAPj4+SEpKQkhICADg4sWLMDc3R58+fQAAkyZNQl5entYYq0yI5TsiIqoJUmieFLv8dHXMmDEVykJCQjB16lRxOTs7GzY2NuKyra0tzp07Jy7fvHkTzz//PCIjI/HHH3+gQ4cOmDNnjtYYq0yI5ZkWAAoLC3Hjxg288MILKCoqgpmZmdYNExE9TddxiPHx8ZDJZGplT7cOAUCpVKptSxAEteXS0lL88ssv+Pbbb+Hq6opPP/0US5YswZIlSzTGqPUaYkpKCry9vTFx4kRkZ2ejb9++OHPmjLa3ERGp0bVTRSaTwd7eXu31bEKUyWSQy+Xislwuh62trbhsY2MDBwcHuLq6AgCGDh2q1oKsitaEuHTpUmzcuBHW1taQyWRYtmwZFi5cqNMHQERUTqLDf7rq3bs3Tp48iZycHDx58gTJycni9UIA6Nq1K3JycnD58mUAwJEjR/Dyyy9r3a7WhFhYWIiOHTuKy56enlAoFDoHTkQElF0/1PbSlZ2dHaZPn47g4GAMHz4cQ4cOhZubGyZMmIDz58/D1NQUq1evRlRUFIYMGYJTp04hPDxc63a1Dsw2NjZGbm6ueH7+559/6h41EZGKrr3MuvLz84Ofn5/auri4OPHnzp07Y+fOndXaptaEOHnyZAQGBkIul2PGjBk4fvw4oqOjq7UTIiJtYw2rMw6xtmhNiP369UOHDh1w/PhxKJVKTJkyBU5OTvqIjYgakAZzL3NpaSmUSiWMjY1hbMzbn4mo+iRaHjJVnU6V2qK1U2XXrl0IDg7G+fPncfr0aYwZMwYHDhzQR2xE1IDU9L3MtUFrc2/jxo3Ys2ePOMbnzp07mDhxInx8fGo9OCJqOKQSwEjjNUQ9BlMFrQnRxMREbcBj69atYWJiUqtBEVHDU69nzL548SIAwNnZGdHR0Xj77bdhZGSE3bt3o1u3bnoLkIgaBm1jDet0C/HpG6kB4OjRo+LPEomEs90QUbXU6xbikSNH9BkHETVwDWLYTU5ODvbt24dHjx5BEAQolUrcuHEDK1as0Ed8RNRA1OsWYrlp06bB1NQUqamp6N27N06cOIHu3bvrIzYiakCkEgmMNFworAt3qmgdh3jnzh2sX78effr0QWBgILZs2cL7mYmo2iQ6vAxNa0J8/vnnAQCOjo64evUq7OzsUFpaWuuBEVHDUpPPVKktWk+ZW7ZsiQ0bNqBLly6IjY2FhYUFCgsL9REbETUg9aFTRWsLMTo6Gk2aNEGPHj3g4uKCVatWYebMmfqIjYgakPJOFU0vQ9OphRgcHAwAmDVrFmbNmlXrQRFRA6TtfmXD58OqE2LXrl01Zmw+V4WIqsNIqrmXWVOZvlSZEL/77jt9xvG3zYmZACsbmfaKVCcMX3/K0CFQNZTkybVX0pEEmscaGj4dakiIbdq00WccRNTASaG500Jrh4YecLZXItKLBnGnChFRTZBome2mDuRD3VqphYWFuHLlCgRBwJMnT2o7JiJqgMo7VTS9DE1rQvz999/h7e2NiRMnIisrC3379mUPMxFVW00+l7nWYtRWYdmyZdi4cSOsra0hk8mwbNkyLFy4UB+xEVEDUh+eqaI1IRYWFqJjx47isqenJxQKRa0GRUQNj0TLfcz1olPF2NgYubm5YrCc6YaI/o4GMexm8uTJCAwMxL179zBjxgwcP34c0dHR+oiNiBqQsoHZmssNTWtC7NevHzp06IDjx49DqVRiypQpcHJy0kdsRNSA1Otb98o9fPgQVlZW8PX1VVtnbW1dq4ERUcNSH8Yhak2I7u7uFS522tjY4Keffqq1oIio4dE2CWy9mCD28uXL4s/FxcX47rvvcP369VoNiogangYxQezTmjRpgtdffx3Hjx+vrXiIqIGqDwOzdbqGWE4QBFy4cAF5eXm1GhQRNTwS1X+ayg1N52uIgiAAKJtBe/bs2bUeGBE1LEYSwFjDOamR4fOh9oS4c+dOuLi46CMWImrA6sP0X1qvIfIZKkRUExrENURnZ2ckJiaie/fuMDc3F9dzHCIRVUd96GXWmhAPHz6MpKQktXUSiQR//PFHrQVFRA1P2cBsTafM1dteYmIiPv/8c5SWlmLs2LEYM2ZMpfWOHj2K6OhoHDlyROs2q0yIxcXFaNKkCc6fP1+9KImIKmEkLXtpKtdVVlYWVq5cid27d6NJkyYYNWoUevXqpTYzFwDcu3cPS5cu1Xm7VYbw9ttv6x4dEZEWUki0vnR14sQJuLu7w9raGubm5vDx8alwJgsAUVFRCAkJ0Xm7VbYQy4fZEBHVBF2vIWZmZlYos7S0hKWlpbicnZ0NGxsbcdnW1hbnzp1Te8+mTZvwr3/9C507d9Y5xioTYlFRES5dulRlYnz55Zd13gkRkQRaJndQ/b+ya4EhISGYOnWquKxUKtWG6QiCoLZ89epVJCcnY+PGjZUm2KpUmRAzMjIwderUShOiRCLB4cOHdd4JEZGukzvEx8dDJpOplT3dOgQAmUyG06dPi8tyuRy2trbiclJSEuRyOd544w2UlJQgOzsbo0ePxubNmzXGWGVC7NixIxISEjS+mYhIV7qeMstkMtjb22vcVu/evREbG4ucnByYmZkhOTkZMTExYnloaChCQ0MBALdu3UJwcLDWZAjUjVm7iagRqMnHkNrZ2WH69OkIDg7G8OHDMXToULi5uWHChAn/aGRMlS3EHj0fu4DMAAAPfUlEQVR6/O2NEhE9SwLNLbDqjsv28/ODn5+f2rq4uLgK9ezt7XUagwhoSIhRUVHVDI+IqGr14V5mrXeqEBHVBAk0twINnw6ZEIlITxrEIwSIiGoCW4hERCoSiQRSDT3JvIZIRI2GFJp7mevCGEAmRCLSC/YyExGp8BoiEZFK2a17NTdBbG1gQiQivZBKJDDisBsiIp4yExGJGsRDpoiIaoK2xwRU5xECtYUJkYj0gi1EIiIVieo/TeWGxoRIRHrBXmYiIhWeMhMRqUigJSHqLZKqMSESkV7wGiIRkYpUovm5zNV4xlStYUIkIr2QaJkxm7PdNHLnjx/B3rXLUFJSDHunTgiMXAqzZs0rrfv7j8nYGDMDnx66oOco6WmvOFjjHfe2MJFKcP3+Y6z84ToelyjU6jg+Z4b3X3OEeRMjKAUBq368jlT5YwNFXHfUh1PmWp2TURAEhIWF4Ysvvqi0/OjRo/Dz84OPjw9CQ0NRUFBQm+HUKfkP7mPTwv+H9xZ9jvlbj+D51u2QsGZZpXWzM65j92eLAEHQc5T0NCtTY8zo1wExSVfx7pZzuJtXhHdebatWp6mxFIv8OmHH2TsI2XEBm0/fQZh3RwNFXLeUnzJrehlarSXEtLQ0jB07FgcOHKi0PCcnBxEREYiNjcWBAwfQtm1bfPzxx7UVTp3zxy8/w/ElN9i2bQ8A6PN6IH5J3gvhmaRXXPgEX82fjjdCZxsiTHpKt7ZWuCovwJ3cIgDA/otZ8HqhZYU6d/OK8OvNXADA/9IfYFHyNb3HWheVTe6g6T/Dq7WEGB8fj5EjR2LQoEGVlh87dgyurq5wdHQEAAQEBCAxMbFCQmioHmTdRQu7VuKytY0MhY/yUfhYvZUcvzQSrw0fDfuOL+k7RHqGjUUTyAuKxWV5QTGaNTWGuYmRuK6NlSlyHpdger/2WPXmy1js10njYOTGpHwcoqaXodXaNcSPPvoIAHD8+PFKyzMzMyGTycRlmUyGgoICPHr0CBYWFmp18/LykJeXV+H99ZlSUFb6L0Aq/euX68dd38DIyBi9h76F+3dv6TM8qoREIqn0qoXiqZXGRhL0bGeFsL1/4Er2I7g7tkD0UGeM3fQ7SpSN4499VTj9lwZKpbLSXiWptGKj9euvv8Znn32mj7D05jm71ki/+Lu4/FCeCfPmVmhqZi6uO/n9ThQXFmLhWF+UlpSguKjs5ykffwVrGztDhN2oyQuK0Mnurz/WzzdrgvzCUhSVKsV19x8VI+NBIa5kPwJQdso8TdIeMqumyHhQqPeY6xLeuqdBq1atkJKSIi5nZWXBysoK5ubmFeqOHTsWI0aMUFuXmZmJMWPG1HqcteWlV17DrtiFyM64Dtu27fFzwmZ0fm2AWp3wL/aKP9+/ewsxgT6Y/fX3+g6VVH7LyMWE3g5obdUUd3KLMMTFDievP1Crc/pGLt7r7YCONuZIlT+GS6vmgABk5hUZKOo6pB40EQ2WED08PLB06VKkp6fD0dERW7duRf/+/Suta2lpCUtLSz1HWLssn3sewbOXY/3s96EoKcHzbRzwn49W4MYf5/DtknAmvjoo90kpPjmShiifF2BsJMXd3EIsP5yGF2yaYVq/9piy/QIePCnB/P+7ipA+7WFqLEWJQkBM0lWUKBr36TJQP4bd6DUhnj9/HlFRUdi7dy9atmyJxYsXIzQ0FCUlJWjXrh2WLl2qz3AMzqV3P7j07qe2rpmldaXJsGUre3x6+KK+QqMq/HozV+xBLndN/ghTtv81PvTC3XxM28Xv6lmc3AHAkiVLxJ9dXV2xd+9fp4Genp7w9PSs7RCIqA6oB2fMvFOFiPSoLmQ9DZgQiUgvpFruZW7UvcxE1LjwlJmIqFw9yIhMiESkJ9ruWDZ8RmRCJCK9qA/Dbmp1+i8ionI1PblDYmIifH19MXDgQMTHx1coP3ToEIYNGwZ/f3+8//77yM3NrWQr6pgQiUgvNE/9Vb0JwLKysrBy5Ups3rwZCQkJ2LZtG1JTU8XygoICzJs3D+vXr8e+ffvg7OyM2NhYrdtlQiQivajJFuKJEyfg7u4Oa2trmJubw8fHB0lJSWJ5SUkJ5s6dCzu7sklQnJ2dcffuXa3b5TVEItILXTuZK5va79n5DLKzs2FjYyMu29ra4ty5c+JyixYtMGBA2WQphYWFWL9+PYKCgrTGyIRIRPqhY0asbBarkJAQTJ06VVx+dvpAQRAqnU4wPz8fU6ZMQadOnSrMmFUZJkQi0gtdZ7uJj49XmzwaQIXZrmQyGU6fPi0uy+Vy2NraqtXJzs7G+PHj4e7ujsjISJ1iZEIkIr2QaHmQVHkDTyaTwd7eXuO2evfujdjYWOTk5MDMzAzJycmIiYkRyxUKBSZNmoTBgwfj/fff1zlGJkQi0p8aGmtoZ2eH6dOnIzg4GCUlJXjzzTfh5uaGCRMmIDQ0FJmZmbh06RIUCoX4oDsXFxcsXLhQ43aZEIlIL2p6glg/Pz/4+fmprYuLiwNQNtXg5cuXqx0jEyIR6UV9uFOFCZGI9KIezO3AhEhEelIPMiITIhHphVSieRJYTT3Q+sKESER6UQ8aiEyIRKQn9SAjMiESkV7wucxEROW0zWhj+HzIhEhE+sFxiEREKjxlJiJSYQuRiEilHnQyMyESkX5IoKWFqLdIqsaESER6UvfbiEyIRKQXUi0TxPLWPSJqPDgOkYioDIfdEBGVq/uXEJkQiUg/6kE+ZEIkIv3gwGwiIhWJRFLpw+SfLjc0JkQi0gueMhMRqfCUmYhIhcNuiIjKcWA2EVEZTu5ARKTCU2YiIhV2qhARqXDYDRFRuXqQEZkQiUgvyvKhpmuIhseESER6wQliiYjK8ZS59igUCgBAfo7cwJFQdZTk3TN0CFQNpfk5AP76ffsnsrOyoCnrlZUbVr1NiHJ5WSLcsXimgSMhavjkcjkcHBz+1nstLCxgZWWFd4LHaK1rZWUFCwuLv7WfmiARBEEw2N7/gcLCQly4cAE2NjYwMjIydDg1KjMzE2PGjEF8fDxkMpmhwyEtGvL3pVAoIJfL4eLiAlNT07+9nYcPH6KgoEBrPQsLC1hbW//t/fxT9baFaGpqih49ehg6jFolk8lgb29v6DBIRw31+/q7LcOnWVtbGzTR6Upq6ACIiOoKJkQiIhUmRCIiFSbEOsjS0hIhISGwtLQ0dCikA35fDUe97WUmIqppbCESEakwIRIRqTAhGtDRo0fh5+cHHx8fhIaGVjpwVZc6pD+CICAsLAxffPFFpeX8vuo3JkQDycnJQUREBGJjY3HgwAG0bdsWH3/8cbXrkP6kpaVh7NixOHDgQKXl/L7qPyZEAzl27BhcXV3h6OgIAAgICEBiYiKe7uPSpQ7pT3x8PEaOHIlBgwZVWs7vq/5jQjSQzMxMtfteZTIZCgoK8OjRo2rVIf356KOP4OfnV2U5v6/6jwnRQJRKJSSVPFVHKpVWqw7VHfy+6j9+UwbSqlUrZGdni8tZWVmwsrKCubl5tepQ3cHvq/5jQjQQDw8PpKSkID09HQCwdetW9O/fv9p1qO7g91X/MSEaSMuWLbF48WKEhoZi8ODBuHr1KsLCwnD+/HkMGzZMYx2qO/h9NSy8dY+ISIUtRCIiFSZEIiIVJkQiIhUmRCIiFSZEIiIVJsQG5tatW3jppZcwbNgw8eXv74+dO3f+421PnDgRu3fvBgAMGzYMeXl5VdbNz89HcHBwtfeRlJSEoKCgCutPnTqFoUOHan2/s7MzcnJyqrXP8PDwKmevocal3j6GlKpmamqKvXv3istZWVkYOnQoXFxc0KlTpxrZx9Pbr0xubi7Onz9fI/si0hcmxEbAzs4ODg4OSE9Px6VLl7Bz5048efIEFhYW+Oabb7Bjxw5s2bIFSqUS1tbWmDNnDpycnJCVlYXw8HBkZ2ejdevWuH//vrhNZ2dnnDx5Es899xzWrVuHPXv2wNjYGA4ODliyZAkiIiJQWFiIYcOGYffu3UhPT8fChQvx8OFDKBQKBAUF4c033wQA/Pe//0ViYiKsra11egbw9evXER0djUePHkEul6NTp0749NNP0bRpUwDAp59+ivPnz0OpVGLatGno168fAFR5nEQigRqUjIwMoUuXLmrrzpw5I/Ts2VO4c+eOsGvXLqFnz55Cfn6+IAiCcOrUKWH06NHC48ePBUEQhJ9//lkYNGiQIAiC8P777wsrV64UBEEQ0tPThS5dugi7du0SBEEQXnzxReH+/fvCoUOHhIEDBwoPHz4UBEEQFi1aJKxZs0YtjpKSEsHX11e4cOGCIAiCkJeXJwwePFg4e/ascPDgQcHX11fIz88XSkpKhPfee08IDAyscFz/+9//hCFDhgiCIAhLliwREhISBEEQhOLiYmHo0KFCUlKSGNe6desEQRCEK1euCK+88opw//59jccZFhYmbNiw4Z998NQgsIXYAJW3zABAoVCgRYsWWL58OVq1agWgrHVnYWEBoGyG5xs3bmDUqFHi+/Py8vDw4UOcOHFCvPXMwcEBvXr1qrCvkydPYtCgQbCysgIAREREACi7llkuPT0dN2/eRGRkpFqMly5dQlpaGgYMGCDG88Ybb+Cbb77ReHyzZs3C8ePHERcXh/T0dGRnZ+Px48dieUBAAADgxRdfhJOTE86ePYvffvutyuMkKseE2AA9ew3xWU/PvqJUKjFs2DDMmjVLXM7OzoaVlRUkEona5KbGxhX/uRgZGalNeZWXl1ehs0WhUKB58+ZqMd27dw/NmzfHsmXL1PZhZGSk9fhmzJgBhUKBwYMHo2/fvrh7967aNp6dQs3Y2FjjcRKVYy9zI+fh4YH9+/eL01Zt2bIFY8eOBQC89tpr2LZtGwDgzp07OHXqVIX39+7dGwcPHhSfHRIbG4uNGzfC2NgYCoUCgiCgffv2akn67t27GDp0KC5cuIA+ffogKSkJeXl5UCqVWjtrgLKZqadMmQJfX18AQEpKChQKhVi+Z88eAMDFixdx8+ZNdO7cWeNxEpVjC7GR8/DwwIQJEzBu3DhIJBJYWFjgs88+g0Qiwdy5cxEREYHBgwdDJpNV2kPt6emJ1NRU8TS1Y8eOiImJgZmZGdzc3DBkyBDEx8djzZo1WLhwITZs2IDS0lJ88MEH6N69OwDgypUreOONN2BpaYlOnTrhwYMHGmOePn06pkyZAnNzc1hYWKBnz564efOmWJ6RkYHhw4dDIpHgk08+gbW1tcbjJCrH2W6IiFR4ykxEpMKESESkwoRIRKTChEhEpMKESESkwoRIRKTChEhEpMKESESk8v8B+q00i7JZzpcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot confusion matrix\n",
    "skplt.metrics.plot_confusion_matrix(labels_test, pred, normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<a id='Thoughts'></a>\n",
    "\n",
    "## Final Thoughts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With an accuracy of 85-95%, precision of 65-75%, and recall of 35-60%, I think our algorithm has done well considering the small amount of data we had to work with.  But, using our validation methods proved useful in creating a reliable algorithm. We've done pretty well, but I don't think I'd want to bet anyones life in prison on any algorithm trained on this data.  We could use more data!  Our Naive Bayes and Decision Tree Classifiers didn't perform as well.  Although, overfitting is a problem as you increase the number of 'n_neighbors' in our KNeighbors Classifier, we can avoid this by keeping the default setting of 5.  That left us with a working algorithm and some pretty solid evaluation metrics.  According to our confusion matrix, we were able to identify 97% of non-POIs and 60% of POIs.  I'd rather get a few POIs wrong than falsely identify a non-POI as a POI.  There may come a day when people will be convicted based on machine learning, so it's important that we be as accurate as possible.  This identifier only gets it wrong around 40% of the time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>salary</th>\n",
       "      <th>bonus</th>\n",
       "      <th>poi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>SKILLING JEFFREY K</td>\n",
       "      <td>1111258.0</td>\n",
       "      <td>5600000.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>LAY KENNETH L</td>\n",
       "      <td>1072321.0</td>\n",
       "      <td>7000000.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>BELDEN TIMOTHY N</td>\n",
       "      <td>213999.0</td>\n",
       "      <td>5249999.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name     salary      bonus   poi\n",
       "59   SKILLING JEFFREY K  1111258.0  5600000.0  True\n",
       "62        LAY KENNETH L  1072321.0  7000000.0  True\n",
       "103    BELDEN TIMOTHY N   213999.0  5249999.0  True"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'poi', 'bonus', 'exercised stock options', and 'fraction to poi' \n",
    "\n",
    "enron[(enron['bonus']>5000000) & (enron['poi'] == True)][['name','salary','bonus','poi']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict POI "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.4, 0.6, 0.6, 0.6, 0.8, 0.2, 0.6, 0. , 0.2, 0.8, 0.2, 0.2, 0.8,\n",
       "       0.6, 0.2, 0. , 0.6, 0.2])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_proba = clf.predict_proba(pd.DataFrame(features)[y==1].values)[:,1]\n",
    "pred_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmo4cob\\AppData\\Local\\conda\\conda\\envs\\tf\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>deferred_income</th>\n",
       "      <th>expenses</th>\n",
       "      <th>bonus</th>\n",
       "      <th>director_fees</th>\n",
       "      <th>salary</th>\n",
       "      <th>email_address</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>restricted_stock</th>\n",
       "      <th>loan_advances</th>\n",
       "      <th>...</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>poi</th>\n",
       "      <th>from_poi_to_this_person</th>\n",
       "      <th>total_be</th>\n",
       "      <th>fraction_to_poi</th>\n",
       "      <th>fraction_from_poi</th>\n",
       "      <th>total_millions</th>\n",
       "      <th>proba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KOENIG MARK E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>127017.0</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>309946.0</td>\n",
       "      <td>mark.koenig@enron.com</td>\n",
       "      <td>1587421.0</td>\n",
       "      <td>1248318.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>671737.0</td>\n",
       "      <td>2271.0</td>\n",
       "      <td>1920055.0</td>\n",
       "      <td>True</td>\n",
       "      <td>53.0</td>\n",
       "      <td>1371737.0</td>\n",
       "      <td>0.245902</td>\n",
       "      <td>0.022325</td>\n",
       "      <td>3.507476</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GLISAN JR BEN F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125978.0</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>274975.0</td>\n",
       "      <td>ben.glisan@enron.com</td>\n",
       "      <td>1272284.0</td>\n",
       "      <td>393818.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>384728.0</td>\n",
       "      <td>874.0</td>\n",
       "      <td>778546.0</td>\n",
       "      <td>True</td>\n",
       "      <td>52.0</td>\n",
       "      <td>984728.0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.059565</td>\n",
       "      <td>2.050830</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>BOWEN JR RAYMOND M</td>\n",
       "      <td>-833.0</td>\n",
       "      <td>65907.0</td>\n",
       "      <td>1350000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>278601.0</td>\n",
       "      <td>raymond.bowen@enron.com</td>\n",
       "      <td>2669589.0</td>\n",
       "      <td>252055.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1593.0</td>\n",
       "      <td>252055.0</td>\n",
       "      <td>True</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1350000.0</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.075350</td>\n",
       "      <td>2.921644</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>DELAINEY DAVID W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86174.0</td>\n",
       "      <td>3000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>365163.0</td>\n",
       "      <td>david.delainey@enron.com</td>\n",
       "      <td>4747979.0</td>\n",
       "      <td>1323148.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2291113.0</td>\n",
       "      <td>2097.0</td>\n",
       "      <td>3614261.0</td>\n",
       "      <td>True</td>\n",
       "      <td>66.0</td>\n",
       "      <td>5291113.0</td>\n",
       "      <td>0.198436</td>\n",
       "      <td>0.021339</td>\n",
       "      <td>8.362240</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>SKILLING JEFFREY K</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29336.0</td>\n",
       "      <td>5600000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1111258.0</td>\n",
       "      <td>jeff.skilling@enron.com</td>\n",
       "      <td>8682716.0</td>\n",
       "      <td>6843672.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>19250000.0</td>\n",
       "      <td>2042.0</td>\n",
       "      <td>26093672.0</td>\n",
       "      <td>True</td>\n",
       "      <td>88.0</td>\n",
       "      <td>24850000.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.024262</td>\n",
       "      <td>34.776388</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>HANNON KEVIN P</td>\n",
       "      <td>-3117011.0</td>\n",
       "      <td>34039.0</td>\n",
       "      <td>1500000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>243293.0</td>\n",
       "      <td>kevin.hannon@enron.com</td>\n",
       "      <td>288682.0</td>\n",
       "      <td>853064.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5538001.0</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>6391065.0</td>\n",
       "      <td>True</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7038001.0</td>\n",
       "      <td>0.656250</td>\n",
       "      <td>0.030622</td>\n",
       "      <td>6.679747</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>LAY KENNETH L</td>\n",
       "      <td>-300000.0</td>\n",
       "      <td>99832.0</td>\n",
       "      <td>7000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1072321.0</td>\n",
       "      <td>kenneth.lay@enron.com</td>\n",
       "      <td>103559793.0</td>\n",
       "      <td>14761694.0</td>\n",
       "      <td>81525000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34348384.0</td>\n",
       "      <td>2411.0</td>\n",
       "      <td>49110078.0</td>\n",
       "      <td>True</td>\n",
       "      <td>123.0</td>\n",
       "      <td>41348384.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.028785</td>\n",
       "      <td>152.669871</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>SHELBY REX</td>\n",
       "      <td>-4167.0</td>\n",
       "      <td>22884.0</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211844.0</td>\n",
       "      <td>rex.shelby@enron.com</td>\n",
       "      <td>2003885.0</td>\n",
       "      <td>869220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1624396.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2493616.0</td>\n",
       "      <td>True</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1824396.0</td>\n",
       "      <td>0.358974</td>\n",
       "      <td>0.057778</td>\n",
       "      <td>4.497501</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>HIRKO JOSEPH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77978.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>joe.hirko@enron.com</td>\n",
       "      <td>91093.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>30766064.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30766064.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30766064.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.857157</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>CAUSEY RICHARD A</td>\n",
       "      <td>-235000.0</td>\n",
       "      <td>30674.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>415189.0</td>\n",
       "      <td>richard.causey@enron.com</td>\n",
       "      <td>1868758.0</td>\n",
       "      <td>2502063.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>2502063.0</td>\n",
       "      <td>True</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>0.244898</td>\n",
       "      <td>0.030655</td>\n",
       "      <td>4.370821</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>BELDEN TIMOTHY N</td>\n",
       "      <td>-2334434.0</td>\n",
       "      <td>17355.0</td>\n",
       "      <td>5249999.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>213999.0</td>\n",
       "      <td>tim.belden@enron.com</td>\n",
       "      <td>5501630.0</td>\n",
       "      <td>157569.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>953136.0</td>\n",
       "      <td>5521.0</td>\n",
       "      <td>1110705.0</td>\n",
       "      <td>True</td>\n",
       "      <td>228.0</td>\n",
       "      <td>6203135.0</td>\n",
       "      <td>0.223140</td>\n",
       "      <td>0.028532</td>\n",
       "      <td>6.612335</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>COLWELL WESLEY</td>\n",
       "      <td>-144062.0</td>\n",
       "      <td>16514.0</td>\n",
       "      <td>1200000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>288542.0</td>\n",
       "      <td>wes.colwell@enron.com</td>\n",
       "      <td>1490344.0</td>\n",
       "      <td>698242.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1132.0</td>\n",
       "      <td>698242.0</td>\n",
       "      <td>True</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1200000.0</td>\n",
       "      <td>0.275000</td>\n",
       "      <td>0.136519</td>\n",
       "      <td>2.188586</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>FASTOW ANDREW S</td>\n",
       "      <td>-1386055.0</td>\n",
       "      <td>55921.0</td>\n",
       "      <td>1300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>440698.0</td>\n",
       "      <td>andrew.fastow@enron.com</td>\n",
       "      <td>2424083.0</td>\n",
       "      <td>1794412.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1794412.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.218495</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>CALGER CHRISTOPHER F</td>\n",
       "      <td>-262500.0</td>\n",
       "      <td>35818.0</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240189.0</td>\n",
       "      <td>christopher.calger@enron.com</td>\n",
       "      <td>1639297.0</td>\n",
       "      <td>126027.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2188.0</td>\n",
       "      <td>126027.0</td>\n",
       "      <td>True</td>\n",
       "      <td>199.0</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>0.173611</td>\n",
       "      <td>0.076597</td>\n",
       "      <td>1.765324</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>KOPPER MICHAEL J</td>\n",
       "      <td>NaN</td>\n",
       "      <td>118134.0</td>\n",
       "      <td>800000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>224305.0</td>\n",
       "      <td>michael.kopper@enron.com</td>\n",
       "      <td>2652612.0</td>\n",
       "      <td>985032.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>985032.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>800000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.637644</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>RICE KENNETH D</td>\n",
       "      <td>-3504386.0</td>\n",
       "      <td>46950.0</td>\n",
       "      <td>1750000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>420636.0</td>\n",
       "      <td>ken.rice@enron.com</td>\n",
       "      <td>505050.0</td>\n",
       "      <td>2748364.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>19794175.0</td>\n",
       "      <td>864.0</td>\n",
       "      <td>22542539.0</td>\n",
       "      <td>True</td>\n",
       "      <td>42.0</td>\n",
       "      <td>21544175.0</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.046409</td>\n",
       "      <td>23.047589</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>YEAGER F SCOTT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53947.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158403.0</td>\n",
       "      <td>scott.yeager@enron.com</td>\n",
       "      <td>360300.0</td>\n",
       "      <td>3576206.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>8308552.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11884758.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8308552.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.245058</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>RIEKER PAULA H</td>\n",
       "      <td>-100000.0</td>\n",
       "      <td>33271.0</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>249201.0</td>\n",
       "      <td>paula.rieker@enron.com</td>\n",
       "      <td>1099100.0</td>\n",
       "      <td>283649.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1635238.0</td>\n",
       "      <td>1258.0</td>\n",
       "      <td>1918887.0</td>\n",
       "      <td>True</td>\n",
       "      <td>35.0</td>\n",
       "      <td>2335238.0</td>\n",
       "      <td>0.585366</td>\n",
       "      <td>0.026355</td>\n",
       "      <td>3.017987</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name  deferred_income  expenses      bonus  \\\n",
       "5           KOENIG MARK E              NaN  127017.0   700000.0   \n",
       "17        GLISAN JR BEN F              NaN  125978.0   600000.0   \n",
       "37     BOWEN JR RAYMOND M           -833.0   65907.0  1350000.0   \n",
       "55       DELAINEY DAVID W              NaN   86174.0  3000000.0   \n",
       "59     SKILLING JEFFREY K              NaN   29336.0  5600000.0   \n",
       "61         HANNON KEVIN P       -3117011.0   34039.0  1500000.0   \n",
       "62          LAY KENNETH L        -300000.0   99832.0  7000000.0   \n",
       "63             SHELBY REX          -4167.0   22884.0   200000.0   \n",
       "73           HIRKO JOSEPH              NaN   77978.0        NaN   \n",
       "82       CAUSEY RICHARD A        -235000.0   30674.0  1000000.0   \n",
       "103      BELDEN TIMOTHY N       -2334434.0   17355.0  5249999.0   \n",
       "105        COLWELL WESLEY        -144062.0   16514.0  1200000.0   \n",
       "107       FASTOW ANDREW S       -1386055.0   55921.0  1300000.0   \n",
       "120  CALGER CHRISTOPHER F        -262500.0   35818.0  1250000.0   \n",
       "131      KOPPER MICHAEL J              NaN  118134.0   800000.0   \n",
       "132        RICE KENNETH D       -3504386.0   46950.0  1750000.0   \n",
       "139        YEAGER F SCOTT              NaN   53947.0        NaN   \n",
       "140        RIEKER PAULA H        -100000.0   33271.0   700000.0   \n",
       "\n",
       "     director_fees     salary                 email_address  total_payments  \\\n",
       "5              NaN   309946.0         mark.koenig@enron.com       1587421.0   \n",
       "17             NaN   274975.0          ben.glisan@enron.com       1272284.0   \n",
       "37             NaN   278601.0       raymond.bowen@enron.com       2669589.0   \n",
       "55             NaN   365163.0      david.delainey@enron.com       4747979.0   \n",
       "59             NaN  1111258.0       jeff.skilling@enron.com       8682716.0   \n",
       "61             NaN   243293.0        kevin.hannon@enron.com        288682.0   \n",
       "62             NaN  1072321.0         kenneth.lay@enron.com     103559793.0   \n",
       "63             NaN   211844.0          rex.shelby@enron.com       2003885.0   \n",
       "73             NaN        NaN           joe.hirko@enron.com         91093.0   \n",
       "82             NaN   415189.0      richard.causey@enron.com       1868758.0   \n",
       "103            NaN   213999.0          tim.belden@enron.com       5501630.0   \n",
       "105            NaN   288542.0         wes.colwell@enron.com       1490344.0   \n",
       "107            NaN   440698.0       andrew.fastow@enron.com       2424083.0   \n",
       "120            NaN   240189.0  christopher.calger@enron.com       1639297.0   \n",
       "131            NaN   224305.0      michael.kopper@enron.com       2652612.0   \n",
       "132            NaN   420636.0            ken.rice@enron.com        505050.0   \n",
       "139            NaN   158403.0        scott.yeager@enron.com        360300.0   \n",
       "140            NaN   249201.0        paula.rieker@enron.com       1099100.0   \n",
       "\n",
       "     restricted_stock  loan_advances  ...    exercised_stock_options  \\\n",
       "5           1248318.0            NaN  ...                   671737.0   \n",
       "17           393818.0            NaN  ...                   384728.0   \n",
       "37           252055.0            NaN  ...                        NaN   \n",
       "55          1323148.0            NaN  ...                  2291113.0   \n",
       "59          6843672.0            NaN  ...                 19250000.0   \n",
       "61           853064.0            NaN  ...                  5538001.0   \n",
       "62         14761694.0     81525000.0  ...                 34348384.0   \n",
       "63           869220.0            NaN  ...                  1624396.0   \n",
       "73                NaN            NaN  ...                 30766064.0   \n",
       "82          2502063.0            NaN  ...                        NaN   \n",
       "103          157569.0            NaN  ...                   953136.0   \n",
       "105          698242.0            NaN  ...                        NaN   \n",
       "107         1794412.0            NaN  ...                        NaN   \n",
       "120          126027.0            NaN  ...                        NaN   \n",
       "131          985032.0            NaN  ...                        NaN   \n",
       "132         2748364.0            NaN  ...                 19794175.0   \n",
       "139         3576206.0            NaN  ...                  8308552.0   \n",
       "140          283649.0            NaN  ...                  1635238.0   \n",
       "\n",
       "     shared_receipt_with_poi  total_stock_value   poi  \\\n",
       "5                     2271.0          1920055.0  True   \n",
       "17                     874.0           778546.0  True   \n",
       "37                    1593.0           252055.0  True   \n",
       "55                    2097.0          3614261.0  True   \n",
       "59                    2042.0         26093672.0  True   \n",
       "61                    1035.0          6391065.0  True   \n",
       "62                    2411.0         49110078.0  True   \n",
       "63                      91.0          2493616.0  True   \n",
       "73                       NaN         30766064.0  True   \n",
       "82                    1585.0          2502063.0  True   \n",
       "103                   5521.0          1110705.0  True   \n",
       "105                   1132.0           698242.0  True   \n",
       "107                      NaN          1794412.0  True   \n",
       "120                   2188.0           126027.0  True   \n",
       "131                      NaN           985032.0  True   \n",
       "132                    864.0         22542539.0  True   \n",
       "139                      NaN         11884758.0  True   \n",
       "140                   1258.0          1918887.0  True   \n",
       "\n",
       "     from_poi_to_this_person    total_be  fraction_to_poi  fraction_from_poi  \\\n",
       "5                       53.0   1371737.0         0.245902           0.022325   \n",
       "17                      52.0    984728.0         0.375000           0.059565   \n",
       "37                     140.0   1350000.0         0.555556           0.075350   \n",
       "55                      66.0   5291113.0         0.198436           0.021339   \n",
       "59                      88.0  24850000.0         0.277778           0.024262   \n",
       "61                      32.0   7038001.0         0.656250           0.030622   \n",
       "62                     123.0  41348384.0         0.444444           0.028785   \n",
       "63                      13.0   1824396.0         0.358974           0.057778   \n",
       "73                       NaN  30766064.0              NaN                NaN   \n",
       "82                      58.0   1000000.0         0.244898           0.030655   \n",
       "103                    228.0   6203135.0         0.223140           0.028532   \n",
       "105                    240.0   1200000.0         0.275000           0.136519   \n",
       "107                      NaN   1300000.0              NaN                NaN   \n",
       "120                    199.0   1250000.0         0.173611           0.076597   \n",
       "131                      NaN    800000.0              NaN                NaN   \n",
       "132                     42.0  21544175.0         0.222222           0.046409   \n",
       "139                      NaN   8308552.0              NaN                NaN   \n",
       "140                     35.0   2335238.0         0.585366           0.026355   \n",
       "\n",
       "     total_millions  proba  \n",
       "5          3.507476    0.4  \n",
       "17         2.050830    0.6  \n",
       "37         2.921644    0.6  \n",
       "55         8.362240    0.6  \n",
       "59        34.776388    0.8  \n",
       "61         6.679747    0.2  \n",
       "62       152.669871    0.6  \n",
       "63         4.497501    0.0  \n",
       "73        30.857157    0.2  \n",
       "82         4.370821    0.8  \n",
       "103        6.612335    0.2  \n",
       "105        2.188586    0.2  \n",
       "107        4.218495    0.8  \n",
       "120        1.765324    0.6  \n",
       "131        3.637644    0.2  \n",
       "132       23.047589    0.0  \n",
       "139       12.245058    0.6  \n",
       "140        3.017987    0.2  \n",
       "\n",
       "[18 rows x 27 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_df = enron[enron.poi == 1]\n",
    "predict_df['proba'] = pred_proba\n",
    "predict_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_names = predict_df[predict_df['proba']>.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>deferred_income</th>\n",
       "      <th>expenses</th>\n",
       "      <th>bonus</th>\n",
       "      <th>director_fees</th>\n",
       "      <th>salary</th>\n",
       "      <th>email_address</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>restricted_stock</th>\n",
       "      <th>loan_advances</th>\n",
       "      <th>...</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>poi</th>\n",
       "      <th>from_poi_to_this_person</th>\n",
       "      <th>total_be</th>\n",
       "      <th>fraction_to_poi</th>\n",
       "      <th>fraction_from_poi</th>\n",
       "      <th>total_millions</th>\n",
       "      <th>proba</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>GLISAN JR BEN F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125978.0</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>274975.0</td>\n",
       "      <td>ben.glisan@enron.com</td>\n",
       "      <td>1272284.0</td>\n",
       "      <td>393818.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>384728.0</td>\n",
       "      <td>874.0</td>\n",
       "      <td>778546.0</td>\n",
       "      <td>True</td>\n",
       "      <td>52.0</td>\n",
       "      <td>984728.0</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.059565</td>\n",
       "      <td>2.050830</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>BOWEN JR RAYMOND M</td>\n",
       "      <td>-833.0</td>\n",
       "      <td>65907.0</td>\n",
       "      <td>1350000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>278601.0</td>\n",
       "      <td>raymond.bowen@enron.com</td>\n",
       "      <td>2669589.0</td>\n",
       "      <td>252055.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1593.0</td>\n",
       "      <td>252055.0</td>\n",
       "      <td>True</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1350000.0</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.075350</td>\n",
       "      <td>2.921644</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>DELAINEY DAVID W</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86174.0</td>\n",
       "      <td>3000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>365163.0</td>\n",
       "      <td>david.delainey@enron.com</td>\n",
       "      <td>4747979.0</td>\n",
       "      <td>1323148.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2291113.0</td>\n",
       "      <td>2097.0</td>\n",
       "      <td>3614261.0</td>\n",
       "      <td>True</td>\n",
       "      <td>66.0</td>\n",
       "      <td>5291113.0</td>\n",
       "      <td>0.198436</td>\n",
       "      <td>0.021339</td>\n",
       "      <td>8.362240</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>SKILLING JEFFREY K</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29336.0</td>\n",
       "      <td>5600000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1111258.0</td>\n",
       "      <td>jeff.skilling@enron.com</td>\n",
       "      <td>8682716.0</td>\n",
       "      <td>6843672.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>19250000.0</td>\n",
       "      <td>2042.0</td>\n",
       "      <td>26093672.0</td>\n",
       "      <td>True</td>\n",
       "      <td>88.0</td>\n",
       "      <td>24850000.0</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.024262</td>\n",
       "      <td>34.776388</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>LAY KENNETH L</td>\n",
       "      <td>-300000.0</td>\n",
       "      <td>99832.0</td>\n",
       "      <td>7000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1072321.0</td>\n",
       "      <td>kenneth.lay@enron.com</td>\n",
       "      <td>103559793.0</td>\n",
       "      <td>14761694.0</td>\n",
       "      <td>81525000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>34348384.0</td>\n",
       "      <td>2411.0</td>\n",
       "      <td>49110078.0</td>\n",
       "      <td>True</td>\n",
       "      <td>123.0</td>\n",
       "      <td>41348384.0</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.028785</td>\n",
       "      <td>152.669871</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>CAUSEY RICHARD A</td>\n",
       "      <td>-235000.0</td>\n",
       "      <td>30674.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>415189.0</td>\n",
       "      <td>richard.causey@enron.com</td>\n",
       "      <td>1868758.0</td>\n",
       "      <td>2502063.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>2502063.0</td>\n",
       "      <td>True</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>0.244898</td>\n",
       "      <td>0.030655</td>\n",
       "      <td>4.370821</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>FASTOW ANDREW S</td>\n",
       "      <td>-1386055.0</td>\n",
       "      <td>55921.0</td>\n",
       "      <td>1300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>440698.0</td>\n",
       "      <td>andrew.fastow@enron.com</td>\n",
       "      <td>2424083.0</td>\n",
       "      <td>1794412.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1794412.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1300000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.218495</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>CALGER CHRISTOPHER F</td>\n",
       "      <td>-262500.0</td>\n",
       "      <td>35818.0</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240189.0</td>\n",
       "      <td>christopher.calger@enron.com</td>\n",
       "      <td>1639297.0</td>\n",
       "      <td>126027.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2188.0</td>\n",
       "      <td>126027.0</td>\n",
       "      <td>True</td>\n",
       "      <td>199.0</td>\n",
       "      <td>1250000.0</td>\n",
       "      <td>0.173611</td>\n",
       "      <td>0.076597</td>\n",
       "      <td>1.765324</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>YEAGER F SCOTT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53947.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>158403.0</td>\n",
       "      <td>scott.yeager@enron.com</td>\n",
       "      <td>360300.0</td>\n",
       "      <td>3576206.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>8308552.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11884758.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8308552.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.245058</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name  deferred_income  expenses      bonus  \\\n",
       "17        GLISAN JR BEN F              NaN  125978.0   600000.0   \n",
       "37     BOWEN JR RAYMOND M           -833.0   65907.0  1350000.0   \n",
       "55       DELAINEY DAVID W              NaN   86174.0  3000000.0   \n",
       "59     SKILLING JEFFREY K              NaN   29336.0  5600000.0   \n",
       "62          LAY KENNETH L        -300000.0   99832.0  7000000.0   \n",
       "82       CAUSEY RICHARD A        -235000.0   30674.0  1000000.0   \n",
       "107       FASTOW ANDREW S       -1386055.0   55921.0  1300000.0   \n",
       "120  CALGER CHRISTOPHER F        -262500.0   35818.0  1250000.0   \n",
       "139        YEAGER F SCOTT              NaN   53947.0        NaN   \n",
       "\n",
       "     director_fees     salary                 email_address  total_payments  \\\n",
       "17             NaN   274975.0          ben.glisan@enron.com       1272284.0   \n",
       "37             NaN   278601.0       raymond.bowen@enron.com       2669589.0   \n",
       "55             NaN   365163.0      david.delainey@enron.com       4747979.0   \n",
       "59             NaN  1111258.0       jeff.skilling@enron.com       8682716.0   \n",
       "62             NaN  1072321.0         kenneth.lay@enron.com     103559793.0   \n",
       "82             NaN   415189.0      richard.causey@enron.com       1868758.0   \n",
       "107            NaN   440698.0       andrew.fastow@enron.com       2424083.0   \n",
       "120            NaN   240189.0  christopher.calger@enron.com       1639297.0   \n",
       "139            NaN   158403.0        scott.yeager@enron.com        360300.0   \n",
       "\n",
       "     restricted_stock  loan_advances  ...    exercised_stock_options  \\\n",
       "17           393818.0            NaN  ...                   384728.0   \n",
       "37           252055.0            NaN  ...                        NaN   \n",
       "55          1323148.0            NaN  ...                  2291113.0   \n",
       "59          6843672.0            NaN  ...                 19250000.0   \n",
       "62         14761694.0     81525000.0  ...                 34348384.0   \n",
       "82          2502063.0            NaN  ...                        NaN   \n",
       "107         1794412.0            NaN  ...                        NaN   \n",
       "120          126027.0            NaN  ...                        NaN   \n",
       "139         3576206.0            NaN  ...                  8308552.0   \n",
       "\n",
       "     shared_receipt_with_poi  total_stock_value   poi  \\\n",
       "17                     874.0           778546.0  True   \n",
       "37                    1593.0           252055.0  True   \n",
       "55                    2097.0          3614261.0  True   \n",
       "59                    2042.0         26093672.0  True   \n",
       "62                    2411.0         49110078.0  True   \n",
       "82                    1585.0          2502063.0  True   \n",
       "107                      NaN          1794412.0  True   \n",
       "120                   2188.0           126027.0  True   \n",
       "139                      NaN         11884758.0  True   \n",
       "\n",
       "     from_poi_to_this_person    total_be  fraction_to_poi  fraction_from_poi  \\\n",
       "17                      52.0    984728.0         0.375000           0.059565   \n",
       "37                     140.0   1350000.0         0.555556           0.075350   \n",
       "55                      66.0   5291113.0         0.198436           0.021339   \n",
       "59                      88.0  24850000.0         0.277778           0.024262   \n",
       "62                     123.0  41348384.0         0.444444           0.028785   \n",
       "82                      58.0   1000000.0         0.244898           0.030655   \n",
       "107                      NaN   1300000.0              NaN                NaN   \n",
       "120                    199.0   1250000.0         0.173611           0.076597   \n",
       "139                      NaN   8308552.0              NaN                NaN   \n",
       "\n",
       "     total_millions  proba  \n",
       "17         2.050830    0.6  \n",
       "37         2.921644    0.6  \n",
       "55         8.362240    0.6  \n",
       "59        34.776388    0.8  \n",
       "62       152.669871    0.6  \n",
       "82         4.370821    0.8  \n",
       "107        4.218495    0.8  \n",
       "120        1.765324    0.6  \n",
       "139       12.245058    0.6  \n",
       "\n",
       "[9 rows x 27 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_names.sort_values(by ='proba', ascending=False)\n",
    "predict_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict POI probability new executive "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bonus                    : 400000\n",
      "exercised stock options  : 45000\n"
     ]
    }
   ],
   "source": [
    "bonus = int(input ('bonus                    : '))\n",
    "stock = int(input ('exercised stock options  : ') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mails from this person to poi : 20000\n",
      "from_messages : 25022\n"
     ]
    }
   ],
   "source": [
    "fraction_poi = float(input ('mails from this person to poi : '))/float(input ('from_messages : '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOT POI..\n"
     ]
    }
   ],
   "source": [
    "if (clf.predict([[bonus, stock, fraction_poi]]) == 1):\n",
    "    print ('POI!!!...')\n",
    "else:\n",
    "    print ('NOT POI..')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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