Identify Fraud Using Machine Learning .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#Project-Overview\" data-toc-modified-id=\"Project-Overview-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Project Overview</a></div><div class=\"lev2 toc-item\"><a href=\"#Scope\" data-toc-modified-id=\"Scope-11\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Scope</a></div><div class=\"lev2 toc-item\"><a href=\"#Needed-Libraries\" data-toc-modified-id=\"Needed-Libraries-12\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Needed Libraries</a></div><div class=\"lev2 toc-item\"><a href=\"#Features\" data-toc-modified-id=\"Features-13\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Features</a></div><div class=\"lev1 toc-item\"><a href=\"#Understanding-the-Dataset\" data-toc-modified-id=\"Understanding-the-Dataset-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Understanding the Dataset</a></div><div class=\"lev2 toc-item\"><a href=\"#Data-Exploration\" data-toc-modified-id=\"Data-Exploration-21\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Data Exploration</a></div><div class=\"lev2 toc-item\"><a href=\"#Outlier-Investigation\" data-toc-modified-id=\"Outlier-Investigation-22\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Outlier Investigation</a></div><div class=\"lev1 toc-item\"><a href=\"#Optimize-Feature-Selection/Engineering\" data-toc-modified-id=\"Optimize-Feature-Selection/Engineering-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Optimize Feature Selection/Engineering</a></div><div class=\"lev2 toc-item\"><a href=\"#Create-new-features\" data-toc-modified-id=\"Create-new-features-31\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Create new features</a></div><div class=\"lev2 toc-item\"><a href=\"#Feature-Selection\" data-toc-modified-id=\"Feature-Selection-32\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Feature Selection</a></div><div class=\"lev2 toc-item\"><a href=\"#Features-Scaling\" data-toc-modified-id=\"Features-Scaling-33\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>Features Scaling</a></div><div class=\"lev1 toc-item\"><a href=\"#Algorithm-Selection-and-Tuning\" data-toc-modified-id=\"Algorithm-Selection-and-Tuning-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Algorithm Selection and Tuning</a></div><div class=\"lev2 toc-item\"><a href=\"#Algorithm-Family-Selection\" data-toc-modified-id=\"Algorithm-Family-Selection-41\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Algorithm Family Selection</a></div><div class=\"lev2 toc-item\"><a href=\"#Algorithm-Selection\" data-toc-modified-id=\"Algorithm-Selection-42\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>Algorithm Selection</a></div><div class=\"lev2 toc-item\"><a href=\"#Algorithm-Tuning\" data-toc-modified-id=\"Algorithm-Tuning-43\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>Algorithm Tuning</a></div><div class=\"lev1 toc-item\"><a href=\"#Project-Closure\" data-toc-modified-id=\"Project-Closure-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Project Closure</a></div><div class=\"lev2 toc-item\"><a href=\"#Dumping-files\" data-toc-modified-id=\"Dumping-files-51\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Dumping files</a></div><div class=\"lev1 toc-item\"><a href=\"#Future-Improvements\" data-toc-modified-id=\"Future-Improvements-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Future Improvements</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2000, Enron was one of the largest companies in the United States. By 2002, it had collapsed into bankruptcy due to widespread corporate fraud. In the resulting Federal investigation, a significant amount of typically confidential information entered into the public record, including tens of thousands of emails and detailed financial data for top executives.  \n",
    "These data have been combined with a hand-generated list of persons of interest in the fraud case, which means individuals who were indicted, reached a settlement or plea deal with the government, or testified in exchange for prosecution immunity.  \n",
    "\n",
    "In this project, I am building **a person of interest identifier based on financial and email data**, made public as a result of the Enron scandal.  \n",
    "\n",
    "The data have been combined in the form of a dictionary, where each key-value pair in the dictionary corresponds to one person. The dictionary key is the person's name, and the value is another dictionary, which contains the names of all the features and their values for that person."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Needed Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"./code/\")\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import seaborn as sns\n",
    "import re\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier\n",
    "from sklearn.feature_selection import SelectPercentile, SelectKBest\n",
    "from sklearn.model_selection import GridSearchCV, StratifiedShuffleSplit\n",
    "from sklearn.neighbors import KNeighborsClassifier, NearestCentroid\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import MaxAbsScaler, StandardScaler\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "\n",
    "from tester import test_classifier\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features included in the dataset can be divided in three categories, Salary Features, Stock Features and Email Features. Bellow you may find the full feature list with  brief definition of each one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Salary Features\n",
    "\n",
    "| Payments            | Definitions of Category Groupings                                                                                                                                                                                                                                                                                                                                                                                                |\n",
    "|:--------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
    "| ***Salary***              | Reflects items such as base salary, executive cash allowances, and benefits payments.                                                                                                                                                                                                                                                                                                                                            |\n",
    "| ***Bonus***               | Reflects annual cash incentives paid based upon company performance. Also may include other retention payments.                                                                                                                                                                                                                                                                                                                  |\n",
    "| ***Long Term Incentive*** | Reflects long-term incentive cash payments from various long-term incentive programs designed to tie executive compensation to long-term success as measuredagainst key performance drivers and business objectives over a multi-year period, generally 3 to 5 years.                                                                                                                                                            |\n",
    "| ***Deferred Income***     | Reflects voluntary executive deferrals of salary, annual cash incentives, and long-term cash incentives as well as cash fees deferred by non-employee directorsunder a deferred compensation arrangement. May also reflect deferrals under a stock option or phantom stock unit in lieu of cash arrangement.                                                                                                                     |\n",
    "| ***Deferral Payments***   | Reflects distributions from a deferred compensation arrangement due to termination of employment or due to in-service withdrawals as per plan provisions.                                                                                                                                                                                                                                                                        |\n",
    "| ***Loan Advances***       | Reflects total amount of loan advances, excluding repayments, provided by the Debtor in return for a promise of repayment. In certain instances, the terms of thepromissory notes allow for the option to repay with stock of the company.                                                                                                                                                                                       |\n",
    "| ***Other***               | Reflects items such as payments for severence, consulting services, relocation costs, tax advances and allowances for employees on international assignment (i.e.housing allowances, cost of living allowances, payments under Enron’s Tax Equalization Program, etc.). May also include payments provided with respect toemployment agreements, as well as imputed income amounts for such things as use of corporate aircraft. |\n",
    "| ***Expenses***            | Reflects reimbursements of business expenses. May include fees paid for consulting services.                                                                                                                                                                                                                                                                                                                                     |\n",
    "| ***Director Fees***       | Reflects cash payments and/or value of stock grants made in lieu of cash payments to non-employee directors.                                                                                                                                                                                                                                                                                                                     |\n",
    "| ***Total Payments***      | Sum of the above values                                                                                                                                                                                                                                                                                                                                                                                                         |\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stock Features\n",
    "\n",
    "| Stock Value              | Definitions of Category Groupings                                                                                                                                                                                                                                                                                                                                                       |\n",
    "|:-------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n",
    "| ***Exercised Stock Options***  | Reflects amounts from exercised stock options which equal the market value in excess of the exercise price on the date the options were exercised either throughcashless (same-day sale), stock swap or cash exercises. The reflected gain may differ from that realized by the insider due to fluctuations in the market price andthe timing of any subsequent sale of the securities. |\n",
    "| ***Restricted Stock***         | Reflects the gross fair market value of shares and accrued dividends (and/or phantom units and dividend equivalents) on the date of release due to lapse of vestingperiods, regardless of whether deferred.                                                                                                                                                                             |\n",
    "| ***Restricted StockDeferred*** | Reflects value of restricted stock voluntarily deferred prior to release under a deferred compensation arrangement.                                                                                                                                                                                                                                                                     |\n",
    "| ***Total Stock Value***        | Sum of the above values                                                                                                                                                                                                                                                                                                                                                                 |\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### email features\n",
    "\n",
    "| Variable                      | Definition                                                                    |\n",
    "|:------------------------------|:------------------------------------------------------------------------------|\n",
    "| ***to messages***             | Total number of emails received (person's inbox)                              |\n",
    "| ***email address***           | Email address of the person                                                   |\n",
    "| ***from poi to this person*** | Number of emails received by POIs                                             |\n",
    "| ***from messages***           | Total number of emails sent by this person                                    |\n",
    "| ***from this person to poi*** | Number of emails sent by this person to a POI.                                |\n",
    "| ***shared receipt with poi*** | Number of emails addressed by someone else to a POI where this person was CC. |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146, 21)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Load the dictionary containing the dataset\n",
    "with open(\"./dataset/final_project_dataset.pkl\", \"rb\") as data_file:\n",
    "    data_init = pickle.load(data_file)\n",
    "\n",
    "#Converting the dataset from a python dictionary to a pandas dataframe\n",
    "data_df = pd.DataFrame.from_dict(data_init, orient='index')\n",
    "data_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "      <th>to_messages</th>\n",
       "      <th>deferral_payments</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>loan_advances</th>\n",
       "      <th>bonus</th>\n",
       "      <th>email_address</th>\n",
       "      <th>restricted_stock_deferred</th>\n",
       "      <th>deferred_income</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>...</th>\n",
       "      <th>from_poi_to_this_person</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>from_messages</th>\n",
       "      <th>other</th>\n",
       "      <th>from_this_person_to_poi</th>\n",
       "      <th>poi</th>\n",
       "      <th>long_term_incentive</th>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <th>restricted_stock</th>\n",
       "      <th>director_fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALLEN PHILLIP K</th>\n",
       "      <td>201955</td>\n",
       "      <td>2902</td>\n",
       "      <td>2869717</td>\n",
       "      <td>4484442</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4175000</td>\n",
       "      <td>phillip.allen@enron.com</td>\n",
       "      <td>-126027</td>\n",
       "      <td>-3081055</td>\n",
       "      <td>1729541</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>1729541</td>\n",
       "      <td>2195</td>\n",
       "      <td>152</td>\n",
       "      <td>65</td>\n",
       "      <td>False</td>\n",
       "      <td>304805</td>\n",
       "      <td>1407</td>\n",
       "      <td>126027</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BADUM JAMES P</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178980</td>\n",
       "      <td>182466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>257817</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>257817</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BANNANTINE JAMES M</th>\n",
       "      <td>477</td>\n",
       "      <td>566</td>\n",
       "      <td>NaN</td>\n",
       "      <td>916197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>james.bannantine@enron.com</td>\n",
       "      <td>-560222</td>\n",
       "      <td>-5104</td>\n",
       "      <td>5243487</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>4046157</td>\n",
       "      <td>29</td>\n",
       "      <td>864523</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>465</td>\n",
       "      <td>1757552</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAXTER JOHN C</th>\n",
       "      <td>267102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1295738</td>\n",
       "      <td>5634343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1386055</td>\n",
       "      <td>10623258</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6680544</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2660303</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1586055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3942714</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAY FRANKLIN R</th>\n",
       "      <td>239671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>260455</td>\n",
       "      <td>827696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400000</td>\n",
       "      <td>frank.bay@enron.com</td>\n",
       "      <td>-82782</td>\n",
       "      <td>-201641</td>\n",
       "      <td>63014</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>145796</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    salary to_messages deferral_payments total_payments  \\\n",
       "ALLEN PHILLIP K     201955        2902           2869717        4484442   \n",
       "BADUM JAMES P          NaN         NaN            178980         182466   \n",
       "BANNANTINE JAMES M     477         566               NaN         916197   \n",
       "BAXTER JOHN C       267102         NaN           1295738        5634343   \n",
       "BAY FRANKLIN R      239671         NaN            260455         827696   \n",
       "\n",
       "                   loan_advances    bonus               email_address  \\\n",
       "ALLEN PHILLIP K              NaN  4175000     phillip.allen@enron.com   \n",
       "BADUM JAMES P                NaN      NaN                         NaN   \n",
       "BANNANTINE JAMES M           NaN      NaN  james.bannantine@enron.com   \n",
       "BAXTER JOHN C                NaN  1200000                         NaN   \n",
       "BAY FRANKLIN R               NaN   400000         frank.bay@enron.com   \n",
       "\n",
       "                   restricted_stock_deferred deferred_income  \\\n",
       "ALLEN PHILLIP K                      -126027        -3081055   \n",
       "BADUM JAMES P                            NaN             NaN   \n",
       "BANNANTINE JAMES M                   -560222           -5104   \n",
       "BAXTER JOHN C                            NaN        -1386055   \n",
       "BAY FRANKLIN R                        -82782         -201641   \n",
       "\n",
       "                   total_stock_value      ...      from_poi_to_this_person  \\\n",
       "ALLEN PHILLIP K              1729541      ...                           47   \n",
       "BADUM JAMES P                 257817      ...                          NaN   \n",
       "BANNANTINE JAMES M           5243487      ...                           39   \n",
       "BAXTER JOHN C               10623258      ...                          NaN   \n",
       "BAY FRANKLIN R                 63014      ...                          NaN   \n",
       "\n",
       "                   exercised_stock_options from_messages    other  \\\n",
       "ALLEN PHILLIP K                    1729541          2195      152   \n",
       "BADUM JAMES P                       257817           NaN      NaN   \n",
       "BANNANTINE JAMES M                 4046157            29   864523   \n",
       "BAXTER JOHN C                      6680544           NaN  2660303   \n",
       "BAY FRANKLIN R                         NaN           NaN       69   \n",
       "\n",
       "                   from_this_person_to_poi    poi long_term_incentive  \\\n",
       "ALLEN PHILLIP K                         65  False              304805   \n",
       "BADUM JAMES P                          NaN  False                 NaN   \n",
       "BANNANTINE JAMES M                       0  False                 NaN   \n",
       "BAXTER JOHN C                          NaN  False             1586055   \n",
       "BAY FRANKLIN R                         NaN  False                 NaN   \n",
       "\n",
       "                   shared_receipt_with_poi restricted_stock director_fees  \n",
       "ALLEN PHILLIP K                       1407           126027           NaN  \n",
       "BADUM JAMES P                          NaN              NaN           NaN  \n",
       "BANNANTINE JAMES M                     465          1757552           NaN  \n",
       "BAXTER JOHN C                          NaN          3942714           NaN  \n",
       "BAY FRANKLIN R                         NaN           145796           NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The dataset contains information of 21 features from 146 employees.  \n",
    "\n",
    "\"NaN\"s are actually strings so I will replace them with Numpy's \"NaN\"s so I can count the information I have across the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_df.replace(to_replace='NaN', value=np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loan_advances                  4\n",
       "director_fees                 17\n",
       "restricted_stock_deferred     18\n",
       "deferral_payments             39\n",
       "deferred_income               49\n",
       "long_term_incentive           66\n",
       "bonus                         82\n",
       "to_messages                   86\n",
       "shared_receipt_with_poi       86\n",
       "from_this_person_to_poi       86\n",
       "from_poi_to_this_person       86\n",
       "from_messages                 86\n",
       "other                         93\n",
       "salary                        95\n",
       "expenses                      95\n",
       "exercised_stock_options      102\n",
       "restricted_stock             110\n",
       "email_address                111\n",
       "total_payments               125\n",
       "total_stock_value            126\n",
       "poi                          146\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.count().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the dataset is quite sparse with some variables like *Total Payments* and *Total Stock Value* having values for most of the employees but some others like *Loan Advances* and *Director Fees* that we have information for too few employees.  \n",
    "I am wondering if there are any records for all missing values. For this I will remove the *email_address* field since we cannot use it somehow in the analysis and I will create a temporary copy without the label (POI).\n",
    "\n",
    "By paying attention to the [Payments Schedule](dataset/enron61702insiderpay.pdf) which is the source of the dataset, we can see that the empty values, except the *email_address*, are actually \"0\". After cleaning the dataset I will impute the empty values with \"0\". For now, I will remove the *email_address* field since we cannot use it somehow in the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "      <th>to_messages</th>\n",
       "      <th>deferral_payments</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>loan_advances</th>\n",
       "      <th>bonus</th>\n",
       "      <th>restricted_stock_deferred</th>\n",
       "      <th>deferred_income</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>expenses</th>\n",
       "      <th>from_poi_to_this_person</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>from_messages</th>\n",
       "      <th>other</th>\n",
       "      <th>from_this_person_to_poi</th>\n",
       "      <th>long_term_incentive</th>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <th>restricted_stock</th>\n",
       "      <th>director_fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LOCKHART EUGENE E</th>\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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   salary  to_messages  deferral_payments  total_payments  \\\n",
       "LOCKHART EUGENE E     NaN          NaN                NaN             NaN   \n",
       "\n",
       "                   loan_advances  bonus  restricted_stock_deferred  \\\n",
       "LOCKHART EUGENE E            NaN    NaN                        NaN   \n",
       "\n",
       "                   deferred_income  total_stock_value  expenses  \\\n",
       "LOCKHART EUGENE E              NaN                NaN       NaN   \n",
       "\n",
       "                   from_poi_to_this_person  exercised_stock_options  \\\n",
       "LOCKHART EUGENE E                      NaN                      NaN   \n",
       "\n",
       "                   from_messages  other  from_this_person_to_poi  \\\n",
       "LOCKHART EUGENE E            NaN    NaN                      NaN   \n",
       "\n",
       "                   long_term_incentive  shared_receipt_with_poi  \\\n",
       "LOCKHART EUGENE E                  NaN                      NaN   \n",
       "\n",
       "                   restricted_stock  director_fees  \n",
       "LOCKHART EUGENE E               NaN            NaN  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping 'poi' and 'email_address' variables\n",
    "data_df = data_df.drop([\"email_address\"], axis=1)\n",
    "data_temp = data_df.drop([\"poi\"], axis=1)\n",
    "data_temp[data_temp.isnull().all(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"LOCKHART EUGENE E\" has all the values missing and will be removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_df = data_df.drop([\"LOCKHART EUGENE E\"], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, since some values are related I would like to rearrange the columns in he following order:  \n",
    "\n",
    "| **POI** | *All payment features* | **Total Payments** | *All stock features* | **Total Stocks** | *Incoming emails features* | **All incoming mails** | *sent emails features* | **All sent emails** |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>poi</th>\n",
       "      <th>salary</th>\n",
       "      <th>bonus</th>\n",
       "      <th>long_term_incentive</th>\n",
       "      <th>deferred_income</th>\n",
       "      <th>deferral_payments</th>\n",
       "      <th>loan_advances</th>\n",
       "      <th>other</th>\n",
       "      <th>expenses</th>\n",
       "      <th>director_fees</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>restricted_stock</th>\n",
       "      <th>restricted_stock_deferred</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>from_poi_to_this_person</th>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <th>to_messages</th>\n",
       "      <th>from_this_person_to_poi</th>\n",
       "      <th>from_messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ALLEN PHILLIP K</th>\n",
       "      <td>False</td>\n",
       "      <td>201955.0</td>\n",
       "      <td>4175000.0</td>\n",
       "      <td>304805.0</td>\n",
       "      <td>-3081055.0</td>\n",
       "      <td>2869717.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152.0</td>\n",
       "      <td>13868.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4484442.0</td>\n",
       "      <td>1729541.0</td>\n",
       "      <td>126027.0</td>\n",
       "      <td>-126027.0</td>\n",
       "      <td>1729541.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1407.0</td>\n",
       "      <td>2902.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BADUM JAMES P</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>178980.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3486.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182466.0</td>\n",
       "      <td>257817.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>257817.0</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>BANNANTINE JAMES M</th>\n",
       "      <td>False</td>\n",
       "      <td>477.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5104.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>864523.0</td>\n",
       "      <td>56301.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>916197.0</td>\n",
       "      <td>4046157.0</td>\n",
       "      <td>1757552.0</td>\n",
       "      <td>-560222.0</td>\n",
       "      <td>5243487.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>566.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAXTER JOHN C</th>\n",
       "      <td>False</td>\n",
       "      <td>267102.0</td>\n",
       "      <td>1200000.0</td>\n",
       "      <td>1586055.0</td>\n",
       "      <td>-1386055.0</td>\n",
       "      <td>1295738.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2660303.0</td>\n",
       "      <td>11200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5634343.0</td>\n",
       "      <td>6680544.0</td>\n",
       "      <td>3942714.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10623258.0</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>BAY FRANKLIN R</th>\n",
       "      <td>False</td>\n",
       "      <td>239671.0</td>\n",
       "      <td>400000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-201641.0</td>\n",
       "      <td>260455.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>129142.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>827696.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>145796.0</td>\n",
       "      <td>-82782.0</td>\n",
       "      <td>63014.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      poi    salary      bonus  long_term_incentive  \\\n",
       "ALLEN PHILLIP K     False  201955.0  4175000.0             304805.0   \n",
       "BADUM JAMES P       False       NaN        NaN                  NaN   \n",
       "BANNANTINE JAMES M  False     477.0        NaN                  NaN   \n",
       "BAXTER JOHN C       False  267102.0  1200000.0            1586055.0   \n",
       "BAY FRANKLIN R      False  239671.0   400000.0                  NaN   \n",
       "\n",
       "                    deferred_income  deferral_payments  loan_advances  \\\n",
       "ALLEN PHILLIP K          -3081055.0          2869717.0            NaN   \n",
       "BADUM JAMES P                   NaN           178980.0            NaN   \n",
       "BANNANTINE JAMES M          -5104.0                NaN            NaN   \n",
       "BAXTER JOHN C            -1386055.0          1295738.0            NaN   \n",
       "BAY FRANKLIN R            -201641.0           260455.0            NaN   \n",
       "\n",
       "                        other  expenses  director_fees  total_payments  \\\n",
       "ALLEN PHILLIP K         152.0   13868.0            NaN       4484442.0   \n",
       "BADUM JAMES P             NaN    3486.0            NaN        182466.0   \n",
       "BANNANTINE JAMES M   864523.0   56301.0            NaN        916197.0   \n",
       "BAXTER JOHN C       2660303.0   11200.0            NaN       5634343.0   \n",
       "BAY FRANKLIN R           69.0  129142.0            NaN        827696.0   \n",
       "\n",
       "                    exercised_stock_options  restricted_stock  \\\n",
       "ALLEN PHILLIP K                   1729541.0          126027.0   \n",
       "BADUM JAMES P                      257817.0               NaN   \n",
       "BANNANTINE JAMES M                4046157.0         1757552.0   \n",
       "BAXTER JOHN C                     6680544.0         3942714.0   \n",
       "BAY FRANKLIN R                          NaN          145796.0   \n",
       "\n",
       "                    restricted_stock_deferred  total_stock_value  \\\n",
       "ALLEN PHILLIP K                     -126027.0          1729541.0   \n",
       "BADUM JAMES P                             NaN           257817.0   \n",
       "BANNANTINE JAMES M                  -560222.0          5243487.0   \n",
       "BAXTER JOHN C                             NaN         10623258.0   \n",
       "BAY FRANKLIN R                       -82782.0            63014.0   \n",
       "\n",
       "                    from_poi_to_this_person  shared_receipt_with_poi  \\\n",
       "ALLEN PHILLIP K                        47.0                   1407.0   \n",
       "BADUM JAMES P                           NaN                      NaN   \n",
       "BANNANTINE JAMES M                     39.0                    465.0   \n",
       "BAXTER JOHN C                           NaN                      NaN   \n",
       "BAY FRANKLIN R                          NaN                      NaN   \n",
       "\n",
       "                    to_messages  from_this_person_to_poi  from_messages  \n",
       "ALLEN PHILLIP K          2902.0                     65.0         2195.0  \n",
       "BADUM JAMES P               NaN                      NaN            NaN  \n",
       "BANNANTINE JAMES M        566.0                      0.0           29.0  \n",
       "BAXTER JOHN C               NaN                      NaN            NaN  \n",
       "BAY FRANKLIN R              NaN                      NaN            NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [\n",
    "    'poi', 'salary', 'bonus', 'long_term_incentive', 'deferred_income',\n",
    "    'deferral_payments', 'loan_advances', 'other', 'expenses', 'director_fees',\n",
    "    'total_payments', 'exercised_stock_options', 'restricted_stock',\n",
    "    'restricted_stock_deferred', 'total_stock_value',\n",
    "    'from_poi_to_this_person', 'shared_receipt_with_poi', 'to_messages',\n",
    "    'from_this_person_to_poi', 'from_messages'\n",
    "]\n",
    "data_df = data_df[cols]\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_df.replace(to_replace=\"NaN\", value=0, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the features are in the right order, we can examine the statistics of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>long_term_incentive</th>\n",
       "      <th>deferred_income</th>\n",
       "      <th>deferral_payments</th>\n",
       "      <th>loan_advances</th>\n",
       "      <th>other</th>\n",
       "      <th>expenses</th>\n",
       "      <th>director_fees</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>restricted_stock</th>\n",
       "      <th>restricted_stock_deferred</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>from_poi_to_this_person</th>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <th>to_messages</th>\n",
       "      <th>from_this_person_to_poi</th>\n",
       "      <th>from_messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>145.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.683342e+05</td>\n",
       "      <td>1.342671e+06</td>\n",
       "      <td>6.692680e+05</td>\n",
       "      <td>-3.854019e+05</td>\n",
       "      <td>4.418227e+05</td>\n",
       "      <td>1.157586e+06</td>\n",
       "      <td>5.894693e+05</td>\n",
       "      <td>7.123619e+04</td>\n",
       "      <td>1.955643e+04</td>\n",
       "      <td>4.380626e+06</td>\n",
       "      <td>4.211583e+06</td>\n",
       "      <td>1.761321e+06</td>\n",
       "      <td>2.065786e+04</td>\n",
       "      <td>5.886335e+06</td>\n",
       "      <td>38.489655</td>\n",
       "      <td>697.765517</td>\n",
       "      <td>1230.013793</td>\n",
       "      <td>24.455172</td>\n",
       "      <td>361.075862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.211001e+06</td>\n",
       "      <td>8.121319e+06</td>\n",
       "      <td>4.059716e+06</td>\n",
       "      <td>2.386279e+06</td>\n",
       "      <td>2.750583e+06</td>\n",
       "      <td>9.682311e+06</td>\n",
       "      <td>3.694784e+06</td>\n",
       "      <td>4.341759e+05</td>\n",
       "      <td>1.194559e+05</td>\n",
       "      <td>2.702539e+07</td>\n",
       "      <td>2.615843e+07</td>\n",
       "      <td>1.093676e+07</td>\n",
       "      <td>1.444650e+06</td>\n",
       "      <td>3.636916e+07</td>\n",
       "      <td>74.088359</td>\n",
       "      <td>1075.128126</td>\n",
       "      <td>2232.153003</td>\n",
       "      <td>79.527073</td>\n",
       "      <td>1445.944684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.799289e+07</td>\n",
       "      <td>-1.025000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.604490e+06</td>\n",
       "      <td>-7.576788e+06</td>\n",
       "      <td>-4.409300e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-3.834600e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.025000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.246000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.520550e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.106920e+05</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.720000e+02</td>\n",
       "      <td>2.153000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.665220e+05</td>\n",
       "      <td>6.087500e+05</td>\n",
       "      <td>3.605280e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>9.760370e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.714420e+05</td>\n",
       "      <td>8.000000e+05</td>\n",
       "      <td>3.753040e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.025900e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.506560e+05</td>\n",
       "      <td>5.394700e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.979596e+06</td>\n",
       "      <td>1.729541e+06</td>\n",
       "      <td>8.530640e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.332399e+06</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>1607.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>52.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.670423e+07</td>\n",
       "      <td>9.734362e+07</td>\n",
       "      <td>4.852193e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.208340e+07</td>\n",
       "      <td>8.392500e+07</td>\n",
       "      <td>4.266759e+07</td>\n",
       "      <td>5.235198e+06</td>\n",
       "      <td>1.398517e+06</td>\n",
       "      <td>3.098866e+08</td>\n",
       "      <td>3.117640e+08</td>\n",
       "      <td>1.303223e+08</td>\n",
       "      <td>1.545629e+07</td>\n",
       "      <td>4.345095e+08</td>\n",
       "      <td>528.000000</td>\n",
       "      <td>5521.000000</td>\n",
       "      <td>15149.000000</td>\n",
       "      <td>609.000000</td>\n",
       "      <td>14368.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             salary         bonus  long_term_incentive  deferred_income  \\\n",
       "count  1.450000e+02  1.450000e+02         1.450000e+02     1.450000e+02   \n",
       "mean   3.683342e+05  1.342671e+06         6.692680e+05    -3.854019e+05   \n",
       "std    2.211001e+06  8.121319e+06         4.059716e+06     2.386279e+06   \n",
       "min    0.000000e+00  0.000000e+00         0.000000e+00    -2.799289e+07   \n",
       "25%    0.000000e+00  0.000000e+00         0.000000e+00    -3.834600e+04   \n",
       "50%    2.106920e+05  3.000000e+05         0.000000e+00     0.000000e+00   \n",
       "75%    2.714420e+05  8.000000e+05         3.753040e+05     0.000000e+00   \n",
       "max    2.670423e+07  9.734362e+07         4.852193e+07     0.000000e+00   \n",
       "\n",
       "       deferral_payments  loan_advances         other      expenses  \\\n",
       "count       1.450000e+02   1.450000e+02  1.450000e+02  1.450000e+02   \n",
       "mean        4.418227e+05   1.157586e+06  5.894693e+05  7.123619e+04   \n",
       "std         2.750583e+06   9.682311e+06  3.694784e+06  4.341759e+05   \n",
       "min        -1.025000e+05   0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%         0.000000e+00   0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "50%         0.000000e+00   0.000000e+00  9.720000e+02  2.153000e+04   \n",
       "75%         1.025900e+04   0.000000e+00  1.506560e+05  5.394700e+04   \n",
       "max         3.208340e+07   8.392500e+07  4.266759e+07  5.235198e+06   \n",
       "\n",
       "       director_fees  total_payments  exercised_stock_options  \\\n",
       "count   1.450000e+02    1.450000e+02             1.450000e+02   \n",
       "mean    1.955643e+04    4.380626e+06             4.211583e+06   \n",
       "std     1.194559e+05    2.702539e+07             2.615843e+07   \n",
       "min     0.000000e+00    0.000000e+00             0.000000e+00   \n",
       "25%     0.000000e+00    1.025000e+05             0.000000e+00   \n",
       "50%     0.000000e+00    9.665220e+05             6.087500e+05   \n",
       "75%     0.000000e+00    1.979596e+06             1.729541e+06   \n",
       "max     1.398517e+06    3.098866e+08             3.117640e+08   \n",
       "\n",
       "       restricted_stock  restricted_stock_deferred  total_stock_value  \\\n",
       "count      1.450000e+02               1.450000e+02       1.450000e+02   \n",
       "mean       1.761321e+06               2.065786e+04       5.886335e+06   \n",
       "std        1.093676e+07               1.444650e+06       3.636916e+07   \n",
       "min       -2.604490e+06              -7.576788e+06      -4.409300e+04   \n",
       "25%        3.246000e+04               0.000000e+00       2.520550e+05   \n",
       "50%        3.605280e+05               0.000000e+00       9.760370e+05   \n",
       "75%        8.530640e+05               0.000000e+00       2.332399e+06   \n",
       "max        1.303223e+08               1.545629e+07       4.345095e+08   \n",
       "\n",
       "       from_poi_to_this_person  shared_receipt_with_poi   to_messages  \\\n",
       "count               145.000000               145.000000    145.000000   \n",
       "mean                 38.489655               697.765517   1230.013793   \n",
       "std                  74.088359              1075.128126   2232.153003   \n",
       "min                   0.000000                 0.000000      0.000000   \n",
       "25%                   0.000000                 0.000000      0.000000   \n",
       "50%                   4.000000               114.000000    312.000000   \n",
       "75%                  41.000000               900.000000   1607.000000   \n",
       "max                 528.000000              5521.000000  15149.000000   \n",
       "\n",
       "       from_this_person_to_poi  from_messages  \n",
       "count               145.000000     145.000000  \n",
       "mean                 24.455172     361.075862  \n",
       "std                  79.527073    1445.944684  \n",
       "min                   0.000000       0.000000  \n",
       "25%                   0.000000       0.000000  \n",
       "50%                   0.000000      17.000000  \n",
       "75%                  14.000000      52.000000  \n",
       "max                 609.000000   14368.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the 3 categories have different orders of magnitude with the biggest different between the *email features* and the other two categories. Most probably we will need to scale the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlier Investigation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My first attempt to spot any possible outliers will be visual.  \n",
    "I will use Seaborn's pairplot which present in the same time the distribution of the variables and a scatter plot representation of them. Since the number of variables are too many to plot them all, I will use the 4 with the higher variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f65ba942f60>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwwAAALPCAYAAADPdEfVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65ba960080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=data_df, vars=[\"total_payments\", \"exercised_stock_options\", \"restricted_stock\", \"total_stock_value\"], hue=\"poi\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two datapoints far away from the cluster of the rest. I will use the *Total Payments* to find them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TOTAL            309886585.0\n",
       "LAY KENNETH L    103559793.0\n",
       "Name: total_payments, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.total_payments.nlargest(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first one 'TOTAL', is the totals on the [Payments Schedule](dataset/enron61702insiderpay.pdf) and not a person so it should be removed.  \n",
    "The second one is not an outlier, it is just the huge payment and stock value of the CEO and chairman of Enron, Kenneth Lay. Datapoints like this are not outliers; in fact anomalies like this may lead us to the rest of the POIs. \n",
    "These extreme values lead the rest of the employees to the bottom left corner of the scatterplot. Let's use a logarithmic scale for both axes to unclutter the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_df.drop(\"TOTAL\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x7f65b96734e0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwcAAALPCAYAAAA3lLwiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkdD/D1EhiCp3TenLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65b9673518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=data_df, vars=[\"total_payments\", \"exercised_stock_options\", \"restricted_stock\", \"total_stock_value\"], hue=\"poi\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the \"TOTAL\" removed the scatter plots are much more uncluttered and we can see some trends on them.  \n",
    "We can notice also a negative value on *Restricted Stock* variable, an indication that more outliers may exist.  \n",
    "Since the data have been taken from a financial statement an error may have introduced during the data entry / scrapping.  \n",
    "We can make a first sanity by checking if the individual values sum with the totals of each category (*Total Payments*, *Total Stock Value*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "salary                  26704229.0\n",
      "bonus                   97343619.0\n",
      "long_term_incentive     48521928.0\n",
      "deferred_income        -27890391.0\n",
      "deferral_payments       31980896.0\n",
      "loan_advances           83925000.0\n",
      "other                   42805453.0\n",
      "expenses                 5094049.0\n",
      "director_fees            1437166.0\n",
      "total_payments         325304226.0\n",
      "dtype: float64\n",
      "---\n",
      "Sum all 'payment' variables: 309921949.0\n"
     ]
    }
   ],
   "source": [
    "print(data_df.sum()[1:11])\n",
    "print(\"---\")\n",
    "print(\"Sum all 'payment' variables:\", sum(data_df.sum()[1:10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "exercised_stock_options      298915485.0\n",
      "restricted_stock             125069226.0\n",
      "restricted_stock_deferred     10572178.0\n",
      "total_stock_value            419009128.0\n",
      "dtype: float64\n",
      "---\n",
      "Sum all 'stock' variables: 434556889.0\n"
     ]
    }
   ],
   "source": [
    "print(data_df.sum()[11:15])\n",
    "print(\"---\")\n",
    "print(\"Sum all 'stock' variables:\", sum(data_df.sum()[11:14]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the totals do not match. I need to check employee by employee to find the errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>poi</th>\n",
       "      <th>salary</th>\n",
       "      <th>bonus</th>\n",
       "      <th>long_term_incentive</th>\n",
       "      <th>deferred_income</th>\n",
       "      <th>deferral_payments</th>\n",
       "      <th>loan_advances</th>\n",
       "      <th>other</th>\n",
       "      <th>expenses</th>\n",
       "      <th>director_fees</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>restricted_stock</th>\n",
       "      <th>restricted_stock_deferred</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>from_poi_to_this_person</th>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <th>to_messages</th>\n",
       "      <th>from_this_person_to_poi</th>\n",
       "      <th>from_messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BELFER ROBERT</th>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-102500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3285.0</td>\n",
       "      <td>102500.0</td>\n",
       "      <td>3285.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44093.0</td>\n",
       "      <td>-44093.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BHATNAGAR SANJAY</th>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137864.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>137864.0</td>\n",
       "      <td>15456290.0</td>\n",
       "      <td>2604490.0</td>\n",
       "      <td>-2604490.0</td>\n",
       "      <td>15456290.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>463.0</td>\n",
       "      <td>523.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    poi  salary  bonus  long_term_incentive  deferred_income  \\\n",
       "BELFER ROBERT     False     0.0    0.0                  0.0              0.0   \n",
       "BHATNAGAR SANJAY  False     0.0    0.0                  0.0              0.0   \n",
       "\n",
       "                  deferral_payments  loan_advances     other  expenses  \\\n",
       "BELFER ROBERT             -102500.0            0.0       0.0       0.0   \n",
       "BHATNAGAR SANJAY                0.0            0.0  137864.0       0.0   \n",
       "\n",
       "                  director_fees  total_payments  exercised_stock_options  \\\n",
       "BELFER ROBERT            3285.0        102500.0                   3285.0   \n",
       "BHATNAGAR SANJAY       137864.0      15456290.0                2604490.0   \n",
       "\n",
       "                  restricted_stock  restricted_stock_deferred  \\\n",
       "BELFER ROBERT                  0.0                    44093.0   \n",
       "BHATNAGAR SANJAY        -2604490.0                 15456290.0   \n",
       "\n",
       "                  total_stock_value  from_poi_to_this_person  \\\n",
       "BELFER ROBERT              -44093.0                      0.0   \n",
       "BHATNAGAR SANJAY                0.0                      0.0   \n",
       "\n",
       "                  shared_receipt_with_poi  to_messages  \\\n",
       "BELFER ROBERT                         0.0          0.0   \n",
       "BHATNAGAR SANJAY                    463.0        523.0   \n",
       "\n",
       "                  from_this_person_to_poi  from_messages  \n",
       "BELFER ROBERT                         0.0            0.0  \n",
       "BHATNAGAR SANJAY                      1.0           29.0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alist = []\n",
    "for line in data_df.itertuples():\n",
    "    if sum(line[2:11]) != line[11] or sum(line[12:15]) != line[15]:\n",
    "        alist.append(line[0])\n",
    "data_df.loc[alist]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the above results with the [Payments Schedule](dataset/enron61702insiderpay.pdf) we can see that there are some errors in the data.  \n",
    "The right values are:\n",
    "\n",
    "|               | poi | salary | deferral_payments | loan_advances | bonus | deferred_income | expenses | other | long_term_incentive | director_fees | total_payments | restricted_stock_deferred | exercised_stock_options | restricted_stock | total_stock_value |\n",
    "|-------------------|--------|-------------------|---------------|-------|-----------------|----------|-------|---------------------|---------------|----------------|---------------------------|-------------------------|------------------|-------------------|\n",
    "| **BELFER ROBERT** | False | 0      | 0                 | 0             | 0     | -102500        | 3285    | 0     | 0                   | 102500       | 3285          | -44093                   | 0                       | 44093           | 0                 |\n",
    "| **BHATNAGAR SANJAY** | False | 0      | 0                 | 0             | 0     | 0               | 137864  |   0    | 0                   | 0             | 137864        | -2604490                | 15456290              | 2604490        | 15456290        |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_df.loc[\"BELFER ROBERT\", :] = [\n",
    "    False, 0, 0, 0, 0, -102500, 3285, 0, 0, 102500, 3285, -44093, 0, 44093, 0,\n",
    "    0, 0, 0, 0, 0\n",
    "]\n",
    "data_df.loc[\"BHATNAGAR SANJAY\", :] = [\n",
    "    False, 0, 0, 0, 0, 0, 137864, 0, 0, 0, 137864, -2604490, 15456290, 2604490,\n",
    "    15456290, 0, 463, 523, 1, 29\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we do not have any more outliers we can plot the two aggregated variables, *Total Payments* and *Total Stock Value*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAENCAYAAADgwHn9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65b8a20898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax = plt.subplots()\n",
    "for poi, data in data_df.groupby(by=\"poi\"):\n",
    "    ax.plot(data['total_payments'],data['total_stock_value'],'o', label=poi)\n",
    "ax.legend()\n",
    "plt.xscale('symlog')\n",
    "plt.yscale('symlog')\n",
    "plt.xlabel(\"Total Payments\")\n",
    "plt.ylabel(\"Total Stock Value\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are some persons with zero salary or bonus (or both) and none of them is a POI. Since we have a sparse number of POIs it might be beneficial to remove them to have a more dense dataset. I will create a copy of the dataset with the specific persons removed for future evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(81, 20)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nbs = data_df[data_df.salary > 0]\n",
    "data_nbs = data_nbs[data_nbs.bonus > 0]\n",
    "data_nbs.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"TOTAL\" was a false entry in the dataset, I want to investigate if there are more. We can notice that the indexes / names in the dataset are in the form *Sirname Name Initial*. I will search all the indexes using regular expressions and print the indexes that do not follow this pattern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BLAKE JR. NORMAN P\n",
      "BOWEN JR RAYMOND M\n",
      "DERRICK JR. JAMES V\n",
      "DONAHUE JR JEFFREY M\n",
      "GARLAND C KEVIN\n",
      "GLISAN JR BEN F\n",
      "OVERDYKE JR JERE C\n",
      "PEREIRA PAULO V. FERRAZ\n",
      "SULLIVAN-SHAKLOVITZ COLLEEN\n",
      "THE TRAVEL AGENCY IN THE PARK\n",
      "WALLS JR ROBERT H\n",
      "WHITE JR THOMAS E\n",
      "WINOKUR JR. HERBERT S\n",
      "YEAGER F SCOTT\n"
     ]
    }
   ],
   "source": [
    "for index in data_df.index:\n",
    "    if re.match('^[A-Z]+\\s[A-Z]+(\\s[A-Z])?$', index):\n",
    "        continue\n",
    "    else:\n",
    "        print(index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a \"suspicious\" index. The **THE TRAVEL AGENCY IN THE PARK**, isn't obviously a name of an employee."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>poi</th>\n",
       "      <th>salary</th>\n",
       "      <th>bonus</th>\n",
       "      <th>long_term_incentive</th>\n",
       "      <th>deferred_income</th>\n",
       "      <th>deferral_payments</th>\n",
       "      <th>loan_advances</th>\n",
       "      <th>other</th>\n",
       "      <th>expenses</th>\n",
       "      <th>director_fees</th>\n",
       "      <th>total_payments</th>\n",
       "      <th>exercised_stock_options</th>\n",
       "      <th>restricted_stock</th>\n",
       "      <th>restricted_stock_deferred</th>\n",
       "      <th>total_stock_value</th>\n",
       "      <th>from_poi_to_this_person</th>\n",
       "      <th>shared_receipt_with_poi</th>\n",
       "      <th>to_messages</th>\n",
       "      <th>from_this_person_to_poi</th>\n",
       "      <th>from_messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>THE TRAVEL AGENCY IN THE PARK</th>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>362096.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>362096.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 poi  salary  bonus  long_term_incentive  \\\n",
       "THE TRAVEL AGENCY IN THE PARK  False     0.0    0.0                  0.0   \n",
       "\n",
       "                               deferred_income  deferral_payments  \\\n",
       "THE TRAVEL AGENCY IN THE PARK              0.0                0.0   \n",
       "\n",
       "                               loan_advances     other  expenses  \\\n",
       "THE TRAVEL AGENCY IN THE PARK            0.0  362096.0       0.0   \n",
       "\n",
       "                               director_fees  total_payments  \\\n",
       "THE TRAVEL AGENCY IN THE PARK            0.0        362096.0   \n",
       "\n",
       "                               exercised_stock_options  restricted_stock  \\\n",
       "THE TRAVEL AGENCY IN THE PARK                      0.0               0.0   \n",
       "\n",
       "                               restricted_stock_deferred  total_stock_value  \\\n",
       "THE TRAVEL AGENCY IN THE PARK                        0.0                0.0   \n",
       "\n",
       "                               from_poi_to_this_person  \\\n",
       "THE TRAVEL AGENCY IN THE PARK                      0.0   \n",
       "\n",
       "                               shared_receipt_with_poi  to_messages  \\\n",
       "THE TRAVEL AGENCY IN THE PARK                      0.0          0.0   \n",
       "\n",
       "                               from_this_person_to_poi  from_messages  \n",
       "THE TRAVEL AGENCY IN THE PARK                      0.0            0.0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.loc[[\"THE TRAVEL AGENCY IN THE PARK\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By cross-checking the above record, we can find that it is a travel agency.\n",
    "\n",
    ">Payments were made by Enron employees on account of business-related travel to The Travel Agency in the Park (later Alliance Worldwide), which was coowned\n",
    "by the sister of Enron's former Chairman. Payments made by the Debtor to reimburse employees for these expenses have not been included.\n",
    "\n",
    "Since it is not an employee we should remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_df = data_df.drop([\"THE TRAVEL AGENCY IN THE PARK\"], axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a final step in outlier investigation, I will search for extreme values. I suspect that because of the nature of the problem, the extreme values is an essential information and they should be kept but let's spot them first.  \n",
    "I'm using Tukey Fences with 3 IQRs for every single feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def outliers_iqr(dataframe, features):\n",
    "    result = set()\n",
    "    for feature in features:\n",
    "        ys = dataframe[[feature]]\n",
    "        quartile_1, quartile_3 = np.percentile(ys, [25, 75])\n",
    "        iqr = quartile_3 - quartile_1\n",
    "        lower_bound = int(round(quartile_1 - (iqr * 3)))\n",
    "        upper_bound = int(round(quartile_3 + (iqr * 3)))\n",
    "        partial_result = list(np.where((ys > upper_bound) | (ys < lower_bound))[0])\n",
    "        print(feature, len(partial_result))\n",
    "        result.update(partial_result)\n",
    "        \n",
    "    print(\"-----------------------------------------------------\")\n",
    "    print(\"\")\n",
    "    print(\"Total number of records with extreme values: \" + str(len(result)))\n",
    "    \n",
    "    return list(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "salary 1\n",
      "bonus 5\n",
      "long_term_incentive 11\n",
      "deferred_income 24\n",
      "deferral_payments 34\n",
      "loan_advances 5\n",
      "other 13\n",
      "expenses 2\n",
      "director_fees 15\n",
      "total_payments 5\n",
      "exercised_stock_options 11\n",
      "restricted_stock 10\n",
      "restricted_stock_deferred 17\n",
      "total_stock_value 11\n",
      "from_poi_to_this_person 11\n",
      "shared_receipt_with_poi 4\n",
      "to_messages 6\n",
      "from_this_person_to_poi 13\n",
      "from_messages 20\n",
      "-----------------------------------------------------\n",
      "\n",
      "Total number of records with extreme values: 93\n"
     ]
    }
   ],
   "source": [
    "cols.remove(\"poi\")\n",
    "xtr_values =outliers_iqr(data_df, cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POI density on the original dataset: 0.125874125874\n",
      "POI density on the subset with the extreme values: 0.161290322581\n",
      "Difference: 0.281362007168\n"
     ]
    }
   ],
   "source": [
    "a = data_df.loc[:, \"poi\"].value_counts()\n",
    "poi_density = a[1]/(a[0]+a[1])\n",
    "print(\"POI density on the original dataset: \" + str(poi_density))\n",
    "a = data_df.ix[xtr_values, \"poi\"].value_counts()\n",
    "poi_density_xtr = a[1]/(a[0]+a[1])\n",
    "poi_density_xtr = a[1]/(a[0]+a[1])\n",
    "print(\"POI density on the subset with the extreme values: \" + str(poi_density_xtr))\n",
    "\n",
    "print(\"Difference: \" +str((poi_density_xtr - poi_density) / poi_density))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that in the subset of employees with extreme value to at least one variable, there are 28% more POIs than in the general dataset. This justify our intuition that the extreme values are related with being a POI, thus we will not remove them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the dataset is clear of outliers we can find the final dimensions and split the labels from the features and have a first scoring as a baseline for the rest of the analysis. I will use the LinearSVC classifier which seems the more appropriate to begin.  \n",
    "The dataset is quite sparse to use the usual *training*/*testing* splitting so instead I will use the whole dataset for training and cross validation for testing. The procedure has been code in the ```test_classifier()``` function, part of the *tester.py* file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(143, 20)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    125\n",
       "True      18\n",
       "Name: poi, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.loc[:, \"poi\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def do_split(data):\n",
    "    X = data.copy()\n",
    "    #Removing the poi labels and put them in a separate array, transforming it\n",
    "    #from True / False to 0 / 1\n",
    "    y = X.pop(\"poi\").astype(int)\n",
    "    \n",
    "    return X, y, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = do_split(data_nbs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "run_control": {
     "marked": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
      "     verbose=0)\n",
      "\tAccuracy: 0.72867\tPrecision: 0.20293\tRecall: 0.35350\tF1: 0.25784\tF2: 0.30782\n",
      "\tTotal predictions: 15000\tTrue positives:  707\tFalse positives: 2777\tFalse negatives: 1293\tTrue negatives: 10223\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#test_classifier() demands the dataset in a dictionary and the features labels\n",
    "#in a list with 'poi' first.\n",
    "features = data_df.columns.tolist()\n",
    "data = data_df.to_dict(orient='index')\n",
    "\n",
    "test_classifier(LinearSVC(random_state=42), data, features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are interested in the ability of the classifier not to label as Person Of Interest (POI) a person that is not, and also to find all the POIs so the metrics that we are most interested are **Precision** and **Recall**. Since we want to maximize both in the same time we will try to maximize the **F1** score which can be interpreted as a weighted average of the precision and recall.  \n",
    "We can see that the initial scores are very low with the LinearSVC classifier being poor in classifying the right persons. This result might be either due to inability of the specific algorithm to make a good prediction on the specific dataset or because of the need of preprocessing of the dataset (or both).  \n",
    "In the following steps, I will explore the impact of adding and transforming features to the performance of the model. Finally, I will try several algorithm families to end up with a combination of features / algorithm to build the best performing model for the specific problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimize Feature Selection/Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create new features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In some cases the value of a variable is less important than its proportion to an aggregated value. As an example from the current dataset a bonus of 100,000 is less informative than a bonus 3 times the salary, or \"500 sent email to POIs\" is far less informative than \"half of the sent emails have been sent to POIs\".  \n",
    "For this reason I will add the proportions of each variable to its category's sum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data_df.copy()\n",
    "data.loc[:, \"salary_p\"] = data.loc[:, \"salary\"]/data.loc[:, \"total_payments\"]\n",
    "data.loc[:, \"deferral_payments_p\"] = data.loc[:, \"deferral_payments\"]/data.loc[:, \"total_payments\"]\n",
    "data.loc[:, \"loan_advances_p\"] = data.loc[:, \"loan_advances\"]/data.loc[:, \"total_payments\"]\n",
    "data.loc[:, \"bonus_p\"] = data.loc[:, \"bonus\"]/data.loc[:, \"total_payments\"]\n",
    "data.loc[:, \"deferred_income_p\"] = data.loc[:, \"deferred_income\"]/data.loc[:, \"total_payments\"]\n",
    "data.loc[:, \"expenses_p\"] = data.loc[:, \"expenses\"]/data.loc[:, \"total_payments\"]\n",
    "data.loc[:, \"other_p\"] = data.loc[:, \"other\"]/data.loc[:, \"total_payments\"]\n",
    "data.loc[:, \"long_term_incentive_p\"] = data.loc[:, \"long_term_incentive\"]/data.loc[:, \"total_payments\"]\n",
    "data.loc[:, \"director_fees_p\"] = data.loc[:, \"director_fees\"]/data.loc[:, \"total_payments\"]\n",
    "\n",
    "data.loc[:, \"restricted_stock_deferred_p\"] = data.loc[:, \"restricted_stock_deferred\"]/data.loc[:, \"total_stock_value\"]\n",
    "data.loc[:, \"exercised_stock_options_p\"] = data.loc[:, \"exercised_stock_options\"]/data.loc[:, \"total_stock_value\"]\n",
    "data.loc[:, \"restricted_stock_p\"] = data.loc[:, \"restricted_stock\"]/data.loc[:, \"total_stock_value\"]\n",
    "\n",
    "data.loc[:, \"from_poi_to_this_person_p\"] = data.loc[:, \"from_poi_to_this_person\"]/data.loc[:, \"to_messages\"]\n",
    "data.loc[:, \"shared_receipt_with_poi_p\"] = data.loc[:, \"shared_receipt_with_poi\"]/data.loc[:, \"to_messages\"]\n",
    "\n",
    "data.loc[:, \"from_this_person_to_poi_p\"] = data.loc[:, \"from_this_person_to_poi\"]/data.loc[:, \"from_messages\"]\n",
    "    \n",
    "data.replace(to_replace=np.NaN, value=0, inplace=True)\n",
    "data.replace(to_replace=np.inf, value=0, inplace=True)\n",
    "data.replace(to_replace=-np.inf, value=0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the importance of the features of the \"enriched\" dataset by using the same classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_importance(dataset):\n",
    "    X, y = do_split(dataset)\n",
    "\n",
    "    selector = SelectPercentile(percentile=100)\n",
    "    a = selector.fit(X, y)\n",
    "\n",
    "    plt.figure(figsize=(12,9))\n",
    "    sns.barplot(y=X.columns, x=a.scores_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAy8AAAIICAYAAABn89EKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/T9Y49hSZtYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65b3fb4c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_importance(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the newly created features with the original we can see that the proportions of \"Long Term Incentive\", \"Restricted Stock Deferred\" and \"From This Person to POI\" score higher than the original features. We will keep these and remove the original values. to avoid bias the model towards a specific feature by using both the original value and its proportion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Adding the proportions\n",
    "data_df.loc[:, \"long_term_incentive_p\"] = data_df.loc[:, \"long_term_incentive\"]/data_df.loc[:, \"total_payments\"]\n",
    "data_df.loc[:, \"restricted_stock_deferred_p\"] = data_df.loc[:, \"restricted_stock_deferred\"]/data_df.loc[:, \"total_stock_value\"]\n",
    "data_df.loc[:, \"from_this_person_to_poi_p\"] = data_df.loc[:, \"from_this_person_to_poi\"]/data_df.loc[:, \"from_messages\"]\n",
    "#Removing the original values.\n",
    "data_df.drop(\"long_term_incentive\", axis=1)\n",
    "data_df.drop(\"restricted_stock_deferred\", axis=1)\n",
    "data_df.drop(\"from_this_person_to_poi\", axis=1)\n",
    "#Correcting NaN and infinity values created by zero divisions\n",
    "data_df.replace(to_replace=np.NaN, value=0, inplace=True)\n",
    "data_df.replace(to_replace=np.inf, value=0, inplace=True)\n",
    "data_df.replace(to_replace=-np.inf, value=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAy8AAAIICAYAAABn89EKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJmRAH9ZZbtIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65b8071400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_importance(data_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For feature selection I will use LinearSVC classifier and I will test both univariate feature selection (KBest) and Primary Component Analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X, y = do_split(data_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIlCAYAAAD1429MAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naaiFEEIIIYRoBWmohRBCCCGEaIX/AIGs9bHPdgLFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f205713ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('reduce_dim', SelectKBest(k=7, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0))])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pipe = Pipeline([('reduce_dim', PCA(random_state=42)),\n",
    "                 ('classify', LinearSVC(random_state=42))])\n",
    "\n",
    "N_FEATURES_OPTIONS = list(range(2,21))\n",
    "\n",
    "param_grid = [\n",
    "    {\n",
    "        'reduce_dim': [PCA(random_state=42)],\n",
    "        'reduce_dim__n_components': N_FEATURES_OPTIONS,\n",
    "    },\n",
    "    {\n",
    "        'reduce_dim': [SelectKBest()],\n",
    "        'reduce_dim__k': N_FEATURES_OPTIONS,\n",
    "    },\n",
    "]\n",
    "reducer_labels = ['PCA', 'KBest']\n",
    "cv = StratifiedShuffleSplit(random_state=42)\n",
    "grid = GridSearchCV(\n",
    "    pipe, param_grid=param_grid, cv=cv, scoring='f1', n_jobs=-1)\n",
    "\n",
    "X, y = do_split(data_df)\n",
    "grid.fit(X, y)\n",
    "\n",
    "mean_scores = np.array(grid.cv_results_['mean_test_score'])\n",
    "mean_scores = mean_scores.reshape(-1, len(N_FEATURES_OPTIONS))\n",
    "bar_offsets = (np.arange(len(N_FEATURES_OPTIONS)) *\n",
    "               (len(reducer_labels) + 1) + .5)\n",
    "plt.figure(figsize=(12,9))\n",
    "\n",
    "for i, (label, reducer_scores) in enumerate(zip(reducer_labels, mean_scores)):\n",
    "    plt.bar(bar_offsets + i, reducer_scores, label=label)\n",
    "\n",
    "plt.title(\"Comparing feature reduction techniques\")\n",
    "plt.xlabel('Reduced number of features')\n",
    "plt.xticks(bar_offsets + len(reducer_labels) / 2, N_FEATURES_OPTIONS)\n",
    "plt.ylabel('Classification accuracy')\n",
    "plt.ylim((0, 0.4))\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()\n",
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we get the best result if we reduce the features of the model to :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['salary',\n",
       " 'bonus',\n",
       " 'exercised_stock_options',\n",
       " 'total_stock_value',\n",
       " 'long_term_incentive_p',\n",
       " 'from_this_person_to_poi_p']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector = SelectKBest(k=6)\n",
    "selector.fit(X, y)\n",
    "X.columns[selector.get_support()].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The score of LinearSVC on the reduced dataset is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('reduce_dim', SelectKBest(k=7, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
      "     verbose=0))])\n",
      "\tAccuracy: 0.76007\tPrecision: 0.24481\tRecall: 0.38350\tF1: 0.29885\tF2: 0.34447\n",
      "\tTotal predictions: 15000\tTrue positives:  767\tFalse positives: 2366\tFalse negatives: 1233\tTrue negatives: 10634\n",
      "\n"
     ]
    }
   ],
   "source": [
    "features = data_df.columns.tolist()\n",
    "data_dict = data_df.to_dict(orient='index')\n",
    "test_classifier(grid.best_estimator_, data_dict, features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a 17% improvement on the f1 score with 20% better precision and 12% better recall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are features in the dataset with big differences in scaling. For example *salary* appears with values between 0 and 2.5 millions while *from_this_person_to_poi_p* takes values in the range [0,1). Some of the algorithms we will evaluate may behave badly because of these differences so we need to scale them.  \n",
    "\n",
    "Having sparse data it is better to use ```MaxAbsScaler()``` for this transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAIlCAYAAAD1429MAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEEK0wX8AonLqf7XjPEYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2050397e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scale', MaxAbsScaler(copy=True)), ('reduce_dim', SelectKBest(k=7, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0))])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#warnings.filterwarnings('ignore')\n",
    "\n",
    "pipe = Pipeline([('scale', MaxAbsScaler()),\n",
    "                 ('reduce_dim', PCA(random_state=42)),\n",
    "                 ('classify', LinearSVC(random_state=42))])\n",
    "\n",
    "N_FEATURES_OPTIONS = list(range(2, 21))\n",
    "\n",
    "param_grid = [\n",
    "    {\n",
    "        'reduce_dim': [PCA(random_state=42)],\n",
    "        'reduce_dim__n_components': N_FEATURES_OPTIONS,\n",
    "    },\n",
    "    {\n",
    "        'reduce_dim': [SelectKBest()],\n",
    "        'reduce_dim__k': N_FEATURES_OPTIONS,\n",
    "    },\n",
    "]\n",
    "reducer_labels = ['PCA', 'KBest']\n",
    "cv = StratifiedShuffleSplit(random_state=42)\n",
    "grid = GridSearchCV(\n",
    "    pipe, param_grid=param_grid, cv=cv, scoring='f1', n_jobs=-1)\n",
    "\n",
    "grid.fit(X, y)\n",
    "\n",
    "mean_scores = np.array(grid.cv_results_['mean_test_score'])\n",
    "mean_scores = mean_scores.reshape(-1, len(N_FEATURES_OPTIONS))\n",
    "bar_offsets = (np.arange(len(N_FEATURES_OPTIONS)) *\n",
    "               (len(reducer_labels) + 1) + .5)\n",
    "plt.figure(figsize=(12,9))\n",
    "\n",
    "for i, (label, reducer_scores) in enumerate(zip(reducer_labels, mean_scores)):\n",
    "    plt.bar(bar_offsets + i, reducer_scores, label=label)\n",
    "\n",
    "plt.title(\"Comparing feature reduction techniques\")\n",
    "plt.xlabel('Reduced number of features')\n",
    "plt.xticks(bar_offsets + len(reducer_labels) / 2, N_FEATURES_OPTIONS)\n",
    "plt.ylabel('Classification accuracy')\n",
    "plt.ylim((0, 0.4))\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()\n",
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('scale', MaxAbsScaler(copy=True)), ('reduce_dim', SelectKBest(k=7, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
      "     verbose=0))])\n",
      "\tAccuracy: 0.87140\tPrecision: 0.55868\tRecall: 0.16900\tF1: 0.25950\tF2: 0.19640\n",
      "\tTotal predictions: 15000\tTrue positives:  338\tFalse positives:  267\tFalse negatives: 1662\tTrue negatives: 12733\n",
      "\n"
     ]
    }
   ],
   "source": [
    "features = data_df.columns.tolist()\n",
    "data = data_df.to_dict(orient='index')\n",
    "test_classifier(grid.best_estimator_, data, features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a quite interesting result. After scaling, model's Precision doubled but Recall fell lower than half, giving the model lower f1 score than before. I'll edit the pipeline to add the option of no-scaling and also the StandardScaler which is a more frequently used option but not very good for sparse data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scale', None), ('reduce_dim', SelectKBest(k=7, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
       "     verbose=0))])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe = Pipeline([('scale', MaxAbsScaler()),\n",
    "                 ('reduce_dim', PCA(random_state=42)),\n",
    "                 ('classify', LinearSVC(random_state=42))])\n",
    "\n",
    "N_FEATURES_OPTIONS = list(range(2, 21))\n",
    "\n",
    "param_grid = [\n",
    "    {\n",
    "        'scale':[None, MaxAbsScaler(), StandardScaler()],\n",
    "        'reduce_dim': [PCA(random_state=42)],\n",
    "        'reduce_dim__n_components': N_FEATURES_OPTIONS,\n",
    "    },\n",
    "    {\n",
    "        'scale':[None, MaxAbsScaler(), StandardScaler()],\n",
    "        'reduce_dim': [SelectKBest()],\n",
    "        'reduce_dim__k': N_FEATURES_OPTIONS,\n",
    "    },\n",
    "]\n",
    "reducer_labels = ['PCA', 'KBest']\n",
    "cv = StratifiedShuffleSplit(random_state=42)\n",
    "grid = GridSearchCV(\n",
    "    pipe, param_grid=param_grid, cv=cv, scoring='f1', n_jobs=-1)\n",
    "\n",
    "grid.fit(X, y)\n",
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "source": [
    "It's now clear that LinearSVC performs better without scaling but I will keep the scaling options in the pipeline because other algorithms may have different behaviour."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algorithm Selection and Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Having finished with the preprocessing of the dataset I will begin evaluating the performance of suitable algorithms. First I will evaluate different algorithms' families by using their most usual member with some generalized attributes and once I conclude with the family I will evaluate different members."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algorithm Family Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_style": "center"
   },
   "source": [
    "From Support Vector Machines, I will evaluate Support Vector Classifier with the default kernel (RBF) and Penalty Parameters 0.1, 1, 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('scale', MaxAbsScaler(copy=True)), ('reduce_dim', SelectKBest(k=3, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=42, shrinking=True,\n",
      "  tol=0.001, verbose=False))])\n"
     ]
    }
   ],
   "source": [
    "#warnings.filterwarnings('ignore')\n",
    "\n",
    "pipe = Pipeline([('scale', MaxAbsScaler()),\n",
    "                 ('reduce_dim', PCA(random_state=42)),\n",
    "                 ('classify', SVC(random_state=42))])\n",
    "\n",
    "N_FEATURES_OPTIONS = list(range(2, 10))\n",
    "C_VALUES = [0.1, 1, 10]\n",
    "\n",
    "\n",
    "param_grid = [\n",
    "    {\n",
    "        'scale':[None, MaxAbsScaler()],\n",
    "        'reduce_dim': [PCA(random_state=42)],\n",
    "        'reduce_dim__n_components': N_FEATURES_OPTIONS,\n",
    "        'classify__C': C_VALUES\n",
    "    },\n",
    "    {\n",
    "        'scale':[None, MaxAbsScaler()],\n",
    "        'reduce_dim': [SelectKBest()],\n",
    "        'reduce_dim__k': N_FEATURES_OPTIONS,\n",
    "        'classify__C': C_VALUES\n",
    "    },\n",
    "]\n",
    "\n",
    "cv = StratifiedShuffleSplit(random_state=42)\n",
    "grid = GridSearchCV(\n",
    "    pipe, param_grid=param_grid, cv=cv, scoring='f1', n_jobs=-1)\n",
    "\n",
    "grid.fit(X, y)\n",
    "\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('scale', MaxAbsScaler(copy=True)), ('reduce_dim', SelectKBest(k=3, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=42, shrinking=True,\n",
      "  tol=0.001, verbose=False))])\n",
      "\tAccuracy: 0.86927\tPrecision: 0.88235\tRecall: 0.02250\tF1: 0.04388\tF2: 0.02795\n",
      "\tTotal predictions: 15000\tTrue positives:   45\tFalse positives:    6\tFalse negatives: 1955\tTrue negatives: 12994\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_classifier(grid.best_estimator_, data, features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support Vector Machines gave as the highest Precision so far but in the same time the worst Recall achieving an F1 score of 0.05405"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFnCAYAAACPasF4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nircQQggRZaR4CyGEEFFGircQQggRZf4BZPRUi4lNPpQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2050247be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = .001  #step size in the mesh\n",
    "\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "scale = MaxAbsScaler()\n",
    "X_trans = scale.fit_transform(X)\n",
    "select = SelectKBest(k=2)\n",
    "X_trans = select.fit_transform(X_trans, y)\n",
    "y_trans = y.values\n",
    "\n",
    "clf = SVC(C=1,\n",
    "          cache_size=200,\n",
    "          class_weight=None,\n",
    "          coef0=0.0,\n",
    "          decision_function_shape=None,\n",
    "          degree=3,\n",
    "          gamma='auto',\n",
    "          kernel='rbf',\n",
    "          max_iter=-1,\n",
    "          probability=False,\n",
    "          random_state=42,\n",
    "          shrinking=True,\n",
    "          tol=0.001,\n",
    "          verbose=False)\n",
    "clf.fit(X_trans, y_trans)\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "x_min, x_max = X_trans[:, 0].min() - 0.1, X_trans[:, 0].max() + 0.1\n",
    "y_min, y_max = X_trans[:, 1].min() - 0.1, X_trans[:, 1].max() + 0.1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
    "\n",
    "# Plot the training points\n",
    "plt.scatter(X_trans[:, 0], X_trans[:, 1], c=y, cmap=cmap_bold, alpha=0.3)\n",
    "\n",
    "# Legend Data\n",
    "classes = ['POI','Non-POI']\n",
    "class_colours = ['#0000FF', '#FF0000']\n",
    "recs = []\n",
    "for i in range(0,len(class_colours)):\n",
    "    recs.append(mpatches.Rectangle((0,0),1,1,fc=class_colours[i]))\n",
    "\n",
    "# Plot Legend ('loc' == position):\n",
    "plt.legend(recs,classes,loc=4)\n",
    "plt.title(\"Support Vectors Classifier with RBF kernel\")\n",
    "plt.xlabel(X.columns[select.get_support()][0])\n",
    "plt.ylabel(X.columns[select.get_support()][1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nearest Neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Nearest Neighbors family, I will evaluate *KNeighborsClassifier*. Since this algorithm is very fast, I'm able to evaluate several parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('scale', None), ('reduce_dim', SelectKBest(k=5, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
      "           weights='uniform'))])\n"
     ]
    }
   ],
   "source": [
    "#warnings.filterwarnings('ignore')\n",
    "\n",
    "pipe = Pipeline([('scale', MaxAbsScaler()),\n",
    "                 ('reduce_dim', PCA(random_state=42)),\n",
    "                 ('classify', KNeighborsClassifier())])\n",
    "\n",
    "N_FEATURES_OPTIONS = list(range(2, 21))\n",
    "N_NEIGHBORS = [1, 3, 5]\n",
    "\n",
    "param_grid = [\n",
    "    {\n",
    "        'scale': [None, MaxAbsScaler()],\n",
    "        'reduce_dim': [PCA(random_state=42)],\n",
    "        'reduce_dim__n_components': N_FEATURES_OPTIONS,\n",
    "        'classify__weights': [\"uniform\", \"distance\"],\n",
    "        'classify__n_neighbors': N_NEIGHBORS,\n",
    "        'classify__p':[1, 2]\n",
    "    },\n",
    "    {\n",
    "        'scale': [None, MaxAbsScaler()],\n",
    "        'reduce_dim': [SelectKBest()],\n",
    "        'reduce_dim__k': N_FEATURES_OPTIONS,\n",
    "        'classify__weights': [\"uniform\", \"distance\"],\n",
    "        'classify__n_neighbors': N_NEIGHBORS,\n",
    "        'classify__p':[1, 2]\n",
    "    },\n",
    "]\n",
    "\n",
    "cv = StratifiedShuffleSplit(random_state=42)\n",
    "grid = GridSearchCV(\n",
    "    pipe, param_grid=param_grid, cv=cv, scoring='f1', n_jobs=-1)\n",
    "\n",
    "grid.fit(X, y)\n",
    "\n",
    "\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('scale', None), ('reduce_dim', SelectKBest(k=5, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
      "           weights='uniform'))])\n",
      "\tAccuracy: 0.85193\tPrecision: 0.43233\tRecall: 0.35300\tF1: 0.38866\tF2: 0.36645\n",
      "\tTotal predictions: 15000\tTrue positives:  706\tFalse positives:  927\tFalse negatives: 1294\tTrue negatives: 12073\n",
      "\n"
     ]
    }
   ],
   "source": [
    "features = data_df.columns.tolist()\n",
    "data = data_df.to_dict(orient='index')\n",
    "test_classifier(grid.best_estimator_, data, features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*KNeighborsClassifier* so far has the most balanced result across Precision and Recall. It has the best F1 score so far."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ensemble Methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Ensemble Methods, I will evaluate both Random Forest from the Averaging subcategory and AdaBoost from Boosting Methods.  \n",
    "Both classifiers are based on recursive partitioning, so they do not require features to be normalized or scaled, since it is invariant to monotonic transformations of the features, thus we can remove the scaling from the pipeline."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Averaging Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('reduce_dim', SelectKBest(k=15, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            n_estimators=1, n_jobs=1, oob_score=False, random_state=42,\n",
      "            verbose=0, warm_start=False))])\n"
     ]
    }
   ],
   "source": [
    "#warnings.filterwarnings('ignore')\n",
    "\n",
    "pipe = Pipeline([('reduce_dim', PCA(random_state=42)),\n",
    "                 ('classify',  RandomForestClassifier(random_state=42))])\n",
    "\n",
    "N_FEATURES_OPTIONS = list(range(2, 21))\n",
    "N_TREES = [1, 2, 3]\n",
    "\n",
    "param_grid = [\n",
    "    {\n",
    "        'reduce_dim': [PCA(random_state=42)],\n",
    "        'reduce_dim__n_components': N_FEATURES_OPTIONS,\n",
    "        'classify__n_estimators': N_TREES,\n",
    "        'classify__criterion': [\"gini\", \"entropy\"]\n",
    "    },\n",
    "    {\n",
    "        'reduce_dim': [SelectKBest()],\n",
    "        'reduce_dim__k': N_FEATURES_OPTIONS,\n",
    "        'classify__n_estimators': N_TREES,\n",
    "        'classify__criterion': [\"gini\", \"entropy\"]\n",
    "    },\n",
    "]\n",
    "\n",
    "cv = StratifiedShuffleSplit(random_state=42)\n",
    "grid = GridSearchCV(\n",
    "    pipe, param_grid=param_grid, cv=cv, scoring='f1', n_jobs=-1)\n",
    "\n",
    "grid.fit(X, y)\n",
    "\n",
    "\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('reduce_dim', SelectKBest(k=15, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            n_estimators=1, n_jobs=1, oob_score=False, random_state=42,\n",
      "            verbose=0, warm_start=False))])\n",
      "\tAccuracy: 0.83293\tPrecision: 0.36427\tRecall: 0.33950\tF1: 0.35145\tF2: 0.34418\n",
      "\tTotal predictions: 15000\tTrue positives:  679\tFalse positives: 1185\tFalse negatives: 1321\tTrue negatives: 11815\n",
      "\n"
     ]
    }
   ],
   "source": [
    "features = data_df.columns.tolist()\n",
    "data = data_df.to_dict(orient='index')\n",
    "test_classifier(grid.best_estimator_, data, features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest has also a balanced behavior but the scores are significantly lower comparing to Nearest Neighbor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boosting methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('reduce_dim', SelectKBest(k=14, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
      "          learning_rate=1.0, n_estimators=10, random_state=42))])\n"
     ]
    }
   ],
   "source": [
    "#warnings.filterwarnings('ignore')\n",
    "\n",
    "pipe = Pipeline([('reduce_dim', PCA(random_state=42)),\n",
    "                 ('classify',  AdaBoostClassifier(random_state=42))])\n",
    "\n",
    "N_FEATURES_OPTIONS = list(range(2, 21))\n",
    "N_ESTIMATORS = [1, 10, 100]\n",
    "\n",
    "param_grid = [\n",
    "    {\n",
    "        'reduce_dim': [PCA(random_state=42)],\n",
    "        'reduce_dim__n_components': N_FEATURES_OPTIONS,\n",
    "        'classify__n_estimators': N_ESTIMATORS,\n",
    "        'classify__algorithm': ['SAMME', 'SAMME.R']\n",
    "    },\n",
    "    {\n",
    "        'reduce_dim': [SelectKBest()],\n",
    "        'reduce_dim__k': N_FEATURES_OPTIONS,\n",
    "        'classify__n_estimators': N_ESTIMATORS,\n",
    "        'classify__algorithm': ['SAMME', 'SAMME.R']\n",
    "    },\n",
    "]\n",
    "\n",
    "cv = StratifiedShuffleSplit(random_state=42)\n",
    "grid = GridSearchCV(\n",
    "    pipe, param_grid=param_grid, cv=cv, scoring='f1', n_jobs=-1)\n",
    "\n",
    "grid.fit(X, y)\n",
    "\n",
    "\n",
    "print(grid.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('reduce_dim', SelectKBest(k=14, score_func=<function f_classif at 0x7f2059e582f0>)), ('classify', AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
      "          learning_rate=1.0, n_estimators=10, random_state=42))])\n",
      "\tAccuracy: 0.84893\tPrecision: 0.40404\tRecall: 0.28000\tF1: 0.33077\tF2: 0.29832\n",
      "\tTotal predictions: 15000\tTrue positives:  560\tFalse positives:  826\tFalse negatives: 1440\tTrue negatives: 12174\n",
      "\n"
     ]
    }
   ],
   "source": [
    "features = data_df.columns.tolist()\n",
    "data = data_df.to_dict(orient='index')\n",
    "test_classifier(grid.best_estimator_, data, features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AdaBoost performed a little better than Random Forest but still the difference with Nearest Neighbor is quite strong. It seems like a safe conclusion that the best classifiers family for the specific problem is Nearest Neighbor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algorithm Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have already evaluated K-Nearest Neighbor and we have the following results:  \n",
    "```Accuracy: 0.85193\tPrecision: 0.43233\tRecall: 0.35300\tF1: 0.38866\tF2: 0.36645\n",
    "\tTotal predictions: 15000\tTrue positives:  706\tFalse positives:  927\tFalse negatives: 1294\tTrue negatives: 12073```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nearest Centroid Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scale', MaxAbsScaler(copy=True)), ('reduce_dim', PCA(copy=True, iterated_power='auto', n_components=2, random_state=42,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)), ('classify', NearestCentroid(metric='manhattan', shrink_threshold=None))])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#warnings.filterwarnings('ignore')\n",
    "\n",
    "pipe = Pipeline([('scale', MaxAbsScaler()),\n",
    "                 ('reduce_dim', PCA(random_state=42)),\n",
    "                 ('classify', NearestCentroid())])\n",
    "\n",
    "N_FEATURES_OPTIONS = list(range(2, 5))\n",
    "\n",
    "param_grid = [\n",
    "    {\n",
    "        'scale': [None, MaxAbsScaler()],\n",
    "        'reduce_dim': [PCA(random_state=42)],\n",
    "        'reduce_dim__n_components': N_FEATURES_OPTIONS,\n",
    "        'classify__metric': [\"euclidean\", \"manhattan\"],\n",
    "        'classify__shrink_threshold': [None, 0.1, 1, 10]\n",
    "    },\n",
    "    {\n",
    "        'scale': [None, MaxAbsScaler()],\n",
    "        'reduce_dim': [SelectKBest()],\n",
    "        'reduce_dim__k': N_FEATURES_OPTIONS,\n",
    "        'classify__metric': [\"euclidean\", \"manhattan\"],\n",
    "        'classify__shrink_threshold': [None, 0.1, 1, 10]\n",
    "    },\n",
    "]\n",
    "\n",
    "cv = StratifiedShuffleSplit(random_state=42)\n",
    "grid = GridSearchCV(\n",
    "    pipe, param_grid=param_grid, cv=cv, scoring='f1', n_jobs=-1)\n",
    "\n",
    "grid.fit(X, y)\n",
    "\n",
    "my_classifier = grid.best_estimator_\n",
    "my_classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('scale', MaxAbsScaler(copy=True)), ('reduce_dim', PCA(copy=True, iterated_power='auto', n_components=2, random_state=42,\n",
      "  svd_solver='auto', tol=0.0, whiten=False)), ('classify', NearestCentroid(metric='manhattan', shrink_threshold=None))])\n",
      "\tAccuracy: 0.73833\tPrecision: 0.30975\tRecall: 0.78350\tF1: 0.44397\tF2: 0.59997\n",
      "\tTotal predictions: 15000\tTrue positives: 1567\tFalse positives: 3492\tFalse negatives:  433\tTrue negatives: 9508\n",
      "\n"
     ]
    }
   ],
   "source": [
    "features = data_df.columns.tolist()\n",
    "data = data_df.to_dict(orient='index')\n",
    "test_classifier(my_classifier, data, features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAFnCAYAAAC/5tBZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSCipi4iIBAkldRERkSChpC4iIhIk/j9N/U6pkqEhzQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f204dddd8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = .02  #step size in the mesh\n",
    "\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "scale = MaxAbsScaler()\n",
    "X_trans = scale.fit_transform(X)\n",
    "pca = PCA(copy=True,\n",
    "          iterated_power='auto',\n",
    "          n_components=2,\n",
    "          random_state=42,\n",
    "          svd_solver='auto',\n",
    "          tol=0.0,\n",
    "          whiten=False)\n",
    "X_trans = pca.fit_transform(X_trans)\n",
    "y_trans = y.values\n",
    "\n",
    "clf = NearestCentroid(metric='manhattan', shrink_threshold=None)\n",
    "clf.fit(X_trans, y_trans)\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "x_min, x_max = X_trans[:, 0].min() - 0.1, X_trans[:, 0].max() + 0.1\n",
    "y_min, y_max = X_trans[:, 1].min() - 0.1, X_trans[:, 1].max() + 0.1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure()\n",
    "plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
    "\n",
    "# Plot the training points\n",
    "plt.scatter(X_trans[:, 0], X_trans[:, 1], c=y_trans, cmap=cmap_bold, alpha=0.3)\n",
    "\n",
    "# Legend Data\n",
    "classes = ['POI','Non-POI']\n",
    "class_colours = ['#0000FF', '#FF0000']\n",
    "recs = []\n",
    "for i in range(0,len(class_colours)):\n",
    "    recs.append(mpatches.Rectangle((0,0),1,1,fc=class_colours[i]))\n",
    "\n",
    "# Plot Legend ('loc' == position):\n",
    "plt.legend(recs,classes,loc=4)\n",
    "\n",
    "plt.title(\"NearestCentroid classifier with manhattan metric\")\n",
    "plt.xlabel(\"First Principal Component\")\n",
    "plt.ylabel(\"Second Principal Component\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nearest Centroid Classifier achieved an even better f1 score (0.44) with an average Precision and an excellent Recall."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Algorithm Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nearest Centroid Classifier is a very fast algorithm and additionally it accepts too few arguments, as a result we were able to try all the different combination during the evaluation.  \n",
    "We can conclude that the best classification of the employees to POIs can be achieved with the following steps:  \n",
    "\n",
    "|Step             |Algorithm(attributes)                                                                                           |\n",
    "|:---------------:|:--------------------------------------------------------------------------------------------------------------:|\n",
    "|Scaling          |MaxAbsScaler(copy=True)                                                                                         |\n",
    "|Feature Selection|PCA(copy=True, iterated_power='auto', n_components=2, random_state=42, svd_solver='auto', tol=0.0, whiten=False)|\n",
    "|Classification   |NearestCentroid(metric='manhattan', shrink_threshold=None)                                                      |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Closure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dumping files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pickle.dump(data_df.to_dict(orient='index'), open(\"my_dataset.pkl\", \"wb\"))\n",
    "pickle.dump(my_classifier, open(\"my_classifier.pkl\", \"wb\"))\n",
    "pickle.dump(features, open(\"my_feature_list.pkl\", \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Future Improvements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are at least two improvements I could have done to the Project.  \n",
    "\n",
    "The first one is a tweak in the classification algorithm.  \n",
    "We noticed in the scatterplot between Total Stock Value vs Total Payments, that none of the persons who had zero Salary or zero Stocks was a POI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfQAAAFeCAYAAABzUe0CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f20503a22b0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Actually, this is so sensible that we could say that it is always true and thus create a rule out of this. If you consider that the persons fall under this category are {{146-81}} out of 146, we could improve the performance of our classification up to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6893150684931507"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((65*1)+(81*0.44))/(65+81)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(*of course this score isn't exact because the algorithm selection and evaluation process should be followed again with the dataset without the zero Payments / Stocks employees*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, I'm leaving for a future improvement the creation of a custom classifier that would classify as non-POI every employee with zero Payments or Stocks and by using a machine learning algorithm for the rest.\n",
    "***\n",
    "The second one is the usage of the content of the emails on the classification. Currently, I just used aggregated values from email headers in the form of the \"emails features. Using NLP to dig into the actual content of the emails and vectorize them, may improve the classification further."
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "81px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": "2",
   "toc_cell": true,
   "toc_position": {
    "height": "224px",
    "left": "0px",
    "right": "1525px",
    "top": "130px",
    "width": "330px"
   },
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}