Starbucks-Promotion_Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Starbucks - Advertising Promotion Optimization\n",
    "<b>Éverton Bin</b>\n",
    "<br>\n",
    "\n",
    "<img src=\"https://opj.ca/wp-content/uploads/2018/02/New-Starbucks-Logo-1200x969.jpg\" width=\"200\" height=\"200\">\n",
    "<br>\n",
    "<br>\n",
    " \n",
    "#### Background Information\n",
    "\n",
    "The dataset provided in this project was originally used as a take-home assignment provided by Starbucks for their job candidates. The data consists of about 120,000 data points split in a 2:1 ratio among training and test files. In the experiment simulated by the data, an advertising promotion was tested to see if it would bring more customers to purchase a specific product priced at $10. Since it costs the company 0.15 to send out each promotion, it would be best to limit that promotion only to those that are most receptive to the promotion. Each data point includes one column indicating whether or not an individual was sent a promotion for the product, and one column indicating whether or not that individual eventually purchased that product. Each individual also has seven additional features associated with them, which are provided abstractly as V1-V7.\n",
    "\n",
    "#### Optimization Strategy\n",
    "\n",
    "The task is to use the training data to understand what patterns in V1-V7 to indicate that a promotion should be provided to a user. Specifically, the goal is to maximize the following metrics:\n",
    "\n",
    "* **Incremental Response Rate (IRR)** \n",
    "\n",
    "IRR depicts how many more customers purchased the product with the promotion, as compared to if they didn't receive the promotion. Mathematically, it's the ratio of the number of purchasers in the promotion group to the total number of customers in the purchasers group (_treatment_) minus the ratio of the number of purchasers in the non-promotional group to the total number of customers in the non-promotional group (_control_).\n",
    "\n",
    "$$ IRR = \\frac{purch_{treat}}{cust_{treat}} - \\frac{purch_{ctrl}}{cust_{ctrl}} $$\n",
    "\n",
    "\n",
    "* **Net Incremental Revenue (NIR)**\n",
    "\n",
    "NIR depicts how much is made (or lost) by sending out the promotion. Mathematically, this is 10 times the total number of purchasers that received the promotion minus 0.15 times the number of promotions sent out, minus 10 times the number of purchasers who were not given the promotion.\n",
    "\n",
    "$$ NIR = (10\\cdot purch_{treat} - 0.15 \\cdot cust_{treat}) - 10 \\cdot purch_{ctrl}$$\n",
    "\n",
    "For a full description of what Starbucks provides to candidates see the [instructions available here](https://drive.google.com/open?id=18klca9Sef1Rs6q8DW4l7o349r8B70qXM).\n",
    "\n",
    "\n",
    "#### How To Test The Strategy?\n",
    "\n",
    "When reaching an optimization strategy, the `promotion_strategy` function will be run to pass to the `test_results` function.  \n",
    "From past data, we know there are four possible outomes:\n",
    "\n",
    "Table of actual promotion vs. predicted promotion customers:  \n",
    "\n",
    "<table>\n",
    "<tr><th></th><th colspan = '2'>Actual</th></tr>\n",
    "<tr><th>Predicted</th><th>Yes</th><th>No</th></tr>\n",
    "<tr><th>Yes</th><td>I</td><td>II</td></tr>\n",
    "<tr><th>No</th><td>III</td><td>IV</td></tr>\n",
    "</table>\n",
    "\n",
    "The metrics are only being compared for the individuals we predict should obtain the promotion – that is, quadrants I and II.  Since the first set of individuals that receive the promotion (in the training set) receive it randomly, we can expect that quadrants I and II will have approximately equivalent participants.  \n",
    "\n",
    "Comparing quadrant I to II then gives an idea of how well the promotion strategy will work in the future. \n",
    "\n",
    "Each strategy will be tested against the test dataset used in the final `test_results` function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of Contents</h2>\n",
    "<ol>\n",
    "    <a href = '#ab'><li><b>Checking A/B Test</b></li></a>\n",
    "        <ol>\n",
    "            <a href = '#inva'><li><b>Invariant Metric</b></li></a>\n",
    "            <a href = '#eval'><li><b>Evaluation Metric</b></li></a>\n",
    "        </ol>\n",
    "    <a href = '#analysis'><li><b>Exploratory Analysis</b></li></a>\n",
    "        <ol>\n",
    "            <a href = '#corr'><li><b>Correlation</b></li></a>\n",
    "                <ol>\n",
    "                    <a href = '#corranal'><li><b>Considerations on Correlation Analysis</b></li></a>\n",
    "                </ol>\n",
    "            <a href = '#hist'><li><b>Histogram</b></li></a>\n",
    "                <ol>\n",
    "                    <a href = '#histanal'><li><b>Considerations on Histogram Analysis</b></li></a>\n",
    "                </ol>\n",
    "        </ol>\n",
    "    <a href = '#start'><li><b>Starting Parameters</b></a>\n",
    "        <ol>\n",
    "            <a href = '#appr1'><li><b>All the Clients</b></li></a>\n",
    "            <a href = '#appr2'><li><b>Client Segmentation Using Individual Correlation</b></li></a>\n",
    "        </ol>\n",
    "    <a href = '#diff'><li><b>Testing Different Approaches</b></a>\n",
    "        <ol>\n",
    "            <a href = '#appr3'><li><b>Logistic Regression</b></li></a>\n",
    "            <a href = '#appr4'><li><b>XGBoost Classifier</b></li></a>\n",
    "                <ol>\n",
    "                    <a href = '#appr40'><li><b>Basic Approach</b></li></a>\n",
    "                    <a href = '#appr41'><li><b>Manipulating Features - Approach 1</b></li></a>\n",
    "                    <a href = '#appr42'><li><b>Manipulating Features - Approach 2</b></li></a>\n",
    "                    <a href = '#appr43'><li><b>Manipulating Features - Approach 3</b></li></a>\n",
    "                </ol>\n",
    "        </ol>\n",
    "     <a href = '#conclusion'><li><b>Conclusion</b></li></a>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in packages\n",
    "from itertools import combinations\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from test_results import test_results, score\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import sklearn as sk\n",
    "\n",
    "from imblearn.over_sampling import SMOTENC, SMOTE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Promotion</th>\n",
       "      <th>purchase</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>30.443518</td>\n",
       "      <td>-1.165083</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32.159350</td>\n",
       "      <td>-0.645617</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>30.431659</td>\n",
       "      <td>0.133583</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.588914</td>\n",
       "      <td>-0.212728</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>28.044331</td>\n",
       "      <td>-0.385883</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID Promotion  purchase  V1         V2        V3  V4  V5  V6  V7\n",
       "0   1        No         0   2  30.443518 -1.165083   1   1   3   2\n",
       "1   3        No         0   3  32.159350 -0.645617   2   3   2   2\n",
       "2   4        No         0   2  30.431659  0.133583   1   1   4   2\n",
       "3   5        No         0   0  26.588914 -0.212728   2   1   4   2\n",
       "4   8       Yes         0   3  28.044331 -0.385883   1   1   2   2"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading the data:\n",
    "train_data = pd.read_csv('training.csv')\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Checking A/B Test</h2>\n",
    "<p>\n",
    "    Since we are dealing with the results of the A/B test applied, we are going to run a statistical test over both invariant and experimental metrics - in this case, number of clients assigned to the control and the experimental groups and the number of purchases for each group respectively.\n",
    "</p>\n",
    "<p>\n",
    "    In both tests, we are going to consider a significance of 5%.\n",
    "</p>\n",
    "<h3>Invariant Metric</h3>\n",
    "<p>\n",
    "    First, let's check whether or not the invariant metric (number os clients designated to control and experimental groups) is statistically equal by computing the p-value when comparing the two groups:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Yes    42364\n",
       "No     42170\n",
       "Name: Promotion, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of clients designated to each group in training set:\n",
    "train_data.Promotion.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z score for the invariant metric is 0.67\n"
     ]
    }
   ],
   "source": [
    "# expected probability when randomly designating clients to promotion and non-promotion groups:\n",
    "mean = 0.5\n",
    "\n",
    "# creating experiment list:\n",
    "exp_list = [1 if x == 'Yes' else 0 for x in train_data.Promotion]\n",
    "\n",
    "# computing sample mean:\n",
    "s_mean = (np.sum(exp_list) + 0.5) / len(exp_list)\n",
    "\n",
    "# computing standard deviation:\n",
    "s_std = np.std(exp_list)\n",
    "\n",
    "# computing z score:\n",
    "z_im = (s_mean - mean) / (s_std / np.sqrt(len(exp_list)))\n",
    "print('Z score for the invariant metric is {:.2f}'.format(z_im))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Invariant metric: p-value is 0.25, which means we failed to reject null hypothesis.\n"
     ]
    }
   ],
   "source": [
    "# computing p-value:\n",
    "p_value_im = sp.stats.norm.cdf(-z_im)\n",
    "print('Invariant metric: p-value is {:.2f}, which means we failed to reject null hypothesis.'.format(p_value_im))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Evaluation Metric</h3>\n",
    "<p>\n",
    "    Once the composition of control and experimental groups is considered statistically correct, we're are going to check if the experiment resulted effectively in increasing the number of purchases in comparison to the control group:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z score for the experimental metric is 12.47\n"
     ]
    }
   ],
   "source": [
    "# experimental group size:\n",
    "n1 = np.sum(exp_list)\n",
    "\n",
    "# control group size:\n",
    "n0 = len(exp_list) - n1\n",
    "\n",
    "# purchase overall mean:\n",
    "p_null = train_data.purchase.mean()\n",
    "\n",
    "# purchase mean in control group:\n",
    "p0 = train_data[train_data.Promotion == 'No'].purchase.mean()\n",
    "\n",
    "# purchase mean in experimental group:\n",
    "p1 = train_data[train_data.Promotion == 'Yes'].purchase.mean()\n",
    "\n",
    "# computing standard error:\n",
    "se = np.sqrt(p_null * (1-p_null) * (1/n0 + 1/n1))\n",
    "\n",
    "# computing z score:\n",
    "z_em = (p1 - p0) / se\n",
    "print('Z score for the experimental metric is {:.2f}'.format(z_em))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experimental metric: p-value is 0.00, which means that we can reject null hypthesis with over 95% of confidence.\n"
     ]
    }
   ],
   "source": [
    "# computing p-value:\n",
    "p_value_em = sp.stats.norm.cdf(-z_em)\n",
    "print('Experimental metric: p-value is {:.2f}, which means that we can reject null hypthesis with over 95% of confidence.'.format(p_value_em))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Exploratory Analysis</h2>\n",
    "<p>\n",
    "    In the experiment, the promotion was randomly sent to Starbucks' clients. The goal is to use the data collected to improve the strategy by sending the promotion only to the ones who are more likely to use it.\n",
    "</p>\n",
    "<p>\n",
    "    In other words, we need to differenciate customers that would buy the product <b>because of the promotion</b> from the ones that would buy it even if they didn't get the promotion and the group that wouldn't buy it despite of any promotion.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>purchase</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>84534.000000</td>\n",
       "      <td>84534.000000</td>\n",
       "      <td>84534.000000</td>\n",
       "      <td>84534.000000</td>\n",
       "      <td>84534.000000</td>\n",
       "      <td>84534.000000</td>\n",
       "      <td>84534.000000</td>\n",
       "      <td>84534.000000</td>\n",
       "      <td>84534.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>62970.972413</td>\n",
       "      <td>0.012303</td>\n",
       "      <td>1.500662</td>\n",
       "      <td>29.973600</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>1.679608</td>\n",
       "      <td>2.327643</td>\n",
       "      <td>2.502898</td>\n",
       "      <td>1.701694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>36418.440539</td>\n",
       "      <td>0.110234</td>\n",
       "      <td>0.868234</td>\n",
       "      <td>5.010626</td>\n",
       "      <td>1.000485</td>\n",
       "      <td>0.466630</td>\n",
       "      <td>0.841167</td>\n",
       "      <td>1.117349</td>\n",
       "      <td>0.457517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.104007</td>\n",
       "      <td>-1.684550</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31467.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26.591501</td>\n",
       "      <td>-0.905350</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>62827.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>29.979744</td>\n",
       "      <td>-0.039572</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>94438.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>33.344593</td>\n",
       "      <td>0.826206</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>126184.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>50.375913</td>\n",
       "      <td>1.691984</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID      purchase            V1            V2            V3  \\\n",
       "count   84534.000000  84534.000000  84534.000000  84534.000000  84534.000000   \n",
       "mean    62970.972413      0.012303      1.500662     29.973600      0.000190   \n",
       "std     36418.440539      0.110234      0.868234      5.010626      1.000485   \n",
       "min         1.000000      0.000000      0.000000      7.104007     -1.684550   \n",
       "25%     31467.250000      0.000000      1.000000     26.591501     -0.905350   \n",
       "50%     62827.500000      0.000000      2.000000     29.979744     -0.039572   \n",
       "75%     94438.750000      0.000000      2.000000     33.344593      0.826206   \n",
       "max    126184.000000      1.000000      3.000000     50.375913      1.691984   \n",
       "\n",
       "                 V4            V5            V6            V7  \n",
       "count  84534.000000  84534.000000  84534.000000  84534.000000  \n",
       "mean       1.679608      2.327643      2.502898      1.701694  \n",
       "std        0.466630      0.841167      1.117349      0.457517  \n",
       "min        1.000000      1.000000      1.000000      1.000000  \n",
       "25%        1.000000      2.000000      2.000000      1.000000  \n",
       "50%        2.000000      2.000000      3.000000      2.000000  \n",
       "75%        2.000000      3.000000      4.000000      2.000000  \n",
       "max        2.000000      4.000000      4.000000      2.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# general statistical overview:\n",
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID           0.0\n",
       "Promotion    0.0\n",
       "purchase     0.0\n",
       "V1           0.0\n",
       "V2           0.0\n",
       "V3           0.0\n",
       "V4           0.0\n",
       "V5           0.0\n",
       "V6           0.0\n",
       "V7           0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for null values:\n",
    "train_data.isnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    41851\n",
       "1      319\n",
       "Name: purchase, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# purchase count in control group:\n",
    "train_data[train_data.Promotion == 'No'].purchase.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    41643\n",
       "1      721\n",
       "Name: purchase, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# purchase count in experimental group:\n",
    "train_data[train_data.Promotion == 'Yes'].purchase.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Correlation</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filtering data for control group:\n",
    "control = train_data[train_data.Promotion == 'No']\n",
    "sel_features = ['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'purchase']\n",
    "\n",
    "# evaluating features' correlation for experimental group:\n",
    "contr_corr = control[sel_features].corr()\n",
    "\n",
    "# creating correlation plot:\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (10, 10))\n",
    "\n",
    "sns.heatmap(contr_corr, annot = True, fmt = '.3g', vmin = -.3, vmax = .3, center = 0, \n",
    "                        cmap = 'RdYlBu', square = True)\n",
    "ax.set_title('Correlation Matrix - Control Group')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9ewmfiT8SE3OPsuUeZ8Zn/6VUKXvzOq/+c52Xuozmv+91582BnYmNvc/woV8TejkCg8FA6zZPMGzEK48c0wO7g08xdeoKkpKS6NX7OQYPfiFdbFOnriB410ns7GyZNn0AdetWzLLuls2H+eab9Vy4EMbKVWOoX9+4L+LjExg//mf++OMSiQlJdO3WnHfeMdaJi0vAx2cZBw+ew6AUQ4d1o0OHJ3Mdn9aaqb6r2RV8Gjt7W6b7vkHdOun7wuXQ6wwfsYhbt2KoU8eLmdP7JffNTOp7t/uUkiUfw2AwYGVtYO2qjwG4efMuw0Ys5MqVSMqVc+WLz9/CyakEe/aeYfbn64iPT8DGxpqRH3WjebOauY9v2prk9k3tm3l8H/1oiq88M6eliC+D+v+7GM6wEYtS1L/BB0M6MaBfG4aOWMjFixEA3L4di4ODPQFrR5vL/vNPJJ1fmsqQ9zrx1pttcxVfSrt3n8J36jJjf+vVgkGDO6XbF75TlxEcbOyrvtMGJvfVTOp+NnMVO3cex8bGCq8K7vj6vomjYwmuhF6nc+dPqFzZE4CGDaswcdIbeRZLyjZP9V1l3P92Nkz37Zf5uXPEQm7dvGvsnzMGpOifmddPTEyiZ+/peLg788Oc/5qX//zLTn5ZsgtrKytatarLqJE9CL1yg06dJ1O5socp5kpMnvh6nscsHl2xGdKt33CWIUM3FHYzMhQcfIqQSxFsDZyMz+Q+TJy0NMNys2atZUD/tmwN9MHRsQSr1+zJtn6P7s2ZP+/9dOsaN/5nRozozvr1n9KufSPmmwYxL3VpSoD/eAL8xzNzxpuUK+eWq8FMYmISU3x+4vu5I1m3fgabNu7jwvkrqcrsDj7O35fC2bRlFhMnDcRn8qJs6074ZAFDh7+M37pptG33FIsWbEy1zhnTl9CiRYNUy94c2In1m2ayeu0Ufv/9T3YHH3/kuB60b/Lkpcyb/wEbNk5i44ZDnD//T6oywcGnuBQSTuDWKUz2eYNJE5dkW7d6jXJ89fV/eKpJ9VTr2rLlCPFx8axfP5E1a8exYkUwoaHXAZgzZxNurg4EBk5h46ZJPN2kRq5iS27/H4RcusbWLRPwmfQaEyctz7DcrNkBDOjfhq1bJuDoaM/qtftyVP+nHz8kwG+MeTADMHf+Npo3q8nWLRNo3qwmc+dvBcDFuRTff/cO6wPGMX3aG4wavTj38e3+w/je2fwpPhNfZeLkjC92Zn2+jgH92rB186fG996D+DKpX6WyBwFrRxOwdjRrV43C3s6G9u0aAvDF7IHm155v39C8/IFpM9bSokWdXMeWUmJiEj6TlzB33lDWb/Bh48aDGfTVk1y6FMGWQF8mTe7H5Em/ZFv3mWfqsG79JALWTaJSJQ/mzt1kXp9XhdL4+U/Az39CvgxmjG0+bdz/WybiM6kPEydn1j/9GdDPm62Bk3B0KsHqNXtzVH/xzzupWsUz1bL9B84RFHSC9QHj2LjhE94a2N78WgWvxwnwG0uA39giN5gxWKkC+ymqis2A5uixq9yKvl/YzchQUNAJunVthlKKRo2qEB0dS0TErVRltNbs33+ODh0aA9C9W3OCth/Ptn6TJtVxciqRbpsXL4bTxPSB+ewztdm69Wi6Mhs3HuLFzk/lKraTJy5QoYIHXl7u2Nha80KnZuzYcSRVmZ07jvJS1+dQStGwUTVuR8dwLeJmlnVDLl7lqSa1AGj+TD22bTtkXl/Q9sOU93KnarXy5mX29o/xdFPjh4SNrTW161QiPCwyV7GdOHGRChXd8fIqja2tNZ06NyEoKPUgKSjoGF27NU9zbG5mWbdq1TJUSXMSBVBKERMbR0JCIvfuxWNjY2XOSq1ds4fBpmyNwWDAxdUhV7GZ27/jBN26Pm1sf8PKRN+OJeJaBn3zwJ90eP4JALp3a2qOJSf1M9xmt6YAdOvWlO1BJwCoU8cLD3dnAKpXK0Pc/Xji4uJzGd9Jur2U0/gaGePr2pQgU5tyUn/f/nN4eT1OubKu6da7OfB3XuycnEnbHnSc8l6PU71amVzFldaJExepUCFFf+v0NDuCjqUqsyPoGF27PuirVYmOjknuq5nUffa5ulhbWwHGLEx4WFSetjs7xv7V1NTmyqY2Z3buNPXPrs3S9M+M64eFRfHrrlP06vVsqvUtW76bwYM6YGtrA4CbW96810T+e6QBjVKqffalxAPh4TfxLONi/t3T05nw8JupykTdvIujYwnzycPT05nwiJs5rp9WjeplCdphfFNv2XKUq1fTn4g2bT5M585NHiGiZBERUXh6Jp/IPTxciQhPva3w8DRlPF0Jj4jMsm616uXZucM4CNsaeJCwq8bBSUzMPRbO38h//9s90zZFR99l187fadq8bq5iCw+/SZkU7fP0cCY8XWw3KeOZ8ti4EB5+M0d10+rQoTEl7G1p8dxIvNuMZuDA53F2Lkl0dAwAX34ZQI/uPnz4wRyuX4/OVWzm9kfcxDNl+z0y6ZsO9sl908OF8PBb2ddXirfe/oYevWawYuVv5jI3btzGvbQTAO6lnYiMvJ2uXYFbj1G7tpf5QyVv40v9gZg+PmfCI7KKL3X9jZuP8mKn9Lf/Dh+5gJubA5UqugMQE3OfeQu2M+Q/L6Qrm1sR4VGpzhEeni4Z9lXPMin6pKcLEeE3c1QXYO2a32jRsp759yuh1+nRfRJv9J3J4cN/5mU4qduc9v1lOi8+kOG509QHs6rvO201Iz/qnu7poJCQCA4fOU/vV2bS943POXEyxPxa6JUbdOvhS983Pufw4fN5F2geMBhUgf0UVY+aoVmQp60o5jQ63TKVtk/oDMo8TP00pvr2Y+mSXfTo4cvdu/ewtUk9Xer48YvY29lSo0a5rFeUDZ1Ru9M0LrMyWdX1mTqIZUu383LPT7h7NxYbU/u//WYtb/TvSImSdhm2JyEhkVEffUefvs/j5eX+0PGkaXim7cu2TE7qpnHyRAgGg4Hg3TPZHuTLooXbuHz5GokJiYSFRdG4cVXW+n1CoyeqMnPGqoeLJRMZNDOHMWZff9mSYfitGc28H/7LkmW7OZTDD4C//rrKrM8DmDzx1RyVz0rGfSxdoUzLZFc/Li6BHTtP0tGUHUhpw6YjqQY6X3+7if792lCy5GM5a/xDyOAwpH8fZnIeyUndOXM2YGVtRZcuzQAo7e5E0I6ZrPWbwOjRLzPyo3ncuRP7qM3PVE7OL1m91zKrv3PnSVxdS1Evg/k4iQmJREfHsHL5SEaN7MHQYQvQWuNe2pGdQVPwXzuW0aN7MWLkwnyJWTy6TCcFK6XWZfYS4JbVSpVSg4HBAF6VXuNx9+ceuYGWasmSX1m5ynhVWr9+RcJSZEjCwm7ibkqtP+DiUoro6BgSEhKxtrZKVcbTwyXb+mlVreLJwoUfAsbbT7/uOpnq9Y2bDuU6OwPGrEpYils74eGRlE7TNk/PNGXCInEv7UJ8XEKmdatUKcu8BcZ5FyEXrxK8y5htOnniAtsCD/H5rOXcvh2DMigee8yW1/sYk4YTJyykQkUP3ujfMfexebpwNUX7wsLT73djmZTHJgp3dyfi4xOyrZvWhg0HadGiLjY21ri5OdK4cVVOnbxExxeexN7elvbtjR+aHTs+yZrVv2W5rqwsWbqLlauMcwzq169IWMr2h9/E3d0pVXkXl1JE345N7pvhUeYynh7OmdZ/cPvIzc2B9m0bcOJECE2eqoabmwMR127hXtqJiGu3cE1x+ywsLIohH8xlxrQ3qFCh9CPGF8zK1ab46lV4hPhumjNInh4uWdYP/u0P6tbx4vHHHVOtMyEhkW3bj7N25UjzsuMnQgjceoxZswOIvh2LQSkes7Wmb59WjxRnSh5pzhHhYVHp+pvxPJKiT4ZFUdrdmbj4xCzr+vvt4dedJ1j04wjzQMHW1sacPatbrxJeXqUJuRhOPdME99xYsmQXK1cb5w/Wr5emf4ZFmY/NAxmfO03Hz9Mlw/qBgUfZsfMkwcGnuR+XwJ07sXw0ahGzZr6Jh6cL7ds3QilFgwaVMBgUUVF3cHV1MMdcr24FKniV5mJIBPXrVcx1zHlBFeHMSUHJKkPTAvgBmJ3Bz52sVqq1nqu1fkpr/dS/cTAD0KdPa/Pk23ZtG+EfsB+tNceO/Q8HB7t0J1WlFE2b1iQw0Hibxc9/H95tjZNevb0bZFs/rRs3jLckkpKS+H7OJl59taX5taSkJLZsOUrnXM6fAahXvwp/XwojNDSC+LgENm/aT5s2jVOVad2mMesCfkNrzfFj5ynlUILS7s5Z1r1x45a5rT/MCeDlV7wBWPzLJ2wN+j+2Bv0ffft1YNDgLubBzFdfrOLO7RhGj+mb67gA6tevxKWQCEIvXycuLoFNGw/h7Z16gqe3d0MC/PelODb2uLs756huWmXKuLL/wDm01sTE3Of48YtUqeKJUoo2bRpw8IAxrb9v31mqVn30ORh9Xm9FgN8YAvzG0K5tA/wDDhrbf/yisf2lM+ibT9cgcOvvAPj5H8Db+0HfrJ9h/ZiY+9y5ew8w3mrZs/cs1auXNdZpUx9//wMA+PsfoK1pXdHRMQz+zxyGD3uJJxtXzUV8Lc2Tctu1bYD/uhTtK2WXSXzVCdx6zBhfwAG8veub2lovy/obNx2hcwa3m/buO0eVyh6pbncs/XkYO7ZNYse2SfR/ozXvDH4+TwYzYOqrl8IJDb1m7G+bDtImTX9r492IgIAHffVC6r6aSd3du08xf/4Wvvv+feztkzNLkZG3SUxMAuDy5WtcuhRBea/H8ySWPn1amSfeGvvnAVObTf0rw3NnDQIDTf0zYH9y/2xTP8P6I4Z3I/hXX3YETeHz2QNp1rQms2a+CUC7tg3Yv/8cYLwYjI9PwMWlVJqYrxNyKQKv8nkTs8gbWT22vR+I0VrvSvuCUupc/jXp0fj6tOfJxmVxdrZj8/p+zJl7iID1Zwq7WQC0alWPXcGnaP/8J9jb2eLr29/82qDBXzPF5w08PJwZ+VF3hg2fzxdfrqN2bS96myarZVV/+PD5HDz0J1FRd2jZajTvv9+F3r2eZcPGQyxdYjx07Z9/gp49njHXOXToLzw9XfDyerQr4JSsra0YO74f77z9GYlJSXTv0ZJq1cuzYnkQAK+82paWrRqyO/gYL3T4CHs7W3x8B2VZF2DTxv0sX7odgHbtn6J7j5YZN8AkLCySuT+so3KVsvTu+QkAr73enl69W+cqtk8+fY233v6CpMQkevZ8lurVy7J8mXG/vvpaK1q1qk/wrlM8334cdva2+PoOyLIuwLZtvzPFZxmRkXd4952vqVXbiwULhvJ6n9aMHfMjXV6ciNbQo8cz1Kxl3B8jPurJx6MW4uu7AldXB3yn9c+wzQ+rVcu67Ao+TfuOk7C3s8F3avJgcNA73zHF53U83J0ZOaIrwz5axBdfbjD2zZ7Ns6x/48Zt3vtgHmBM4b/Y+Slamp7sGTyoPUOHLWT1mn2UKePCl//3FgC/LA3m77+v8d33W/ju+y0ALJw/JFeTMo3t+4P2L0w2tm9Kivje/Z4pk1/Hw92JkcNN8X21gdq1y6eJL+P6sbFx7N17lskT0t8a27Q544FOfrG2tmL8J6/z9ltfkJSURI+ez1K9ejmWL/8VgFdfbW3sq8En6fD8WONj275vZlkXYIrPEuLiEnhr4OdA8uPZhw/9yVdfB2BtZcBgZWDixL44O5fK87iM577TtO8wwXTuS36aatDgb5kypY+pf3Zn2IgFfPHVeuPx6/VMtvUz07PHM4wd/zMvdvHBxsaa6dP6o5Ti0OHzfPXVBqysDVgZDEya+BrOziXzPOZHVZTnthQUldE9RgCl1LfAUq31ntxsoHHT7zLeQDFxdH/twm5CvonXRefNmh+sDcX7/rdKjCvsJuSvTM5dxUWSlX32hSyUQRfzvglgaFugI4w+/xdcYG+IJcNaFsnRU1YZmj+BWUqpMsAKYJnW+liBtEoIIYQQOSYZmizm0Gitv9RaNwdaAZHAIqXUGaXUp0qpvPlWLyGEEEKIPJDtY9ta60ta6xla6yeA14HuQNGYnCKEEEIIQQ7+lpNSygboCLwKtAV2AZPyuV1CCCGEyKGi/CcJCkpW30PTHngN6AwcBJYDg7XWdwuobUIIIYQQOZJVhmYssBT4SGuduz+KI4QQQoh8I5OCs54U3EZrPU8GM0IIIYTIKaVUR6XUOaXUeaXU6Axe76OUOmH62auUyvpbR3Mo2zk0QgghhCjaDIZH/dOMeUspZQV8C7QHQoFDSql1Wus/UhS7CLTSWkcppV4A5gJNc7vtorEHhBBCCFEcPA2c11r/T2sdh3H+bdeUBbTWe7XWD/7I1n6gfF5sWDI0QgghhIUrQn+cshxwOcXvoWSdfXkL2JwXG5YBjRBCCCFyTCk1GBicYtFcrfXcBy9nUCXDP8uglGqDcUCTJ3/FWgY0QgghhIUryKecTIOXuZm8HAp4pfi9PPBP2kJKqQbAfOAFrfWNvGiXzKERQgghRF45BFRXSlVWStli/FLedSkLKKUqAGuBN7TWf+bVhiVDI4QQQli4ovJNwVrrBKXUECAQsAIWaq1PK6XeNb0+B/gUcAO+U0oBJGitn8rttmVAI4QQQog8o7XeBGxKs2xOiv+/Dbyd19uVAY0QQghh4eSbgmUOjRBCCCGKAcnQCCGEEBZOMjSSoRFCCCFEMSAZGiGEEMLCSYZGMjRCCCGEKAZkQCOEEEIIiye3nIQQQggLpwySn8j3Ac3R/bXzexOFqnGzM4XdhHxzdF/Nwm5C/tLF/ASgint8hd2A/GVIulfYTcg/xb1vikIhGRohhBDCwhWVP31QmGSYLIQQQgiLJxkaIYQQwsLJY9uSoRFCCCFEMSAZGiGEEMLCSYZGMjRCCCGEKAYkQyOEEEJYOMnQSIZGCCGEEMWAZGiEEEIIC2dQkqGRDI0QQgghLJ5kaIQQQggLJ3NoJEMjhBBCiGJAMjRCCCGEhbOSDI1kaIQQQghh+WRAI4QQQgiLJ7echBBCCAsnk4IlQyOEEEKIYkAyNEIIIYSFky/WkwyNEEIIIYoBydAIIYQQFk7m0EiGRgghhBDFgGRohBBCCAsnGRrJ0AghhBCiGCgSGRqtNVOnrmRX8Cns7GyZPq0/detWSFfucuh1hg+fz61bd6lTpwIzZ7yJra11lvXHjF3Mr7+exM3NgQ3rPzWv6+zZUCZMWEJMzH3KlXNj1qyBlCplz7r1B1iwYJu53LlzV/BbO5batb3yf0dkYcL4NrR4tiKRUbG8/PqKQm1LWlprpvquYlfwaezsbJju2y/z4zdiIbdu3qVOHS9mzhiQfPwyqH/1aiSjRv/E9evRGJSBl19+lv79vFOtc8HCbcz8zI99e2fi6lLKvPyffyLp3MWHIe914q2B7XMXWwH3zaHD5nHxYjgAt6NjcHAsQYD/eADOngtlwqdLuHP3HgalWL16DI89ZpO7+PLh2N2/H0+fNz4nLi6BxIQkOnR4gg/ef9G8vp9/2ckvS3ZhbWVFq1Z1GTWyR3J8E5Zx5849DAbF6lUfF8n4suqbX3+zgZWr9uDq6gDA8KEv0apVPfO28qpvJse32tg+e1um+75B3Trpz1XG+BZx61aMMb7p/VLEl3n9xMQkevaeiYeHEz98/x8Ahg5fmNw/b8fi4GBPgN8Y4uISmDBxGadO/40yGBg3pidNn66RB/EV7PE7c+YyEyYu435cAlZWBiZ++ioNGlQiPj6R8Z/8wh9/XCYhMZFuXZvyzuCOuYovLxkkPVE0MjTBwacIuRTB1sDJ+Ezuw8RJSzMsN2vWWgb0b8vWQB8cHUuwes2ebOv36N6c+fPeT7euceN/ZsSI7qxf/ynt2jdivmkQ81KXpgT4jyfAfzwzZ7xJuXJuhT6YAVi/4SxDhm4o7GZkKDj4tHH/b5mIz6Q+TJy8PMNys2b7M6CfN1sDJ+HoVILVa/ZmWd/KyorRo3qyeeMEVqwYydKlwZw/f9W8vqtXI9m79yxly7im29a06atp0aJOHsRW8H3zi/8bZO6Dzz/fmPbtnwAgISGRkSMXMWlSHzZumMDixcOxtrbKZXz5c+xsba35adGHrPMfh7/fWHb/9gfHjl0EYP+BcwQFnWB9wDg2bvjE/KGekJDIyFE/Mmnia2zc8AmLfxpaZOPLrm8O6O9NgN9YAvzGphrMQN71TWP7/iDk0jW2bpmAz6TXmDgps/gCGNC/DVu3TMDR0Z7Va/flqP7in3dStapHqmVffD6QAL8xBPiN4fn2jWjfvhEAq1Yb+/z6gHEsmj+EGTP9SEpKymV8BX/8Ppvlx3vvdSbAbywfvv8in83yA2BL4FHi4hJYv248a1ePYcWK3wi9ciNX8Ym8VSQGNEFBJ+jWtRlKKRo1qkJ0dCwREbdSldFas3//OTp0aAxA927NCdp+PNv6TZpUx8mpRLptXrwYTpMm1QF49pnabN16NF2ZjRsP8WLnp/I01kd19NhVbkXfL+xmZChoxwm6dW1q2v+ViY6OyeL4GT+cu3dtRlDQ8Szru7s7ma/GSpW0o0pVT8LDb5rXOW36GkZ+1J20X7+wffsxyns9TvVqZXIfWyH0zZTr3bzliLkP7tnzBzVrlqNWrfIAuLiUwsoqd2/h/Dp2SilKlrQDjAOVhPhE83Fatnw3gwd1wNbWmHlxc3MwxXfGYuLLrm9mJi/7ZnL7nja2r2Flom/HEnEtg/gO/EmH503xdWuaJr6M64eFRfHrrtP06vlMhtvWWrM58CgvdnoSgPMXwmjWrCZgPKYODvacOvV3HsRXsMdPKcXdO7EA3L4Ti7u7k2k5xMbeJyEhkXv34rCxsaaUqY8XBVZKFdhPUZXl2UIp5aiUqprB8gZ52Yjw8Jt4lnEx/+7p6Zzu5BB18y6OjiXMV2yens6ER9zMcf20alQvS9AOY6ffsuUoV69GpSuzafNhOndu8ggR/buEh9/E0zPl/ncxH5sHMjx+pmOUk/qhV25w5sxlGjasBBhPVO4eTuYPvwdiYu4zb/42hvy3U97FVsB984HDh8/j5uZApUrGK+SLIREopXjrra/o3mMq8+YHPnpgJvl57BITk+ja3ZdnnvuYZ56pRcOGlQEICYng8JHz9H5lJn3f+JwTJ0OS40Px1ttf073HNObN31qk43sgbd8EWLJkF126TmHMuJ+5dSsGyPu+CRAekaZ9Hpn0Twf75Pg8XAgPv5Vtfd/paxj5UbdMJ5sePnLB1D/dAahVsxxBO06SkJDI5dDrnP7jMlfD0p9XHyq+Qjh+Y8f0YuYsP1q1GcuMmWsZPqwrAB2eb4y9/WM813IMbdqOZ+DAdjg7l8xVfCJvZTqgUUq9DJwF1iilTiulUn6y/5iXjdDoDLaftlAGZR6mfhpTffuxdMkuevTw5e7de9japJ5OdPz4ReztbKlRo1zWKxLojI5N2gOQRZns6t+9e48PPpjL2NG9KFXKntjYOOb8sIUP3++Srt7X32ygf39vc3Ygtwqjbz6wYeMhXkwxoE5MSOTIkfN8NmsgS5eMZPu2Y+zbdzZnK8tEfh47KysDAX5j2bVzKidOhvDnn/+Y44iOjmHl8pGMGtmDocMWoLUmMTGRI0cv8Nlnb7J0yQi2bz9epOOD9H0T4LVXW7Jt62QC/MbiXtqR6TPXAHnfNzNpeg7jy7r+zl9P4urqQL0M5qs8sGHjYV7slJzB7tmjOZ6ezvTsPRPfaWt4olFlrKxyd8uwMI7fsuW7GTO6F7t2+jJmdC/Gjf8FgBMnQzBYGdi9axpB23xYuGg7ly9ff+TY8prBoArsp6jKalLwWOBJrfVVpdTTwM9KqbFa67Ukn68zpJQaDAwG+GHOcAYPfjFdmSVLfmXlqt8AqF+/ImEpMiRhYTdxd3dOVd7FpRTR0TEkJCRibW2Vqoynh0u29dOqWsWThQs/BIy3n37ddTLV6xs3HZLsTBaWLNnFStM98/r1KhIWlnL/R+Fe2ilV+YyPn7GMp6dLpvXj4xP54MN5dOnyNM+bUuZ/X75GaOh1unabaiwffpMePaexasUojp8IITDwd2bN8iP6diwGg+Kxx2zo26f1Q8RWuH0TjLdptm37nbVrxpqXeXq68HST6ubJzy1b1eP0H3/TvHmtHMdmjK9gjt0Djo4laPp0DXb/dpoaNcri4elC+/aNUErRoEElDAZFVNQdPD3SxNeyLqf/uFxk48uobwI8/rij+f+9ez/Hu+9+B5AnfRNgydJdrFxlnCNSv36a+MKT254qvtuxyfGFRyXH5+GcYf3AwN/ZsfMkwcGnuX8/njt37/HRqJ+YNbM/YOqf24+zdtUoc11rayvGju5p/v3V12dTqWLph4oNCv/4+fnvZ9zY3gC80LEx4z9ZAsCGDYdo8VwdbGyscHNzoHHjqpw8dQkvr8cfOkaRP7K65WSttb4KoLU+CLQBximlPoAMLjtT0FrP1Vo/pbV+KqPBDECfPq3NEx/btW2Ef8B+tNYcO/Y/HBzs0r0plVI0bVqTwEDjXBc//314tzXe+fL2bpBt/bRu3IgGICkpie/nbOLVV1uaX0tKSmLLlqN0LiLzZ4qiPn1amSc9tmvbAP+AA6b9fxEHB/tMjl8NAgN/B8AvYD/e3qbj16Z+hvW11owb/zNVqnjy5oC25nXVrFGOfXtmsiNoCjuCpuDp4czaNWMoXdqJpb+MMC/v368N7wzu8NAfGIXdNwH27jtLlcqeqdLlzz1Xh3N/XiE2No6EhEQOHfqLalUffi5GQRy7yMjbREcbb7XcuxdnjgegXdsG7N9/DjBeTMTHJ+DiUsoY37m08XkWyfgy65tAqjke27cdo3r1sgB50jcB+rzeyjwp1xjfQWP7jpvaVzqD+J6uQeBWU3z+B5Lj866fYf0Rw7sSvHMKO7ZP5vPZb9KsaQ3zYAZg775zVKnskap/xsbGERNjnOe3Z+8ZrKwMVHuEuUKFffzc3Z04eOgvAPbvP2celJUp48qBA+fQWhMTc5/jxy9SpUrqCdOFSTI0WWdoopVSVbXWFwBMmZrWgD9QNy8b0apVPXYFn6L9859gb2eLr2/yG2fQ4K+Z4vMGHh7OjPyoO8OGz+eLL9dRu7YXvXs9m2394cPnc/DQn0RF3aFlq9G8/34Xevd6lg0bD7F0yS4A2j//BD17JE98O3ToLzw9XfDyeviri/zi69OeJxuXxdnZjs3r+zFn7iEC1p8p7GYBD/b/adp3mGDa/2+YXxs0+FumTOmDh7szI0d0Z9iIBXzx1Xpq1y5P717PZFn/yNELBKw7SI0aZena3RdI/whswcRWsH0TYNPGQ3R+MXWG0MmpJAMGtKNX72kopWjZsi6tW9fPg/jy/thFXLvF6DGLSUxMQidpOnZ8kjZtjG3t2eMZxo7/mRe7+GBjY830af1RSuHkVIIBA7zp1XsGSlGk48uqb342y4+zZ0NBQblybkye+HquYsgyvpZ1je3rOAl7Oxt8p/ZNju+d75ji87opvq4M+2gRX3y5wdg/ezbPtn5WNm0+QmfTZOAHbkTe5q1B32IwKDzcnZk5vX8mtR8ivkI4fj6T++Dru4qExCQee8yGyZP7ANDn9ZaMGfczL3aZgkbTo3tzatUsjyg6VEb3GAGUUhuB6Vrr3WmW2wAva62X5GgLemeW2RxL17hZ0RhU5Iej+2oWdhPylyoSD/nlH527R2ZFISvOx6+4v/cADG0LNJXxf7svFNhn7bAWVYtkmiarDM1WYKZSqgywAlimtT6mtY4HcjaYEUIIIUS+MxThx6kLSqbDZK31l1rr5kArIBJYpJQ6o5T6VClVvcBaKIQQQgiRjWzzflrrS1rrGVrrJ4DXge4YH+cWQgghRBEgk4JzMKBRStkopboopZYAm4E/gZ7ZVBNCCCGEKDCZzqFRSrUHXgM6AweB5cBgrfXdAmqbEEIIIXKgKGdOCkp2X6y3FPhIax1ZQO0RQgghhHhomQ5otNZtCrIhQgghhHg0VpKhKRp/bVsIIYQQIjeyuuUkhBBCCAsg30MjGRohhBBCFAOSoRFCCCEsnDzlJBkaIYQQQhQDkqERQgghLJxkaCRDI4QQQohiQDI0QgghhIWTDI1kaIQQQghRDMiARgghhBAWT245CSGEEBZOvlhPMjRCCCGEKAYkQyOEEEJYOJkULBkaIYQQQhQDkqERQgghLJyVZGgkQyOEEEIIy5fvGZp4XTK/N1Goju6rWdhNyDeNm58r7Cbkq6P7axd2E/KXKt7XKwnavrCbkK+sDbGF3YT8c+92Ybcg/xVw95SnnCRDI4QQQohiQObQCCGEEBZOnnKSDI0QQgghigHJ0AghhBAWziDpCcnQCCGEEMLySYZGCCGEsHDylJNkaIQQQghRDEiGRgghhLBw8pCTZGiEEEIIUQzIgEYIIYQQFk9uOQkhhBAWTiYFS4ZGCCGEEMWAZGiEEEIICycZGsnQCCGEEKIYkAyNEEIIYeHksW3J0AghhBCiGJABjRBCCGHhrJQqsJ/sKKU6KqXOKaXOK6VGZ/B6LaXUPqXUfaXUR3m1D+SWkxBCCCHyhFLKCvgWaA+EAoeUUuu01n+kKBYJfAB0y8tty4BGCCGEsHBFaA7N08B5rfX/AJRSy4GugHlAo7WOACKUUp3zcsNyy0kIIYQQeaUccDnF76GmZflOMjRCCCGEhSvI76FRSg0GBqdYNFdrPffByxlU0fnfKhnQCCGEEOIhmAYvczN5ORTwSvF7eeCffG8URWRA89vuE0z3/ZnEpCR69mrN24O6pHpda80035/ZHXwcO7vHmOo7mDp1K2VZ9+zZS/hM/JGYmHuULfc4Mz77L6VK2ZvXefWf67zUZTT/fa87bw7sTGzsfYYP/ZrQyxEYDAZat3mCYSNeyZP4tNZM9V3FruDT2NnZMN23H3XrVkhX7nLodYaPWMitm3epU8eLmTMGYGtrnWn9q1cjGTX6J65fj8agDLz88rP07+edap0LFm5j5md+7Ns7E1eXUubl//wTSecuPgx5rxNvDWyfJ3HmxoTxbWjxbEUio2J5+fUVhdoWrTVTp65kV/Ap7OxsmT6tf+bHa/h8bt26S506FZg5483k45VJ/eDdp5k6dSVJSUn07vUsgwd3NK/v55938suSX7G2NtCqVT1GjexJaOh1OnWeROXKHgA0bFiZyZP6ABAXl4CPz3IOHvwTZVAMG9qVDh0aF5lYx4xdzK+/nsTNzYEN6z81r+vs2VAmTFhCTMx9ypVzY9asgZQqZc+69QdYsGCbudy5c1fwWzuW2rW90rUnp3bvPsF03yWm80MrBg16MV3803yXEBx8HHs7W6b6DjKfWzKrO2LYt1wMCQPgdnQMDo4lWOvnQ1xcApMmLuL0qRCUQTFmbB+efro2AF9+sZp1AXu4FX2Xw0cy+xzImcLon198uY6goOMYDAo3VwemTeuPh4czAGfPhTLh0yXcuXsPg1KsXj2Gxx6zYcOGQ/zww2ZQCnd3Jz77bGCqc1Cu98HMAHb9dtZ4Tpz8CnVrl0+/D65EMvzjX7h1K5Y6tcsxc+qr2NpYc+FiBGMnrOD0mSsMG9KRt/q3BuB/IREMG/VLqvof/KcDA/q2yJN256ci9E3Bh4DqSqnKwBXgVeD1gthwoc+hSUxMYorPT3w/dyTr1s9g08Z9XDh/JVWZ3cHH+ftSOJu2zGLipIH4TF6Ubd0Jnyxg6PCX8Vs3jbbtnmLRgo2p1jlj+hJatGiQatmbAzuxftNMVq+dwu+//8nu4ON5EmNw8GlCLkWwdctEfCb1YeLk5RmWmzXbnwH9vNkaOAlHpxKsXrM3y/pWVlaMHtWTzRsnsGLFSJYuDeb8+avm9V29GsnevWcpW8Y13bamTV9NixZ18iS+vLB+w1mGDN1Q2M0AIDj4lHF/B07GZ3IfJk5ammG5WbPWMqB/W7YG+uDoWILVa/ZkWT8xMYnJk5cxf94QNm6YwIaNhzh/3njhsn//OYJ2HGf9uvFs3DAh1SCzQoXSBPiPJ8B/vHkwAzBnzmZc3RwIDJzMpo0TaPJ0jSITK0CP7s2ZP+/9dOsaN/5nRozozvr1n9KufSPmmwYxL3Vpao5z5ow3KVfOLVeDmcTEJKb6LGbO3BGsWz+NTRv3cz7dueUEly6FsXnLTCZOepPJk3/Ktu7s/3uPtX4+rPXzof3zT9Gu3ZMArF71KwD+66Yyf8EoPpuxjKSkJABat27E8hUTHjmWlAqjf779VnvWr/uEAP/xtG5dn2+/M55PExISGTlyEZMm9WHjhgksXjwca2srEhISmeq7kp8WD2f9uk+oWbMcS37ZmSfxAwT/dpaQv6+zdd3H+HzSi4lT12a8D77YyIC+Ldm6/mMcHe1Z7XcQAGenEowb1Y23+rVKVb5KJXcCVg4nYOVw1i4bir2dDe296+VZu/8NtNYJwBAgEDgDrNRan1ZKvauUehdAKeWplAoFhgPjlVKhSinH3G670Ac0J09coEIFD7y83LGxteaFTs3YseNIqjI7dxzlpa7PoZSiYaNq3I6O4VrEzSzrhly8ylNNagHQ/Jl6bNt2yLy+oO2HKe/lTtVqySN6e/vHeLqp8QPextaa2nUqER4WmScxBu04QbeuTVFK0ahRZaKjY4iIuJWqjNaa/fvP0aHDEwB079qMoKDjWdZ3d3cyX1mVKmlHlaqehIffNK9z2vQ1jPyoO2kH7tu3H6O81+NUr1YmT+LLC0ePXeVW9P3CbgYAQUEn6Na1mWl/VyE6OjaL42XMiHTv1pyg7cezrH/iRAgVK7jj5VUaW1trOndqQlDQCQCWLd/F4EEdsLW1AcDNLfv39pq1e3nHdAVtMBge6eo3v2IFaNKkOk5OJdJt8+LFcJo0qQ7As8/UZuvWo+nKbNx4iBc7P/XQ8aR08sT/8DKdH2xtrenUqSk7d6Te1o4dR3mp67MZnFuyr6u1JnDLQTp3bgbAhQv/0KyZ8Rzi5uaIg2NJTp26CEDDRtUo7e6cq3geKIz+mTK7HRsbhzKdVPbs+YOaNctRq5bxXOriUgorKwNaG9sQG3MfrTV37tzDPY/iBwj69TTdXnzSGEODikTfvkfEtej0++DQeTq0q2/cB12eJGjnaQDcXEvRoJ4X1tZWmW5j34G/8CrvRrmyLnnW7vxkUAX3kx2t9SatdQ2tdVWt9VTTsjla6zmm/4dprctrrR211s6m/0dnvdYc7IPcriC3IiKi8PRMziB4eLgSER6Vqkx4eJoynq6ER0RmWbda9fLmE9DWwIOEXTUOTmJi7rFw/kb++9/umbYpOvouu3b+TtPmdXMfIBAefhNPz+Q3haenC+ERN1OVibp5F0fHEuY3mKens3lwkpP6oVducObMZRo2rAQYB0HuHk7mE80DMTH3mTd/G0P+2ylPYiuOwsNv4lkm5f52TjVQhEyOl+mYZFY/PDwq1XIPT2fCTf01JCSCw4fP0/vl6fTtO5sTJ0PM5UJDr9Ot+1T69p3N4cN/ARAdHQPAl1+uo3uPqXzw4VyuX3/480F+xZqVGtXLErTD+OG6ZctRrl6NSldm0+bDdO7c5KHjSSk8Iooyac4P4WnOLRHhUXh6uiWX8XQlPCIqR3WPHD6Hm5sjFSt5AlCzlhc7dvxOQkIioaHX+ON0CGF5dFGUKq5C6J8A//d//rRqPYb1Gw7y4QfGW/sXQyJQSvHWW1/RvcdU5s0PBMDGxoqJE16jy0s+tGj5MRcuXKVXr2fzbh9EROPp6Zwcg4cT4WkGdVE3Y3B0sE/eBx7O6cpkZWPgcV584Yk8aa8oGFkOaExpIU/T/0srpXoopfLmU95E6/STn1WalEJmZbKq6zN1EMuWbuflnp9w924sNjbG6ULffrOWN/p3pERJuwzbk5CQyKiPvqNP3+fx8nJ/6HgykpMYyaJMdvXv3r3HBx/MZezoXpQqZU9sbBxzftjCh+93SVfv62820L+/NyUziV+AzmBCfrrb0xkdk2zqZzTN/8FxTExMIjo6hpUrPmbUqB4MHToPrTXu7k7s3OGLv984Ro/uxYiPFnLnTiwJiUmEhUXRuHFV/NaO44lGVZgxc81DRpp/sWZlqm8/li7ZRY8evty9ew9bm9RT+Y4fv4i9nS01auTySc8cnVvSV1MqZ3U3bdxPJ1N2BqBHj5Z4eLjwcu+JTJ+2hEaNqmFtlXkG4FEVRv8EGDasG7t+nUaXF5/ml19+BSAxIZEjR87z2ayBLF0yku3bjrFv31ni4xNZtjwYf79x7A6eQc0a5fhh7pYcRpi93J5TsxMXn8COXafp2L5B9oVFkZHppGCl1DvAaON/1QxgAHAamKaUmqm1XpBFXfMjXd99P5q3B2eeDfHwcE11FRMeHpkuNevpmaZMWCTupV2Ij0vItG6VKmWZt+BjwHj7KXiX8Yrw5IkLbAs8xOezlnP7dgzKoHjsMVte72OcszBxwkIqVPTgjf7JkzUfxZIlu1i52njPun69ioSFJV/phIVF4V7aKVV5F5dSREfHkJCQiLW1FWFhN3F3dzLF75Jp/fj4RD74cB5dujzN888bryb+vnyN0NDrdO021Vg+/CY9ek5j1YpRHD8RQmDg78ya5Uf07VgMBsVjj9nQt0/rXMVr6ZYs+ZWVq34DoH79ioRdTbm/b6ZLl2d8vIxlPD1cMqwfH5+Yanl4ijoeHs60b98IpRQNGlTGYFBERd3B1dXBfBuqXr2KVPB6nIsXI6hXrwL29ra0b98IgI4dG5vnSBSFWLNStYonCxd+CBhvP/2662Sq1zduOpTr7AwYzy1X05wf0rbNw9OFsLAbyWXM55bELOsmJCSyffsRVq6eZF5mbW3F6DHJc5z6vOZDhYoeuY4DCr9/pvTii014591v+eCDLnh6uvB0k+rm250tW9Xj9B9/U7KU8YKpQoXSALzwwlPMnZe7Ac2S5XtYufaAcR/U9SIs7GZyDOG3cC+d+jati0tJom/HJu+D8JvpymQm+Lez1K1VjsfdHHLV5oJUhCYFF5qsMjRDgLrAk8BnQFet9UCgGZB+pl8KWuu5WuuntNZPZTWYAahXvwp/XwojNDSC+LgENm/aT5s2qZ/UaN2mMesCfkNrzfFj5ynlUILS7s5Z1r1xw5haTEpK4oc5Abz8ivHpn8W/fMLWoP9ja9D/0bdfBwYN7mIezHz1xSru3I5h9Ji+WbY5J/r0aUWA31gC/MbSrm0D/AMOoLXm2LGLODjYmwcrDyilaNq0BoGBvwPgF7Afb2/j1YF3m/oZ1tdaM278z1Sp4smbA9qa11WzRjn27ZnJjqAp7AiagqeHM2vXjKF0aSeW/jLCvLx/vza8M7jDv34wA9CnT2vzhNR2bRvhH7DftL//h4ODXSbHqyaBgcbbmn7++/Buazpe3g0yrF+/fkVCLkVwOfQ6cXEJbNx0yHyM27VrxP4D5wDjh3x8fCIuLqWIjLxNYqJxYunly9cIuRSBl9fjKKVo06YBBw7+CcC+fWepWjVnc6IKItas3LhhvDWWlJTE93M28eqrLc2vJSUlsWXLUTrncv4MQL36lfn7UjihodeIi0tg06YDtGmT+hZCmzZPsC5gT4pzi73p3JJ13X37TlO5cplUt7xjY+8TE2OcB7Z3zymsrAxUq5Y33ydW2P0zJCTcvO4dO05QxfTU3XPP1eHcn1eIjY0jISGRQ4f+olrVMni4O3PhwlUiI28DsGfvGapWyd2cvT6vPmuesNuuTT38NxwxxnDiEg6l7NINVpRSNH2qGoHbjQNmv/VH8G6dsxsMG7cco3NHud1kabJ6bDtBax0DxCilLmitwwC01lFKqTz7khxrayvGju/HO29/RmJSEt17tKRa9fKsWB4EwCuvtqVlq4bsDj7GCx0+wt7OFh/fQVnWBWM6ePnS7QC0a/8U3Xu0zLgBJmFhkcz9YR2Vq5Sld89PAHjt9fb06t061zG2alWPXcGnad9hAvZ2tvj6vmF+bdDgb5kypQ8e7s6MHNGdYSMW8MVX66lduzy9ez2TZf0jRy8QsO4gNWqUpWt3XwCGD32JVq0sb1a+r097nmxcFmdnOzav78ecuYcIWH+mUNpi3N+naP/8J6b93d/82qDBXzPF5w08PJwZ+VF3hg2fzxdfrqN2bS96m+YIZFbf2tqKTz95hbff+sr4KHDPZ6hevSwAPXs8w9hxi3mxy2RsbKyYPr0/SikOHfqLr75ej5WVASsrA5Mm9sHZuSQAH43ozqiPF+HruwpX11JMS9HOwo4VYPjw+Rw89CdRUXdo2Wo077/fhd69nmXDxkMsXbILgPbPP0HPHs+Y6xw69Beeni54eZV+6FjSsra2Ytz4Nxj89mckpTq37ADglVe9admqIcHBJ3ihw0js7B5jiu/bWdZ9YPOmA6luNwFERkYz+O1ZGAwKd3cXps94x/zarM9WsGnjPu7FxuHdeig9e7XivSFZX+xlpjD65+zZ/lwMCUcpRbmyrkyaZHwK18mpJAMGtKNX72kopWjZsi6tWxsn4b733ov06Tsba2srypV1Zdq0h++fme6DFrXY9dsZ2neZboxh0svJ++C9BUyZ0AsPdydGDu3EsI+X8MW3W6hdsxy9uz8NwLXr0fR8/Svzo+Y/LfmNTWs/olQpO2Jj49i7/y8mj++ZZ+0tCEXoTx8UGpXRvUgApdRhoLnWOl4pVV5rHWpabgcc0Fo3zMkG4pMOFsg3BBYWG24XdhPyTePm5wq7Cfnq6P7ahd0EkQsJ2j77QhbMWsUWdhPyz73ie940s3+pQIcYe6/eKrDP2mfKOBXJ4VNWGZoTQFPgtweDGRM3YES+tkoIIYQQOSZzaLKeQ3Mc+EwpFaKUmqGUagSgtb6itd5eIK0TQgghhMiBTAc0WusvtdbNgVZAJLBIKXVGKfWpUurhv5JUCCGEEPnCoFSB/RRV2X6xntb6ktZ6htb6CYx/j6E7xq8zFkIIIYQoErL945RKKRugI8Y/MNUW2AVMyrKSEEIIIQqMPOWU9RfrtQdeAzoDB4HlwGCt9d0CapsQQgghRI5klaEZCywFPtJa5/0fJBFCCCFEnijKc1sKSqYDGq11m4JsiBBCCCHEo8p2Do0QQgghijaZQ5ODp5yEEEIIIYo6ydAIIYQQFk7m0EiGRgghhBDFgAxohBBCCGHx5JaTEEIIYeGs5I6TZGiEEEIIYfkkQyOEEEJYOJkULBkaIYQQQhQDkqERQgghLJxkaCRDI4QQQohiQDI0QgghhIWTP30gGRohhBBCFAOSoRFCCCEsnMyhkQyNEEIIIYoBydAIIYQQFk7m0BTAgMbaEJvfmyhcuvgmuY7ur13YTchXjZudKewm5KsjB+oWdhPylbW+W9hNyF+6sBuQf64ktS7sJuS7coXdgH8hydAIIYQQFk7m0MgcGiGEEEIUA5KhEUIIISycIqmwm1DoJEMjhBBCCIsnAxohhBBCWDy55SSEEEJYOKXklpNkaIQQQghh8SRDI4QQQlg4A4mF3YRCJxkaIYQQQlg8ydAIIYQQFk7m0EiGRgghhBDFgGRohBBCCAsnX6wnGRohhBBCFAOSoRFCCCEsnMyhkQyNEEIIIYoBydAIIYQQFk6+h0YyNEIIIYQoBiRDI4QQQlg4mUMjGRohhBBCFAOSoRFCCCEsnHwPjWRohBBCCFEMyIBGCCGEEBavSNxy2h18iqlTV5CUlESv3s8xePALqV7XWjN16gqCd53Ezs6WadMHULduxSzrbtl8mG++Wc+FC2GsXDWG+vUrARAfn8D48T/zxx+XSExIomu35rzzjrFOXFwCPj7LOHjwHAalGDqsGx06PJnr+IztX8mu4FPY2dkyfVp/6tatkK7c5dDrDB8+n1u37lKnTgVmzngTW1vrLOuPGbuYX389iZubAxvWf2pe19Bh87h4MRyA29ExODiWIMB/PABnz4Uy4dMl3Ll7D4NSrF49hscesykS8QTvPs3UqStJSkqid69nGTy4o3l9P/+8k1+W/Iq1tYFWreoxamRPQkOv06nzJCpX9gCgYcPKTJ7UB3hwPJdz8OCfKINi2NCudOjQOMdx5pUJ49vQ4tmKREbF8vLrKwp8+2nlx/vt5s27DB82lytXblCunBv/98VgnJxKEhp6nc6dJqQ4PlWYNLkvABs2HOSHHzahULi7O/PZZwNxcXUAYPMm4/tXKahZy4vZs99+pFi11kz1XcWu4NPY2dkw3bdf5n11xEJu3bxLnTpezJwxILmvZlD/6tVIRo3+ievXozEoAy+//Cz9+3mnWueChduY+Zkf+/bOxNWlFHFxCUyYuJRTp/5GGRTjxvam6dM1Himu/I7v/v14+rzxOXFxCSQmJNGhwxN88P6LAGzecpRvvtnIhf+FsWrlKOrXM/aNqKg7fDB0HqdO/U33bs349JNXchVbWgf37OWbWbNJSkyiU/euvP7mgHT74pvPZnPgtz3Y2dkxatIEatSuBcBrnV+iRMkSGAwGrKysmbNkMQALv/uevb8GowwKZ1dXPp40gcdLl+bMqdN8PmWqab3Q/51BtPBuk6fx5DWZFFwEBjSJiUlMnryUhYuG4eHhQu9evnh7N6RatbLmMsHBp7gUEk7g1ikcP36RSROXsHLV2CzrVq9Rjq++/g8TJvySantbthwhPi6e9esnEht7n86dJ9K5cxPKl3+cOXM24ebqQGDgFJKSkrh1826exBgcfIqQSxFsDZzM8eMXmThpKatWjk5XbtastQzo35bOnZvw6YQlrF6zh9dfa5Vl/R7dm9O3T2s+Hv1jqnV98X+DzP+fPn01pRzsAUhISGTkyEV8NvNNatUqT1TUHaytrYpEPMbjuYxFCz/Ew8OFXr2n4e3dgGrVyrJ//zmCdhxn/brx2NracONGtHk7FSqUNg/WUpozZzOubg4EBk4mKSmJm7diHirOvLJ+w1lWrDrJ5AltC2X7KeXX+23e3M00a16LwYNfYO7czcybu4WPRvYEjMfHP+DTVO1ISEjEd+oKNm6ciIurA5/NXM0vS3by/vsvERISzty5m1m6bBROTiVTHeuHFRx82tjXtkzk+PEQJk5ezqoVo9KVmzXbnwH9vOnc+Sk+nbiU1Wv28vprLTOtb2VlxehRPalbtwJ37t6jZ8/pPPtMbapVKwPA1auR7N17lrJlXM3bWLVqDwDr143nxo3bDBr8DatXfYzB8OiJ8vyKz9bWmp8WfUjJknbExyfyet/ZtGxRl0aNKlOjehm+/nowEyYsTbWNxx6z4cMPuvDXX//w119XHzmmjCQmJvLljJl89t03lPbw4D99+/NMq5ZUqlLFXObAnr1c+ftvfg5Yy5mTp/hi2nS+W/yj+fXPf5iDk4tzqvW+0u8NBv73PwCsXbacn+fOZ9i4MVSuWpU5vyzGytqaG9euM+jV13mmZQusrAv9I1NkodBvOZ04cZEKFd3x8iqNra01nTo3ISjoeKoyQUHH6NqtOUopGjWqQnR0LBERN7OsW7VqGapU8Uy3PaUUMbFxJCQkcu9ePDY2VpQqZfywX7tmD4NN2RqDwWC+WsytoKATdOvaLE37b6Uqo7Vm//5z5gxC927NCdp+PNv6TZpUx8mpRKbb1lqzecsRXuz8FAB79vxBzZrlqFWrPAAuLqWwsnq4bpBf8Zw4EULFCsnHs3OnJgQFnQBg2fJdDB7UAVtbYybJzc0x23auWbuXd0wZHoPBgKtLqYeKM68cPXaVW9H3C2XbaeXX+y0o6DjdujUHoFu35mzffizLdmit0VoTExuH1po7d+7h7u4MwKqVu3m9T2ucnEoCOTvWmQnacYJuXZuaYqlMdHRMFn31CQC6d22WHFcm9d3dncyZkFIl7ahS1ZPw8JvmdU6bvoaRH3VHqeTtnL9wlWbNappicsDBsQSnTv39yLHlZ3xKKUqWtAOMg8+E+ERzLFWrlqGKKeOWUokSj/HUk9UeKtubU2dPnaZceS/Kli+PjY0N3h3as/fXXanK7P11F+1f7IxSijoN6nPn9m1uXLue5XpLlko+J9yLjeVBkHb2dubBS1zcfVTKA1lEKRIL7KeoeqhPMqWUb143IDz8JmU8k69iPD2cCQ+PyqCMS3IZTxfCw2/mqG5aHTo0poS9LS2eG4l3m9EMHPg8zs4liY42Xr1/+WUAPbr78OEHc7h+/dGvDNO237NMyvY7pzr5AUTdvIujYwlztsTT05nwiJs5rp+Zw4fP4+bmQKVKxhPQxZAIlFK89dZXdO8xlXnzA4tMPOHhUamWe3gmH8+QkAgOHz5P75en07fvbE6cDDGXCw29TrfuU+nbdzaHD/8FkOJ4rqN7j6l88OHcPDueliy/3m83bkSbByTu7s5ERt42lwsNvU73bj707fuZ+fjY2FgzYWIfXuoyiZYtRnLhwlV69XoOgJCQcEIuhvPaqzN45eVp7A4+lat4PdPGYuqHD2TYV039OSf1Q6/c4MyZyzRsWAkwDhLcPZzMFw0P1KpVnqAdJ0hISORy6HVOn/6bq2FZn68KM77ExCS6dvflmec+5plnatGwYeVctTU3rl+7hrtn8iDqcXcPrkVcS10m4hruHsllSru7c/1aBGC8kB353hDeef0NNqxZm6regm++45UXOrN98xbe/M875uVnTp7izV4v89bLrzF07GjJzliATAc0Sqmv0vx8Dfz3we951gKtM9p2zsrkpG4aJ0+EYDAYCN49k+1BvixauI3Ll6+RmJBIWFgUjRtXZa3fJzR6oiozZ6x6uFgyocmonWkLZVDmYepnYsPGQ7zYuYn598SERI4cOc9nswaydMlItm87xr59Z3O2sodpzyPEk35p8vFMTEwiOjqGlSs+ZtSoHgwdOg+tNe7uTuzc4Yu/3zhGj+7FiI8WcudOLAmJSebj6bd2HE80qsKMmWseKs5iqYDfb+7uTuzYOR0//08YPfplPhoxnzt3YomPT2D5sl34+Y8nePdn1KhZjrk/bAYgITGJS5ciWPzzCGbPHsT48YvNA9SHpXMTbw7q3717jw8+mMvY0b0oVcqe2Ng45vywhQ/f75KuXs8ezfH0cKFn7xn4TlvNE42qPHR2NK38jM/KykCA31h27ZzKiZMh/PnnP7lqa27kJM4Mzyums85Xi+Yzd+kvTP/mS/xXrub4kaPmMm8N+S8rNm+k3Qsd8V++0ry8dv16LFq9ku9//omli34k7n7RyLJmxqCSCuynqMrq3dQDcAUOA0dM/8ab/n8kq5UqpQYrpQ4rpQ7Pnbs+ywZ4eLpwNSzS/HtY+E3zlV7qMslXMmFhUbi7O+WoblobNhykRYu62NhY4+bmSOPGVTl18hLOLqWwt7elfXtjWrZjxyf5449HTwcvWfIrXbtNoWu3Kbi7OxF2NWX707fTxaUU0dExJCQkpivj6eGSbf2MJCQksm3b73Tq9JR5maenC083qY6rKd6WrepxOgdxFkQ8aZeHp6jj4eFM+/aNUErRoEFlDAZFVNQdbG1tcDHdSqpXryIVvB7n4sUIXJxLmo5nIwA6dmycq+NZXOTX+83NzZEI05V9RMRNXE23a9MeH68Kpbl4MZyzZ0IBqFDBHaUUL7zwFL//fgEw9g/vto2wsbGmvNfjVK7syaWQiBzHuGTJLrp296Vrd1/c3Z0JSxtLaadU5TPuq8Yynp4umdaPj0/kgw/n0aXL0zz/vPG88ffla4SGXqdrt6l4tx1PWPhNevScxrVrt7C2tmLsmF4E+I3l+2/f5fbtGCpVdM9xXAUd3wOOjiVo+nQNdv92+qHbmldKu7sTERZu/v16RDiPl348fZnw5DLXIiJwK10agMdN/7q4uvJcm9acPZ0+Fu+OHQnesSPd8opVKmNvb8/FCxfyIhSRj7Ia0NQBrgMdge1a65+A21rrn0z/z5TWeq7W+imt9VODB6e/Ukmpfv1KXAqJIPTydeLiEti08RDe3g1TlfH2bkiA/z601hw79j8cHOxxd3fOUd20ypRxZf+Bc8b79zH3OX78IlWqeKKUok2bBhw88CcA+/adpWrVMlmuKyt9+rQmwH88Af7jade2Ef4B+1O03858QnlAKUXTpjUJDDReOfj578O7bQNT/A2yrZ+RvfvOUqWyZ6qU8nPP1eHcn1eINc0jOnToL6rlIM6CiKd+/YqEXIrgcqjxeG7cdAhvb2Oddu0asf/AOQAuXgwnPj4RF5dSREbeJjHReMVw+fI1Qi5F4OX1uPl4HjiYN8ezuMiv95u3d0P8/fcB4O+/j7ZtjcvTHp9LIRF4eZXG3cOZCxf+Md+a2rvnDFVMx6ddu0YcMB3rqMjbhISEU94r9YdXVvr0aUWA31gC/MbSrm0D/AMOmGK5aIolo75ag8DA3wHwC9hv7nfebepnWF9rzbjxP1OliidvDkie7F2zRjn27ZnJjqAp7AiagqeHM2vXjKF0aSdiY+OIiTFe5e/ZcwYrKyvzJOKHURDxRUbeNmfF7t2LM59LCkutunW4cvlvrl65Qnx8PDsCt9G8VctUZZ5p1ZJtGzaiteaPEycpWaoUbqUfJzY2lpi7xgc8YmNjObx/P5WrVgUg9O/ki5y9wcFUqFQJgKtXrpCYkABA2D9XuRxyCc8yZSnKFEkF9lNUqYxSeakKKPUkMAvYCAzRWld6mA1odmW9AWDXrpP4+q4gKTGJnj2f5d3/dGb5MuOEr1dfa4XWGp/Jy9i9+xR29rb4+g4wP4adUV2Abdt+Z4rPMiIj7+DoaE+t2l4sWDCUu3fvMXbMj1y4cBWtoUePZ3jr7Q4AXLlyg49HLSQ6OgZXVwd8p/WnbFm3rPePzv7gaq2Z7LOc3btPY29ni69vf+rXNz7qOGjw10zxeQMPD2cuX77GsOHzuXUrhtq1vZj12ZvY2tpkWX/48PkcPPQnUVF3cHNz5P33u9C717MAjB79Iw0bVeG1V1O/8QPWHWDu3C0opWjZsi6jTE+j5FR+xmM8nqtITEqiZ89n+M+7nQDjI9hjxy3m7NlQbGysGDWqJ82b1SIw8Chffb0eKysDVlYG3h/SxXyyvnLlBqM+XkR0dCyurqWY5tufsmWT54A0bnbmoeJ+VL4+7XmycVmcne2IjIxlztxDBKzP/20fOVA3w+X58X6LirrDsKFzuXo1kjJlXPniy3dwdi5JYOARvv5qHVZWVlhZKYa8/5J5ELR82S4WLw7C2tqKsuXcmDZtAC4updBaM336Kn7bfRqDleLddzvRufPT6eJQSQnZ7gNjX1vB7t/+MPW1N8yPGQ8a/C1TpvTBw92Zy5evM2zEAlNfLc+smQNS9NX09Q8fOU+fvp9To0ZZ81NKw4e+RKtW9VJt37vteFavHo2rSylCr9zgrbe/xmBQeLg7M3VKX8qVy/r8UljxnT0Xyugxi0lMTEInaTp2fJIh7xnfi9u2HcNn6krzubV2rfIsmP++Od47d+8RH5+Ig4M9C+e/n+Gg7Upsk3TLsrP/tz18N+tzEpMSeeGll+j79kDWrTbeRn6pV0+01nw1fSYH9+0zPrY98VNq1qnDP6GhfDrC+ORXYmICbTt2pO/bAwGY8NEoLl+6hEEZcC/jybBxYyjt7s7WDZtY9uOPWFtbowwG+g16m+fatH6o9pYr6VigM4lvxZ3P9rM2rzjZViuSs6QzHdAopb4Blmqt9yrjzcr/As211n0fZgM5GdBYspwMaETRVFADmsKS2YCmuMjJgEYUTY8yoLE0BT2giY7/s8A+ax1tahTJAU1Wt5z+AmYrpUKA6cCehx3MCCGEEEIUhEwHNFrrL7XWzYFWQCSwSCl1Rin1qVIqd19vKYQQQog8I99Dk4PvodFaX9Jaz9BaPwG8DnQHineuXgghhBAWJdtvClJK2WB80ulVoC2wC5iUz+0SQgghRA7J33LKYkCjlGoPvAZ0Bg4Cy4HBWuu8+QNHQgghhBB5JKsMzVhgKfCR1joyi3JCCCGEKESGIvz9MAUl0wGN1rpo/610IYQQQgiTQv9r20IIIYQQuSV/PlQIIYSwcEX5TxIUFMnQCCGEEMLiSYZGCCGEsHDy2LZkaIQQQghRDEiGRgghhLBwRflPEhQUydAIIYQQwuJJhkYIIYSwcAaZQyMZGiGEEEJYPsnQCCGEEBZOvodGMjRCCCGEKAYkQyOEEEJYOPkeGsnQCCGEEKIYkAGNEEIIYeEUiQX2k21blOqolDqnlDqvlBqdwetKKfWV6fUTSqnGebEPZEAjhBBCiDyhlLICvgVeAOoAryml6qQp9gJQ3fQzGPg+L7Ytc2iEEEIIC1eE5tA8DZzXWv8PQCm1HOgK/JGiTFdgsdZaA/uVUs5KqTJa66u52bBkaIQQQgiRV8oBl1P8Hmpa9rBlHlq+Z2hUYlx+b6JwqWI8JizOsQFHDtQt7Cbkqyebni7sJuSr4n78ijN3+7OF3YQC8HSBbk3pgsvQKIMajPFW0QNztdZzH7ycQRWddhU5KPPQ5JaTEEIIIXLMNHiZm8nLoYBXit/LA/88QpmHVrwvwYUQQghRkA4B1ZVSlZVStsCrwLo0ZdYB/UxPOzUDbuV2/gxIhkYIIYSwfAV4yynDG0YPmqF1glJqCBAIWAELtdanlVLvml6fA2wCOgHngRjgzbxolgxohBBCCJFntNabMA5aUi6bk+L/Gngvr7crAxohhBDC0hVkhqaIkjk0QgghhLB4kqERQgghLJ3O9VPPFk8yNEIIIYSweJKhEUIIISydzKGRDI0QQgghLJ9kaIQQQghLlyQZGsnQCCGEEMLiSYZGCCGEsHQyh0YyNEIIIYSwfJKhEUIIISydZGgkQyOEEEIIyycZGiGEEMLSSYZGMjRCCCGEsHwyoBFCCCGExZNbTkIIIYSlky/WkwyNEEIIISyfZGiEEEIIS6d1Ybeg0BWJAY3Wmqm+q9kVfBo7e1um+75B3Tpe6cpdDr3O8BGLuHUrhjp1vJg5vR+2ttZZ1vdu9yklSz6GwWDAytrA2lUfA3Dz5l2GjVjIlSuRlCvnyhefv4WTUwn27D3D7M/XER+fgI2NNSM/6kbzZjXzIL5VxvbZ2TDdtx9161bIJL6F3Lp51xjfjAEp4ktf//79ePq88TlxcQkkJiTRocMTfPD+i+b1/fzLTn5ZsgtrKytatarLqJE9ADh7LpQJE5Zx5849DAbF6lUf89hjNrmLb+pKdgWfws7OlunT+mce3/D53Lp1lzp1KjBzxpvJ8WVSf8zYxfz660nc3BzYsP5T87rOng1lwoQlxMTcp1w5N2bNGkipUvasW3+ABQu2mcudO3cFv7VjqV07fX/KzO7gU0yduoKkpCR69X6OwYNfyCDeFQTvOomdnS3Tpg+gbt2KWda9efMuw4fN5cqVG5Qr58b/fTEYJ6eShIZep3OnCVSu7AFAw4ZVmDS5LwAbNhzkhx82oVC4uzvz2WcDcXF1AGDzpsN88816lIKatbyYPfvtHMeX1yaMb0OLZysSGRXLy6+vKLR2ZCQ/juWWzcZ9f+FCGCtXjaF+/UoAxMcnMH78z/zxxyUSE5Lo2q0577xjrBMXl4CPzzIOHjyHQSmGDutGhw5PFsn4Zs5Yzc6dx7GxsaZChdL4ThuAo2MJ9uz5g9mz15rPjaNG9qJZ81r5Gl9Kv+0+wXTfn0lMSqJnr9a8PahLulin+f7M7uDj2Nk9xlTfwdSpWwmA8ePmEfzr77i6OuK/frq5TuCWA3z3jR//+98/LFs5kXr1qgCwYf0eFi3cZC7357nLrFrjQ63aFfM0JpG3isQtp+DgPwi5dI2tWybgM+k1Jk5anmG5WbMDGNC/DVu3TMDR0Z7Va/flqP5PP35IgN8Y82AGYO78bTRvVpOtWybQvFlN5s7fCoCLcym+/+4d1geMY/q0Nxg1enEexHeakEsRbN0yEZ9JfZg4ObP4/BnQz5utgZNwdCrB6jV7s6xva2vNT4s+ZJ3/OPz9xrL7tz84duwiAPsPnCMo6ATrA8axccMnvDWwPQAJCYmMHPUjkya+xsYNn7D4p6FYW1vlMr5TxvYFTsZnch8mTlqacXyz1jKgf1u2Bvrg6FiC1Wv2ZFu/R/fmzJ/3frp1jRv/MyNGdGf9+k9p174R802DmJe6NCXAfzwB/uOZOeNNypVze6jBTGJiEpMnL2Xe/A/YsHESGzcc4vz5f9LFeykknMCtU5js8waTJi7Jtu68uZtp1rwWgVun0Kx5LebN3WJeX4UKpfEP+BT/gE/Ng5mEhER8p65g8U8jWLd+AjVrluOXJTsBCAkJZ+7czSxdNooNGycxduzLOY4vP6zfcJYhQzcUahsykl/HsnqNcnz19X94qkn1VOvasuUI8XHxrF8/kTVrx7FiRTChodcBmDNnE26uDgQGTmHjpkk83aRGkY3vmWdrs37DRNatn0ClSh7M/WEzAC4upfj++yGsXz+R6dPfZNSohebt5Ed8aWOd4vMT388dybr1M9i0cR8Xzl9JVWZ38HH+vhTOpi2zmDhpID6TF5lf69atBXPmjkq33mrVy/PF1x/y5FOpL1pf7PIsa/ymssZvKtNmvEu5co8X/cGMTiq4nyKqSAxognacoFvXp1FK0ahhZaJvxxJx7VaqMlpr9h/4kw7PPwFA925NCQo6nuP6GW6zW1MAunVryvagEwDUqeOFh7szANWrlSHufjxxcfF5EF9TY/saVSY6OoaIiAzi23+ODh1M8XVtlia+9PWVUpQsaQcYPwAT4hNRyri+Zct3M3hQB2xtjZkXNzfjlf2ePWeoWbMctWqVB4wnKSur3HWDoKATdOvazNS+KkRHx2YRX2NjfN2aE7T9eLb1mzSpjpNTiXTbvHgxnCamD5Rnn6nN1q1H05XZuPEQL3Z+6qFiOXHiIhUquuPlVRpbW2s6dW5iPg7J8R6ja7fmadp7M8u6QUHH6datOQDdujVn+/ZjWbZDa43WmpjYOLTW3LlzD3dTv1y1cjev92mNk1NJANzcHB8qxrx29NhVbkXfL9Q2ZCS/jmXVqmWoUsUz3faUUsTExpGQkMi9e/HY2FhRqpQ9AGvX7GGwKVtjMBjMmbaiGN9zz9U1X+Q0bFSFsLAoAOrUqYCHhzMA1auX5X5c8rkxP+JL6eSJC1So4IGXlzs2tta80KkZO3YcSVVm546jvNT1OZRSNGxUjdvRMVyLuAnAU01q4eRcMt16q1YtR+XKZbLc9qaN+3ihc/M8i0Xknxx/kimlKiuleiilauV1I8IjbuLp6WL+3dPDmfDwm6nKRN28i6ODvfmN5unhQnj4rezrK8Vbb39Dj14zWLHyN3OZGzdu417aCQD30k5ERt5O167ArceoXdvLPCh45PjC07TP04XwiAzicyyRHJ9ncgxZ1U9MTKJrd1+eee5jnnmmFg0bVgYgJCSCw0fO0/uVmfR943NOnAwB4GJIBArFW29/Tfce05hnykzlOr4yKduXyfFLG1/EzRzXT6tG9bIE7TCegLdsOcrVq1HpymzafJjOnZs8dCxlPF2T2+LhTHh4VAZl0hyP8JtZ1r1xI9o8IHF3d07V30JDr9O9mw99+37G4cN/AWBjY82EiX14qcskWrYYyYULV+nV6znAmKEJuRjOa6/O4JWXp7E7+NRDxfhvkV/HMjMdOjSmhL0tLZ4biXeb0Qwc+DzOziWJjo4B4MsvA+jR3YcPP5jD9evRFhHfmjV7aNmyXrrlgYFHqWM6N+ZXfClFREThmaK9Hh6uRKSLNU0ZT1fCIyJzve0tmw/QqVOzXK8n30mGJvMBjVLKP8X/uwI7gC5AgFJqQF42IqO5TOpBqiGLQg+KZFV/2ZJh+K0Zzbwf/suSZbs5dPh8jtr0119XmfV5AJMnvpqj8lnRGbY9J/GpbOtbWRkI8BvLrp1TOXEyhD//NKaNExMSiY6OYeXykYwa2YOhwxagtSYxMZEjRy/w2WdvsnTJCLZvP86+fWdzFx+ZH5ss43uY+mlM9e3H0iW76NHDl7t372Frk3o62PHjF7G3s6VGjXJZryit3ByrnNRNw93diR07p+Pn/wmjR7/MRyPmc+dOLPHxCSxftgs///EE7/6MGjXLmVP/CYlJXLoUweKfRzB79iDGj19s/lARKRTwsTx5IgSDwUDw7plsD/Jl0cJtXL58jcSERMLComjcuCpr/T6h0RNVmTlj1cPFkpF8jm/O9xuxtjLQ5aWmqZb/9dc/zJ61xnx7NN/iSyEn59AcnWcf0onj57G3s6V6jZzfthaFJ6tJwSlvGH4MeGutLyqlHgeCgB8zq6iUGgwMBvjh+w8ZPKhzujJLlu5i5SrjHJH69Sua05oAYeE3cXd3SlXexaUU0bdjSUhIxNrairDwKHMZTw/nTOs/uH3k5uZA+7YNOHEihCZPVcPNzYGIa7dwL+1ExLVbuKZIkYaFRTHkg7nMmPYGFSqUzmIXZW7Jkl2sXG2cI1K/Xpr4wqLM2aFU8UXHJMcXlhyDp6dLtvUdHUvQ9Oka7P7tNDVqlMXD04X27RuhlKJBg0oYDIqoqDt4erjwdJPquLqUAqBly7qc/uMyzZs/XOJtyZJfWbnKmPGqX78iYVdTtu+mORuRdXzGMp4eLtnWT6tqFU8WLvwQMN5++nXXyVSvb9x06KGzMwAeni5cDUu+qjP2JecMyqQ5Hu5OxMcnZFrXzc2RiAjj7xERN839zdbWxpwBrFevIl4VSnPxYjgPxngVKrgD8MILT5nn3Xh6uNCwURVsbKwp7/U4lSt7cikkgvoNKj10vMVZfh3LzGzYcJAWLepiY2ONm5sjjRtX5dTJS3R84Uns7W1p3954O7ljxydZs/q3LNdV2PH5+e1l568n+fHHYakGBWFhUQwZ8h0zZgw0901nl1L5El+qODxcCUvR3vDwSEqnidXTM02ZsEjcS7uQG5s37beY2026ADMnuRsm5p+sbjmlHO5aa60vAmitrwNZ7jmt9Vyt9VNa66cyGswA9Hm9FQF+YwjwG0O7tg3wDziI1ppjxy/i4GCf7gNbKUXTp2sQuPV3APz8D+Dt3QAAb+/6GdaPibnPnbv3AIiJuc+evWepXr2ssU6b+vj7HwDA3/8AbU3rio6OYfB/5jB82Es82bhqVmFmqU+fVgT4jSXAb6wpvgPG9h0ztc89g/ia1iAw0BRfwP7k+NrUz7B+ZORt85X5vXtx7N13liqVjff227VtwP795wDjB358fAIuLqV47rk6nDt3hVjTvf5Dh/6iWtX08wGyj6+1efJtu7aN8A/Yb2rf/3BwsMskvpoEBhrnuvj578O77YPj1yDb+mnduGFMaSclJfH9nE28+mpL82tJSUls2XKUzg85fwagfv1KXAqJIPTydeLiEti08RDe3g1TlfH2bkiA/74U7bXH3d05y7re3g3x9zdOYvf330fbtsblkZG3SUw0vp0uX77GpZAIvLxK4+7hzIUL/5hvTe3dc4YqVY33+tu1a8SBA8ZjGxV5m5CQcMp7Pf7QsRZ3+XUsM1OmjCv7D5wzzn2Kuc/x4xepUsUTpRRt2jTg4IE/Adi37yxVq2Y9b6Mw49sdfIr58wL5/vv3sLd/zLyu6OgY3hn8NcOHd6fxk9XMy/MrvpTq1a/C35fCCA2NID4ugc2b9tOmTeNUZVq3acy6gN/QWnP82HlKOZRIN+h5GElJSWwNPMgLlnC7SQCgMkrTASilEoAYjIMxO8BLax2mlLIFDmutG+RoC4nbsn04XmvN5Ckr2f3bGeztbPCd2pf69YwJokHvfMcUn9fxcHfm8uXrDPtoEbdu3qV2bS9mzeyHra1NpvUvX77Oex/MMzYjIZEXOz/Ff97tCEDUzTsMHbaQq1ejKFPGhS//7y2cnUvy3ZwtzJ23lYopMjML5w8xT6pNv6Oyn4aktWayzwp2//YH9na2+Pq+kRzf4G+ZMqVPcnwjFnDrVgy1a5dn1swByfFlUP/suVBGj1lMYmISOknTseOTDHmvE2B8jHLs+J85eybU+IjlqB7mx88D1h1g7tytKGXM0Dx4nPtRYkuObzm7d582ta8/9es/iO9rpvi8gYeHM5cvX2PY8Pmm+LyY9dmbKeLLuP7w4fM5eOhPoqLu4ObmyPvvd6F3r2f5aXEQS5fsAqD9808wYng385XkgQPnmP25PytXfJxxgx+0O5P4du06ia/vCpISk+jZ81ne/U9nli8zbuvV11qhtcZn8jJ27z6Fnb0tvr4DzI/uZlQXICrqDsOGzuXq1UjKlHHliy/fwdm5JIGBR/j6q3VYWVlhZaUY8v5L5g+W5ct2sXhxENbWVpQt58a0aQNwcSmF1prp01fx2+7TGKwU777bic6dn04Xx5NNT+fo+OWWr097nmxcFmdnOyIjY5kz9xAB68/k+3aPHKibbZn8OJbbtv3OFJ9lREbewdHRnlq1vViwYCh3795j7JgfuXDhKlpDjx7P8NbbHQC4cuUGH49aSHR0DK6uDvhO60/Zsm653gf5Ed/z7ccRF5eAs2kS7YOvEvj+u43MnbuZihXdzdtfsHAobm6ODx1fQpL9Q8cavOsYM6YtITEpie49WvLOu11ZsTwIgFdebWt8RN3nJ3777ST2drb4+A4yP4Y9csS3HDp4hps3jeeR/w7pQc9erdm+7TDTpi4mMvI2Do4lqFWrInPnG5+GOnjwDF/MXsHSFRMfuq0ANoanCzSRoSOXFNgX0SjXPkUySZPVgOZbYKnWek+a5c5Aba31vhxtIQcDGouWww99i1ScYyPzAU1xUVADmsKSkwGNKJoeZUBjaWRAU/CymkPzJzBLKVUGWAEs01of01rfBHI2mBFCCCFE/ivCTx8VlEwvUbXWX2qtmwOtgEhgkVLqjFLqU6VU3n5rkhBCCCFELmSbc9daX9Jaz9BaPwG8DnQH8v8GuRBCCCFyRuuC+ymish3QKKVslFJdlFJLgM0Yb0X1zPeWCSGEEELkUKZzaJRS7YHXgM7AQWA5MFhrfbeA2iaEEEIIkSNZTQoeCywFPtJa5/77o4UQQgiRP2RScOYDGq11m4JsiBBCCCHEo8oqQyOEEEIISyAZmpz/tW0hhBBCiKJKMjRCCCGEpUuSDI1kaIQQQghh8SRDI4QQQlg6mUMjGRohhBBCWD7J0AghhBCWTjI0kqERQgghhOWTDI0QQghh6YrwH40sKJKhEUIIIYTFkwyNEEIIYenke2gkQyOEEEIIyycZGiGEEMLSyVNOkqERQgghhOWTAY0QQgghLJ7cchJCCCEsndxyKoABTXF/Nl4VdgPyT4K2L+wm5Ctrfbewm5CvjhyoW9hNyFdPNj1d2E3IV0f31SzsJuQbG1W833uicEiGRgghhLB08ti2zKERQgghhOWTDI0QQghh6SRDIxkaIYQQQlg+ydAIIYQQlk4yNJKhEUIIIYTlkwyNEEIIYemSivlXpOSAZGiEEEIIYfEkQyOEEEJYOplDIxkaIYQQQlg+ydAIIYQQlk4yNJKhEUIIIYTlkwyNEEIIYekkQyMZGiGEEEJYPhnQCCGEEMLiyS0nIYQQwtLJLSfJ0AghhBDC8kmGRgghhLB0kqGRDI0QQgghLJ9kaIQQQghLJ3+cUjI0QgghhLB8kqERQgghLJ3MoZEMjRBCCCEsX5HI0GitmTptDbuCT2Nnb8v0qX2pW8crXbnLodcZ/tGP3LoVQ5065Zk5rR+2ttaZ1v/fxXCGjViUov4NPhjSiQH92jB0xEIuXowA4PbtWBwc7AlYO9pc9p9/Iun80lSGvNeJt95sm/v4fFcZ22dnw3TfftStWyHj+EYs5NbNu9Sp48XMGQOS48ug/tWrkYwa/RPXr0djUAZefvlZ+vfzBuDrbzawctUeXF0dABg+9CVataqXOr4uPsb4BrbPVXy7d59guu8SEpOS6NmrFYMGvZgu/mm+SwgOPo69nS1TfQdRp26lLOuOGPYtF0PCALgdHYODYwnW+vkQF5fApImLOH0qBGVQjBnbh6efrg3Al1+sZl3AHm5F3+Xwkbm5iill2wv62D2wYOE2Zn7mx769M3F1KUVcXAITJi7l1Km/UQbFuLG9afp0jVzFtzv4FFOnriApKYlevZ9j8OAX0sc/dQXBu05iZ2fLtOkDqFu3YpZ1t2w+zDffrOfChTBWrhpD/fqVAIiPT2D8+J/5449LJCYk0bVbc955x1gnLi4BH59lHDx4DoNSDB3WjQ4dnsxVbHllwvg2tHi2IpFRsbz8+orCbk4q+dU/H0hMTKJn7+l4uDvzw5z/AnDz5l2GDV/AlSs3KFfOjS/+722cnEqwZ88ZZn/uT3x8IjY2Vowc2YPmzWrmPr6pK9kVfAo7O1umT+ufeXzD53Pr1l3q1KnAzBlvJseXQf2rVyMZ9fGPxvefQfHyy8/Rv1/bFPHNSxHfIJycShIaep1OnSdRubIHAA0bVmbypD65ii9PSYamaGRognf/QcilCLZu/hSfia8ycXLGJ41Zn69jQL82bN38KY6OJVi9dl+W9atU9iBg7WgC1o5m7apR2NvZ0L5dQwC+mD3Q/Nrz7Rualz8wbcZaWrSokzfxBZ82tm/LRHwm9WHi5OUZxzfbnwH9vNkaOAlHpxKsXrM3y/pWVlaMHtWTzRsnsGLFSJYuDeb8+avm9Q3o702A31gC/MamGswATJu+Ok/iS0xMYqrPYubMHcG69dPYtHE/589fSVVmd/AJLl0KY/OWmUyc9CaTJ/+Ubd3Z//cea/18WOvnQ/vnn6JdO+OH2+pVvwLgv24q8xeM4rMZy0gyvZFbt27E8hUTch1TSoV17K5ejWTv3rOULeNqXrZq1R4A1q8bz6IFHzBjxhpz7I8iMTGJyZOXMm/+B2zYOImNGw5x/vw/aeI/xaWQcAK3TmGyzxtMmrgk27rVa5Tjq6//w1NNqqda15YtR4iPi2f9+omsWTuOFSuCCQ29DsCcOZtwc3UgMHAKGzdN4ukmuRuo5aX1G84yZOiGwm5GhvKrfz6w+OedVK3imWrZ3HmBNG9ek62Bk2jevCZz5wUC4OJSiu+//w/r141n+rT+jPr4xzyI75SxfYGT8Znch4mTlmYc36y1DOjflq2BPsbPhjV7sqxvZWXF6I97sXnTRFYs/5ilS3aZ++/ceVto3qwWWwN9aN6sljk+gAoVShPgP54A//FFazAjgCIyoAnacZJuLz2NUopGDSsTfTuWiGu3UpXRWrP/wJ90eL4RAN27NiUo6ESO6+/bfw4vr8cpV9Y13Xo3B/7Oi52Trwa3Bx2nvNfjVK9WJo/iO0G3rk2N7WtUmejoGCIiMohv/zk6dHjCFF8zgoKOZ1nf3d3JfLVSqqQdVap6Eh5+M9v2bN9+LM/iO3nif3hV8MDLyx1bW2s6dWrKzh1HU5XZseMoL3V9FqUUDRtV43Z0DNcibuaortaawC0H6dy5GQAXLvxDs2bGgZibmyMOjiU5deoiAA0bVaO0u3OuY0qpsI7dtOlrGPlRd5RK3s75C1dpZrridXNzwMGxBKdO/f3IsZ04cZEKFd3x8ipt3P+dm5jbbY4/6BhduzU3tb8K0dGxRETczLJu1aplqJLmQxBAKUVMbBwJCYncuxePjY0VpUrZA7B2zR4Gm7I1BoMBF1NmsSg4euwqt6LvF3YzMpRf/RMgLCyKX3edolevZzPYpvH92K1rM7ab1lWnjhcepvdf9epliLufQFxcfO7iCzJuK3X/yyy+xsb4ujUnaPvxLOunev+VSv3+Cwo6QbduzY3xdWvO9u2p3xNFldZJBfZTVGU6oFFKVVBK2Zn+r5RSbyqlvlZK/Ucplae3qsIjbuLp6WL+3dPDmfDw1J026uZdHB3ssba2Si5j6tg5qb9x81Fe7JQ+hX34yAXc3ByoVNEdgJiY+8xbsJ0h/3khXdlHFR6epn2eLoRH3ExVJurmXRwdSyTH5+lsfoPlpH7olRucOXOZhg0rmZctWbKLLl2nMGbcz9y6FZMc3/xtDPlvp7yJLSKKMp7Jg0QPD1fCw6NSlYkIj8LT0y25jKcr4RFROap75PA53NwcqVjJ+AFZs5YXO3b8TkJCIqGh1/jjdAhhYZF5EktGCuPYBe04gbuHE7VqlU9Vrlat8gTtOEFCQiKXQ69z+vTfXA1Lvb8eNraU+9/4vonKoEya9offzFHdtDp0aEwJe1taPDcS7zajGTjweZydSxIdbeybX34ZQI/uPnz4wRyuX49+5Lj+TfKzf/pOW83Ij7pjMKhU67tx4zbu7k4AuLs7ERl5O127Arf+Tu3a5bG1tcl9fGVSts853UVbhvFF3Mxx/dDQ66b3X2VTfNGZxhcaep1u3afSt+9sDh/+K1exibyXVYZmU4rXpwOdgQNAEyBvJiiYaJ3++Xml0hXKtEx29ePiEtix8yQdTVcoKW3YdCTVQOfrbzfRv18bSpZ8LGeNz4GM26fSFsq0THb17969xwcfzGXs6F7mK97XXm3Jtq2TCfAbi3tpR6bPXAMY59b07+9NyZJ2jxxPTtudRRHj8clB3U0b99PJlJ0B6NGjJR4eLrzceyLTpy2hUaNqWFtZPVrbc6Cgj11sbBxzftjCh+93SVevZ4/meHq40LP3DHynreaJRlWwsspFkjU3seWkbhonT4RgMBgI3j2T7UG+LFq4jcuXr5GYkEhYWBSNG1dlrd8nNHqiKjNnrHq4WP6l8qt/7tx5ElfXUtTLYL5Kdv766x9mzfZn8qTXH7puWppH/GzIYX3z+2/My+ZzZ2bc3Z3YucMXf79xjB7dixEfLeTOndjsQig4SUkF91NEZZVpMWitY0z/bwc00cZc0y9KqSxzcEqpwcBggB+++5DBg9JnA5YsDWblauN93Pr1KhCW4kozLPymeYT8gItLKaJvx5KQkIi1tZWxTGljGU8PlyzrB//2B3XrePH4446p1pmQkMi27cdZu3KkednxEyEEbj3GrNkBRN+OxaAUj9la07dPq6xCTh/fkl2sXL3HFF/F1O0LizK3PVV80THJ8YUlx+Dp6ZJp/fj4RD74cB5dujzN888nD9hSxtq793O8++53yfEF/s6sWX7G+AyKxx6zoW+f1g8V3wMeHq5cTZEhCQ+PxD3NbR8PTxfCwm4klwmLxL20C/FxiVnWTUhIZPv2I6xcPcm8zNraitFjku9d93nNhwoVPR6p7ZkpzGP39+VrhIZep2u3qcby4Tfp0XMaq1aMonRpJ8aO6WVe16uvfWbOLD4KD0+XVPvf+L5xzqBMmva7OxEfn5Bt3bQ2bDhIixZ1sbGxxs3NkcaNq3Lq5CU6vvAk9va2tG9v3AcdOz7JmtW/PXJcxV1B9M/AwKPs2HmS4ODT3I9L4M6dWD4atYhZM9/Ezc3BfNsmIuKW+cGDB/WHvD+XGdP7U6FC6UeM71dWrjIe//r1KxJ2NWX70vezjOMzlvH0cMm0fnx8Ih98MDfdudPNzTHD+GxtbcwZp3r1KlLB63EuXoygfv2KjxSnyHtZXd5dVko9eOwiBPACUEq5ZVrDRGs9V2v9lNb6qYwGMwB9Xm9pnpTbrm0D/NcdRGvNseMXcShll+5NqZSi6dPVCdx6DAC/gAN4e9cHwLtNvSzrb9x0hM4Z3G7au+8cVSp7pEq5Lv15GDu2TWLHtkn0f6M17wx+/qEHMwB9+rQyT8ht17YB/gEHjO07dhEHB/t0AzalFE2b1iAw8HdTfPvx9m5giq9+hvW11owb/zNVqnjy5oDUT2KlvM+8fdsxqlcva4zvlxHsCJrCjqAp9O/XhncGd3jkwQxAvfqV+ftSOKGh14iLS2DTpgO0aZM6E9amzROsC9iD1prjx85TysGe0u7O2dbdt+80lSuXwTPFrY3Y2PvExBjnM+zdcworKwPVqpV75PZnpDCPXc0a5di3Z6b5GHl6OLN2zRhKl3YiNjbOHPuePWewsrKiWi7mQdWvX4lLIRGEXr5u3P8bD+HtnXpyvLd3QwL895na/z9T+51zVDetMmVc2X/gHFprYmLuc/z4RapU8UQpRZs2DTh44E8A9u07S9WqeTN/rTgqiP45Yng3gn/1ZUfQFD6fPZBmTWsya+abxjreDfAP2A+Af8B+2prWFR0dw+B3v2P48K482bhqLuJrbZ54265tI/wD9qfof3aZxFeTwEDj/Ds//314t22Qqq1p6xvff4upUtWTN99sl2p93t4N8Pc3PnDi77+PtqZ1RUbeJjHRmJ24fPkaIZci8PJ6/JHjzHOSoUFllHIEUEp5AYsBK+AW8BzwO+ACfKS1DsrRFhK2Zvt9zFprJk9Zxe49Z7C3s8F3Sl/q1zOmOge9+z1TJr+Oh7sTly9fZ9hHi7h1K4batcsza0Y/bG1tsqwfGxtH67afsD1wIg4OqVOKo8f+TMOGlXntlecybNfX326iRInHsn5s25D97Q6tNZN9VrD7tz+wt7PF1/cN6tczjuoHDf6WKVP64OHubIxvxILk+GYOSI4vg/qHj5ynT9/PqVGjLAaDcWz64PHskaN+5OzZUFBQrpwbkye+nu5E8PU3G4zxZfLYdgIls40NIHjXcaZPW0JSUhLde7TknXdfYsXyHQC88qo3Wmum+PzMnt9OYGf3GFN836ZevcqZ1n1g7Jh5NGxYlVdeTX6c+cqVawx+exYGg8Ld3QWfKW9RtpzxpDLrsxVs2riPiAjjVVjPXq14b0j3TNttzd1sYyuMY5eSd9vxrF49GleXUoReucFbb3+NwaDwcHdm6pS+lCuX+fWFNmQ/1W3XrpP4+q4gKTGJnj2f5d3/dGb5sl0AvPpaK7TW+Exexu7dp7Czt8XXd4D5MeyM6gJs2/Y7U3yWERl5B0dHe2rV9mLBgqHcvXuPsWN+5MKFq2gNPXo8w1tvdwDgypUbfDxqIdHRMbi6OuA7rT9ly2Z97fRk09PZxpcXfH3a82Tjsjg72xEZGcucuYcIWH8m37d7dF/2jzznV/9M6cDBP1m4cLv5se2oqDsMHb6Aq/9EUqasK1/+39s4O5fku+83M3deIBVTZA0Xzn8fN7cMJnirnN0qNbZvObt3nza1r785IzJo8NdM8XkDDw9nLl++xrDh803xeTHrszdTxJe+/uEj5+nTZxY1apQzzxEaPqwrrVrVN8Y3bB5Xr0ZSpowrX34xGGfnkgQGHuWrr9djZWXAysrA+0O6mAeGGVJtsr4Hm8eSfhtZYH/7wPDcZwUaW05lNaD5BlgGRAHVMd6eCgUO6YeZ5pyDAY1Fy8GAxlLldEBjqXIyoLFkORnQWLKCGtAUlpwMaCxWDgc0Fk0GNBlSSrkCK4BKGO/+vKy1TvdEgVJqIfAiEKG1rpf29QzblcVrfwGzME4Ofga4oLU+8FCDGSGEEELkvyRdcD+5MxoI0lpXB4JMv2fkR6Djw6w40wGN1vpLrXVzoBUQCSxSSp1RSn2qlCo633olhBBCCEvRFfjJ9P+fgG4ZFdJaB2Mce+RYtnk/rfUlrfUMrfUTwOtAdyD/byALIYQQImcsZ1Kwh9b6KoDp30d/VDONbG+yK6VsMKZ9XgXaAruASVlWEkIIIUSxlPKrWUzmaq3npnh9O5D+68JhXH62K9MBjVKqPfAaxi/UOwgsBwZrrYv3TEohhBDC0hTg49SmwUumX7CrtW6X2WtKqXClVBmt9VWlVBkgIq/aldUtp7HAPqC21rqL1nqJDGaEEEIIkQvrgP6m//cHAvJqxZlmaLTWbfJqI0IIIYTIR0X4C+/SmA6sVEq9BfwN9AZQSpUF5mutO5l+Xwa0Bh5XSoUCE7TWC7JacfH+ogohhBBCFBla6xsY5+OmXf4P0CnF76897LplQCOEEEJYOsvJ0OSbf8HXNQohhBCiuJMMjRBCCGHpJEMjGRohhBBCWD7J0AghhBCWTjI0kqERQgghhOWTDI0QQghh6XL/V7AtnmRohBBCCGHxZEAjhBBCCIsnt5yEEEIISyeTgiVDI4QQQgjLJxkaIYQQwtJJhkYyNEIIIYSwfJKhEUIIISydZGgkQyOEEEIIy5fvGZokK/v83kShMiTdK+wm5BtrQ2xhNyF/yfdQWbSj+2oWdhPyVePm5wq7Cfnm6N7qhd2E/GdVsJvTiXJCkwyNEEIIISyezKERQgghLJ386QPJ0AghhBDC8kmGRgghhLB0ModGMjRCCCGEsHySoRFCCCEsnJY5NJKhEUIIIYTlkwyNEEIIYelkDo1kaIQQQghh+WRAI8T/t3fncVFV7wPHP4dNUEHABMzcy33LLLVNxUxLS0y/fksrNcs2M5e0NEsRxSW19VuGpmWZSyrglli44G5q7ktZamgCKiAuqCzn98cdB5ABBmEYxt/zfr14MTOcc+/zcOfeOfOce2eEEEI4PJlyEkIIIRxdhnw5pVRohBBCCOHwpEIjhBBCODi5bFsqNEIIIYS4DUiFRgghhHB0ctm2VGiEEEII4fikQiOEEEI4OjmHRio0QgghhHB8UqERQgghHJyWc2ikQiOEEEIIxycVGiGEEMLRZconBUuFRgghhBAOTyo0QgghhKOTc2hK34Bm48YDhE6YT2ZmJj16PMIrA57M8XetNaET5hMTsx93dzdCJ75Ew4bV8+370ZSfWLduL66uzlSt5kdoaD+8vMpy+tQ5Onf+gJo1AwBo2rQWY4NfKPactNZMCF3MhpiDuHu4MSn0BRo2qJqrXeypcwwdNocLF67QoEFVpkx6ETc3lwL7Z2Rk0v0/U/D3r8DXX70OwOChszl+PB6AixdT8fT0IDJ8JNevpzNm7HwOHPwH5eTE+yO70/KBOoXPZ8IiNsQcwN3djUkT+9CwYTXL+QydxYULl2nQoBpTJvfLyieP/jEbDzJhwiIyMzP5T4+HGDCgEwCffLqM6Oi9ODkpKvp6MnFiH/z9vQE4cvQUYz6cx6XLV3FSisWLR1KmjCsrVvzG11//DErh51eBjz56CV+f8oXPNfQn43/v7sqk0BfzznXYbC4kXza23eS+2bZd7v7XrqXR+4XpXL+eTkZ6Jh073sugt7oA8PPq3XzxxUr++juOnxaNoHEj4/mdlHSJQYNncuDAP3QLasWHH/y3ULlYsjHmABMmLDT2mf88zIABT+TOf8JCYjYY+9vESX2z9rc8+k6ZvNi0v7lQrVolQif2xcurLJs3H2LatKWkpaXj6urCiOE9aNW6HgDXr6cTEjKfHTuO4qQUg4cE0bHjfUXOz1bb7wZj35uEv583X894A4Dk5MsMGfoNp0+fp0qVinzy8ctUqFCWzZsPM216BGlpGbi6OjN8+DO0blW3yDkW1ZjR7XjkoeokJqXSs9dCe4eTgy2PnYGPfUi5cmVwcnLC2cWJpT+9C5i237DZnD6dSJUqvnwyvT8VKpQlKfkSgwZ/w4H9J+nWrRUfju5Zov8LUbBSNeWUkZFJyLh5hM0czPIVIaxcuYNjx/7N0SYmZj8nTyawOiqU4HEvMi74hwL7PvhgA5YtDyZyWTA1avgTFrbKvLyq1SoRHjGG8IgxNhnMGDEf4sTJs6xZPYaQ4OcYG7zAYrup0yLp26cda1aPwcvLg8VLt1rVf+7366hd2z/HY59Mf4nI8JFEho/k8Q7N6NChGQA/Ld4MwPLI95kzayCTp4STWci515iYA5w4mcCaqHGEjOvN2OAfLeczdSl9+7RnTVQIXl5lWbxkc779MzIyGTduPrNmDmTlijGsWPmbeRu+3L8Dy5d9QGTEaNq2bcz/vlwJQHp6BsOHzyE4uDcrV4xh7tyhuLg4k56ewYTQRXw3dyjLl31A3bpVmPfDukLlacR60Ih19VhCgnszdlxe2y6Cvi8GsiYqGK8KZVm8ZEu+/d3cXPhuztssi3ifiPBRbNx0iD17jgNQ557KfP75AO5vcXeOdZQp48rbg55ixPBuhc7DEuP//SMzZw1ixcpgVq74zcL+doCTJ+KJWjOecSEvEDx2XoF9H3yoPstXjGXZ8jHG/vb1zwD4+JTnq68Gsnz5WCZN6seIEbPN65kxYxUVfT2JihrPylXBPHB/4QbZebHV9rth7vfrqF0rIMdjYTOjaN26Lmuigmndui5hM6Oy5f86y5eNZtLEPox499tiybGolq84wsDBK+wdhkW2PnZ+9+3bRIaPNA9mAMJm/ULrVnVZs3oMrVvVJWzWGgDKuLny9ltdim3/K246U5fYT2lVqgY0+/Ydp1o1P6pWrYSbmwtPPvkAa6P35GizNnoPXbu2RilFs2a1SUm5QkJCcr59H3q4IS4uzoBRhYmPSyrRvKLX7iOo6wNGzE1rknIxlYSzF3K00VqzbfsfdHz8XgC6BbUkOnpvgf3j4pJYv+EgPbo/aHHdWmt+jtpNlyeNd7vH/oqjleldYcWKnnh6enDgwD+Fyyd6H0FdW5m2QS1SUlJJSLCQz7ajdOzY3JRPa6J/3Ztv/337TlA92zbs/OT9REfvA6B8eQ/zslNTr6OUAmDz5kPUrVuFevXuAowXDWdnJ7Q2Yki9cg2tNZcuXcXPz7tQecKN/31LU6w1Tc+3vHI1bbuurW7adrn7K6UoV84dMAZl6WkZmFKidu3K1KqZc4AKULZsGVrcdzdlyrgWOg9L9u07TrXq2faZzveb4zbnH72HrkGtb9pWyfn2fTj7/tasFnGm/a1Bg2rmqto999zJtetpXL+eBsDSJZsZ8KpR4XFycsLH17NYcrTV9oMb+94BevR4yMI6WwEQ1LUVv5qW1aBBVfz9buRfmevX0s3529PuPWe4kHLN3mFYZMtjZ77rDGoJQFBQS341HYOM/a92se1/ovjlOaBRSj2tlHIvyWAS4pMIqOxjvu8f4EN8fM7BR3x8MgGVfc33AwJ8SIhPtqovwNIlm3jk0Ubm+6dPneOZbsG88PwUdu78ozjTyYo5IZmAgKzYAvy9iY9PztEmKfkyXp4e5heCAH8f4uMvFNg/dNIShr8ThJOTsrjunbv+omJFT2rU8AOgXt0qRK/dT3p6BrGnznHwUCxnCjnAM7ZBtngC8sjHq2xWPgHexCck59s/Ptc29M6xDT/+OII2bUeyfMUO3h70FADHTySglKJ//8/o9swEZs4y3g27ujozdsxzPPV0CI88+i5//XUm1wuP1blm/98H+JjzyDfX+OQC+2dkZNK1WygPPvwuDz5Yj6ZNaxY6vqKIj0+mckC2fcnf2+L+Vvnm+OOTreoLsGTJZh7Ntr/dEBW1mwb1q+Lm5kpKyhUAPv00kme6hfD2oBmcO5dS5PxuxG+r7Rc6cTHD3+mWa987f/4ifn4VAPDzq0Bi4sVccUWt+Z369e/CzU1eHPNjy2MnStH/5S94psdkFi7aZG5z/vxF/CqZtl8ly9tPlE75VWgWAqeUUt8rpZ5USjnbOhhLhawb78Sz2uRupZR1fWfMWIGzizNPPWW8e6rkV4HotVNYGj6G997ryfB3ZnLpUuqthp8nbSG4m2Oz1OhGk7z6r1u/H19fTxpZOCfghhUrd9LlyRbm+92faU1AgDfd/zOF0IlLuLdZTZydC7dp89oGORtZaFNA/4K24ZAhQWxYP5GnujzADz+sByAjPYNdu47x0dSX+HHecH79ZQ9btx4hLS2D+QtiiAh/n40xk6lbpwpfh622MsPsaViK1Zptpwrs7+zsRGT4KDasm8C+/Sf4449/c7W1qaLkZkXfGV+txMXZiaeebpnj8T///JdpU5cQPO55wNiGcXFJNG9em6XhH9Ds3tpMmfxTYbOxyFbbb926/fj6ls9338vLn3/+y9RpEYwL7lXovv/f2OrYCTB/3hDCl7zHzK/fYN78jfy281hRw7WvDF1yP6VUficFHwECgR7AMGCOUiocmK+13pDfQpVSA4ABAF/NeIcBA562Khh/fx/izmS9y4uPS8o1TRDg70PcmUTz/bi4JCr5eXM9LSPfvhHhm1m/bh9zvh1mfkK7ubma3yE1bFSDqlUrceJ4PI0a17Aq3vzM+3EDi34y5uEbN65uLrsDxMUnm9/B3eDjU56Ui6mkp2fg4uJMXHySuU2Av7fF/lFRv7N23X5iYg5y7Voaly5f5Z0R3zF1Sh/AmMr45de9LP1phLmvi4szo97rbr7/bK9p1KheqeB85q1n0U+bsvLJ9r+Oi0vOtZ18fMqTknIlK59sbQJu2s43/paWaxvmXi5Aly738+pr/2PQoKcICPDhgfvvMZ/s+2ibRhw89A/lyhvFxWrVjNyeeKIFYTOtG9DMm7eBRaZzjRo3umnbxSWZ373ln6tp2wX4FNjfy6ssLR+ow8ZNB6lT506rYiwO/gE+nInLti/F5/5/G21uit+vAmlp6fn2DQ/fwrr1+/n22yE5XoDi4pIYOPBLJk9+iWrVjKqht095PDzc6NDBmDLo1Ok+lizOesdcWCWx/aKidmfte9fTuXQplXdGzGHqlH5UrOhJQsIF/PwqkJBwAd9s02dxcUkMfCuMyZP6mJ+bIqeSOHYC5um/ihU96dC+Cfv2neD+Fncb2+/sBfwqVSDhbM7tJ0q3/Co0WmudpLWeqbVuDzQFDgGTlFKx+S1Uax2mtW6htW5h7WAGoHHjGpw8Gc+pU2e5fj2dVat20C6waY427QKbERm5Fa01e/b8haenB35+3vn23bjxALNmrebLr97Cw6OMeVmJiRfJyDBOiI2NPcvJkwncVfUOq+PNT+9ebcwn5T7WvgkRkTuMmPceN2K+6aCqlKLlA3WIWvM7AOER2wkMbAJAYGBji/2HDe1KzLrxrP11HNOn9aNVyzrmwQzAlq1HqVXTP0fJNTX1OleuGPPlm7ccxtnZibvvrlxwPr3bEhkxmsiI0TzWvhkRkdtM2+BvPD3dcx1klFK0bFmXqKjdpny2Etj+Rj5NLPZv3Lg6J04mEHvqHNevp7Ny1W/m/8GJE/HmZa9du898jsnDDzfg6B+nSU29Tnp6Br/99id3166Mv583f/11xlwu3rzlMLVrFZynkWsbIsNHERk+yrTttptiNf3vLeZah6go07aL3Ja17do1ttg/MfGiearl6tXrbNl6hFo1c55camuNG9fg5IkETsUa/+9VK38j8Kb9LTCwKZERW7Ntq2z7Wx59N8YcYNbMKL766s0c+1tKyhVeHfA5Q4d2o/l9WSc8K6Vo164JO7YbU75btx6hdm3rtpUlJbH9hg0NImZ9KGujxzN92ku0almXqVP6mf5nxvMbICJyG+1Ny0pJucKA175k6NCu3Ne89i3nd7sriWPnlSvXuHT5KgBXrlxj85Yj3HOP8WYisF1jIiK2AxARsd28/Uo9qdCgLJVUAZRSu7XWzfP4W3Wt9UlrVpCpNxYq+w0b9jEx1LgU9JnuD/Haa11YsGA9AM8+2xatNSEhP7Jpo3HJb2hoP3NFxVJfgI6PG5cre3sb7+JvXJ69JmoXn30eiYuzE07OTrw18GnaBTYrTLg4ZV4tsI3WmnHjF7Fx02E83F0JnfC8+VLcV179kvEhvfD38yY29hxD3pnDheTL1K9flalTXsTNzTXf/jds3/EHs+dEmy/bBnhv1Pc0bVKD5559xPzYqdPn6f/K/3ByUvj7eTMhpDdVqvhikZPlAp7WmnEhC9i48SAe7m6EhvahcWNTPgM+Z3zIC/j7exMbe5YhQ2dx4cIVI5+P+mXlk0f/DRv2Exr6ExmZmXTv/iCvv2Zcev/WW19z/EQ8Simq3OlLcHAv/P2NgVrksu2Eha1GKcWjjzZkxHCjAjV/QQxz567FxcWZKnf6MnFiH3yyX7atC766y4h1IRs3HTLF+kLWthvwP8aP75217YZ9Y8r1LqZO6Zst19z9jxw9xXsj55KRkYnO1HTqdB8D3zRy/eWXPYRMWERi4iW8vDyoX+8uvpn1FgCB7Udz6fJV0tIy8PT0YPast/IckOo8tl92xv97IZkZmXTv/hCvvd6ZBfONAuyzz7Ux9rdx89m48QDuHm6EhvalsXl/y90X4PEO75v2t3KAsb8Fj3uer75cSVjYz1Sv7mde/zezB1OxohenT5/n3RGzSUm5gq+vJ6ET+3DnnRXzjV1lpheYn622X3bbd/zB7Nm/mi/bTkq6xOCh33Dm30Qq3+nLpx+/jLd3Ob786mfCZkblyH/2rLeoWNFyBaB566MF5lccQkM6cF/zO/H2dicxMZUZYb8RufywTde5e8s9VrWz1bEzNvYcbw6aCRhTnl06t+D114yPiEhKvsTgIbM5cyaJypV9+PTj/ubncuBjH3Lp0lXS0tLx9CrL7Jlv5v2G0LmD5RMbbeTaxKASG2mUGRlRorlZK78BzSHgZa31lqKsoLADGkdjzYDGYVnxgujQrBjQODJrBjSOzJoBjSMrqQGNPVg7oHFoJTyguTqha4m91rq/H1kqBzT5TTl9DUxTSp1QSk1WSjUroZiEEEIIIQolz7dwWutPgU+VUtWBZzFOCnYH5gMLtNa2ucZZCCGEEIWTcXtXnK1R4Afraa1Paq0na63vBXoB3QDbTrAKIYQQQhRCgZPsSilXoBNGlaY9sAEItnFcQgghhLBSaf5KgpKS54BGKdUBeA7oDOwAFgADtNaXSyg2IYQQQgir5FehGQX8CLyjtU7Mp50QQggh7KkUfz5MScnvpOB2JRmIEEIIIcStur0/qEIIIYT4/0DOoSn4KichhBBCiNJOKjRCCCGEg9NyDo1UaIQQQgjh+GRAI4QQQgiHJ1NOQgghhKOTk4KlQiOEEEIIxycVGiGEEMLRyZdTSoVGCCGEEI5PKjRCCCGEg5Mvp5QKjRBCCCFuA1KhEUIIIRydfLCeVGiEEEII4fikQiOEEEI4ODmHRio0QgghhLgNSIVGCCGEcHDy5ZQlMKBx0tdtvQr7UrdxkevqRXtHYFOnM9vaOwSb8vM4Yu8QbMpVXbZ3CDa1e8s99g7BZpo/+Ke9Q7C53ds72DuE/3ekQiOEEEI4ODmHRs6hEUIIIcRtQCo0QgghhIPLlHNopEIjhBBCCMcnFRohhBDCwck5NFKhEUIIIcRtQAY0QgghhHB4MuUkhBBCODidmWnvEOxOKjRCCCGEcHhSoRFCCCEcnKN89YFSyhdYCNQATgA9tdZJN7WpCswFAoBMIExr/WlBy5YKjRBCCCFKyntAtNb6HiDadP9m6cAwrXV9oBXwplKqQUELlgqNEEII4eAc6LLtrkBb0+3vgPXAu9kbaK3PAGdMty8qpQ4DVYBD+S1YKjRCCCGEKCn+pgHLjYGLX36NlVI1gHuB7QUtWCo0QgghhIMryXNolFIDgAHZHgrTWodl+/uvGOe/3Oz9Qq6nPLAEGKy1TimovQxohBBCCGE10+AlLJ+/P5bX35RS8UqpylrrM0qpykBCHu1cMQYz87TWS62JS6achBBCCAenM3WJ/RTRMqCP6XYfIPLmBkopBXwDHNZaT7d2wTKgEUIIIURJmQR0UEr9CXQw3UcpdadSapWpzUPAC0CgUmqP6efJghYsU05CCCGEg8t0kKuctNbngfYWHv8XeNJ0exOgCrtsqdAIIYQQwuFJhUYIIYRwcI7yScG2JBUaIYQQQjg8qdAIIYQQDs6BPinYZkrFgEZrzYTQn9gQcxB3d1cmhb5Iw4bVcrWLPXWOocNmcyH5Mg0aVGXK5L64ubkU2D8jI5Pu/5mEv583X894w/z49z+s44d5G3BxdqZNm4aMGP4Mp06f58nO46hZ0x+Apk1rMG5sr1KZ35kziYx47zvOnUvBSTnRs+dD9HkxEIDDh2MZM3Y+166n4+zsxNgPn6VJkxqkpWUw+oMfOHQolvSMDIK6tuTVAZ2KlF++eU+JZMOmI0bc4/5Lw/p35c77dCJD3/2BCxdSaVC/ClMmPIubqwt/HU9g1JiFHDx8miEDO9G/T1sA/j6RwJARP+ToP+j1jvR9/hGb5JHdjs1b+GLqNDIzMnmyW1d69eub4+9aa774aBrbN23G3d2dEcFjqFO/HgDPdX6asuXK4uTkhLOzCzPmzQVg9pdfsWV9DMpJ4e3ry7vBY7ijUiUOHzjI9PETTMuFPq++wiOB7WyW26aN+5gU+j0ZmZl079GWl195KlduE0O/Z2PMXtzdyzAhdAANGtYAYPT7M4lZ/zu+vl5ELJ9k7hO1ejtffhHO33//y/xFY2nUqBYAK5ZvZs7sVeZ2fxyN5aclIdSrX71Yc9JaM2HCIjbEHMDd3Y1JE/vkve8NncWFC5dp0KAaUyb3y9r3LPQ/cyaREe9+a+x7ToqePR+mz4vGeY7JyZcZMnQmp0+fp0qVinzy8StUqFCOU6fO8WTn4GzHlpqMC+5d9PxCFxvHBg83JoW+QMMGVS3nN2wOFy5cMY4tk17Mdmyx3D/wsQ8pV66M8Xx1cWLpT+9m5TdsNqdPJ1Klii+fTO9PhQplSUq+xKDB33Bg/0m6dWvFh6N7Fim34jJmdDseeag6iUmp9Oy10N7hCBsqFVNOMTEHOXEygTWrxxIS3Jux4xZYbDd1WgR9XwxkTVQwXhXKsnjJFqv6z/1+HbVr5fzQwm3bjxIdvY/lke+zcsUH9H+pg/lv1areQWT4KCLDRxV5MGPL/JydnXlvRHd+XjmGhQuH8+OPMRw7dgaAj6aG8+abnYkMH8Xbb3Xho6nhAKyO2s316+ksXzaapYtHsnDhJk6dPl/kHC3mvekIJ/45x5pl7xLyQQ/GTrD82UhTP1lJ3+cfZc3yd/Hy8mBx+A4AvCuU5f0RQfR/sU2O9rVq+BG5aCiRi4aydP5gPNxd6RDYyCY5ZJeRkcGnk6cw6fNPmbNkEWtXr+HE33/naLN98xZO//MP30cuZejoUXwycVKOv0//egYzF/xoHswA/PfFF5i1aD4zF/xI60ce5vuwWQDUrF2bGT/MZeaCH5n8xWd8PGEiGenpNsotk/Eh3/FV2HCWLZ/MqpVb+evY6RxtNsbs5Z+T8axaPZWxwS8RMm6O+W9BQY8wI2xEruXefc9dfPL529zXom6Ox7s89RBLwiewJHwCEye/RpUqdxT7YAYgJuaAse9EjSNkXG/GBv9osd3UqUvp26c9a6JC8PIqy+Ilm/Pt7+zszHvv9uDnVWNZuOBdfpy3gWPH/gUgbOZqWreqx5qoEFq3qkfYzCjzeqpVq0RkxGgiI0YXeTBjxHeIEyfPsmb1GEKCn2NscF7Hlkj69mnHmtVjjH1s6Var+n/37dtEho80D2YAwmb9QutWdVmzegytW9UlbNYaAMq4ufL2W10YMbxbkfMqTstXHGHg4BX2DkOUgFIxoIleu4+gri1RStGsWU1SUq6QkHAhRxutNdu2HaVjx3sB6Na1FdHRewvsHxeXxPoNB+jR46Ecy5u/YCMDXumIm5srABUrejpcfn5+FczvNsuXc6dW7QDi45MBUEpx+VIqABcvpeLnV8H0OKSmXiM9PYOrV6/j6upC+XLutsl7/UGCutxnxN2kOikXr5JwNuenV2ut2fbbMTo+1tjI+6n7iF53EICKvuVp0qgqLi7Oea5j6/Y/qXpXRarc6WOTHLI7cuAgVe6qyp133YWrqyuBHTuwZf2GHG22rN9Ahy6dUUrRoEljLl28yPmz5/Jdbrny5c23r6amGhsJcPdwx9nFKKJev34NpQp9FaPV9u/7i2rV/Kla1Q9XNxeeeLIVa9fuytFm3drdPN31YZRSNG12NxdTrnA2IRmAFvfXo4J3uVzLrV27CjVrVs533atWbuWJzq2LLZfsoqP3EdS1lWnfqUVKSmo++15zALoFtSb617359s+x75XPue9FR+8jKMjIJyioNb+almWT/NbuI6jrA0Z8TWuScjGVhLMW8tv+Bx0fNx1bglredGzJv7/FdQa1BCAoqCW/Ru8DoGzZMrS4rzZlyrgWd5pFsnvPGS6kXLN3GDbnQB+sZzNWD2iUUtWVUo+ZbnsopYptBBAfn0xAQNYLUkCAD/GmA+UNScmX8fIqa35xCwjwNh9A8usfOnExw9/phpNTzheDEycS2LnrGP/57xSef2E6+/afMP/t1OnzBD0TyvMvTGfnzmOlOr/sMR8+HEvTpjUAGDWyB1OmhtOm3SgmT1nK0CFdAej4eHM8PMrw8KMjadd+NC+99BjeFl6IikN8QgoBAd5ZcftXIP6mF5Ok5Ct4eXpk5e3vnatNflZG7aXLE/cWS7wFOXf2LH4B/ub7d/j5czbhbM42CWfx889qU8nPj3NnjU/2Vkox/M2BvNrrBVYsyVmt+uaLL/nvE5359efV9Hv9VfPjh/cfoF+PnvTv+RyDR71nHuAUt4SEJAICfM33/f19SYhPytEmPv6mNgG+xCckFnndq3/ezpNPtiryciyJj08moHL2fSdrv7rB4r5n2r+s6X/q1DnTvlcTgPPnU8xvIPz8KpCYeDFH26BuE3j++Wns3Pln0fNLuOnY4J9Hfjn2MR/i4y8U3F8p+r/8Bc/0mMzCRZvMbc6fv4hfJVN+lXLmJ4Q9WXV0VEq9gvFFVL5AbeAuYAYWPhznVmide8SX691oPm3y6r9u3X58fcvTqGE1tu/4I8ffM9IzSEm5wqIFw9m//ySDh3xD9C/j8Kvkxbro8fj4lOfAwX94c+AMVi7/gPLlPUpdfjdcvnyVQYPCGPVeD3Oc8xdsZOR7Pej4+L2s+nkX74/+gW/nvM2+/SdwcnZi44aJpKRcodfz03iwdT2qVr3jlvPLS1HzLsj1tHTWbjjIsEFP3FJ8hWVNPhoLbUyfD/XZnFncUakSSYmJDH99IFVr1KDpfUZVoP/AN+g/8A1+nD2HiAWL6Gsa1NRv3Ig5ixdx8u/jTBozlpYPPYhbmTLFnZp1uRVhW+Vl395jeLi7cU+d3Od9FAeL2+PmkC3lZWV/8743smeBxwg/vwqsWxtqHFsOnDSOLSs+LOKxJfdj1u1jBfefP28I/n7enD9/kX4vf0GtWgHc3+LuW45V2JZctm19heZNjI8iTgHQWv9JPl/5rZQaoJTaqZTaGRZmee5y3rwNdO0WStduofj5eRMXl/VuMC4uyfwO4AYfn/KkpFwhPT3D1CbZ/C4oIMDHYv/dv//F2nX7CWw/mqHDZrNt+1HeGWHM+/sH+NChQzOUUjRpUgMnJ0VS0iXc3Fzx8TGmABo1rEa1qpU4fsLid2flqyTyA0hLy2DQ2zN56qkHePzxrEpFeMQ2Hu/QDIAnOjVn3/6TAKxY8RuPPNwAV1dnKlb0pHnz2uw/cLLQ+eWZ94LNdO05na49p+NXyYu4uOSsuOMv4FfJ66a8y5FyMTUr7/jkXG3yErPpCA3rVeEOG04XZlfJz4+EuHjz/XMJ8dxR6Y7cbeKz2pxNSKBipUoA3GH67ePry8Pt2nLk4MFc6wjs1ImYtWtzPV69Vk08PDw4/tdfxZFKLv7+vsTFZVVb4uMTqeTnnaNNQMBNbeIS8atUtKm+n1dtK/bppnnz1tM1aDxdg8bj51eBuDPZ951k/G7Ky/K+Z7QJ8PfJs39aWgaDBoXl2vcqVvQyT2slJFzA19d4fuY4tjSqTrWqd3D8+C0cW37cQNduE+nabaKRX/ZjQ3zWcSNHfjn2saSsY4u/d579/U15VqzoSYf2Tdi374T5/o1pqYSzWfkJYW/WDmiuaa2v37ijlHIBC29dTLTWYVrrFlrrFgMGdLHYpnfvNuYTbx9r34SIyO1ordmz5zienh65dkqlFC1b1iEq6ncAwiO3ERjYBIDAdo0t9h82NIiY9aGsjR7P9Gkv0aplXaZO6QfAY+2bsG3bUQCOH48nLS0dH5/yJCZeJCMjE4DY2HOcOJlA1bsKX70oify01rw/+ntq1QqgX9+cxTI/vwrs+M0oaW/bdpQa1Y0X08qVfdm+/Shaa65cucbevcepVcuf4tL72YfMJ+w+1q4RESt2GXHvO4lnefdcgxWlFC1b3E3Ur/uNvJfvIrBtQ6vWtXL1Hjp3KpnpJoB6DRtwOvYfzpw+TVpaGmujfqF1m0dztHmwzaP8smIlWmsO7dtPufLlqVjpDlJTU7ly+TIAqamp7Ny2jZq1awNw6p9/zP23xMRQrUYNAM6cPm0+CTju3zPEnjhJQOU7bZJbo8a1+OdkHKdOJZB2PZ2fV22jXbvmOdq0bdecZZGb0Fqzd88xynuWzTXoKYzMzEzWRO3giWKeburdu635xNvH2jcjInKbad/5G09P9zz2vbpERe0GIDxiK4HtTfteYBOL/Y19by61agfQr1/OLxYODGxCRIRx0m1ExFbam5aV89hy1ji23EJltHevNkSGjyQyfKTp2LLDiG+v6dhQyUJ+D9Qhao3p2BKxPevYEtjYYv8rV65x6fJVAK5cucbmLUe45x7juRfYrjEREdtN+W2nvWlZwr50ZmaJ/ZRWylIZOVcjpaYAycCLwFvAG8AhrfX7BXbOjC5wBVprxoUsZOOmQ3i4uxEa+gKNGxlXPLwy4H+MH98bfz9vYmPPMWTYN1y4cIX69e9i6pS+uLm55tv/hu07/mD27F/Nl21fv57OqNHfc+TwKVxdXRgx4hlat6pL1Jrf+eyzFTi7OOHs5MRbb3UmsF3Rdlhb5bdz1zF6Pz+dOnXuxMnJGJsOHfw0bdo0YueuY4SG/kR6RiZlyrgy5sNnadSwGpcvX2Xk+9/z17E4NJpnurXm5f4dLAd+7XLR854YzsYtR424g3vSuKExtfDKm98wfkwP/P0qEHvqPEPenceFlCvUr1uFqaHP4ebmwtlzKXTv9RmXLl/FSSnKli3DqqXvUL68O6mp12nbaQK/rngPT89bK9mfzmxb6D7bNm3my6nTycjM4Imnn+b5l19i2eIlADzdoztaaz6bNIUdW7cal22P/ZC6DRrw76lTfDjMuAooIyOd9p068fzLLwEw5p0RxJ48iZNywq9yAEPeH0klPz/WrFjF/G+/xcXFBeXkxIuvvMzD7ayP2c/jSKFyi9mwh8kT55GRmUm3Zx7l1de6snBBNAD/fba9cYlvyHds2rQfD3c3QkJfMV+GPXzY//htx2GSky9RsaIXbwx8hu492vLrLzuZOGEuiYkX8fQqS7161QmbZfwfduw4zCfTFvLjwrGFivMGV1Xw89PYdxawceNB077Th8aNb+x7nzM+5AX8/b2JjT3LkKGzTPteVaZ+1C/bvpe7/85dx+jdeyp16lQxn583dEhX2rRpTFLSJQYPmcmZM4lUruzLp58MwNu7HFFRu/ns8+U4Ozvh7OzEWwOfMg8sLMos+Io2rTXjxi9i46bDeLi7Ejrh+axjy6tfMj6kV9ax5Z05XEi+bOQ35cWs/Cz0j409x5uDZgLG9HyXzi14/TXj4x2Ski8xeMhszpxJonJlHz79uL/5PLzAxz7k0qWrpKWl4+lVltkz3+Tuu3OfFN78waKfP2St0JAO3Nf8Try93UlMTGVG2G9ELj9s8/Xu3v6G7c7ityC2U/MSm3Oqunp3ieZmLWsHNE5Af+BxjOnlKGCWtqazFQMaUUoVcUBT2t3KgMaRFHZA42isGdA4NCsGNI6qJAc09lLSA5p/OtxbYq+11X75vVQOaKw6KVhrnQnMBGYqpXyBu6wazAghhBBClABrr3JaDzxtar8HOKuU2qC1Hmq70IQQQghhjdL8+TAlxdqTgitorVOAZ4A5Wuv7gMcK6COEEEIIUSKs/ZQuF6VUZaAnUPCJwEIIIYQoMZlSobG6QjMO40TgY1rr35RStYDb/6wuIYQQQjgEa08K/gn4Kdv9v4HutgpKCCGEENaTTwq2/qRgd4zLthsC5m8y1Fq/ZKO4hBBCCCGsZu2U0/dAANAR2IDxXU7yjWRCCCFEKSDftm39gOZurfUHwGWt9XdAZ6Cx7cISQgghhLCetQOaNNPvZKVUI6ACUMMmEQkhhBBCFJK1l22HKaV8gA+AZUB54EObRSWEEEIIq8lJwdZf5TTLdHMDUMt24QghhBBCFJ61VzmVwbhMu0b2PlrrcbYJSwghhBDWKs0n65YUa6ecIoELwC7gmu3CEUIIIYQoPGsHNHdprTvZNBIhhBBC3BKp0Fh/ldMWpZRcpi2EEEKIUinfCo1Saj+gTe36KaX+xphyUoDWWjexfYhCCCGEyI9c5VTwlFOXEolCCCGEEKII8h3QaK1PAiilWgEHtdYXTfc9gQbASZtHKIQQQoh8Zco5NFafQ/MVcCnb/cumx4QQQggh7M7aq5yU1to8/NNaZyqlrO0rhBBCCBvKzLR3BPZnbYXmb6XUIKWUq+nnbeBvWwYmhBBCCGEtawc0rwEPAqeBU0BLYICtghJCCCGE9TIzS+6ntCpw2kgp5QxM11o/WwLxCCGEEEIUWoEDGq11hlKqklLKTWt9vdBrcGqvbimyW6SUGqC1DivJdZakEs3Po0TWkkNJ5lelJFZyk5J9fj5QMqsxkX2vmDmX2JqAks1v9/YOJbGaHG7352dprpyUFGunnE4Am5VSHyilht74sWFcRXG7T4VJfo7tds7vds4NJD9Hd7vn9/+etVcq/Wv6cQI8bReOEEIIIUThWTWg0VoH2zoQIYQQQtwa+Vw9Kwc0Sql1GN/plIPWOrDYIyq623aO1ETyc2y3c363c24g+Tm62z2///dUts/Ly7uRUvdlu+sOdAfStdYjbBWYEEIIIayzq269EqvR3Hf0SIle7GMta6ecdt300Gal1AYbxCOEEEIIUWhWXeWklPLN9nOHUqoTEGDj2AqKab1SquNNjw1WSn2plFqtlEpWSq2wV3xFkU9uq5RSW5VSB5VS+5RS/7VXjEWRT35zlFK7lFJ7TDm+Zq8YiyK/56bptpdS6rRS6gv7RFg0Bex7Gabtt0cptcxeMRZFAflVU0qtUUodVkodUkrVsFOYtySf3A5n2257lFJXlVJBdgqz2CmlaiilDtg7DluSD9az/iqnXWSdQ5OOcRl3f1sEVAjzgWeBqGyPPQsMB9yAssCrdoirOOSV27vAv1rrP5VSdwK7lFJRWutkO8RYFPnlt01rfU0pVR44oJRaprX+1x5BFkF+z02AEMCRK5z55fei1rqZPYIqRvnlNxeYoLX+xfQcLcWHd4vyym2A1nojGG9ggWPAmpIPr2iUUi5a63R7xyHsw9rPoWkA/A/YCxwAfgZ22iooKy0GuiilyoAxAgfuBDZpraOBi3aMrajyyi1Ga/0ngOlFPgGoZK8giyC//K6Z2pTB+udnaZPnc9N0Ppo/DvhikU2e+dkzqGKUV36JgIvW+hcArfUlrfUVu0V5a6zZdj2An+2Vm6mackQp9Z2pEr1YKVVWKXVCKXWHqU0LpdR60+2xSqkwpdQaYK5Syl8pFa6U2mv6edC0aGel1ExT9XeNUsrD1P8VpdRvprZLlFJlTY//Ryl1wPR4jOkxZ6XUR6b2+5RSpeZNs1RorH/B+A6oD3wGfG66/b2tgrKG1vo8sAPoZHroWWChtuYs51LOmtyUUg9gVKL+KvkIiya//JRSVZVS+4BYYLIDVmfyzA9QwDSyKjUOqYDnp7tSaqdSapujTlnks/3uAZKVUkuVUr+bXthK+PN8i8bK4+azGJUce6oLhGmtmwApwBsFtL8P6Kq17oXxOrVBa90UaA4cNLW5B/if1rohkIxxcQvAUq31/ab2h8maffgQ6Gh6/GnTY/2BC1rr+4H7gVeUUjWLlqooLtYOaOpqrV/WWq8z/QwA6tgyMCvdKJ9C6dgJi1OeuSmlKmMMKPtprUvxeDlfFvPTWseaDmJ3A32UUv52iq+oLOX3BrBKax1rt6iKT17Pz2pa6xZAL+ATpVRtewRXDCzl5wI8AryD8WJWC+hrj+CKqKBjS2NyTknZQ6zWerPp9g/AwwW0X6a1TjXdDgS+AuOre7TWF0yPH9da7zHd3gXUMN1upJTaqJTaD/QGGpoe3wx8q5R6hawvongceFEptQfYDlTEGCjZnVRorB/Q/K6UanXjjlKqJcbGtrcIoL1SqjngobXebed4ilMEFnJTSnkBK4HRWuttdoyvqCLIZ9uZKjMHMV5AHFEEufNrDQxUSp0ApmIcGCfZL8QiicDC9rtRUdNa/w2sB+61V4BFFEHu/E4Bv2ut/zadpxGBUQFwNBHkve/1BMK11ml2iSzLzZV2jXH+5o3XLPeb/n7ZimVey3Y7g6xzSL8FBmqtGwPBN5attX4NGA1UBfYopSpiVFnf0lo3M/3U1Fo78vTxbcXaAU1LYItpDvMEsBVoo5Tab5oesAut9SWMg+Zsbq/qjMXclFJuQDgwV2v9k/2iK7o88rsr27y2D/AQcNReMRaFpfy01r211tW01jUw3uXP1Vq/Z7cgiyCP7eeT7dyMOzC23yF7xVgUeRxbfgN8lFI3zlsLxAHzK+C4+ZyFx+yhmlKqten2cxjn+JzAmFqCrOkiS6KB18F8zotXAevyBM4opVwxKjSY+tbWWm/XWn8InMMY2EQBr5vaopSqo5QqV6jMbEQqNNZf5dSp4CZ2Mx9YSlYJFaXURqAeUF4pdQror7W2dwn1VtycW0/gUaCiUqqv6bG+2cqojubm/OoD05RSGuOd0FSt9X57BVcMcj03bzOWtt/XSqlMjDdLk7TWDveCn02O/LTWGUqpd4BopZTCmLaYacf4isLScbMGxot2abgC7zDGlPPXwJ8YU0g7gG+UUqMwpnvy8jYQppTqj1GJeR04k0/7D0zLOwnsJ+v7Cj9SSt2DcSyKxrgoZh/GVNVu03PgLBB0C/kJG7Dqk4KFEEKIkmAaWK3QWjeydyyOZGPVkvuk4EdiS+cnBTvqZbFCCCGEEGbWTjkJIYQQNqe1PgFIdaaQSvO5LSVFKjRCCCGEcHgyoBFCCCGEw5MpJyGEEMLByZSTVGiEEEIIcRuQCo0QQgjh4OQjWKRCI4QQQojbgFRohBBCCAcn59BIhUYIIYQQtwGp0AghhBAOTio0UqERQgghxG1AKjRCCCGEg5MKjVRohBBCCHEbkAqNEEII4eCkQiMVGiGEEELcBqRCI4QQQjg4qdBIhUYIIYQQtwGp0AghhBAOTio0UqERQgghxG1ABjRCCCGEcHgy5SSEEEI4OJlykgqNEEIIIW4DUqERQgghHFymtncE9icVGiGEEEI4PKnQCCGEEA5OzqGRCo0QQgghbgNSoRFCCCEcnFRopEIjhBBCiNuAVGiEEEIIB+coFRqllC+wEKgBnAB6aq2TbmrjDsQAZTDGKYu11mMKWrZUaIQQQghRUt4DorXW9wDRpvs3uwYEaq2bAs2ATkqpVgUtWCo0QgghhINzlAoN0BVoa7r9HbAeeDd7A621Bi6Z7rqafgr8pB2p0AghhBCipPhrrc8AmH77WWqklHJWSu0BEoBftNbbC1qwVGiEEEIIB9dLH1UltS6l1ABgQLaHwrTWYdn+/isQYKHr+9auQ2udATRTSnkD4UqpRlrrA/nGZVR2hBBCCCFsSyl1FGirtT6jlKoMrNda1y2gzxjgstZ6an7tZMpJCCGEECVlGdDHdLsPEHlzA6VUJVNlBqWUB/AYcKSgBUuFRgghhBAlQilVEVgEVAP+Af6jtU5USt0JzNJaP6mUaoJxwrAzRuFlkdZ6XIHLlgGNEEIIIRydTDkJIYQQwuHJgEYIIYQQDk8GNEIIIYRweDKgEUIIIYTDkwGNEEIIIRyeDGiEEEII4fBkQCOEEEIIhycDGiGEEEI4vP8Dw4NV/jWMnt8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# filtering data for experimental group:\n",
    "experimental = train_data[train_data.Promotion == 'Yes']\n",
    "\n",
    "# evaluating features' correlation for experimental group:\n",
    "exper_corr = experimental[sel_features].corr()\n",
    "\n",
    "# creating correlation plot:\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 1, figsize = (10, 10))\n",
    "\n",
    "sns.heatmap(exper_corr, annot = True, fmt = '.3g', vmin = -.3, vmax = .3, center = 0, \n",
    "                        cmap = 'RdYlBu', square = True)\n",
    "ax.set_title('Correlation Matrix - Experimental Group')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>control</th>\n",
       "      <th>experimental</th>\n",
       "      <th>perc_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>V1</th>\n",
       "      <td>-0.006088</td>\n",
       "      <td>-0.004651</td>\n",
       "      <td>-23.605724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V2</th>\n",
       "      <td>0.000090</td>\n",
       "      <td>-0.002885</td>\n",
       "      <td>-3306.003045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3</th>\n",
       "      <td>0.008325</td>\n",
       "      <td>-0.017012</td>\n",
       "      <td>-304.357526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V4</th>\n",
       "      <td>0.002344</td>\n",
       "      <td>0.053348</td>\n",
       "      <td>2176.227435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V5</th>\n",
       "      <td>0.002771</td>\n",
       "      <td>0.011711</td>\n",
       "      <td>322.600958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V6</th>\n",
       "      <td>-0.000980</td>\n",
       "      <td>-0.002049</td>\n",
       "      <td>109.222660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V7</th>\n",
       "      <td>0.004786</td>\n",
       "      <td>-0.005005</td>\n",
       "      <td>-204.587034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purchase</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           control  experimental     perc_var\n",
       "V1       -0.006088     -0.004651   -23.605724\n",
       "V2        0.000090     -0.002885 -3306.003045\n",
       "V3        0.008325     -0.017012  -304.357526\n",
       "V4        0.002344      0.053348  2176.227435\n",
       "V5        0.002771      0.011711   322.600958\n",
       "V6       -0.000980     -0.002049   109.222660\n",
       "V7        0.004786     -0.005005  -204.587034\n",
       "purchase  1.000000      1.000000    -0.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# observing the percentage variation in the correlation between control and experimental group: \n",
    "purch_corr = pd.DataFrame()\n",
    "purch_corr['control'] = contr_corr['purchase']\n",
    "purch_corr['experimental'] = exper_corr['purchase']\n",
    "purch_corr['perc_var'] = -((purch_corr.control - purch_corr.experimental)/purch_corr.control)*100\n",
    "purch_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    It's interesting to observe that, considering the only difference between control and experimental groups is whether or not they were given a promotion ticket, among the features with higher correlation, the one that was most responsive to the experiment is represented by <b>V4</b>.\n",
    "</p>\n",
    "<p>\n",
    "    V4 already had a positive correlation with the purchase feature, considering the control group. With the promotion event, its positive correlation increased by over 2000%, which is impressive. Considering the ideal experiment, where all the other variables are controlled, we could say that the higher V4 is, the more susceptible the client is to use the promotion and purchase the product. Since V4 is a binary feature (1 or 2), we can translate it as <b>class 2</b> being more receptive to the promotion event.\n",
    "</p>\n",
    "<p>\n",
    "    <b>V5</b> behaves the same as V4, but with lower intensity. In the control group, it already showed a positive correlation with the purchase, and this correlation was powered with the promotion, increasing by over 300%. V5 represents categories from 1 to 4, indicating that categories represented by higher numbers tend to be more affected by the promotion, in a positive way.\n",
    "</p>\n",
    "<p>\n",
    "    A different response is shown when observing the <b>V3</b> feature. In the control group, this variable represented the highest positive correlation, while in the experimental group this tendency was reversed: it became the second-highest correlation score, and this time in a negative direction. It looks like clients related to higher values of V3 have a naturally higher propensity for purchasing the product. At the same time, these clients are not quite receptive to promotional events.\n",
    "</p>\n",
    "<p>\n",
    "    Other features like <b>V2</b> show a great variation when comparing control and experimental groups. However, they don't stand out because their correlation values are too low in comparison to the other features.\n",
    "</p>\n",
    "<h4>Considerations on Correlation Analysis</h4>\n",
    "<p>\n",
    "    Just by looking at the correlation values for both groups, we could say that, in general, clients that belong to class 2 in feature V4, higher classes in V5, and are related to lower V3 values are the clients with more potential of being positively responsive to the promotion. \n",
    "</p>\n",
    "<p>\n",
    "    Also, we could see that clients associated with higer values of V3 seems to be more willing to purchase the product, despite any promotional event. Actually, promotional events seem to cause negative impact on their interest in purchasing the product.\n",
    "</p>\n",
    "<p>\n",
    "    At the same time, since even the highest correlation values represent in fact low correlation, we have to say that there must be other important clients' aspects not being captured by these features that affect the positive or negative responses to the promotional event.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Histogram</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histograms for Control Group V1-V7 \n",
      "red: no-purchase | blue: purchase\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'V5'}>,\n",
       "        <AxesSubplot:title={'center':'V6'}>,\n",
       "        <AxesSubplot:title={'center':'V7'}>]], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x180 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x180 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x180 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JkqRWuxD46SSXAt8MfFuS91TVz/U2GmXROJifha7MOV7zkhPatWjeILseP7jmnE2+AydJkqQWq6pfq6otVbWVznoFf7G8eJO0PljASZIkSdKccAqlJEnSOlJVS8DSjGNImhDPwEmSJEnSnLCAkyRJkqQ5YQEnSZIkSXPCAk6SJEmS5oQFnCRJkiTNCQs4SZIkSZoTFnCSFkqSi5N8LsmeJG8c0O77kxxN8opp5pMkSRrEAk7SwkiyAXgHcAlwNnBFkrNXaPc7wJ3TTShJkjSYBZykRXIBsKeqHq6qJ4Gbgcv6tHsd8CfAo9MMJ0mSNIwFnKRFshl4pOf+3u62pyXZDLwcuG6KuSRJkhrZOOsAa3Hf4cNcdP/usT5mnXfOWB9PUqukz7Zadv8twBuq6mjSr3nPgyU7gB0AmzZtYmlpaWiALU89xc4D+5pkXZWlpYMjHXfo0KFGucdl5+HDK+4btW9Gfe6DTKJfBj33QQb1yySe+zCj9s2oz39glqNHp/r6laQ2mOsCTpJWaS9wes/9LcD+ZW22ATd3i7dTgUuTHKmqDy1/sKq6HrgeYNu2bbV9+/ahAd58++1ce9rmoe1Wa9QPn5aWlmiSe1wGfei288C+kfpmEh+8TaJfRv3AcVC/zOJDx1H7ZtwfuALsevzgVF+/ktQGjaZQDlu1Lcn2JI8neaD786amx0rSFN0LnJXkzCQnApcDt/Y2qKozq2prVW0F/hj4pX7FmyRJ0iwMPQPXs2rbT9D59PreJLdW1WeWNf1oVb10xGMlaeKq6kiSa+isLrkBuLGqHkxydXe/33uTJEmt1mQK5dOrtgEkObZqW5MibC3HStLYVdVtwG3LtvUt3KrqF6aRSZIkqakmBVy/Vdte2KfdDyT5JJ3vk1xbVQ+u4tjWLAYwLwsBgIsBjKJNiwHM4jUzSNvySJIk6XhNCrgmq7Z9AviOqjqU5FLgQ8BZDY/tbGzJYgDzshAAuBjAKNq0GMAsXjODtC2PJEmSjtekgBu6altVfbXn9m1J3pnk1CbHSpIkqd28dJPUHk1WoRy6aluS09JdczvJBd3H/UqTYyVJkiRJzQw9A9dw1bZXAK9NcgQ4DFxeVQX0PXZCz0WSJEmS1rVGF/IetmpbVb0deHvTYyVJkiRJq9foQt6SJEmSpNmzgJMkSZKkOWEBJ0mSJElzwgJOkiRJkuaEBZwkSZIkzQkLOEmSJEmaExZwkiRJcy7J6Ul2JXkoyYNJXj/rTJImo9F14CRJktRqR4D/UFWfSHIycF+Sj1TVZ2YdTNJ4eQZOkiRpzlXVl6vqE93bXwMeAjbPNpWkSfAMnCRJ0jqSZCtwHvCxPvt2ADsANm3axNLSUqPH3PLUU+w8sG98IYGlpYNjfTyAQ4cONX5Os2TO8ZtE1p2HD4/18QAOHT265pwWcJIkSetEkmcAfwL8SlV9dfn+qroeuB5g27ZttX379kaP++bbb+fa08Z7Qq/OO2esjwewtLRE0+c0S+Ycv0lkvej+3WN9PIBdjx9cc06nUEqSJK0DSU6gU7y9t6pumXUeSZNhASdJkjTnkgT4Q+Chqvq9WeeRNDkWcJIWSpKLk3wuyZ4kb+yz/1VJdnd/7k5y7ixyStIqXQhcCfxYkge6P5fOOpSk8fM7cJIWRpINwDuAnwD2AvcmuXXZMttfAH60qv4uySV0vivywumnlaTmquqvgMw6h6TJ8wycpEVyAbCnqh6uqieBm4HLehtU1d1V9Xfdu/cAW6acUZIkaUWNCri1TDlK8sUkn+qeyv/4OMNL0iptBh7pub+XwddJ+kXg9okmkiRJWoWhUyjHNOXooqp6bIy5JWkU/aYXVd+GyUV0CrgfWvHBRrie0iSupQSjX09p2tf4GXRNnVH7Zl6uJTXq9YQG9csknvswo/ZNW6+nJEnzpsl34J6ecgSQ5NiUo6cLuKq6u6e9U44ktdVe4PSe+1uA/csbJTkHuAG4pKq+stKDjXI9pUlcSwlGv57StK/xM+iaOjsP7Bupb+blWlKjXk9oUL9M4rkPM2rftPV6SpI0b5pMoVzrlKMCPpzkvu6n1ZI0K/cCZyU5M8mJwOXArb0NkpwB3AJcWVWfn0FGSZKkFTU5A7fWKUcXVtX+JM8BPpLks1V1V59jWzEVaV6mIYFTkUbRpqlIs3jNDNK2PJNQVUeSXAPcCWwAbqyqB5Nc3d1/HfAm4FnAOzuXVeJIVW2bVWZJkqReTQq4NU05qqr93f8+muSDdKZkHlfAtWUq0rxMQwKnIo2iTVORZvGaGaRteSalqm4Dblu27bqe268BXjPtXJIkSU00KeCennIE7KMz5eiVvQ1WmnKU5CTgm6rqa93bLwF+c1zhpUWUCXyPBGDXRB5VkiRJ4zS0gFvjlKNNwAe72zYC76uqOybyTCRJkiRpnWtyBm7kKUfdlSvPXb5dkiRJkrR6jS7kLUmSJEmaPQs4SZIkSZoTFnCSJEmSNCcs4CRJkiRpTljASZIkSdKcsICTJEmSpDlhASdJkiRJc8ICTpIkSZLmhAWcJEmSJM0JCzhJkiRJmhMWcJIkSZI0JyzgJEmSJGlOWMBJkiRJ0pywgJMkSZKkOWEBJ0mSJElzwgJOkiRJkuZEowIuycVJPpdkT5I39tmfJG/r7t+d5Pymx0rSNK1lPJOkNvM9l7QYhhZwSTYA7wAuAc4Grkhy9rJmlwBndX92AL+/imMlaSrWMp5JUpv5nktaHE3OwF0A7Kmqh6vqSeBm4LJlbS4D3l0d9wCnJHluw2MlaVrWMp5JUpv5nktaEE0KuM3AIz3393a3NWnT5FhJmpa1jGeS1GaOXdKC2NigTfpsq4ZtmhzbeYBkB53pSgCHknyuQbZTgccatGusX+CGxp5lLa4dMc8anv8grembQf0yoec+SGv6BeCi5nm+Y9JZJmgt49nxD9aScQvWx9jluNVfy8YtaFHfLMi41VSjsWvEcQva9Z5rkNa8Pocw5/jNRdZVjFuwwtjVpIDbC5zec38LsL9hmxMbHAtAVV0PXN8gz9OSfLyqtq3mmElpUxZoVx6z9NemLNC+PBOylvHsOPM+bkG78pilvzZlgXblaVOWFmg0do0ybsH89LU5x2tecsL8ZB1HziZTKO8FzkpyZpITgcuBW5e1uRW4qrt624uAx6vqyw2PlaRpWct4Jklt5nsuaUEMPQNXVUeSXAPcCWwAbqyqB5Nc3d1/HXAbcCmwB/gH4NWDjp3IM5GkIdYynklSm/meS1ocTaZQUlW30XlT07vtup7bBfxy02PHaNVTACaoTVmgXXnM0l+bskD78kzEWsazMWlbP7cpj1n6a1MWaFeeNmWZuQV6zzWIOcdrXnLC/GRdc8503qtIkiRJktquyXfgJEmSJEkt0PoCLsmNSR5N8ukV9ifJ25LsSbI7yfkzzLI9yeNJHuj+vGmCWU5PsivJQ0keTPL6Pm2m2TdN8kylf5J8c5K/SfLJbpb/0qfNVPqmYZapvW66v29DkvuT/HmffVN7zax3jl0rZmnN2OW4taYsjlvrUJvGrUEa5HxVN9/uJHcnOXfaGbs5Bubsaff9SY4mecW0svXJMDRr99/9A91x4S+nma8nw7D/99+e5M96xq+ZfI994n/rqqrVP8CPAOcDn15h/6XA7XQuJ/Ii4GMzzLId+PMp9ctzgfO7t08GPg+cPcO+aZJnKv3Tfb7P6N4+AfgY8KJZ9E3DLFN73XR/378H3tfvd07zNbPefxy7VszSmrHLcWtNWRy31uFPm8atNeb8QeCZ3duXtDVnt80G4C/ofHfxFS3+f38K8BngjO7957Q0538Cfqd7+9nAQeDEGeSc6N+61p+Bq6q76HT+Si4D3l0d9wCnJHnujLJMTVV9uao+0b39NeAhYPOyZtPsmyZ5pqL7fA91757Q/Vn+Zc+p9E3DLFOTZAvwU8ANKzSZ2mtmvXPs6q9NY5fj1pqyTI3j1vS0adwaZFjOqrq7qv6ue/ceOtfEm7qGY+/rgD8BHp18opU1yPpK4Jaq+lK3/UzyNshZwMlJAjyj2/bINLJ9Q4gJ/61rfQHXwGbgkZ77e5nRH+CuH+ietr09yT+fxi9MshU4j86npL1m0jcD8sCU+qc73eYBOgPiR6pqZn3TIAtM73XzFuBXgX9cYX/b/j2tZ23r64Ueuxy3Vp0FHLcW0Tz29S/SOcvROkk2Ay8HrhvWtgW+G3hmkqUk9yW5ataBVvB24HvpXMT+U8Drq2qlsWMqJvG3bj0UcOmzbVafFH4C+I6qOhf478CHJv0LkzyDzic3v1JVX12+u88hE+2bIXmm1j9VdbSqnk/nU7cLknzf8qj9DptRlqn0S5KXAo9W1X2DmvXZ5lK1k9Gmvl7osctxa6QsjluLaa76OslFdAq4N8w6ywreAryhqo7OOkgDG4EX0Dkb/pPAryf57tlG6usngQeAfwo8H3h7km+bVZhJ/a1bDwXcXuD0nvtb6FTdU1dVXz027aQ612I5Icmpk/p9SU6g86J4b1Xd0qfJVPtmWJ5p90/39/w9sARcvGzX1F83K2WZYr9cCPx0ki8CNwM/luQ9y9q05t/TAmhNXy/y2OW4NVoWx62FNTd9neQcOtNuL6uqr8w6zwq2ATd3X9+vAN6Z5F/MNNHK9gJ3VNUTVfUYcBcwk8Vhhng1nameVVV7gC8A3zOLIJP8W7ceCrhbgau6K7m8CHi8qr48iyBJTuvOuSXJBXT6dyKDRvf3/CHwUFX93grNptY3TfJMq3+SPDvJKd3b3wL8OPDZZc2m0jdNskyrX6rq16pqS1VtBS4H/qKqfm5Zs9b8e1oArenrRR27HLdGz+K4tbDmoq+TnAHcAlxZVZ+fdZ6VVNWZVbW1+/r+Y+CXqupDs021oj8FfjjJxiTfCryQzve62uZLwIsBkmwCngc8PO0Qk/5bt3FMOScmyfvprHZ1apK9wG/Q+UI1VXUdnVV7LgX2AP9Ap/KeVZZXAK9NcgQ4DFxeVZOaWnAhcCXwqXS+pwCdlXfO6Mkztb5pmGda/fNc4F1JNtB5U/GBqvrzJFf3ZJlW3zTJMs3XzXFm1C/rnmPXito0djlujZ7FcWsdatO4tcacbwKeReeMFsCRqtrWwpytMSxrVT2U5A5gN53vo95QVQMvjzCLnMBvATcl+RSdKYpv6J4xnLaJ/q3LFMdbSZIkSdIarIcplJIkSZK0ECzgJEmSJGlOWMBJkiRJ0pywgJMkSZKkOWEBJ0mSJElzwgJOkiRJkuaEBZwkSZIkzQkLOEmSJEmaE/8fGDGtBR6RZoEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x180 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Defining features:\n",
    "features = ['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7']\n",
    "\n",
    "# Features histograms for control group:\n",
    "print('Histograms for Control Group V1-V7 \\nred: no-purchase | blue: purchase')\n",
    "control[control.purchase == 0].hist(column = features[:4], figsize=(15,2.5), layout = (1, 4), \n",
    "                                    density = True, color = 'tomato')\n",
    "\n",
    "control[control.purchase == 1].hist(column = features[:4], figsize=(15,2.5), layout = (1, 4), \n",
    "                                              density = True, color = 'darkturquoise')\n",
    "\n",
    "control[control.purchase == 0].hist(column = features[4:], figsize=(15,2.5), layout = (1, 3), \n",
    "                                    density = True, color = 'tomato')\n",
    "\n",
    "control[control.purchase == 1].hist(column = features[4:], figsize=(15,2.5), layout = (1, 3), \n",
    "                                              density = True, color = 'darkturquoise')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    In the <b>control group</b>, we can see some differences between the ones who purchased the product or not. For example, people related to the class 1 in the V1 feature seem to be more likely to purchase the product in comparison to the other classes.\n",
    "</p>\n",
    "<p>\n",
    "    V3 reinforces what we concluded from the correlation analysis: clients with higher V3 values are more likely to buy the product without any promotional event.\n",
    "</p>\n",
    "<p>\n",
    "    V6 shows a slightly advantage in class 2 as a trend for naturally purchasing the product.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Histograms for Experimental Group V1-V7 \n",
      "red: no-purchase | blue: purchase\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'V5'}>,\n",
       "        <AxesSubplot:title={'center':'V6'}>,\n",
       "        <AxesSubplot:title={'center':'V7'}>]], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x180 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x180 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x180 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x180 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Features histograms for experimental group:\n",
    "print('Histograms for Experimental Group V1-V7 \\nred: no-purchase | blue: purchase')\n",
    "experimental[experimental.purchase == 0].hist(column = features[:4], figsize=(15,2.5), layout = (1, 4), \n",
    "                                    density = True, color = 'tomato')\n",
    "\n",
    "experimental[experimental.purchase == 1].hist(column = features[:4], figsize=(15,2.5), layout = (1, 4), \n",
    "                                              density = True, color = 'darkturquoise')\n",
    "\n",
    "experimental[experimental.purchase == 0].hist(column = features[4:], figsize=(15,2.5), layout = (1, 3), \n",
    "                                    density = True, color = 'tomato')\n",
    "\n",
    "experimental[experimental.purchase == 1].hist(column = features[4:], figsize=(15,2.5), layout = (1, 3), \n",
    "                                              density = True, color = 'darkturquoise')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    In the <b>experimental group</b> we can see some interesting changes. In V1, for example, the trend presented by class 1 in the control group is reverted, since proportionally class 2 increases its participation with the promotion, and class 1 decreases the participation among the buyers.\n",
    "</p>\n",
    "<p>\n",
    "    V2 shows that clients with values closer to the mean are more responsive to the promotion.\n",
    "</p>\n",
    "<p>\n",
    "    Again, we can see through V3 that its distribution resembles a mirrored-image of the control group, considering the ones who purchased the product. In other words, lower values of V3 represent consumers that purchase the product because of the promotion, while higher values of V3 represent consumers that are discouraged to buy the product because of the promotion.\n",
    "</p>\n",
    "<p>\n",
    "    Class 1 in V4 tends to be not responsive to the promotional event, as well as class 2 in V6 and more significantly in the V5 feature. In the other hand, class 3 in V5 seems to increase its purchase rate with the promotional event.\n",
    "</p>\n",
    "\n",
    "<h4>Considerations on Histogram Analysis</h4>\n",
    "<p>\n",
    "    We could reinforce some of the conclusions settled before, and we could see some changes in the behavior of the clients depending on whether they were submitted to the promotional event or not.\n",
    "</p>\n",
    "<p>\n",
    "    Although we don't know exactly what each feature is representing, I would guess that V2 represents something like ages, being that the subjects who are around their thirties tend to be more responsive to the promotion.\n",
    "</p>\n",
    "<p>\n",
    "    V3 could easily represent the subject's incomes, once the ones who have higher incomes would buy the product whenever they wanted to, and the ones with lower incomes would increase their purchases once they were exposed to some promotional event.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Starting Parameters</h2>\n",
    "<p>\n",
    "    Once we have identified one consumer's potential profile that is more receptive to the promotion offer, let's simply test what would be the test result if we sent the promotion to:\n",
    "<ol type=\"1\">\n",
    "    <li>all the clients;</li>\n",
    "    <li>the clients within features' values with higher positive correlation.</li>\n",
    "</ol>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Approach 1 - All the Clients</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job!  See how well your strategy worked on our test data below!\n",
      "\n",
      "Your irr with this strategy is 0.0096.\n",
      "\n",
      "Your nir with this strategy is -1132.20.\n",
      "We came up with a model with an irr of 0.0188 and an nir of 189.45 on the test set.\n",
      "\n",
      " How did you do?\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.009593158278250108, -1132.1999999999998)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining function to test promotion to all the clients:\n",
    "def promotion_strategy_all(df):\n",
    "    '''\n",
    "    INPUT \n",
    "    df - a dataframe with *only* the columns V1 - V7 (same as train_data)\n",
    "\n",
    "    OUTPUT\n",
    "    promotion_df - np.array with the values\n",
    "                   'Yes' or 'No' related to whether or not an \n",
    "                   individual should recieve a promotion \n",
    "                   should be the length of df.shape[0]\n",
    "                \n",
    "    Ex:\n",
    "    INPUT: df\n",
    "    \n",
    "    V1\tV2\t  V3\tV4\tV5\tV6\tV7\n",
    "    2\t30\t-1.1\t1\t1\t3\t2\n",
    "    3\t32\t-0.6\t2\t3\t2\t2\n",
    "    2\t30\t0.13\t1\t1\t4\t2\n",
    "    \n",
    "    OUTPUT: promotion\n",
    "    \n",
    "    array(['Yes', 'Yes', 'No'])\n",
    "    indicating the first two users would recieve the promotion and \n",
    "    the last should not.\n",
    "    '''\n",
    "    prom_list = ['Yes' for i in range(df.shape[0])]\n",
    "    \n",
    "    promotion = np.array(prom_list)\n",
    "    \n",
    "    return promotion\n",
    "\n",
    "# Testing IRR and NIR for this strategy:\n",
    "test_results(promotion_strategy_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    As expected, the strategy of sending the promotion to all the clients does not optimize <i>Incremental Response Rate</i> nor <i>Net Incremented Revenue</i>.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Approach 2 - Client Segmentation Using Individual Correlation</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job!  See how well your strategy worked on our test data below!\n",
      "\n",
      "Your irr with this strategy is 0.0119.\n",
      "\n",
      "Your nir with this strategy is -531.40.\n",
      "We came up with a model with an irr of 0.0188 and an nir of 189.45 on the test set.\n",
      "\n",
      " How did you do?\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.01191694716124083, -531.4000000000001)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining function using Correlation for client segmentation:\n",
    "def promotion_strategy_corr(df):\n",
    "    '''\n",
    "    INPUT \n",
    "    df - a dataframe with *only* the columns V1 - V7 (same as train_data)\n",
    "\n",
    "    OUTPUT\n",
    "    promotion_df - np.array with the values\n",
    "                   'Yes' or 'No' related to whether or not an \n",
    "                   individual should recieve a promotion \n",
    "                   should be the length of df.shape[0]\n",
    "                \n",
    "    Ex:\n",
    "    INPUT: df\n",
    "    \n",
    "    V1\tV2\t  V3\tV4\tV5\tV6\tV7\n",
    "    2\t30\t-1.1\t1\t1\t3\t2\n",
    "    3\t32\t-0.6\t2\t3\t2\t2\n",
    "    2\t30\t0.13\t1\t1\t4\t2\n",
    "    \n",
    "    OUTPUT: promotion\n",
    "    \n",
    "    array(['Yes', 'Yes', 'No'])\n",
    "    indicating the first two users would recieve the promotion and \n",
    "    the last should not.\n",
    "    '''\n",
    "    prom_list = ['Yes' if (df.V4.iloc[i] == 2) | (df.V5.iloc[i] >= 3) else 'No' for i in range(df.shape[0])]\n",
    "    \n",
    "    promotion = np.array(prom_list)\n",
    "    \n",
    "    return promotion\n",
    "\n",
    "# Testing IRR and NIR for this strategy:\n",
    "test_results(promotion_strategy_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    In comparison to the first approach, this simple client segmentation based on individual feature correlation already showed some advances. Still, we need to test approaches that consider the incremental contribution of each one of the features acting together.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Testing Different Approaches</h2>\n",
    "<h3>Approach 3 - Logistic Regression</h3>\n",
    "<p>\n",
    "    In this approach, we are going to train a logistic regression model to predict whether or not a client should be sent the promotion: we are only using the experimental group, considering the ones who made the purchase as the group that should recieve the promotion.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    41643\n",
      "1    41643\n",
      "Name: purchase, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=LogisticRegression(),\n",
       "             param_grid={'C': [1, 10, 100], 'penalty': ['l1', 'l2']})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting training set:\n",
    "X = experimental[features]\n",
    "y = experimental['purchase']\n",
    "\n",
    "# Balancing train set with SMOTE:\n",
    "smotenc = SMOTENC(categorical_features = [0, 3, 4, 5, 6], random_state = 101)\n",
    "X_bal, y_bal = smotenc.fit_sample(X, y)\n",
    "print(y_bal.value_counts())\n",
    "\n",
    "# Training Logistic Regression model, using GridSearchCV for best parameters:\n",
    "lr = LogisticRegression()\n",
    "param = {'penalty': ['l1', 'l2'], \n",
    "         'C': [1, 10, 100]}\n",
    "log_r_cv = GridSearchCV(lr, param_grid = param)\n",
    "log_r_cv.fit(X_bal, y_bal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job!  See how well your strategy worked on our test data below!\n",
      "\n",
      "Your irr with this strategy is 0.0145.\n",
      "\n",
      "Your nir with this strategy is -72.55.\n",
      "We came up with a model with an irr of 0.0188 and an nir of 189.45 on the test set.\n",
      "\n",
      " How did you do?\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.014468322486757366, -72.54999999999995)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining function using Logistic Regression for client segmentation:\n",
    "def promotion_strategy_log(df):\n",
    "    '''\n",
    "    INPUT \n",
    "    df - a dataframe with *only* the columns V1 - V7 (same as train_data)\n",
    "\n",
    "    OUTPUT\n",
    "    promotion_df - np.array with the values\n",
    "                   'Yes' or 'No' related to whether or not an \n",
    "                   individual should recieve a promotion \n",
    "                   should be the length of df.shape[0]\n",
    "                \n",
    "    Ex:\n",
    "    INPUT: df\n",
    "    \n",
    "    V1\tV2\t  V3\tV4\tV5\tV6\tV7\n",
    "    2\t30\t-1.1\t1\t1\t3\t2\n",
    "    3\t32\t-0.6\t2\t3\t2\t2\n",
    "    2\t30\t0.13\t1\t1\t4\t2\n",
    "    \n",
    "    OUTPUT: promotion\n",
    "    \n",
    "    array(['Yes', 'Yes', 'No'])\n",
    "    indicating the first two users would recieve the promotion and \n",
    "    the last should not.\n",
    "    '''\n",
    "    X_test = df[features]\n",
    "    y_pred = log_r_cv.predict(X_test)\n",
    "    prom_list = ['Yes' if y_pred[i] == 1 else 'No' for i in range(len(y_pred))]\n",
    "    \n",
    "    promotion = np.array(prom_list)\n",
    "    \n",
    "    return promotion\n",
    "\n",
    "# Testing IRR and NIR for this strategy:\n",
    "test_results(promotion_strategy_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    <b>Logistic Regression</b> improved our previous correlation approach, but it's not a good model yet.\n",
    "</p>\n",
    "<p>\n",
    "    Let's check for most important features using Decision Tree algorithm:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "V3 0.52\n",
      "V2 0.2\n",
      "V4 0.09\n",
      "V5 0.07\n",
      "V6 0.05\n",
      "V1 0.04\n",
      "V7 0.03\n"
     ]
    }
   ],
   "source": [
    "# Training Decision Tree Classifier to check for most important features:\n",
    "tree = DecisionTreeClassifier(random_state = 101)\n",
    "tree.fit(X_bal, y_bal)\n",
    "\n",
    "# Checking feature importance\n",
    "feature_imp = tree.feature_importances_\n",
    "for i, value in zip(np.argsort(-feature_imp), -np.sort(-feature_imp)):\n",
    "    print(features[i], round(value, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    Next step, let's try the same Logistic Regression approach, this time selecting only the 4 most important variables to see if it improves the model:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    41643\n",
      "1    41643\n",
      "Name: purchase, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=LogisticRegression(),\n",
       "             param_grid={'C': [1, 10, 100], 'penalty': ['l1', 'l2']})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting training set:\n",
    "features_imp = ['V2', 'V3', 'V4', 'V5']\n",
    "X_imp = experimental[features_imp]\n",
    "y_imp = experimental['purchase']\n",
    "\n",
    "# Balancing train set with SMOTE:\n",
    "smotenc_imp = SMOTENC(categorical_features = [2, 3], random_state = 201)\n",
    "X_ibal, y_ibal = smotenc_imp.fit_sample(X_imp, y_imp)\n",
    "print(y_ibal.value_counts())\n",
    "\n",
    "# Training Logistic Regression model, using GridSearchCV for best parameters:\n",
    "log_r_cv_imp = GridSearchCV(lr, param_grid = param)\n",
    "log_r_cv_imp.fit(X_ibal, y_ibal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job!  See how well your strategy worked on our test data below!\n",
      "\n",
      "Your irr with this strategy is 0.0147.\n",
      "\n",
      "Your nir with this strategy is -39.60.\n",
      "We came up with a model with an irr of 0.0188 and an nir of 189.45 on the test set.\n",
      "\n",
      " How did you do?\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0147094528569421, -39.59999999999991)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def promotion_strategy_log_imp(df):\n",
    "    '''\n",
    "    INPUT \n",
    "    df - a dataframe with *only* the columns V1 - V7 (same as train_data)\n",
    "\n",
    "    OUTPUT\n",
    "    promotion_df - np.array with the values\n",
    "                   'Yes' or 'No' related to whether or not an \n",
    "                   individual should recieve a promotion \n",
    "                   should be the length of df.shape[0]\n",
    "                \n",
    "    Ex:\n",
    "    INPUT: df\n",
    "    \n",
    "    V1\tV2\t  V3\tV4\tV5\tV6\tV7\n",
    "    2\t30\t-1.1\t1\t1\t3\t2\n",
    "    3\t32\t-0.6\t2\t3\t2\t2\n",
    "    2\t30\t0.13\t1\t1\t4\t2\n",
    "    \n",
    "    OUTPUT: promotion\n",
    "    \n",
    "    array(['Yes', 'Yes', 'No'])\n",
    "    indicating the first two users would recieve the promotion and \n",
    "    the last should not.\n",
    "    '''\n",
    "    X_test = df[features_imp]\n",
    "    y_pred = log_r_cv_imp.predict(X_test)\n",
    "    prom_list = ['Yes' if y_pred[i] == 1 else 'No' for i in range(len(y_pred))]\n",
    "    \n",
    "    promotion = np.array(prom_list)\n",
    "    \n",
    "    return promotion\n",
    "\n",
    "# Testing IRR and NIR for this strategy:\n",
    "test_results(promotion_strategy_log_imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    Feature selection did improve the model, however, we couldn't achieve our purposes yet.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Approach 4 - XGBoost Classifier</h3>\n",
    "<h4>Basic Approach</h4>\n",
    "<p>\n",
    "    Up until now, we have trained the models using only our experimental group. In this fourth approach, let's try one kind of one-versus-rest classification.\n",
    "</p>\n",
    "<p>\n",
    "    In this attempt, let's consider that only the ones who recieved the promotion and purchased the product build the features' composition of those who should recieve the promotion. All the other clients represent the profile that should not recieve the promotion:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>send_prom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>30.443518</td>\n",
       "      <td>-1.165083</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>32.159350</td>\n",
       "      <td>-0.645617</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>30.431659</td>\n",
       "      <td>0.133583</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>26.588914</td>\n",
       "      <td>-0.212728</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>28.044331</td>\n",
       "      <td>-0.385883</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   V1         V2        V3  V4  V5  V6  V7  send_prom\n",
       "0   2  30.443518 -1.165083   1   1   3   2          0\n",
       "1   3  32.159350 -0.645617   2   3   2   2          0\n",
       "2   2  30.431659  0.133583   1   1   4   2          0\n",
       "3   0  26.588914 -0.212728   2   1   4   2          0\n",
       "4   3  28.044331 -0.385883   1   1   2   2          0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting training set:\n",
    "data_xgb = train_data[features]\n",
    "\n",
    "# Transforming labels:\n",
    "data_xgb['send_prom'] = [1 if (train_data.Promotion.iloc[i] == 'Yes') & (train_data.purchase.iloc[i] == 1) else 0 \\\n",
    "                         for i in range(train_data.shape[0])]\n",
    "data_xgb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    83813\n",
      "1    83813\n",
      "Name: send_prom, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, gamma=None,\n",
       "                                     gpu_id=None, importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_delta_step=None,\n",
       "                                     max_depth=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=100, n_jobs=None,\n",
       "                                     num_parallel_tree=None, random_state=None,\n",
       "                                     reg_alpha=None, reg_lambda=None,\n",
       "                                     scale_pos_weight=None, seed=301,\n",
       "                                     subsample=None, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=0),\n",
       "             param_grid={'gamma': [0.1, 3, 5], 'learning_rate': [0.1, 0.3],\n",
       "                         'max_depth': [5, 7], 'min_child_weight': [1, 5],\n",
       "                         'reg_lambda': [1, 5]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying SMOTE:\n",
    "smotenc_xgb = SMOTENC(categorical_features = [0, 3, 4, 5, 6], random_state = 301)\n",
    "X_xgb, y_xgb = smotenc_xgb.fit_sample(data_xgb[features], data_xgb['send_prom'])\n",
    "print(y_xgb.value_counts())\n",
    "\n",
    "# Training XGBClassifier model:\n",
    "xgb = XGBClassifier(objective = 'binary:logistic', seed = 301, verbosity = 0)\n",
    "param_xgb = {'learning_rate': [0.1, 0.3], 'max_depth': [5, 7], \n",
    "             'min_child_weight': [1, 5], 'reg_lambda': [1, 5], 'gamma': [.1, 3, 5]}\n",
    "\n",
    "xgb_cl = GridSearchCV(xgb, param_grid = param_xgb, scoring = 'roc_auc')\n",
    "xgb_cl.fit(X_xgb, y_xgb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job!  See how well your strategy worked on our test data below!\n",
      "\n",
      "Your irr with this strategy is 0.0164.\n",
      "\n",
      "Your nir with this strategy is -1.10.\n",
      "We came up with a model with an irr of 0.0188 and an nir of 189.45 on the test set.\n",
      "\n",
      " How did you do?\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.016444154998371867, -1.1000000000000014)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining function using XGBClassifier for client segmentation:\n",
    "def promotion_strategy_xgb(df):\n",
    "    '''\n",
    "    INPUT \n",
    "    df - a dataframe with *only* the columns V1 - V7 (same as train_data)\n",
    "\n",
    "    OUTPUT\n",
    "    promotion_df - np.array with the values\n",
    "                   'Yes' or 'No' related to whether or not an \n",
    "                   individual should recieve a promotion \n",
    "                   should be the length of df.shape[0]\n",
    "                \n",
    "    Ex:\n",
    "    INPUT: df\n",
    "    \n",
    "    V1\tV2\t  V3\tV4\tV5\tV6\tV7\n",
    "    2\t30\t-1.1\t1\t1\t3\t2\n",
    "    3\t32\t-0.6\t2\t3\t2\t2\n",
    "    2\t30\t0.13\t1\t1\t4\t2\n",
    "    \n",
    "    OUTPUT: promotion\n",
    "    \n",
    "    array(['Yes', 'Yes', 'No'])\n",
    "    indicating the first two users would recieve the promotion and \n",
    "    the last should not.\n",
    "    '''\n",
    "    X_test = df[features]\n",
    "    y_pred = xgb_cl.predict(X_test)\n",
    "    prom_list = ['Yes' if y_pred[i] == 1 else 'No' for i in range(len(y_pred))]\n",
    "    \n",
    "    promotion = np.array(prom_list)\n",
    "    \n",
    "    return promotion\n",
    "\n",
    "# Testing IRR and NIR for this strategy:\n",
    "test_results(promotion_strategy_xgb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    <b>XGBoost Classifier</b> improved our <i>IRR</i> and <i>NIR</i> metrics, but the last one still needs improvement.\n",
    "</p>\n",
    "<h4>Manipulating Features - Approach 1</h4>\n",
    "<p>\n",
    "    In the next trial, let's transform the continuous numeric variables into classes distributed as observerd during the histogram analysis to see if it improves the generalization. Once again, let's use only the experimental group:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    V1  V2  V3  V4  V5  V6  V7  purchase\n",
       "4    3   2   3   1   1   2   2         0\n",
       "8    2   3   2   2   3   1   2         0\n",
       "10   1   3   5   2   1   4   1         0\n",
       "12   1   4   2   2   3   1   1         0\n",
       "14   3   4   4   2   2   4   1         0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting data and applying transformations:\n",
    "data_xgb2 = experimental[sel_features]\n",
    "# Transforming V2 and V3:\n",
    "V2_cat = [0 if ((data_xgb2.V2.iloc[i] < 20) | (data_xgb2.V2.iloc[i] > 40)) \\\n",
    "          else 1 if (20 <= data_xgb2.V2.iloc[i] < 25) \\\n",
    "          else 2 if (25 <= data_xgb2.V2.iloc[i] < 30) \\\n",
    "          else 3 if (30 <= data_xgb2.V2.iloc[i] < 35) \\\n",
    "          else 4 for i in range(data_xgb2.shape[0])]\n",
    "data_xgb2['V2'] = V2_cat\n",
    "V3_cat = [5 if (data_xgb2.V3.iloc[i] <= -1) \\\n",
    "          else 4 if (-1 < data_xgb2.V3.iloc[i] <= -0.5) \\\n",
    "          else 3 if (-0.5 < data_xgb2.V3.iloc[i] <= 0) \\\n",
    "          else 2 if (0 < data_xgb2.V3.iloc[i] <= 0.5) \\\n",
    "          else 1 if (0.5 < data_xgb2.V3.iloc[i] <= 1) \\\n",
    "          else 0 for i in range(data_xgb2.shape[0])]\n",
    "data_xgb2['V3'] = V3_cat\n",
    "data_xgb2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    41643\n",
      "1    41643\n",
      "Name: purchase, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, gamma=None,\n",
       "                                     gpu_id=None, importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_delta_step=None,\n",
       "                                     max_depth=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=100, n_jobs=None,\n",
       "                                     num_parallel_tree=None, random_state=None,\n",
       "                                     reg_alpha=None, reg_lambda=None,\n",
       "                                     scale_pos_weight=None, seed=1001,\n",
       "                                     subsample=None, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=0),\n",
       "             param_grid={'gamma': [0.1, 3, 5], 'learning_rate': [0.1, 0.3],\n",
       "                         'max_depth': [3, 5], 'min_child_weight': [1, 5],\n",
       "                         'reg_lambda': [1, 5]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying SMOTE:\n",
    "smote_xgb2 = SMOTE(random_state = 1001)\n",
    "X_xgb2, y_xgb2 = smote_xgb2.fit_sample(data_xgb2[features], data_xgb2['purchase'])\n",
    "print(y_xgb2.value_counts())\n",
    "\n",
    "# Training XGBClassifier model:\n",
    "xgb = XGBClassifier(objective = 'binary:logistic', seed = 1001, verbosity = 0)\n",
    "param_xgb = {'learning_rate': [0.1, 0.3], 'max_depth': [3, 5], \n",
    "             'min_child_weight': [1, 5], 'reg_lambda': [1, 5], 'gamma': [.1, 3, 5]}\n",
    "\n",
    "xgb_cl2 = GridSearchCV(xgb, param_grid = param_xgb, scoring = 'roc_auc')\n",
    "xgb_cl2.fit(X_xgb2, y_xgb2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job!  See how well your strategy worked on our test data below!\n",
      "\n",
      "Your irr with this strategy is 0.0178.\n",
      "\n",
      "Your nir with this strategy is 153.70.\n",
      "We came up with a model with an irr of 0.0188 and an nir of 189.45 on the test set.\n",
      "\n",
      " How did you do?\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.017830221770241402, 153.70000000000005)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining function using XGBClassifier for client segmentation:\n",
    "def promotion_strategy_xgb2(df):\n",
    "    '''\n",
    "    INPUT \n",
    "    df - a dataframe with *only* the columns V1 - V7 (same as train_data)\n",
    "\n",
    "    OUTPUT\n",
    "    promotion_df - np.array with the values\n",
    "                   'Yes' or 'No' related to whether or not an \n",
    "                   individual should recieve a promotion \n",
    "                   should be the length of df.shape[0]\n",
    "                \n",
    "    Ex:\n",
    "    INPUT: df\n",
    "    \n",
    "    V1\tV2\t  V3\tV4\tV5\tV6\tV7\n",
    "    2\t30\t-1.1\t1\t1\t3\t2\n",
    "    3\t32\t-0.6\t2\t3\t2\t2\n",
    "    2\t30\t0.13\t1\t1\t4\t2\n",
    "    \n",
    "    OUTPUT: promotion\n",
    "    \n",
    "    array(['Yes', 'Yes', 'No'])\n",
    "    indicating the first two users would recieve the promotion and \n",
    "    the last should not.\n",
    "    '''\n",
    "    X_test = df[features]\n",
    "    V2_cat = [0 if ((X_test.V2.iloc[i] < 20) | (X_test.V2.iloc[i] > 40)) \\\n",
    "              else 1 if (20 <= X_test.V2.iloc[i] < 25) \\\n",
    "              else 2 if (25 <= X_test.V2.iloc[i] < 30) \\\n",
    "              else 3 if (30 <= X_test.V2.iloc[i] < 35) \\\n",
    "              else 4 for i in range(X_test.shape[0])]\n",
    "    X_test['V2'] = V2_cat\n",
    "    V3_cat = [5 if (X_test.V3.iloc[i] <= -1) \\\n",
    "              else 4 if (-1 < X_test.V3.iloc[i] <= -0.5) \\\n",
    "              else 3 if (-0.5 < X_test.V3.iloc[i] <= 0) \\\n",
    "              else 2 if (0 < X_test.V3.iloc[i] <= 0.5) \\\n",
    "              else 1 if (0.5 < X_test.V3.iloc[i] <= 1) \\\n",
    "              else 0 for i in range(X_test.shape[0])]\n",
    "    X_test['V3'] = V3_cat\n",
    "    y_pred = xgb_cl2.predict(X_test)\n",
    "    prom_list = ['Yes' if y_pred[i] == 1 else 'No' for i in range(len(y_pred))]\n",
    "    \n",
    "    promotion = np.array(prom_list)\n",
    "    \n",
    "    return promotion\n",
    "\n",
    "# Testing IRR and NIR for this strategy:\n",
    "test_results(promotion_strategy_xgb2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    We can see that this strategy did optimize our metrics, not as good as Starbucks itself, but still a significant improvement.\n",
    "</p>\n",
    "<p>\n",
    "    Our understanding of the behavior of the features during the exploratory analysis was fundamental to elaborate this strategy. We are going to keep this same approach, now generalizing a little bit more the classes we created before.\n",
    "</p>\n",
    "<h4>Manipulating Features - Approach 2</h4>\n",
    "<p>\n",
    "    Considering the V2 feature distribution, it looks like there is a similar response to the promotion along with its values with slight differences. Still, the largest proportion of clients is situated around the mean value - like a traditional Gaussian distribution. With that in mind, let's try to focus on the greatest public 'cutting off' the tails in one single group.\n",
    "</p>\n",
    "<p>\n",
    "    At the same time, we are simplifying V3 classes as an attempt to make them more generalistic:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    V1  V2  V3  V4  V5  V6  V7  purchase\n",
       "4    3   1   1   1   1   2   2         0\n",
       "8    2   1   0   2   3   1   2         0\n",
       "10   1   1   2   2   1   4   1         0\n",
       "12   1   1   0   2   3   1   1         0\n",
       "14   3   1   1   2   2   4   1         0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_xgb3 = experimental[sel_features]\n",
    "data_xgb3['V2'] = [0 if ((data_xgb3.V2.iloc[i] < 15) | (data_xgb3.V2.iloc[i] > 45)) \\\n",
    "                   else 1 for i in range(data_xgb3.shape[0])] \n",
    "\n",
    "data_xgb3['V3'] = [-1 if (data_xgb3.V3.iloc[i] > 1) \\\n",
    "                   else 0 if (0 <= data_xgb3.V3.iloc[i] <= 1) \\\n",
    "                   else 1 if (-1 <= data_xgb3.V3.iloc[i] < 0) \\\n",
    "                   else 2 for i in range(data_xgb3.shape[0])] \n",
    "data_xgb3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    41643\n",
      "1    41643\n",
      "Name: purchase, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, gamma=None,\n",
       "                                     gpu_id=None, importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_delta_step=None,\n",
       "                                     max_depth=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=100, n_jobs=None,\n",
       "                                     num_parallel_tree=None, random_state=None,\n",
       "                                     reg_alpha=None, reg_lambda=None,\n",
       "                                     scale_pos_weight=None, seed=401,\n",
       "                                     subsample=None, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=0),\n",
       "             param_grid={'gamma': [0.1, 3, 5], 'learning_rate': [0.1, 0.3],\n",
       "                         'max_depth': [3, 5], 'min_child_weight': [1, 5],\n",
       "                         'reg_lambda': [1, 5]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying SMOTE:\n",
    "smotenc_xgb3 = SMOTE(random_state = 401)\n",
    "X_xgb3, y_xgb3 = smotenc_xgb3.fit_sample(data_xgb3[features], data_xgb3['purchase'])\n",
    "print(y_xgb3.value_counts())\n",
    "\n",
    "# Training XGBClassifier model:\n",
    "xgb = XGBClassifier(objective = 'binary:logistic', seed = 401, verbosity = 0)\n",
    "param_xgb = {'learning_rate': [0.1, 0.3], 'max_depth': [3, 5], \n",
    "             'min_child_weight': [1, 5], 'reg_lambda': [1, 5], 'gamma': [.1, 3, 5]}\n",
    "\n",
    "xgb_cl3 = GridSearchCV(xgb, param_grid = param_xgb, scoring = 'roc_auc')\n",
    "xgb_cl3.fit(X_xgb3, y_xgb3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job!  See how well your strategy worked on our test data below!\n",
      "\n",
      "Your irr with this strategy is 0.0173.\n",
      "\n",
      "Your nir with this strategy is 154.00.\n",
      "We came up with a model with an irr of 0.0188 and an nir of 189.45 on the test set.\n",
      "\n",
      " How did you do?\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.017338303174019454, 154.0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining function using XGBClassifier for client segmentation:\n",
    "def promotion_strategy_xgb3(df):\n",
    "    '''\n",
    "    INPUT \n",
    "    df - a dataframe with *only* the columns V1 - V7 (same as train_data)\n",
    "\n",
    "    OUTPUT\n",
    "    promotion_df - np.array with the values\n",
    "                   'Yes' or 'No' related to whether or not an \n",
    "                   individual should recieve a promotion \n",
    "                   should be the length of df.shape[0]\n",
    "                \n",
    "    Ex:\n",
    "    INPUT: df\n",
    "    \n",
    "    V1\tV2\t  V3\tV4\tV5\tV6\tV7\n",
    "    2\t30\t-1.1\t1\t1\t3\t2\n",
    "    3\t32\t-0.6\t2\t3\t2\t2\n",
    "    2\t30\t0.13\t1\t1\t4\t2\n",
    "    \n",
    "    OUTPUT: promotion\n",
    "    \n",
    "    array(['Yes', 'Yes', 'No'])\n",
    "    indicating the first two users would recieve the promotion and \n",
    "    the last should not.\n",
    "    '''\n",
    "    X_test = df[features]\n",
    "    X_test['V2'] = [0 if ((X_test.V2.iloc[i] < 15) | (X_test.V2.iloc[i] > 45)) \\\n",
    "                    else 2 for i in range(X_test.shape[0])]\n",
    "    X_test['V3'] = [-1 if (X_test.V3.iloc[i] > 1) \\\n",
    "                    else 0 if (0 <= X_test.V3.iloc[i] <= 1) \\\n",
    "                    else 1 if (-1 <= X_test.V3.iloc[i] < 0) \\\n",
    "                    else 2 for i in range(X_test.shape[0])]\n",
    "    y_pred = xgb_cl3.predict(X_test)\n",
    "    prom_list = ['Yes' if y_pred[i] == 1 else 'No' for i in range(len(y_pred))]\n",
    "    \n",
    "    promotion = np.array(prom_list)\n",
    "    \n",
    "    return promotion\n",
    "\n",
    "# Testing IRR and NIR for this strategy:\n",
    "test_results(promotion_strategy_xgb3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    We can see that this more generalist strategy came up with similar results.\n",
    "</p>\n",
    "<h4>Manipulating Features - Approach 3</h4>\n",
    "<p>\n",
    "    As a next attempt, we are going to distinguish the classes not only by their distributions and importances, but the classes' values are going to represent some kind of <b>weight</b> according to their relevance when considering the ones who purchased the product in the experimental group:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    V1  V2  V3  V4  V5  V6  V7  purchase\n",
       "4    3   2   1   1   1   2   2         0\n",
       "8    2   2  -1   2   3   1   2         0\n",
       "10   1   2   3   2   1   4   1         0\n",
       "12   1   1  -1   2   3   1   1         0\n",
       "14   3   1   2   2   2   4   1         0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting data and applying transformations:\n",
    "data_xgb4 = experimental[sel_features]\n",
    "# Transforming V2 and V3:\n",
    "data_xgb4['V2'] = [0 if ((data_xgb4.V2.iloc[i] < 15) | (data_xgb4.V2.iloc[i] > 45)) \\\n",
    "                   else 1 if ((15 <= data_xgb4.V2.iloc[i] < 25) | (35 < data_xgb4.V2.iloc[i] <= 45)) \\\n",
    "                   else 2 for i in range(data_xgb4.shape[0])]\n",
    "\n",
    "data_xgb4['V3'] = [3 if (data_xgb4.V3.iloc[i] <= -1) \\\n",
    "                   else 2 if (-1 < data_xgb4.V3.iloc[i] <= -0.5) \\\n",
    "                   else 1 if (-0.5 < data_xgb4.V3.iloc[i] <= 0) \\\n",
    "                   else -1 if (0 < data_xgb4.V3.iloc[i] <= 0.5) \\\n",
    "                   else -2 if (0.5 < data_xgb4.V3.iloc[i] <= 1) \\\n",
    "                   else -3 for i in range(data_xgb4.shape[0])]\n",
    "data_xgb4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    41643\n",
      "1    41643\n",
      "Name: purchase, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=XGBClassifier(base_score=None, booster=None,\n",
       "                                     colsample_bylevel=None,\n",
       "                                     colsample_bynode=None,\n",
       "                                     colsample_bytree=None, gamma=None,\n",
       "                                     gpu_id=None, importance_type='gain',\n",
       "                                     interaction_constraints=None,\n",
       "                                     learning_rate=None, max_delta_step=None,\n",
       "                                     max_depth=None, min_child_weight=None,\n",
       "                                     missing=nan, monotone_constraints=None,\n",
       "                                     n_estimators=100, n_jobs=None,\n",
       "                                     num_parallel_tree=None, random_state=None,\n",
       "                                     reg_alpha=None, reg_lambda=None,\n",
       "                                     scale_pos_weight=None, seed=1001,\n",
       "                                     subsample=None, tree_method=None,\n",
       "                                     validate_parameters=None, verbosity=0),\n",
       "             param_grid={'gamma': [0.1, 3, 5], 'learning_rate': [0.1, 0.3],\n",
       "                         'max_depth': [3, 5], 'min_child_weight': [1, 5],\n",
       "                         'reg_lambda': [1, 5]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying SMOTE:\n",
    "smote_xgb4 = SMOTE(random_state = 1001)\n",
    "X_xgb4, y_xgb4 = smote_xgb4.fit_sample(data_xgb4[features], data_xgb4['purchase'])\n",
    "print(y_xgb4.value_counts())\n",
    "\n",
    "# Training XGBClassifier model:\n",
    "xgb = XGBClassifier(objective = 'binary:logistic', seed = 1001, verbosity = 0)\n",
    "param_xgb = {'learning_rate': [0.1, 0.3], 'max_depth': [3, 5], \n",
    "             'min_child_weight': [1, 5], 'reg_lambda': [1, 5], 'gamma': [.1, 3, 5]}\n",
    "\n",
    "xgb_cl4 = GridSearchCV(xgb, param_grid = param_xgb, scoring = 'roc_auc')\n",
    "xgb_cl4.fit(X_xgb4, y_xgb4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nice job!  See how well your strategy worked on our test data below!\n",
      "\n",
      "Your irr with this strategy is 0.0185.\n",
      "\n",
      "Your nir with this strategy is 214.70.\n",
      "We came up with a model with an irr of 0.0188 and an nir of 189.45 on the test set.\n",
      "\n",
      " How did you do?\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.01849462654235275, 214.70000000000005)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining function using XGBClassifier for client segmentation:\n",
    "def promotion_strategy_xgb4(df):\n",
    "    '''\n",
    "    INPUT \n",
    "    df - a dataframe with *only* the columns V1 - V7 (same as train_data)\n",
    "\n",
    "    OUTPUT\n",
    "    promotion_df - np.array with the values\n",
    "                   'Yes' or 'No' related to whether or not an \n",
    "                   individual should recieve a promotion \n",
    "                   should be the length of df.shape[0]\n",
    "                \n",
    "    Ex:\n",
    "    INPUT: df\n",
    "    \n",
    "    V1\tV2\t  V3\tV4\tV5\tV6\tV7\n",
    "    2\t30\t-1.1\t1\t1\t3\t2\n",
    "    3\t32\t-0.6\t2\t3\t2\t2\n",
    "    2\t30\t0.13\t1\t1\t4\t2\n",
    "    \n",
    "    OUTPUT: promotion\n",
    "    \n",
    "    array(['Yes', 'Yes', 'No'])\n",
    "    indicating the first two users would recieve the promotion and \n",
    "    the last should not.\n",
    "    '''\n",
    "    X_test = df[features]\n",
    "    V2_cat = [0 if ((X_test.V2.iloc[i] < 15) | (X_test.V2.iloc[i] > 45)) \\\n",
    "              else 1 if ((15 <= X_test.V2.iloc[i] < 25) | (35 < X_test.V2.iloc[i] <= 45)) \\\n",
    "              else 2 for i in range(X_test.shape[0])]\n",
    "    X_test['V2'] = V2_cat\n",
    "    V3_cat = [3 if (X_test.V3.iloc[i] <= -1) \\\n",
    "              else 2 if (-1 < X_test.V3.iloc[i] <= -0.5) \\\n",
    "              else 1 if (-0.5 < X_test.V3.iloc[i] <= 0) \\\n",
    "              else -1 if (0 < X_test.V3.iloc[i] <= 0.5) \\\n",
    "              else -2 if (0.5 < X_test.V3.iloc[i] <= 1) \\\n",
    "              else -3 for i in range(X_test.shape[0])]\n",
    "    X_test['V3'] = V3_cat\n",
    "    y_pred = xgb_cl4.predict(X_test)\n",
    "    prom_list = ['Yes' if y_pred[i] == 1 else 'No' for i in range(len(y_pred))]\n",
    "    \n",
    "    promotion = np.array(prom_list)\n",
    "    \n",
    "    return promotion\n",
    "\n",
    "# Testing IRR and NIR for this strategy:\n",
    "test_results(promotion_strategy_xgb4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>\n",
    "    In this last approach, we finally achieved results that overcome the ones given by Starbucks. While our <i>irr</i> has a similar value in comparison to Starbucks's model, our <i>nir</i> showed a great improvement, close to 15%.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Conclusion</h2>\n",
    "<p>\n",
    "    We could see through this project the importance of data analysis in Data Science projects. Even when it's not clear what a feature represents, through the analysis process we were able to guess what some features could represent, and more importantly how they related to our business problem.\n",
    "</p>\n",
    "<p>\n",
    "    Understanding how differently these features behave when comparing our study groups made it possible to elaborate different feature engineering strategies that resulted in better optimization.\n",
    "</p>\n",
    "<p>\n",
    "    The most successful model came up with this strategy:\n",
    "    <li>V2 was separated into classes labeled with the weight of their occurrences in the dataset, following a normal distribution;</li>\n",
    "    <li>V3 was separated into 6 classes, lower values were labeled with higher numbers since they represent the ones who are more likely to respond to the promotional event;</li>\n",
    "</p>\n",
    "<p>\n",
    "    Besides that, we could guess that that V2 probably represents clients' age, while V3 stands for their incomes.\n",
    "</p>"
   ]
  }
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