Identify_Customer_Segments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Project: Identify Customer Segments\n",
    "\n",
    "In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.\n",
    "\n",
    "This notebook will help you complete this task by providing a framework within which you will perform your analysis steps. In each step of the project, you will see some text describing the subtask that you will perform, followed by one or more code cells for you to complete your work. **Feel free to add additional code and markdown cells as you go along so that you can explore everything in precise chunks.** The code cells provided in the base template will outline only the major tasks, and will usually not be enough to cover all of the minor tasks that comprise it.\n",
    "\n",
    "It should be noted that while there will be precise guidelines on how you should handle certain tasks in the project, there will also be places where an exact specification is not provided. **There will be times in the project where you will need to make and justify your own decisions on how to treat the data.** These are places where there may not be only one way to handle the data. In real-life tasks, there may be many valid ways to approach an analysis task. One of the most important things you can do is clearly document your approach so that other scientists can understand the decisions you've made.\n",
    "\n",
    "At the end of most sections, there will be a Markdown cell labeled **Discussion**. In these cells, you will report your findings for the completed section, as well as document the decisions that you made in your approach to each subtask. **Your project will be evaluated not just on the code used to complete the tasks outlined, but also your communication about your observations and conclusions at each stage.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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    {
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      "text/plain": [
       "'\\nImport note: The classroom currently uses sklearn version 0.19.\\nIf you need to use an imputer, it is available in sklearn.preprocessing.Imputer,\\ninstead of sklearn.impute as in newer versions of sklearn.\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import libraries here; add more as necessary\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline\n",
    "\n",
    "'''\n",
    "Import note: The classroom currently uses sklearn version 0.19.\n",
    "If you need to use an imputer, it is available in sklearn.preprocessing.Imputer,\n",
    "instead of sklearn.impute as in newer versions of sklearn.\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 0: Load the Data\n",
    "\n",
    "There are four files associated with this project (not including this one):\n",
    "\n",
    "- `Udacity_AZDIAS_Subset.csv`: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).\n",
    "- `Udacity_CUSTOMERS_Subset.csv`: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).\n",
    "- `Data_Dictionary.md`: Detailed information file about the features in the provided datasets.\n",
    "- `AZDIAS_Feature_Summary.csv`: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns\n",
    "\n",
    "Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.\n",
    "\n",
    "To start off with, load in the demographics data for the general population into a pandas DataFrame, and do the same for the feature attributes summary. Note for all of the `.csv` data files in this project: they're semicolon (`;`) delimited, so you'll need an additional argument in your [`read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.\n",
    "\n",
    "Once the dataset is loaded, it's recommended that you take a little bit of time just browsing the general structure of the dataset and feature summary file. You'll be getting deep into the innards of the cleaning in the first major step of the project, so gaining some general familiarity can help you get your bearings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the general demographics data.\n",
    "azdias = pd.read_csv('Udacity_AZDIAS_Subset.csv', delimiter=\";\")\n",
    "\n",
    "# Load in the feature summary file.\n",
    "feat_info = pd.read_csv('AZDIAS_Feature_Summary.csv', delimiter=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "      <td>794005.000000</td>\n",
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       "      <td>2.777398</td>\n",
       "      <td>1.522098</td>\n",
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       "      <td>3.074528</td>\n",
       "      <td>2.821039</td>\n",
       "      <td>3.401106</td>\n",
       "      <td>3.033328</td>\n",
       "      <td>2.874167</td>\n",
       "      <td>3.075121</td>\n",
       "      <td>...</td>\n",
       "      <td>2.253330</td>\n",
       "      <td>2.801858</td>\n",
       "      <td>1.595426</td>\n",
       "      <td>0.699166</td>\n",
       "      <td>1.943913</td>\n",
       "      <td>3.612821</td>\n",
       "      <td>3.381087</td>\n",
       "      <td>3.167854</td>\n",
       "      <td>5.293002</td>\n",
       "      <td>3.07222</td>\n",
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       "      <td>1.068775</td>\n",
       "      <td>0.499512</td>\n",
       "      <td>1.595021</td>\n",
       "      <td>1.321055</td>\n",
       "      <td>1.464749</td>\n",
       "      <td>1.322134</td>\n",
       "      <td>1.529603</td>\n",
       "      <td>1.486731</td>\n",
       "      <td>1.353248</td>\n",
       "      <td>...</td>\n",
       "      <td>0.972008</td>\n",
       "      <td>0.920309</td>\n",
       "      <td>0.986736</td>\n",
       "      <td>0.727137</td>\n",
       "      <td>1.459654</td>\n",
       "      <td>0.973967</td>\n",
       "      <td>1.111598</td>\n",
       "      <td>1.002376</td>\n",
       "      <td>2.303739</td>\n",
       "      <td>1.36298</td>\n",
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       "      <td>3.000000</td>\n",
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       "            AGER_TYP  ALTERSKATEGORIE_GROB      ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "count  891221.000000         891221.000000  891221.000000  886367.000000   \n",
       "mean       -0.358435              2.777398       1.522098       3.632838   \n",
       "std         1.198724              1.068775       0.499512       1.595021   \n",
       "min        -1.000000              1.000000       1.000000       1.000000   \n",
       "25%        -1.000000              2.000000       1.000000       2.000000   \n",
       "50%        -1.000000              3.000000       2.000000       4.000000   \n",
       "75%        -1.000000              4.000000       2.000000       5.000000   \n",
       "max         3.000000              9.000000       2.000000       6.000000   \n",
       "\n",
       "       FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  \\\n",
       "count      891221.000000  891221.000000     891221.000000   891221.000000   \n",
       "mean            3.074528       2.821039          3.401106        3.033328   \n",
       "std             1.321055       1.464749          1.322134        1.529603   \n",
       "min             1.000000       1.000000          1.000000        1.000000   \n",
       "25%             2.000000       1.000000          3.000000        2.000000   \n",
       "50%             3.000000       3.000000          3.000000        3.000000   \n",
       "75%             4.000000       4.000000          5.000000        5.000000   \n",
       "max             5.000000       5.000000          5.000000        5.000000   \n",
       "\n",
       "       FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER      ...          PLZ8_ANTG1  \\\n",
       "count          891221.000000     891221.000000      ...       774706.000000   \n",
       "mean                2.874167          3.075121      ...            2.253330   \n",
       "std                 1.486731          1.353248      ...            0.972008   \n",
       "min                 1.000000          1.000000      ...            0.000000   \n",
       "25%                 2.000000          2.000000      ...            1.000000   \n",
       "50%                 3.000000          3.000000      ...            2.000000   \n",
       "75%                 4.000000          4.000000      ...            3.000000   \n",
       "max                 5.000000          5.000000      ...            4.000000   \n",
       "\n",
       "          PLZ8_ANTG2     PLZ8_ANTG3     PLZ8_ANTG4    PLZ8_BAUMAX  \\\n",
       "count  774706.000000  774706.000000  774706.000000  774706.000000   \n",
       "mean        2.801858       1.595426       0.699166       1.943913   \n",
       "std         0.920309       0.986736       0.727137       1.459654   \n",
       "min         0.000000       0.000000       0.000000       1.000000   \n",
       "25%         2.000000       1.000000       0.000000       1.000000   \n",
       "50%         3.000000       2.000000       1.000000       1.000000   \n",
       "75%         3.000000       2.000000       1.000000       3.000000   \n",
       "max         4.000000       3.000000       2.000000       5.000000   \n",
       "\n",
       "            PLZ8_HHZ       PLZ8_GBZ         ARBEIT    ORTSGR_KLS9  \\\n",
       "count  774706.000000  774706.000000  794005.000000  794005.000000   \n",
       "mean        3.612821       3.381087       3.167854       5.293002   \n",
       "std         0.973967       1.111598       1.002376       2.303739   \n",
       "min         1.000000       1.000000       1.000000       0.000000   \n",
       "25%         3.000000       3.000000       3.000000       4.000000   \n",
       "50%         4.000000       3.000000       3.000000       5.000000   \n",
       "75%         4.000000       4.000000       4.000000       7.000000   \n",
       "max         5.000000       5.000000       9.000000       9.000000   \n",
       "\n",
       "           RELAT_AB  \n",
       "count  794005.00000  \n",
       "mean        3.07222  \n",
       "std         1.36298  \n",
       "min         1.00000  \n",
       "25%         2.00000  \n",
       "50%         3.00000  \n",
       "75%         4.00000  \n",
       "max         9.00000  \n",
       "\n",
       "[8 rows x 81 columns]"
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     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "# Check the structure of the data after it's loaded (e.g. print the number of\n",
    "# rows and columns, print the first few rows).\n",
    "azdias.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Tip**: Add additional cells to keep everything in reasonably-sized chunks! Keyboard shortcut `esc --> a` (press escape to enter command mode, then press the 'A' key) adds a new cell before the active cell, and `esc --> b` adds a new cell after the active cell. If you need to convert an active cell to a markdown cell, use `esc --> m` and to convert to a code cell, use `esc --> y`. \n",
    "\n",
    "## Step 1: Preprocessing\n",
    "\n",
    "### Step 1.1: Assess Missing Data\n",
    "\n",
    "The feature summary file contains a summary of properties for each demographics data column. You will use this file to help you make cleaning decisions during this stage of the project. First of all, you should assess the demographics data in terms of missing data. Pay attention to the following points as you perform your analysis, and take notes on what you observe. Make sure that you fill in the **Discussion** cell with your findings and decisions at the end of each step that has one!\n",
    "\n",
    "#### Step 1.1.1: Convert Missing Value Codes to NaNs\n",
    "The fourth column of the feature attributes summary (loaded in above as `feat_info`) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. `[-1,0]`), this will get read in as a string object. You'll need to do a little bit of parsing to make use of it to identify and clean the data. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.\n",
    "\n",
    "**As one more reminder, you are encouraged to add additional cells to break up your analysis into manageable chunks.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Identify missing or unknown data values and convert them to NaNs.\n",
    "for index, row in feat_info.iterrows():\n",
    "    column = row['attribute']\n",
    "    mia = row['missing_or_unknown'][1:-1].split(',')\n",
    "    if len(mia) > 0:\n",
    "        for value in mia:\n",
    "            if value != '':\n",
    "                if value == '-1':\n",
    "                    value = -1\n",
    "                if (value != 'XX' and value != 'X'):\n",
    "                    value = float(value)\n",
    "                azdias[column] = azdias[column].replace(to_replace=value, value=np.NaN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGER_TYP  ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  \\\n",
       "0       NaN                   2.0          1            2.0   \n",
       "1       NaN                   1.0          2            5.0   \n",
       "2       NaN                   3.0          2            3.0   \n",
       "3       2.0                   4.0          2            2.0   \n",
       "4       NaN                   3.0          1            5.0   \n",
       "\n",
       "   FINANZ_MINIMALIST  FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  \\\n",
       "0                  3              4                 3               5   \n",
       "1                  1              5                 2               5   \n",
       "2                  1              4                 1               2   \n",
       "3                  4              2                 5               2   \n",
       "4                  4              3                 4               1   \n",
       "\n",
       "   FINANZ_UNAUFFAELLIGER  FINANZ_HAUSBAUER    ...     PLZ8_ANTG1  PLZ8_ANTG2  \\\n",
       "0                      5                 3    ...            NaN         NaN   \n",
       "1                      4                 5    ...            2.0         3.0   \n",
       "2                      3                 5    ...            3.0         3.0   \n",
       "3                      1                 2    ...            2.0         2.0   \n",
       "4                      3                 2    ...            2.0         4.0   \n",
       "\n",
       "   PLZ8_ANTG3  PLZ8_ANTG4  PLZ8_BAUMAX  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  \\\n",
       "0         NaN         NaN          NaN       NaN       NaN     NaN   \n",
       "1         2.0         1.0          1.0       5.0       4.0     3.0   \n",
       "2         1.0         0.0          1.0       4.0       4.0     3.0   \n",
       "3         2.0         0.0          1.0       3.0       4.0     2.0   \n",
       "4         2.0         1.0          2.0       3.0       3.0     4.0   \n",
       "\n",
       "   ORTSGR_KLS9  RELAT_AB  \n",
       "0          NaN       NaN  \n",
       "1          5.0       4.0  \n",
       "2          5.0       2.0  \n",
       "3          3.0       3.0  \n",
       "4          6.0       5.0  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.2: Assess Missing Data in Each Column\n",
    "\n",
    "How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's [`hist()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html) function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)\n",
    "\n",
    "For the remaining features, are there any patterns in which columns have, or share, missing data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45a4f86f98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform an assessment of how much missing data there is in each column of the\n",
    "# dataset.\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "history=[]\n",
    "for index, row in feat_info.iterrows():\n",
    "    column = row['attribute']\n",
    "    history.append(len(azdias[column]) - azdias[column].count())\n",
    "\n",
    "plt.hist(history, ec='black')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TITEL_KZ 889061 Percentage of missing values in column: 99.76%\n",
      "AGER_TYP 685843 Percentage of missing values in column: 76.96%\n",
      "KK_KUNDENTYP 584612 Percentage of missing values in column: 65.60%\n",
      "KBA05_BAUMAX 476524 Percentage of missing values in column: 53.47%\n",
      "GEBURTSJAHR 392318 Percentage of missing values in column: 44.02%\n",
      "ALTER_HH 310267 Percentage of missing values in column: 34.81%\n",
      "KKK 158064 Percentage of missing values in column: 17.74%\n",
      "REGIOTYP 158064 Percentage of missing values in column: 17.74%\n",
      "W_KEIT_KIND_HH 147988 Percentage of missing values in column: 16.61%\n",
      "KBA05_ANTG1 133324 Percentage of missing values in column: 14.96%\n",
      "KBA05_ANTG2 133324 Percentage of missing values in column: 14.96%\n",
      "KBA05_ANTG3 133324 Percentage of missing values in column: 14.96%\n",
      "KBA05_ANTG4 133324 Percentage of missing values in column: 14.96%\n",
      "KBA05_GBZ 133324 Percentage of missing values in column: 14.96%\n",
      "MOBI_REGIO 133324 Percentage of missing values in column: 14.96%\n",
      "PLZ8_ANTG1 116515 Percentage of missing values in column: 13.07%\n",
      "PLZ8_ANTG2 116515 Percentage of missing values in column: 13.07%\n",
      "PLZ8_ANTG3 116515 Percentage of missing values in column: 13.07%\n",
      "PLZ8_ANTG4 116515 Percentage of missing values in column: 13.07%\n",
      "PLZ8_BAUMAX 116515 Percentage of missing values in column: 13.07%\n",
      "PLZ8_HHZ 116515 Percentage of missing values in column: 13.07%\n",
      "PLZ8_GBZ 116515 Percentage of missing values in column: 13.07%\n",
      "HEALTH_TYP 111196 Percentage of missing values in column: 12.48%\n",
      "SHOPPER_TYP 111196 Percentage of missing values in column: 12.48%\n",
      "VERS_TYP 111196 Percentage of missing values in column: 12.48%\n",
      "NATIONALITAET_KZ 108315 Percentage of missing values in column: 12.15%\n",
      "PRAEGENDE_JUGENDJAHRE 108164 Percentage of missing values in column: 12.14%\n",
      "KBA13_ANZAHL_PKW 105800 Percentage of missing values in column: 11.87%\n",
      "ANZ_HAUSHALTE_AKTIV 99611 Percentage of missing values in column: 11.18%\n",
      "CAMEO_DEUG_2015 99352 Percentage of missing values in column: 11.15%\n",
      "CAMEO_DEU_2015 99352 Percentage of missing values in column: 11.15%\n",
      "CAMEO_INTL_2015 99352 Percentage of missing values in column: 11.15%\n",
      "LP_LEBENSPHASE_FEIN 97632 Percentage of missing values in column: 10.95%\n",
      "ARBEIT 97375 Percentage of missing values in column: 10.93%\n",
      "RELAT_AB 97375 Percentage of missing values in column: 10.93%\n",
      "ORTSGR_KLS9 97274 Percentage of missing values in column: 10.91%\n",
      "ANZ_HH_TITEL 97008 Percentage of missing values in column: 10.88%\n",
      "LP_LEBENSPHASE_GROB 94572 Percentage of missing values in column: 10.61%\n",
      "BALLRAUM 93740 Percentage of missing values in column: 10.52%\n",
      "EWDICHTE 93740 Percentage of missing values in column: 10.52%\n",
      "INNENSTADT 93740 Percentage of missing values in column: 10.52%\n",
      "GEBAEUDETYP_RASTER 93155 Percentage of missing values in column: 10.45%\n",
      "GEBAEUDETYP 93148 Percentage of missing values in column: 10.45%\n",
      "MIN_GEBAEUDEJAHR 93148 Percentage of missing values in column: 10.45%\n",
      "OST_WEST_KZ 93148 Percentage of missing values in column: 10.45%\n",
      "WOHNLAGE 93148 Percentage of missing values in column: 10.45%\n",
      "LP_FAMILIE_FEIN 77792 Percentage of missing values in column: 8.73%\n",
      "LP_FAMILIE_GROB 77792 Percentage of missing values in column: 8.73%\n",
      "KONSUMNAEHE 73969 Percentage of missing values in column: 8.30%\n",
      "SOHO_KZ 73499 Percentage of missing values in column: 8.25%\n",
      "ANZ_PERSONEN 73499 Percentage of missing values in column: 8.25%\n",
      "ANZ_TITEL 73499 Percentage of missing values in column: 8.25%\n",
      "WOHNDAUER_2008 73499 Percentage of missing values in column: 8.25%\n",
      "HH_EINKOMMEN_SCORE 18348 Percentage of missing values in column: 2.06%\n",
      "CJT_GESAMTTYP 4854 Percentage of missing values in column: 0.54%\n",
      "GFK_URLAUBERTYP 4854 Percentage of missing values in column: 0.54%\n",
      "LP_STATUS_FEIN 4854 Percentage of missing values in column: 0.54%\n",
      "LP_STATUS_GROB 4854 Percentage of missing values in column: 0.54%\n",
      "RETOURTYP_BK_S 4854 Percentage of missing values in column: 0.54%\n",
      "ONLINE_AFFINITAET 4854 Percentage of missing values in column: 0.54%\n",
      "ALTERSKATEGORIE_GROB 2881 Percentage of missing values in column: 0.32%\n",
      "ANREDE_KZ 0 Percentage of missing values in column: 0.00%\n",
      "FINANZ_MINIMALIST 0 Percentage of missing values in column: 0.00%\n",
      "FINANZ_SPARER 0 Percentage of missing values in column: 0.00%\n",
      "FINANZ_VORSORGER 0 Percentage of missing values in column: 0.00%\n",
      "FINANZ_ANLEGER 0 Percentage of missing values in column: 0.00%\n",
      "FINANZ_UNAUFFAELLIGER 0 Percentage of missing values in column: 0.00%\n",
      "FINANZ_HAUSBAUER 0 Percentage of missing values in column: 0.00%\n",
      "FINANZTYP 0 Percentage of missing values in column: 0.00%\n",
      "GREEN_AVANTGARDE 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_SOZ 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_FAM 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_REL 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_MAT 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_VERT 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_LUST 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_ERL 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_KULT 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_RAT 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_KRIT 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_DOM 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_KAEM 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_PFLICHT 0 Percentage of missing values in column: 0.00%\n",
      "SEMIO_TRADV 0 Percentage of missing values in column: 0.00%\n",
      "ZABEOTYP 0 Percentage of missing values in column: 0.00%\n"
     ]
    }
   ],
   "source": [
    "# Investigate patterns in the amount of missing data in each column.\n",
    "result = {}\n",
    "limit = len(azdias)\n",
    "for index, row in feat_info.iterrows():\n",
    "    column = row['attribute']\n",
    "    result[column] = (len(azdias[column]) - azdias[column].count())\n",
    "\n",
    "for w in sorted(result, key=result.get, reverse=True):\n",
    "    print(w, result[w], 'Percentage of missing values in column: {:.2f}%'.format(result[w]/(limit/100)))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the outlier columns from the dataset. (You'll perform other data\n",
    "# engineering tasks such as re-encoding and imputation later.)\n",
    "\n",
    "del azdias['TITEL_KZ']\n",
    "del azdias['AGER_TYP']\n",
    "del azdias['KK_KUNDENTYP']\n",
    "del azdias['KBA05_BAUMAX']\n",
    "del azdias['ALTER_HH']\n",
    "del azdias['GEBURTSJAHR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>CJT_GESAMTTYP</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>FINANZTYP</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_ANTG1</th>\n",
       "      <th>PLZ8_ANTG2</th>\n",
       "      <th>PLZ8_ANTG3</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_BAUMAX</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  FINANZ_MINIMALIST  \\\n",
       "0                   2.0          1            2.0                  3   \n",
       "1                   1.0          2            5.0                  1   \n",
       "2                   3.0          2            3.0                  1   \n",
       "3                   4.0          2            2.0                  4   \n",
       "4                   3.0          1            5.0                  4   \n",
       "\n",
       "   FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  \\\n",
       "0              4                 3               5                      5   \n",
       "1              5                 2               5                      4   \n",
       "2              4                 1               2                      3   \n",
       "3              2                 5               2                      1   \n",
       "4              3                 4               1                      3   \n",
       "\n",
       "   FINANZ_HAUSBAUER  FINANZTYP    ...     PLZ8_ANTG1  PLZ8_ANTG2  PLZ8_ANTG3  \\\n",
       "0                 3          4    ...            NaN         NaN         NaN   \n",
       "1                 5          1    ...            2.0         3.0         2.0   \n",
       "2                 5          1    ...            3.0         3.0         1.0   \n",
       "3                 2          6    ...            2.0         2.0         2.0   \n",
       "4                 2          5    ...            2.0         4.0         2.0   \n",
       "\n",
       "   PLZ8_ANTG4  PLZ8_BAUMAX  PLZ8_HHZ  PLZ8_GBZ  ARBEIT  ORTSGR_KLS9  RELAT_AB  \n",
       "0         NaN          NaN       NaN       NaN     NaN          NaN       NaN  \n",
       "1         1.0          1.0       5.0       4.0     3.0          5.0       4.0  \n",
       "2         0.0          1.0       4.0       4.0     3.0          5.0       2.0  \n",
       "3         0.0          1.0       3.0       4.0     2.0          3.0       3.0  \n",
       "4         1.0          2.0       3.0       3.0     4.0          6.0       5.0  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "azdias.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.2: Assess Missing Data in Each Column\n",
    "##### Are there any patterns in missing values? \n",
    "TITEL_KZ - Academic title flag\n",
    "AGER_TYP - Best-ager typology\n",
    "Both columns were related to personal data. And based on the Data Dictionary, this data was rare.\n",
    "\n",
    "##### Which columns were removed from the dataset?\n",
    "TITEL_KZ 889061 Percentage of missing values in column: 99.76%\n",
    "AGER_TYP 685843 Percentage of missing values in column: 76.96%\n",
    "KK_KUNDENTYP 584612 Percentage of missing values in column: 65.60%\n",
    "KBA05_BAUMAX 476524 Percentage of missing values in column: 53.47%\n",
    "GEBURTSJAHR 392318 Percentage of missing values in column: 44.02%\n",
    "ALTER_HH 310267 Percentage of missing values in column: 34.81%\n",
    "I decided to remove columns with more than 20% nan values.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "Now, you'll perform a similar assessment for the rows of the dataset. How much data is missing in each row? As with the columns, you should see some groups of points that have a very different numbers of missing values. Divide the data into two subsets: one for data points that are above some threshold for missing values, and a second subset for points below that threshold.\n",
    "\n",
    "In order to know what to do with the outlier rows, we should see if the distribution of data values on columns that are not missing data (or are missing very little data) are similar or different between the two groups. Select at least five of these columns and compare the distribution of values.\n",
    "- You can use seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) function to create a bar chart of code frequencies and matplotlib's [`subplot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html) function to put bar charts for the two subplots side by side.\n",
    "- To reduce repeated code, you might want to write a function that can perform this comparison, taking as one of its arguments a column to be compared.\n",
    "\n",
    "Depending on what you observe in your comparison, this will have implications on how you approach your conclusions later in the analysis. If the distributions of non-missing features look similar between the data with many missing values and the data with few or no missing values, then we could argue that simply dropping those points from the analysis won't present a major issue. On the other hand, if the data with many missing values looks very different from the data with few or no missing values, then we should make a note on those data as special. We'll revisit these data later on. **Either way, you should continue your analysis for now using just the subset of the data with few or no missing values.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J0knruGaJVtWzwNeBjcCqJCvbrnXAobY+C5wD0Pa/CTgyXJ/XZ6H6jxYZQ5IkaWKMM0t0TZJVbf1U4N3Ao8C9wPtbs63A3W19b9um7f9aVVWrX91mkZ4LbAC+BdwPbGgzQk9hMDFhb+uz0BiSJEkTY+XSTTgb2N1mc/4WsKeqvpTke8CdSf4z8B3gttb+NuAvkswwuLN2NUBVPZJkD/A94CXguqr6OUCSDwH7gBXArqp6pB3rYwuMIUmSNDGWDGxV9RDw9hH1xxm8zza//vfAVQsc60bgxhH1e4B7xh1DkiRpkvhNB5IkSZ0zsEmSJHXOwCZJktQ5A5skSVLnDGySJEmdM7BJkiR1zsAmSZLUOQObJElS5wxskiRJnTOwSZIkdc7AJkmS1DkDmyRJUucMbJIkSZ0zsEmSJHXOwCZJktQ5A5skSVLnDGySJEmdWzKwJTknyb1JHk3ySJIPt/ppSfYnOdiWq1s9SW5JMpPkoSQXDh1ra2t/MMnWofpFSR5ufW5JksXGkCRJmiTj3GF7CfjDqnoLsBG4Lsl5wA7gQFVtAA60bYDLgQ3tsx24FQbhC7geeAdwMXD9UAC7tbU91m9zqy80hiRJ0sRYMrBV1dNV9e22/jzwKLAW2ALsbs12A1e29S3AHTXwTWBVkrOBy4D9VXWkqo4C+4HNbd8bq+obVVXAHfOONWoMSZKkiXFc77AlWQ+8HbgPOKuqnoZBqAPObM3WAk8NdZtttcXqsyPqLDKGJEnSxBg7sCX5beCvgI9U1U8WazqiVq+gPrYk25NMJ5mem5s7nq6SJEndGyuwJXkNg7D22ar6Qis/0x5n0paHW30WOGeo+zrg0BL1dSPqi43xMlW1s6qmqmpqzZo14/xIkiRJJ41xZokGuA14tKr+dGjXXuDYTM+twN1D9WvbbNGNwHPtceY+YFOS1W2ywSZgX9v3fJKNbaxr5x1r1BiSJEkTY+UYbd4J/Fvg4SQPttofAzcBe5JsA54Ermr77gGuAGaAF4APAFTVkSSfAO5v7W6oqiNt/YPA7cCpwFfah0XGkCRJmhhLBraq+p+Mfs8M4NIR7Qu4boFj7QJ2jahPA+ePqP941BiSJEmTxG86kCRJ6pyBTZIkqXMGNkmSpM4Z2CRJkjpnYJMkSeqcgU2SJKlzBjZJkqTOGdgkSZI6Z2CTJEnqnIFNkiSpcwY2SZKkzhnYJEmSOmdgkyRJ6pyBTZIkqXMGNkmSpM4Z2CRJkjpnYJMkSeqcgU2SJKlzSwa2JLuSHE7y3aHaaUn2JznYlqtbPUluSTKT5KEkFw712draH0yydah+UZKHW59bkmSxMSRJkibNOHfYbgc2z6vtAA5U1QbgQNsGuBzY0D7bgVthEL6A64F3ABcD1w8FsFtb22P9Ni8xhiRJ0kRZMrBV1d8AR+aVtwC72/pu4Mqh+h018E1gVZKzgcuA/VV1pKqOAvuBzW3fG6vqG1VVwB3zjjVqDEmSpInySt9hO6uqngZoyzNbfS3w1FC72VZbrD47or7YGL8kyfYk00mm5+bmXuGPJEmS1Kdf96SDjKjVK6gfl6raWVVTVTW1Zs2a4+0uSZLUtVca2J5pjzNpy8OtPgucM9RuHXBoifq6EfXFxpAkSZoorzSw7QWOzfTcCtw9VL+2zRbdCDzXHmfuAzYlWd0mG2wC9rV9zyfZ2GaHXjvvWKPGkCRJmigrl2qQ5HPAu4AzkswymO15E7AnyTbgSeCq1vwe4ApgBngB+ABAVR1J8gng/tbuhqo6NpHhgwxmop4KfKV9WGQMSZKkibJkYKuqaxbYdemItgVct8BxdgG7RtSngfNH1H88agxJkqRJ4zcdSJIkdW7JO2ySJE2y9Tu+vNynoGXwxE3vWe5TeBnvsEmSJHXOwCZJktQ5A5skSVLnDGySJEmdM7BJkiR1zsAmSZLUOQObJElS5wxskiRJnTOwSZIkdc7AJkmS1DkDmyRJUucMbJIkSZ0zsEmSJHXOwCZJktS5lct9ApJ0Mli/48vLfQqSJlj3gS3JZuDPgRXAZ6rqpmU+JU0w/6ctSVoOXT8STbIC+BRwOXAecE2S85b3rCRJkk6srgMbcDEwU1WPV9XPgDuBLct8TpIkSSdU74FtLfDU0PZsq0mSJE2M3t9hy4ha/VKjZDuwvW3+NMljr+pZwRnAj17lMfTKeX365bXpm9enX16bEyyfPK7mv8r1+SfjNOo9sM0C5wxtrwMOzW9UVTuBnSfqpJJMV9XUiRpPx8fr0y+vTd+8Pv3y2vTtRFyf3h+J3g9sSHJuklOAq4G9y3xOkiRJJ1TXd9iq6qUkHwL2MfizHruq6pFlPi1JkqQTquvABlBV9wD3LPd5zHPCHr/qFfH69Mtr0zevT7+8Nn171a9Pqn7pHX5JkiR1pPd32CRJkiaege04Jdmc5LEkM0l2LPf5TLoku5IcTvLdodppSfYnOdiWq5fzHCdVknOS3Jvk0SSPJPlwq3t9llmS1yX5VpK/bdfmT1r93CT3tWvz+TbZS8sgyYok30nypbbttelEkieSPJzkwSTTrfaq/14zsB0HvyqrS7cDm+fVdgAHqmoDcKBt68R7CfjDqnoLsBG4rv334vVZfi8Cl1TV24ALgM1JNgKfBG5u1+YosG0Zz3HSfRh4dGjba9OX36+qC4b+lMer/nvNwHZ8/KqszlTV3wBH5pW3ALvb+m7gyhN6UgKgqp6uqm+39ecZ/M9nLV6fZVcDP22br2mfAi4B7mp1r80ySbIOeA/wmbYdvDa9e9V/rxnYjo9flXVyOKuqnoZBaADOXObzmXhJ1gNvB+7D69OF9sjtQeAwsB/4AfBsVb3Umvj7bfn8GfBHwD+07dPx2vSkgL9O8kD7piU4Ab/Xuv+zHp0Z66uyJP1Ckt8G/gr4SFX9ZHCzQMutqn4OXJBkFfBF4C2jmp3Ys1KS9wKHq+qBJO86Vh7R1GuzfN5ZVYeSnAnsT/L9EzGod9iOz1hflaVl90ySswHa8vAyn8/ESvIaBmHts1X1hVb2+nSkqp4Fvs7gPcNVSY79Q97fb8vjncD7kjzB4LWbSxjccfPadKKqDrXlYQb/2LmYE/B7zcB2fPyqrJPDXmBrW98K3L2M5zKx2ns3twGPVtWfDu3y+iyzJGvanTWSnAq8m8E7hvcC72/NvDbLoKo+XlXrqmo9g//HfK2q/gCvTReSvCHJ7xxbBzYB3+UE/F7zD+cepyRXMPjXzrGvyrpxmU9poiX5HPAu4AzgGeB64L8De4B/DDwJXFVV8ycm6FWW5F8C/wN4mF+8i/PHDN5j8/osoyT/jMGL0SsY/MN9T1XdkOR3GdzVOQ34DvBvqurF5TvTydYeif6nqnqv16YP7Tp8sW2uBP6yqm5Mcjqv8u81A5skSVLnfCQqSZLUOQObJElS5wxskiRJnTOwSZIkdc7AJkmS1DkDmyRJUucMbJIkSZ0zsEmSJHXu/wFJfBSuLLSTiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4598b9d390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How much data is missing in each row of the dataset?\n",
    "missing_row_values = azdias.isnull().sum(axis=1)\n",
    "\n",
    "missing_values = []\n",
    "for i in range(0, len(missing_row_values)):\n",
    "    missing_values.append(missing_row_values[i])\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "n, bins, patches = plt.hist(x=missing_values, bins=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 782046.   15860.   18166.   75149.]\n",
      "[  0.    12.25  24.5   36.75  49.  ]\n"
     ]
    }
   ],
   "source": [
    "print(n)\n",
    "print(bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write code to divide the data into two subsets based on the number of missing\n",
    "# values in each row.\n",
    "azdias_copy = azdias.copy()\n",
    "less_than_12 = azdias_copy[azdias_copy.isnull().sum(axis=1) <= 12.25]\n",
    "more_than_12 = azdias_copy[azdias_copy.isnull().sum(axis=1) > 12.25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "782046"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(less_than_12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "109175"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(more_than_12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891221"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(azdias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4597700d30>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4598b9d710>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_more = more_than_12.count()\n",
    "count_more.sort_values(ascending=True).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f45a4f64940>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4598bf3080>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the distribution of values for at least five columns where there are\n",
    "# no or few missing values, between the two subsets.\n",
    "count_less = less_than_12.count()\n",
    "count_less.sort_values(ascending=True).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_single_comp(feature, ds1, ds2):\n",
    "    fig, ax = plt.subplots(figsize=(12,3), ncols=2)\n",
    "    sns.countplot(x=feature, data=ds1, ax=ax[0])\n",
    "    sns.countplot(x=feature, data=ds2, ax=ax[1])\n",
    "\n",
    "\n",
    "def plot_comparison(ds1, ds2, title):\n",
    "    fig, ax = plt.subplots(figsize=(12,3), ncols=2)\n",
    "    sns.countplot(x='ALTERSKATEGORIE_GROB', data=ds1, ax=ax[0])\n",
    "    sns.countplot(x='ALTERSKATEGORIE_GROB', data=ds2, ax=ax[1])    \n",
    "    fig.suptitle(title, fontsize=20)\n",
    "\n",
    "    plot_single_comp('FINANZ_ANLEGER', ds1, ds2)\n",
    "    plot_single_comp('FINANZ_HAUSBAUER', ds1, ds2)\n",
    "    plot_single_comp('GREEN_AVANTGARDE', ds1, ds2)\n",
    "    plot_single_comp('SEMIO_KAEM', ds1, ds2)\n",
    "    plot_single_comp('ONLINE_AFFINITAET', ds1, ds2)\n",
    "    plot_single_comp('CJT_GESAMTTYP', ds1, ds2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4561cf8be0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4561c13da0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4561bec5c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4561b21f28>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4561b73a90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4561a3a128>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4561955e80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_comparison(less_than_12, more_than_12, \n",
    "                'Subset 1 (few missing values) vs Subset 2 (lots of missing values)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.1.3: Assess Missing Data in Each Row\n",
    "\n",
    "Second subset has 109k rows have less than 12 filled fields of filled data.\n",
    "First subset more than 780k rows have more than 12 fields of filled data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.2: Select and Re-Encode Features\n",
    "\n",
    "Checking for missing data isn't the only way in which you can prepare a dataset for analysis. Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, you need to make a few encoding changes or additional assumptions to be able to make progress. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (`feat_info`) for a summary of types of measurement.\n",
    "- For numeric and interval data, these features can be kept without changes.\n",
    "- Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).\n",
    "- Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.\n",
    "\n",
    "In the first two parts of this sub-step, you will perform an investigation of the categorical and mixed-type features and make a decision on each of them, whether you will keep, drop, or re-encode each. Then, in the last part, you will create a new data frame with only the selected and engineered columns.\n",
    "\n",
    "Data wrangling is often the trickiest part of the data analysis process, and there's a lot of it to be done here. But stick with it: once you're done with this step, you'll be ready to get to the machine learning parts of the project!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How many features are there of each data type?\n",
    "features = {}\n",
    "for index, row in feat_info.iterrows():\n",
    "    column = row['attribute']\n",
    "    row_type = row['type']\n",
    "    if row_type in ['mixed', 'categorical']:\n",
    "        if column not in ['TITEL_KZ', 'AGER_TYP', 'KK_KUNDENTYP', 'KBA05_BAUMAX', 'GEBURTSJAHR', 'ALTER_HH']:\n",
    "            value = less_than_12[column].unique()\n",
    "            if row_type in features:\n",
    "                features[row_type][column] = value\n",
    "            else:\n",
    "                features[row_type] = {column: value}\n",
    "\n",
    "result = []\n",
    "for row, column in features.items():\n",
    "    for feature in column.keys():\n",
    "        result.append('Column: {}. Type: {}. Features: {}. # of features: {}.'.format(feature, row, column[feature], len(column[feature])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "For categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:\n",
    "- For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.\n",
    "- There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.\n",
    "- For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via [OneHotEncoder](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'categorical': {'ANREDE_KZ': array([2, 1]),\n",
       "  'CJT_GESAMTTYP': array([  5.,   3.,   2.,   4.,   1.,   6.,  nan]),\n",
       "  'FINANZTYP': array([1, 6, 5, 2, 4, 3]),\n",
       "  'GFK_URLAUBERTYP': array([ 10.,   1.,   5.,  12.,   9.,   3.,   8.,  11.,   4.,   7.,   2.,\n",
       "           6.,  nan]),\n",
       "  'GREEN_AVANTGARDE': array([0, 1]),\n",
       "  'LP_FAMILIE_FEIN': array([  5.,   1.,  nan,  10.,   2.,   7.,  11.,   8.,   4.,   6.,   9.,\n",
       "           3.]),\n",
       "  'LP_FAMILIE_GROB': array([  3.,   1.,  nan,   5.,   2.,   4.]),\n",
       "  'LP_STATUS_FEIN': array([  2.,   3.,   9.,   4.,   1.,  10.,   5.,   8.,   6.,   7.,  nan]),\n",
       "  'LP_STATUS_GROB': array([  1.,   2.,   4.,   5.,   3.,  nan]),\n",
       "  'NATIONALITAET_KZ': array([  1.,   3.,   2.,  nan]),\n",
       "  'SHOPPER_TYP': array([  3.,   2.,   1.,   0.,  nan]),\n",
       "  'SOHO_KZ': array([ 1.,  0.]),\n",
       "  'VERS_TYP': array([  2.,   1.,  nan]),\n",
       "  'ZABEOTYP': array([5, 3, 4, 1, 6, 2]),\n",
       "  'GEBAEUDETYP': array([ 8.,  1.,  3.,  2.,  6.,  4.,  5.]),\n",
       "  'OST_WEST_KZ': array(['W', 'O'], dtype=object),\n",
       "  'CAMEO_DEUG_2015': array(['8', '4', '2', '6', '1', '9', '5', '7', '3', nan], dtype=object),\n",
       "  'CAMEO_DEU_2015': array(['8A', '4C', '2A', '6B', '8C', '4A', '2D', '1A', '1E', '9D', '5C',\n",
       "         '8B', '7A', '5D', '9E', '9B', '1B', '3D', nan, '4E', '4B', '3C',\n",
       "         '5A', '7B', '9A', '6D', '6E', '2C', '7C', '9C', '7D', '5E', '1D',\n",
       "         '8D', '6C', '6A', '5B', '4D', '3A', '2B', '7E', '3B', '6F', '5F',\n",
       "         '1C'], dtype=object)},\n",
       " 'mixed': {'LP_LEBENSPHASE_FEIN': array([ 21.,   3.,  nan,  32.,   8.,   2.,   5.,  10.,   4.,   6.,  23.,\n",
       "          12.,  20.,   1.,  25.,  13.,  18.,  31.,  38.,  35.,  30.,   7.,\n",
       "          22.,  14.,  29.,  24.,  28.,  37.,  26.,  39.,  27.,  36.,   9.,\n",
       "          34.,  33.,  15.,  11.,  40.,  16.,  19.,  17.]),\n",
       "  'LP_LEBENSPHASE_GROB': array([  6.,   1.,  nan,  10.,   2.,   3.,   5.,   7.,  12.,  11.,   9.,\n",
       "           4.,   8.]),\n",
       "  'PRAEGENDE_JUGENDJAHRE': array([ 14.,  15.,   8.,   3.,  10.,  11.,   5.,   9.,   6.,   4.,  nan,\n",
       "           2.,   1.,  12.,  13.,   7.]),\n",
       "  'WOHNLAGE': array([ 4.,  2.,  7.,  3.,  5.,  1.,  8.,  0.]),\n",
       "  'CAMEO_INTL_2015': array(['51', '24', '12', '43', '54', '22', '14', '13', '15', '33', '41',\n",
       "         '34', '55', '25', nan, '23', '31', '52', '35', '45', '44', '32'], dtype=object),\n",
       "  'PLZ8_BAUMAX': array([  1.,   2.,  nan,   4.,   5.,   3.])}}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Column: ANREDE_KZ. Type: categorical. Features: [2 1]. # of features: 2.',\n",
       " 'Column: CJT_GESAMTTYP. Type: categorical. Features: [  5.   3.   2.   4.   1.   6.  nan]. # of features: 7.',\n",
       " 'Column: FINANZTYP. Type: categorical. Features: [1 6 5 2 4 3]. # of features: 6.',\n",
       " 'Column: GFK_URLAUBERTYP. Type: categorical. Features: [ 10.   1.   5.  12.   9.   3.   8.  11.   4.   7.   2.   6.  nan]. # of features: 13.',\n",
       " 'Column: GREEN_AVANTGARDE. Type: categorical. Features: [0 1]. # of features: 2.',\n",
       " 'Column: LP_FAMILIE_FEIN. Type: categorical. Features: [  5.   1.  nan  10.   2.   7.  11.   8.   4.   6.   9.   3.]. # of features: 12.',\n",
       " 'Column: LP_FAMILIE_GROB. Type: categorical. Features: [  3.   1.  nan   5.   2.   4.]. # of features: 6.',\n",
       " 'Column: LP_STATUS_FEIN. Type: categorical. Features: [  2.   3.   9.   4.   1.  10.   5.   8.   6.   7.  nan]. # of features: 11.',\n",
       " 'Column: LP_STATUS_GROB. Type: categorical. Features: [  1.   2.   4.   5.   3.  nan]. # of features: 6.',\n",
       " 'Column: NATIONALITAET_KZ. Type: categorical. Features: [  1.   3.   2.  nan]. # of features: 4.',\n",
       " 'Column: SHOPPER_TYP. Type: categorical. Features: [  3.   2.   1.   0.  nan]. # of features: 5.',\n",
       " 'Column: SOHO_KZ. Type: categorical. Features: [ 1.  0.]. # of features: 2.',\n",
       " 'Column: VERS_TYP. Type: categorical. Features: [  2.   1.  nan]. # of features: 3.',\n",
       " 'Column: ZABEOTYP. Type: categorical. Features: [5 3 4 1 6 2]. # of features: 6.',\n",
       " 'Column: GEBAEUDETYP. Type: categorical. Features: [ 8.  1.  3.  2.  6.  4.  5.]. # of features: 7.',\n",
       " \"Column: OST_WEST_KZ. Type: categorical. Features: ['W' 'O']. # of features: 2.\",\n",
       " \"Column: CAMEO_DEUG_2015. Type: categorical. Features: ['8' '4' '2' '6' '1' '9' '5' '7' '3' nan]. # of features: 10.\",\n",
       " \"Column: CAMEO_DEU_2015. Type: categorical. Features: ['8A' '4C' '2A' '6B' '8C' '4A' '2D' '1A' '1E' '9D' '5C' '8B' '7A' '5D' '9E'\\n '9B' '1B' '3D' nan '4E' '4B' '3C' '5A' '7B' '9A' '6D' '6E' '2C' '7C' '9C'\\n '7D' '5E' '1D' '8D' '6C' '6A' '5B' '4D' '3A' '2B' '7E' '3B' '6F' '5F' '1C']. # of features: 45.\",\n",
       " 'Column: LP_LEBENSPHASE_FEIN. Type: mixed. Features: [ 21.   3.  nan  32.   8.   2.   5.  10.   4.   6.  23.  12.  20.   1.  25.\\n  13.  18.  31.  38.  35.  30.   7.  22.  14.  29.  24.  28.  37.  26.  39.\\n  27.  36.   9.  34.  33.  15.  11.  40.  16.  19.  17.]. # of features: 41.',\n",
       " 'Column: LP_LEBENSPHASE_GROB. Type: mixed. Features: [  6.   1.  nan  10.   2.   3.   5.   7.  12.  11.   9.   4.   8.]. # of features: 13.',\n",
       " 'Column: PRAEGENDE_JUGENDJAHRE. Type: mixed. Features: [ 14.  15.   8.   3.  10.  11.   5.   9.   6.   4.  nan   2.   1.  12.  13.\\n   7.]. # of features: 16.',\n",
       " 'Column: WOHNLAGE. Type: mixed. Features: [ 4.  2.  7.  3.  5.  1.  8.  0.]. # of features: 8.',\n",
       " \"Column: CAMEO_INTL_2015. Type: mixed. Features: ['51' '24' '12' '43' '54' '22' '14' '13' '15' '33' '41' '34' '55' '25' nan\\n '23' '31' '52' '35' '45' '44' '32']. # of features: 22.\",\n",
       " 'Column: PLZ8_BAUMAX. Type: mixed. Features: [  1.   2.  nan   4.   5.   3.]. # of features: 6.']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column OST_WEST_KZ type is binary. It should be re-encoded: yes. Values: ['W' 'O']\n",
      "Column CAMEO_DEU_2015 type is multilevel. It should be re-encoded: yes. Values: ['8A' '4C' '2A' '6B' '8C' '4A' '2D' '1A' '1E' '9D' '5C' '8B' '7A' '5D' '9E'\n",
      " '9B' '1B' '3D' nan '4E' '4B' '3C' '5A' '7B' '9A' '6D' '6E' '2C' '7C' '9C'\n",
      " '7D' '5E' '1D' '8D' '6C' '6A' '5B' '4D' '3A' '2B' '7E' '3B' '6F' '5F' '1C']\n"
     ]
    }
   ],
   "source": [
    "# Assess categorical variables: which are binary, which are multi-level, and\n",
    "# which one needs to be re-encoded?\n",
    "import re\n",
    "for column in features['categorical']:\n",
    "    check = list(filter(lambda x: str(x) != 'nan', features['categorical'][column]))\n",
    "    block = []\n",
    "    for item in check:\n",
    "        if '.' not in str(item):\n",
    "            try:\n",
    "                int(item)\n",
    "            except ValueError:\n",
    "                block.append(item)\n",
    "    column_type = 'binary' if len(features['categorical'][column]) <= 3 else 'multilevel'\n",
    "    should_be_converted = 'yes' if len(block) > 0 else 'no'\n",
    "    if should_be_converted == 'yes':\n",
    "        print('Column {} type is {}. It should be re-encoded: {}. Values: {}'.format(column, column_type, should_be_converted, features['categorical'][column]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "# Re-encode categorical variable(s) to be kept in the analysis.\n",
    "def dealWithWO(x):\n",
    "    if x == 'nan':\n",
    "        return x\n",
    "    return 1 if x == 'W' else 0\n",
    "\n",
    "less_than_12['OST_WEST_KZ'] = less_than_12.apply(lambda x: dealWithWO(x.OST_WEST_KZ), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "def coerce_to_int(val):\n",
    "    try:\n",
    "       return int(str(val), base=16)\n",
    "    except ValueError:\n",
    "       return val\n",
    "\n",
    "less_than_12['CAMEO_DEU_2015'] = less_than_12.apply(lambda x: coerce_to_int(x.CAMEO_DEU_2015), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 138.,   76.,   42.,  107.,  140.,   74.,   45.,   26.,   30.,\n",
       "        157.,   92.,  139.,  122.,   93.,  158.,  155.,   27.,   61.,\n",
       "         nan,   78.,   75.,   60.,   90.,  123.,  154.,  109.,  110.,\n",
       "         44.,  124.,  156.,  125.,   94.,   29.,  141.,  108.,  106.,\n",
       "         91.,   77.,   58.,   43.,  126.,   59.,  111.,   95.,   28.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_than_12['CAMEO_DEU_2015'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.1: Re-Encode Categorical Features\n",
    "\n",
    "I decided to recode OST_WEST_KZ from str to binary type.\n",
    "I also reworked CAMEO_DEU_2015 so it will be int type instead of mixed object type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "There are a handful of features that are marked as \"mixed\" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:\n",
    "- \"PRAEGENDE_JUGENDJAHRE\" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.\n",
    "- \"CAMEO_INTL_2015\" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).\n",
    "- If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.\n",
    "\n",
    "Be sure to check `Data_Dictionary.md` for the details needed to finish these tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Investigate \"PRAEGENDE_JUGENDJAHRE\" and engineer two new variables.\n",
    "def coerce_to_movement(val):\n",
    "    return 1 if val in [-1, 0, 1, 3, 5, 8, 10, 12, 14] else 0\n",
    "less_than_12['PRAEGENDE_JUGENDJAHRE_MOVEMENT']=less_than_12.apply(lambda x: coerce_to_movement(x.PRAEGENDE_JUGENDJAHRE), axis=1)\n",
    "\n",
    "def coerce_to_age(val):\n",
    "    if val in [-1, 0, 1, 2]:\n",
    "        return 1\n",
    "    if val in [3, 4]:\n",
    "        return 2\n",
    "    if val in [5, 6, 7]:\n",
    "        return 3\n",
    "    if val in [8, 9]:\n",
    "        return 4\n",
    "    if val in [10, 11, 12, 13]:\n",
    "        return 5\n",
    "    return 6\n",
    "\n",
    "less_than_12['PRAEGENDE_JUGENDJAHRE_AGE']=less_than_12.apply(lambda x: coerce_to_age(x.PRAEGENDE_JUGENDJAHRE), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_than_12['PRAEGENDE_JUGENDJAHRE_MOVEMENT'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 4, 2, 5, 3, 1])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_than_12['PRAEGENDE_JUGENDJAHRE_AGE'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n"
     ]
    }
   ],
   "source": [
    "# Investigate \"CAMEO_INTL_2015\" and engineer two new variables.\n",
    "def coerce_to_wealth(val):\n",
    "    if val in ['-1', '11', '12', '13', '14', '15']:\n",
    "        return 1\n",
    "    if val in ['21', '22', '23', '24', '25']:\n",
    "        return 2\n",
    "    if val in ['31', '32', '33', '34', '35']:\n",
    "        return 3\n",
    "    if val in ['41', '42', '43', '44', '45']:\n",
    "        return 4\n",
    "    return 5\n",
    "\n",
    "less_than_12['CAMEO_INTL_2015_WEALTH']=less_than_12.apply(lambda x: coerce_to_wealth(x.CAMEO_INTL_2015), axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 2, 1, 4, 3])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_than_12['CAMEO_INTL_2015_WEALTH'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "def coerce_to_life_stage(val):\n",
    "    if val in ['-1', '11', '21', '31', '41', '51']:\n",
    "        return 1\n",
    "    if val in ['12', '22', '32', '42', '52']:\n",
    "        return 2\n",
    "    if val in ['13', '23', '33', '43', '53']:\n",
    "        return 3\n",
    "    if val in ['14', '24', '34', '44', '54']:\n",
    "        return 4\n",
    "    return 5\n",
    "less_than_12['CAMEO_INTL_2015_LIFE_STAGE']=less_than_12.apply(lambda x: coerce_to_life_stage(x.CAMEO_INTL_2015), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 2, 3, 5])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "less_than_12['CAMEO_INTL_2015_LIFE_STAGE'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion 1.2.2: Engineer Mixed-Type Features\n",
    "\n",
    "I found in PRAEGENDE_JUGENDJAHRE feature:\n",
    "1. that there were only two movements groups: mainstream and avantgarde, so i decided to stick with binary type grouping.\n",
    "2. i also found that all groups could be splitted into 6 periods.\n",
    "\n",
    "In CAMEO_INTL_2015 i discovered:\n",
    "That there were 5 wealth and life-stage, groups which were easily splitted into 5 stages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1.2.3: Complete Feature Selection\n",
    "\n",
    "In order to finish this step up, you need to make sure that your data frame now only has the columns that you want to keep. To summarize, the dataframe should consist of the following:\n",
    "- All numeric, interval, and ordinal type columns from the original dataset.\n",
    "- Binary categorical features (all numerically-encoded).\n",
    "- Engineered features from other multi-level categorical features and mixed features.\n",
    "\n",
    "Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep \"PRAEGENDE_JUGENDJAHRE\", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from **the subset with few or no missing values**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If there are other re-engineering tasks you need to perform, make sure you\n",
    "# take care of them here. (Dealing with missing data will come in step 2.1.)\n",
    "del less_than_12['PRAEGENDE_JUGENDJAHRE']\n",
    "del less_than_12['CAMEO_INTL_2015']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>CJT_GESAMTTYP</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>FINANZTYP</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE_MOVEMENT</th>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE_AGE</th>\n",
       "      <th>CAMEO_INTL_2015_WEALTH</th>\n",
       "      <th>CAMEO_INTL_2015_LIFE_STAGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>3.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  FINANZ_MINIMALIST  \\\n",
       "1                   1.0          2            5.0                  1   \n",
       "2                   3.0          2            3.0                  1   \n",
       "3                   4.0          2            2.0                  4   \n",
       "4                   3.0          1            5.0                  4   \n",
       "5                   1.0          2            2.0                  3   \n",
       "\n",
       "   FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  \\\n",
       "1              5                 2               5                      4   \n",
       "2              4                 1               2                      3   \n",
       "3              2                 5               2                      1   \n",
       "4              3                 4               1                      3   \n",
       "5              1                 5               2                      2   \n",
       "\n",
       "   FINANZ_HAUSBAUER  FINANZTYP             ...              PLZ8_ANTG4  \\\n",
       "1                 5          1             ...                     1.0   \n",
       "2                 5          1             ...                     0.0   \n",
       "3                 2          6             ...                     0.0   \n",
       "4                 2          5             ...                     1.0   \n",
       "5                 5          2             ...                     1.0   \n",
       "\n",
       "   PLZ8_HHZ  PLZ8_GBZ  ARBEIT  ORTSGR_KLS9  RELAT_AB  \\\n",
       "1       5.0       4.0     3.0          5.0       4.0   \n",
       "2       4.0       4.0     3.0          5.0       2.0   \n",
       "3       3.0       4.0     2.0          3.0       3.0   \n",
       "4       3.0       3.0     4.0          6.0       5.0   \n",
       "5       5.0       5.0     2.0          3.0       3.0   \n",
       "\n",
       "   PRAEGENDE_JUGENDJAHRE_MOVEMENT  PRAEGENDE_JUGENDJAHRE_AGE  \\\n",
       "1                               1                          6   \n",
       "2                               0                          6   \n",
       "3                               1                          4   \n",
       "4                               1                          4   \n",
       "5                               1                          2   \n",
       "\n",
       "   CAMEO_INTL_2015_WEALTH  CAMEO_INTL_2015_LIFE_STAGE  \n",
       "1                       5                           1  \n",
       "2                       2                           4  \n",
       "3                       1                           2  \n",
       "4                       4                           3  \n",
       "5                       5                           4  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do whatever you need to in order to ensure that the dataframe only contains\n",
    "# the columns that should be passed to the algorithm functions.\n",
    "mixed_cat = ['LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB', 'WOHNLAGE', 'PLZ8_BAUMAX']\n",
    "\n",
    "final_encoded_azdias = less_than_12.drop(mixed_cat, axis=1)\n",
    "final_encoded_azdias.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1.3: Create a Cleaning Function\n",
    "\n",
    "Even though you've finished cleaning up the general population demographics data, it's important to look ahead to the future and realize that you'll need to perform the same cleaning steps on the customer demographics data. In this substep, complete the function below to execute the main feature selection, encoding, and re-engineering steps you performed above. Then, when it comes to looking at the customer data in Step 3, you can just run this function on that DataFrame to get the trimmed dataset in a single step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(df_copy):\n",
    "    \"\"\"\n",
    "    Perform feature trimming, re-encoding, and engineering for demographics\n",
    "    data\n",
    "    \n",
    "    INPUT: Demographics DataFrame\n",
    "    OUTPUT: Trimmed and cleaned demographics DataFrame\n",
    "    \"\"\"\n",
    "    def dealWithWO(x):\n",
    "        if x == 'nan':\n",
    "            return x\n",
    "        return 1 if x == 'W' else 0\n",
    "\n",
    "    def coerce_to_int(val):\n",
    "        try:\n",
    "           return int(str(val), base=16)\n",
    "        except ValueError:\n",
    "           return val\n",
    "    def coerce_to_movement(val):\n",
    "        return 1 if val in [-1, 0, 1, 3, 5, 8, 10, 12, 14] else 0\n",
    "    \n",
    "    def coerce_to_age(val):\n",
    "        if val in [-1, 0, 1, 2]:\n",
    "            return 1\n",
    "        if val in [3, 4]:\n",
    "            return 2\n",
    "        if val in [5, 6, 7]:\n",
    "            return 3\n",
    "        if val in [8, 9]:\n",
    "            return 4\n",
    "        if val in [10, 11, 12, 13]:\n",
    "            return 5\n",
    "        return 6\n",
    "    \n",
    "    def coerce_to_wealth(val):\n",
    "        if val in ['-1', '11', '12', '13', '14', '15']:\n",
    "            return 1\n",
    "        if val in ['21', '22', '23', '24', '25']:\n",
    "            return 2\n",
    "        if val in ['31', '32', '33', '34', '35']:\n",
    "            return 3\n",
    "        if val in ['41', '42', '43', '44', '45']:\n",
    "            return 4\n",
    "        return 5\n",
    "\n",
    "    def coerce_to_life_stage(val):\n",
    "        if val in ['-1', '11', '21', '31', '41', '51']:\n",
    "            return 1\n",
    "        if val in ['12', '22', '32', '42', '52']:\n",
    "            return 2\n",
    "        if val in ['13', '23', '33', '43', '53']:\n",
    "            return 3\n",
    "        if val in ['14', '24', '34', '44', '54']:\n",
    "            return 4\n",
    "        return 5\n",
    "    \n",
    "    # Put in code here to execute all main cleaning steps:\n",
    "    # convert missing value codes into NaNs, ...\n",
    "\n",
    "    for index, row in feat_info.iterrows():\n",
    "        column = row['attribute']\n",
    "        mia = row['missing_or_unknown'][1:-1].split(',')\n",
    "        if len(mia) > 0:\n",
    "            for value in mia:\n",
    "                if value != '':\n",
    "                    if value == '-1':\n",
    "                        value = -1\n",
    "                    if (value != 'XX' and value != 'X'):\n",
    "                        value = float(value)\n",
    "                    df_copy[column] = df_copy[column].replace(to_replace=value, value=np.NaN)\n",
    "    \n",
    "    # remove selected columns and rows, ...\n",
    "    del df_copy['TITEL_KZ']\n",
    "    del df_copy['AGER_TYP']\n",
    "    del df_copy['KK_KUNDENTYP']\n",
    "    del df_copy['KBA05_BAUMAX']\n",
    "    del df_copy['GEBURTSJAHR']\n",
    "    del df_copy['ALTER_HH']\n",
    "                    \n",
    "    df_copy = df_copy[df_copy.isnull().sum(axis=1) <= 12.25]\n",
    "    \n",
    "    # select, re-encode, and engineer column values.\n",
    "    df_copy['OST_WEST_KZ'] = df_copy.apply(lambda x: dealWithWO(x.OST_WEST_KZ), axis=1)\n",
    "    df_copy['CAMEO_DEU_2015'] = df_copy.apply(lambda x: coerce_to_int(x.CAMEO_DEU_2015), axis=1)\n",
    "    df_copy['PRAEGENDE_JUGENDJAHRE_MOVEMENT']=df_copy.apply(lambda x: coerce_to_movement(x.PRAEGENDE_JUGENDJAHRE), axis=1)\n",
    "    df_copy['PRAEGENDE_JUGENDJAHRE_AGE']=df_copy.apply(lambda x: coerce_to_age(x.PRAEGENDE_JUGENDJAHRE), axis=1)\n",
    "    df_copy['CAMEO_INTL_2015_LIFE_STAGE']=df_copy.apply(lambda x: coerce_to_life_stage(x.CAMEO_INTL_2015), axis=1)\n",
    "    df_copy['CAMEO_INTL_2015_WEALTH']=df_copy.apply(lambda x: coerce_to_wealth(x.CAMEO_INTL_2015), axis=1)\n",
    "    del df_copy['PRAEGENDE_JUGENDJAHRE']\n",
    "    del df_copy['CAMEO_INTL_2015']\n",
    "    mixed_cat = ['LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB', 'WOHNLAGE', 'PLZ8_BAUMAX']\n",
    "\n",
    "    df_copy = df_copy.drop(mixed_cat, axis=1)\n",
    "    # Return the cleaned dataframe.\n",
    "    return df_copy\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Feature Transformation\n",
    "\n",
    "### Step 2.1: Apply Feature Scaling\n",
    "\n",
    "Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll want to keep an eye on the [API reference page for sklearn](http://scikit-learn.org/stable/modules/classes.html) to help you navigate to all of the classes and functions that you'll need. In this substep, you'll need to check the following:\n",
    "\n",
    "- sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an [Imputer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html) to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.\n",
    "- For the actual scaling function, a [StandardScaler](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) instance is suggested, scaling each feature to mean 0 and standard deviation 1.\n",
    "- For these classes, you can make use of the `.fit_transform()` method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If you've not yet cleaned the dataset of all NaN values, then investigate and\n",
    "# do that now.\n",
    "from sklearn.preprocessing import Imputer\n",
    "imr = Imputer(missing_values='NaN', strategy='mean')\n",
    "features_w_nan = final_encoded_azdias.columns[final_encoded_azdias.isna().any()].tolist()\n",
    "for feature in features_w_nan:\n",
    "    final_encoded_azdias[feature] = imr.fit_transform(final_encoded_azdias[[feature]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply feature scaling to the general population demographics data.\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler(copy=True, with_mean=True, with_std=True)\n",
    "final_encoded_azdias[final_encoded_azdias.columns] = scaler.fit_transform(final_encoded_azdias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_azdias = final_encoded_azdias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>CJT_GESAMTTYP</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>FINANZTYP</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE_MOVEMENT</th>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE_AGE</th>\n",
       "      <th>CAMEO_INTL_2015_WEALTH</th>\n",
       "      <th>CAMEO_INTL_2015_LIFE_STAGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.767309</td>\n",
       "      <td>0.958203</td>\n",
       "      <td>0.979706</td>\n",
       "      <td>-1.488579</td>\n",
       "      <td>1.539141</td>\n",
       "      <td>-1.045335</td>\n",
       "      <td>1.466874</td>\n",
       "      <td>0.979056</td>\n",
       "      <td>1.327541</td>\n",
       "      <td>-1.347486</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415570</td>\n",
       "      <td>1.432549</td>\n",
       "      <td>0.560345</td>\n",
       "      <td>-0.174490</td>\n",
       "      <td>-0.132811</td>\n",
       "      <td>0.679783</td>\n",
       "      <td>0.583773</td>\n",
       "      <td>1.104873</td>\n",
       "      <td>1.170518</td>\n",
       "      <td>-1.261688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.198792</td>\n",
       "      <td>0.958203</td>\n",
       "      <td>-0.324902</td>\n",
       "      <td>-1.488579</td>\n",
       "      <td>0.866589</td>\n",
       "      <td>-1.771766</td>\n",
       "      <td>-0.569222</td>\n",
       "      <td>0.261642</td>\n",
       "      <td>1.327541</td>\n",
       "      <td>-1.347486</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.967252</td>\n",
       "      <td>0.399819</td>\n",
       "      <td>0.560345</td>\n",
       "      <td>-0.174490</td>\n",
       "      <td>-0.132811</td>\n",
       "      <td>-0.795300</td>\n",
       "      <td>-1.712994</td>\n",
       "      <td>1.104873</td>\n",
       "      <td>-0.874477</td>\n",
       "      <td>0.749231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.181843</td>\n",
       "      <td>0.958203</td>\n",
       "      <td>-0.977207</td>\n",
       "      <td>0.688223</td>\n",
       "      <td>-0.478515</td>\n",
       "      <td>1.133958</td>\n",
       "      <td>-0.569222</td>\n",
       "      <td>-1.173185</td>\n",
       "      <td>-0.816561</td>\n",
       "      <td>1.045139</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.967252</td>\n",
       "      <td>-0.632911</td>\n",
       "      <td>0.560345</td>\n",
       "      <td>-1.180194</td>\n",
       "      <td>-1.003765</td>\n",
       "      <td>-0.057759</td>\n",
       "      <td>0.583773</td>\n",
       "      <td>-0.260039</td>\n",
       "      <td>-1.556142</td>\n",
       "      <td>-0.591382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.198792</td>\n",
       "      <td>-1.043621</td>\n",
       "      <td>0.979706</td>\n",
       "      <td>0.688223</td>\n",
       "      <td>0.194037</td>\n",
       "      <td>0.407527</td>\n",
       "      <td>-1.247921</td>\n",
       "      <td>0.261642</td>\n",
       "      <td>-0.816561</td>\n",
       "      <td>0.566614</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415570</td>\n",
       "      <td>-0.632911</td>\n",
       "      <td>-0.344327</td>\n",
       "      <td>0.831215</td>\n",
       "      <td>0.302665</td>\n",
       "      <td>1.417324</td>\n",
       "      <td>0.583773</td>\n",
       "      <td>-0.260039</td>\n",
       "      <td>0.488853</td>\n",
       "      <td>0.078924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.767309</td>\n",
       "      <td>0.958203</td>\n",
       "      <td>-0.977207</td>\n",
       "      <td>-0.037377</td>\n",
       "      <td>-1.151067</td>\n",
       "      <td>1.133958</td>\n",
       "      <td>-0.569222</td>\n",
       "      <td>-0.455771</td>\n",
       "      <td>1.327541</td>\n",
       "      <td>-0.868961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415570</td>\n",
       "      <td>1.432549</td>\n",
       "      <td>1.465017</td>\n",
       "      <td>-1.180194</td>\n",
       "      <td>-1.003765</td>\n",
       "      <td>-0.057759</td>\n",
       "      <td>0.583773</td>\n",
       "      <td>-1.624951</td>\n",
       "      <td>1.170518</td>\n",
       "      <td>0.749231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  FINANZ_MINIMALIST  \\\n",
       "1             -1.767309   0.958203       0.979706          -1.488579   \n",
       "2              0.198792   0.958203      -0.324902          -1.488579   \n",
       "3              1.181843   0.958203      -0.977207           0.688223   \n",
       "4              0.198792  -1.043621       0.979706           0.688223   \n",
       "5             -1.767309   0.958203      -0.977207          -0.037377   \n",
       "\n",
       "   FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  \\\n",
       "1       1.539141         -1.045335        1.466874               0.979056   \n",
       "2       0.866589         -1.771766       -0.569222               0.261642   \n",
       "3      -0.478515          1.133958       -0.569222              -1.173185   \n",
       "4       0.194037          0.407527       -1.247921               0.261642   \n",
       "5      -1.151067          1.133958       -0.569222              -0.455771   \n",
       "\n",
       "   FINANZ_HAUSBAUER  FINANZTYP             ...              PLZ8_ANTG4  \\\n",
       "1          1.327541  -1.347486             ...                0.415570   \n",
       "2          1.327541  -1.347486             ...               -0.967252   \n",
       "3         -0.816561   1.045139             ...               -0.967252   \n",
       "4         -0.816561   0.566614             ...                0.415570   \n",
       "5          1.327541  -0.868961             ...                0.415570   \n",
       "\n",
       "   PLZ8_HHZ  PLZ8_GBZ    ARBEIT  ORTSGR_KLS9  RELAT_AB  \\\n",
       "1  1.432549  0.560345 -0.174490    -0.132811  0.679783   \n",
       "2  0.399819  0.560345 -0.174490    -0.132811 -0.795300   \n",
       "3 -0.632911  0.560345 -1.180194    -1.003765 -0.057759   \n",
       "4 -0.632911 -0.344327  0.831215     0.302665  1.417324   \n",
       "5  1.432549  1.465017 -1.180194    -1.003765 -0.057759   \n",
       "\n",
       "   PRAEGENDE_JUGENDJAHRE_MOVEMENT  PRAEGENDE_JUGENDJAHRE_AGE  \\\n",
       "1                        0.583773                   1.104873   \n",
       "2                       -1.712994                   1.104873   \n",
       "3                        0.583773                  -0.260039   \n",
       "4                        0.583773                  -0.260039   \n",
       "5                        0.583773                  -1.624951   \n",
       "\n",
       "   CAMEO_INTL_2015_WEALTH  CAMEO_INTL_2015_LIFE_STAGE  \n",
       "1                1.170518                   -1.261688  \n",
       "2               -0.874477                    0.749231  \n",
       "3               -1.556142                   -0.591382  \n",
       "4                0.488853                    0.078924  \n",
       "5                1.170518                    0.749231  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_azdias.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.1: Apply Feature Scaling\n",
    "\n",
    "I decided to prepare df using imputer and then use StandardScaler as scaling function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "On your scaled data, you are now ready to apply dimensionality reduction techniques.\n",
    "\n",
    "- Use sklearn's [PCA](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).\n",
    "- Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's [`plot()`](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html) function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.\n",
    "- Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply PCA to the data.\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA()\n",
    "X_pca = pca.fit_transform(final_azdias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782046, 77)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4561b219e8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate the variance accounted for by each principal component.\n",
    "def scree_plot(pca):\n",
    "    '''\n",
    "    Creates a scree plot associated with the principal components \n",
    "    \n",
    "    INPUT: pca - the result of instantian of PCA in scikit learn\n",
    "            \n",
    "    OUTPUT:\n",
    "            None\n",
    "    '''\n",
    "    num_components=len(pca.explained_variance_ratio_)\n",
    "    ind = np.arange(num_components)\n",
    "    vals = pca.explained_variance_ratio_\n",
    " \n",
    "    plt.figure(figsize=(24, 6))\n",
    "    ax = plt.subplot(111)\n",
    "    cumvals = np.cumsum(vals)\n",
    "    ax.bar(ind, vals)\n",
    "    ax.plot(ind, cumvals)\n",
    "    for i in range(num_components):\n",
    "        ax.annotate(r\"%s%%\" % ((str(vals[i]*100)[:4])), (ind[i]+0.2, vals[i]), va=\"bottom\", ha=\"center\", fontsize=12)\n",
    " \n",
    "    ax.xaxis.set_tick_params(width=0)\n",
    "    ax.yaxis.set_tick_params(width=2, length=12)\n",
    " \n",
    "    ax.set_xlabel(\"Principal Component\")\n",
    "    ax.set_ylabel(\"Variance Explained (%)\")\n",
    "    plt.title('Explained Variance Per Principal Component')\n",
    "\n",
    "scree_plot(pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-apply PCA to the data while selecting for number of components to retain.\n",
    "pca = PCA(n_components=25)\n",
    "azdis_pca = pca.fit_transform(final_azdias)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.2: Perform Dimensionality Reduction\n",
    "\n",
    "How many principal components / transformed features are you retaining for the next step of the analysis?\n",
    "25 component will provide 80% of coverage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2.3: Interpret Principal Components\n",
    "\n",
    "Now that we have our transformed principal components, it's a nice idea to check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.\n",
    "\n",
    "As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.\n",
    "\n",
    "- To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.\n",
    "- You should investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the *i*-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import operator\n",
    "\n",
    "def map_pca_to_features(pca, features):\n",
    "    mapping = {}\n",
    "    for i in range(0, len(pca)):\n",
    "        feature = features[i]\n",
    "        pcaN = pca[i]\n",
    "        mapping[feature] = pca[i]\n",
    "    return sorted(mapping.items(), key=operator.itemgetter(1))\n",
    "\n",
    "def print_mapping(mapping):\n",
    "    for key, value in mapping:\n",
    "        print('{}: {}'.format(str(key), str(value)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CJT_GESAMTTYP: -3.04164160598\n",
      "ANREDE_KZ: -3.03301175269\n",
      "LP_FAMILIE_FEIN: -1.72227340584\n",
      "GFK_URLAUBERTYP: -1.34645223286\n",
      "SEMIO_FAM: -1.20760431157\n",
      "SEMIO_REL: -1.06411421519\n",
      "SEMIO_SOZ: -0.594009011823\n",
      "FINANZ_MINIMALIST: -0.258125261517\n",
      "FINANZ_SPARER: -0.0434360233743\n",
      "LP_STATUS_FEIN: 0.163179599516\n",
      "FINANZ_UNAUFFAELLIGER: 0.373264617413\n",
      "FINANZ_HAUSBAUER: 0.634503635445\n",
      "LP_FAMILIE_GROB: 0.69209123725\n",
      "GREEN_AVANTGARDE: 0.788393887899\n",
      "SEMIO_VERT: 0.870176621917\n",
      "SEMIO_MAT: 0.946327175208\n",
      "LP_STATUS_GROB: 1.05071627554\n",
      "SEMIO_LUST: 1.16726642489\n",
      "HEALTH_TYP: 1.31659896292\n",
      "FINANZTYP: 1.35972672033\n",
      "FINANZ_ANLEGER: 2.17396312581\n",
      "FINANZ_VORSORGER: 2.93250241207\n",
      "ALTERSKATEGORIE_GROB: 4.46758060331\n",
      "NATIONALITAET_KZ: 6.99980061281\n",
      "RETOURTYP_BK_S: 8.32447015747\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Map weights for the first principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "# HINT: Try defining a function here or in a new cell that you can reuse in the\n",
    "# other cells.\n",
    "print(print_mapping(map_pca_to_features(azdis_pca[0], final_azdias.columns.tolist())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CJT_GESAMTTYP: -3.44459036913\n",
      "FINANZ_SPARER: -2.6897880047\n",
      "FINANZ_HAUSBAUER: -1.98077501315\n",
      "NATIONALITAET_KZ: -1.45871085893\n",
      "SEMIO_MAT: -1.33153864215\n",
      "FINANZ_UNAUFFAELLIGER: -1.07796110322\n",
      "SEMIO_LUST: -0.960197640691\n",
      "GFK_URLAUBERTYP: -0.865718121047\n",
      "ALTERSKATEGORIE_GROB: -0.549917164391\n",
      "GREEN_AVANTGARDE: -0.284325119235\n",
      "LP_FAMILIE_FEIN: -0.185331370771\n",
      "FINANZ_ANLEGER: -0.0905465747817\n",
      "FINANZTYP: -0.0648539676454\n",
      "FINANZ_VORSORGER: 0.0935186313729\n",
      "ANREDE_KZ: 0.162489187774\n",
      "HEALTH_TYP: 0.285640702875\n",
      "SEMIO_VERT: 0.408597563733\n",
      "SEMIO_FAM: 0.434971812523\n",
      "SEMIO_REL: 0.505902821442\n",
      "SEMIO_SOZ: 0.836683332691\n",
      "LP_STATUS_GROB: 0.897368035697\n",
      "FINANZ_MINIMALIST: 0.920663117515\n",
      "LP_STATUS_FEIN: 0.974882720774\n",
      "RETOURTYP_BK_S: 1.01434697089\n",
      "LP_FAMILIE_GROB: 1.30494042154\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Map weights for the second principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "\n",
    "print(print_mapping(map_pca_to_features(azdis_pca[1], final_azdias.columns.tolist())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALTERSKATEGORIE_GROB: -4.73114127374\n",
      "LP_STATUS_GROB: -1.67673542792\n",
      "LP_STATUS_FEIN: -1.36288810006\n",
      "HEALTH_TYP: -1.21002070718\n",
      "FINANZ_VORSORGER: -1.04057948967\n",
      "FINANZ_MINIMALIST: -0.980167614902\n",
      "CJT_GESAMTTYP: -0.640054414023\n",
      "FINANZ_SPARER: -0.437046882798\n",
      "GREEN_AVANTGARDE: -0.373423041362\n",
      "FINANZTYP: -0.229562249315\n",
      "RETOURTYP_BK_S: -0.122157302146\n",
      "SEMIO_REL: -0.0747082685773\n",
      "SEMIO_SOZ: 0.0409020551119\n",
      "LP_FAMILIE_GROB: 0.0710424974695\n",
      "SEMIO_LUST: 0.227710077859\n",
      "FINANZ_ANLEGER: 0.235886353654\n",
      "GFK_URLAUBERTYP: 0.311487927269\n",
      "SEMIO_FAM: 0.334896830547\n",
      "NATIONALITAET_KZ: 0.336836733487\n",
      "LP_FAMILIE_FEIN: 0.411088888785\n",
      "FINANZ_UNAUFFAELLIGER: 0.419245543043\n",
      "FINANZ_HAUSBAUER: 0.534280191811\n",
      "SEMIO_VERT: 0.543148426176\n",
      "SEMIO_MAT: 0.674687422469\n",
      "ANREDE_KZ: 1.70471503549\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Map weights for the third principal component to corresponding feature names\n",
    "# and then print the linked values, sorted by weight.\n",
    "\n",
    "print(print_mapping(map_pca_to_features(azdis_pca[2], final_azdias.columns.tolist())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 2.3: Interpret Principal Components\n",
    "\n",
    "#### 1st Principal Component\n",
    "**RETOURTYP_BK_S: 8.32447015747**\n",
    "Return type\n",
    "**NATIONALITAET_KZ: 6.99980061281**\n",
    "Nationality based on given name\n",
    "**ALTERSKATEGORIE_GROB: 4.46758060331**\n",
    "Estimated age based on given name analysis\n",
    "\n",
    "First component is correlated with personal treats and is based on age, nationality and return type.\n",
    "\n",
    "#### 2nd Principal Component\n",
    "**CJT_GESAMTTYP: -3.44459036913**\n",
    "Customer-Journey-Typology: preferred information and buying channels for\n",
    "consumer\n",
    "**FINANZ_SPARER: -2.6897880047**\n",
    "Financial typology\n",
    "**FINANZ_HAUSBAUER: -1.98077501315**\n",
    "Financial typology\n",
    "\n",
    "It seams that second component is related to where this customer comes from and how he/she spends money.\n",
    "#### 3rd Principal Component\n",
    "**ALTERSKATEGORIE_GROB: -4.73114127374**\n",
    "Estimated age based on given name analysis\n",
    "**ANREDE_KZ: 1.70471503549**\n",
    "Gender\n",
    "**LP_STATUS_GROB: -1.67673542792**\n",
    "Social status, rough scale\n",
    "\n",
    "Third component describes who is our customer: its age, social status and gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Clustering\n",
    "\n",
    "### Step 3.1: Apply Clustering to General Population\n",
    "\n",
    "You've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, you will apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.\n",
    "\n",
    "- Use sklearn's [KMeans](http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans) class to perform k-means clustering on the PCA-transformed data.\n",
    "- Then, compute the average difference from each point to its assigned cluster's center. **Hint**: The KMeans object's `.score()` method might be useful here, but note that in sklearn, scores tend to be defined so that larger is better. Try applying it to a small, toy dataset, or use an internet search to help your understanding.\n",
    "- Perform the above two steps for a number of different cluster counts. You can then see how the average distance decreases with an increasing number of clusters. However, each additional cluster provides a smaller net benefit. Use this fact to select a final number of clusters in which to group the data. **Warning**: because of the large size of the dataset, it can take a long time for the algorithm to resolve. The more clusters to fit, the longer the algorithm will take. You should test for cluster counts through at least 10 clusters to get the full picture, but you shouldn't need to test for a number of clusters above about 30.\n",
    "- Once you've selected a final number of clusters to use, re-fit a KMeans instance to perform the clustering operation. Make sure that you also obtain the cluster assignments for the general demographics data, since you'll be using them in the final Step 3.3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 47717456.3921\n",
      "2 39538051.8074\n",
      "3 36079265.0312\n",
      "4 33885366.3925\n",
      "5 32483020.3714\n",
      "6 31214383.145\n",
      "7 30120905.4184\n",
      "8 29175515.8572\n",
      "9 28596328.7164\n",
      "10 27972215.1048\n",
      "11 27584148.0175\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "scores = []\n",
    "clusters_range = range(1,12)\n",
    "for k in clusters_range:\n",
    "    kmeanModel = KMeans(n_clusters=k)\n",
    "    model = kmeanModel.fit(azdis_pca)\n",
    "    score = np.abs(model.score(azdis_pca))\n",
    "    scores.append(score)\n",
    "    print(k, score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f45619e4780>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Investigate the change in within-cluster distance across number of clusters.\n",
    "# HINT: Use matplotlib's plot function to visualize this relationship.\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(clusters_range, scores, 'bx-')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Distortion')\n",
    "plt.title('The Elbow Method showing the optimal k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-fit the k-means model with the selected number of clusters and obtain\n",
    "# cluster predictions for the general population demographics data.\n",
    "kmeans = KMeans(n_clusters=8)\n",
    "kmeans_clusters = kmeans.fit(azdis_pca)\n",
    "predictions = kmeans_clusters.predict(azdis_pca)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.1: Apply Clustering to General Population\n",
    "Into how many clusters have you decided to segment the population?\n",
    "I decided to break population into 8 clusters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.2: Apply All Steps to the Customer Data\n",
    "\n",
    "Now that you have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Take care to not confuse this for re-fitting all of the models to the customer data. Instead, you're going to use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, you will interpret how the general population fits apply to the customer data.\n",
    "\n",
    "- Don't forget when loading in the customers data, that it is semicolon (`;`) delimited.\n",
    "- Apply the same feature wrangling, selection, and engineering steps to the customer demographics using the `clean_data()` function you created earlier. (You can assume that the customer demographics data has similar meaning behind missing data patterns as the general demographics data.)\n",
    "- Use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, you should not be using a `.fit()` or `.fit_transform()` method to re-fit the old objects, nor should you be creating new sklearn objects! Carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load in the customer demographics data.\n",
    "customers = pd.read_csv('Udacity_CUSTOMERS_Subset.csv', delimiter=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:83: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:84: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:85: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:86: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:87: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:88: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "final_customers = clean_data(customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTERSKATEGORIE_GROB</th>\n",
       "      <th>ANREDE_KZ</th>\n",
       "      <th>CJT_GESAMTTYP</th>\n",
       "      <th>FINANZ_MINIMALIST</th>\n",
       "      <th>FINANZ_SPARER</th>\n",
       "      <th>FINANZ_VORSORGER</th>\n",
       "      <th>FINANZ_ANLEGER</th>\n",
       "      <th>FINANZ_UNAUFFAELLIGER</th>\n",
       "      <th>FINANZ_HAUSBAUER</th>\n",
       "      <th>FINANZTYP</th>\n",
       "      <th>...</th>\n",
       "      <th>PLZ8_ANTG4</th>\n",
       "      <th>PLZ8_HHZ</th>\n",
       "      <th>PLZ8_GBZ</th>\n",
       "      <th>ARBEIT</th>\n",
       "      <th>ORTSGR_KLS9</th>\n",
       "      <th>RELAT_AB</th>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE_MOVEMENT</th>\n",
       "      <th>PRAEGENDE_JUGENDJAHRE_AGE</th>\n",
       "      <th>CAMEO_INTL_2015_LIFE_STAGE</th>\n",
       "      <th>CAMEO_INTL_2015_WEALTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0.979706</td>\n",
       "      <td>1.413824</td>\n",
       "      <td>-1.151067</td>\n",
       "      <td>1.133958</td>\n",
       "      <td>-1.247921</td>\n",
       "      <td>-0.455771</td>\n",
       "      <td>-0.816561</td>\n",
       "      <td>-0.868961</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.967252</td>\n",
       "      <td>1.432549</td>\n",
       "      <td>1.465017</td>\n",
       "      <td>-2.185898</td>\n",
       "      <td>-1.439242</td>\n",
       "      <td>-1.532842</td>\n",
       "      <td>-1.712994</td>\n",
       "      <td>-1.624951</td>\n",
       "      <td>-0.192812</td>\n",
       "      <td>-1.261688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.181843</td>\n",
       "      <td>0.958203</td>\n",
       "      <td>-0.977207</td>\n",
       "      <td>1.413824</td>\n",
       "      <td>-1.151067</td>\n",
       "      <td>1.133958</td>\n",
       "      <td>-1.247921</td>\n",
       "      <td>0.979056</td>\n",
       "      <td>0.612841</td>\n",
       "      <td>-0.868961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415570</td>\n",
       "      <td>-0.632911</td>\n",
       "      <td>-1.248998</td>\n",
       "      <td>-0.174490</td>\n",
       "      <td>-0.132811</td>\n",
       "      <td>-0.057759</td>\n",
       "      <td>-1.712994</td>\n",
       "      <td>-1.624951</td>\n",
       "      <td>0.488853</td>\n",
       "      <td>0.078924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.181843</td>\n",
       "      <td>-1.043621</td>\n",
       "      <td>-0.977207</td>\n",
       "      <td>1.413824</td>\n",
       "      <td>-1.151067</td>\n",
       "      <td>1.133958</td>\n",
       "      <td>-0.569222</td>\n",
       "      <td>-1.173185</td>\n",
       "      <td>-0.816561</td>\n",
       "      <td>1.045139</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.967252</td>\n",
       "      <td>-0.632911</td>\n",
       "      <td>0.560345</td>\n",
       "      <td>-2.185898</td>\n",
       "      <td>-1.003765</td>\n",
       "      <td>-1.532842</td>\n",
       "      <td>0.583773</td>\n",
       "      <td>-2.307407</td>\n",
       "      <td>0.488853</td>\n",
       "      <td>-0.591382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.198792</td>\n",
       "      <td>-1.043621</td>\n",
       "      <td>1.632011</td>\n",
       "      <td>-0.037377</td>\n",
       "      <td>-1.151067</td>\n",
       "      <td>0.407527</td>\n",
       "      <td>0.788176</td>\n",
       "      <td>1.696469</td>\n",
       "      <td>-0.816561</td>\n",
       "      <td>-0.868961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415570</td>\n",
       "      <td>-0.632911</td>\n",
       "      <td>-0.344327</td>\n",
       "      <td>-0.174490</td>\n",
       "      <td>-0.132811</td>\n",
       "      <td>-1.532842</td>\n",
       "      <td>0.583773</td>\n",
       "      <td>-0.260039</td>\n",
       "      <td>-1.556142</td>\n",
       "      <td>0.749231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.198792</td>\n",
       "      <td>-1.043621</td>\n",
       "      <td>0.327402</td>\n",
       "      <td>1.413824</td>\n",
       "      <td>-1.151067</td>\n",
       "      <td>1.133958</td>\n",
       "      <td>-1.247921</td>\n",
       "      <td>-0.455771</td>\n",
       "      <td>-0.101860</td>\n",
       "      <td>0.566614</td>\n",
       "      <td>...</td>\n",
       "      <td>0.415570</td>\n",
       "      <td>1.432549</td>\n",
       "      <td>1.465017</td>\n",
       "      <td>-0.174490</td>\n",
       "      <td>0.738142</td>\n",
       "      <td>1.417324</td>\n",
       "      <td>-1.712994</td>\n",
       "      <td>-1.624951</td>\n",
       "      <td>0.488853</td>\n",
       "      <td>0.078924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ALTERSKATEGORIE_GROB  ANREDE_KZ  CJT_GESAMTTYP  FINANZ_MINIMALIST  \\\n",
       "0              1.181843  -1.043621       0.979706           1.413824   \n",
       "2              1.181843   0.958203      -0.977207           1.413824   \n",
       "3              1.181843  -1.043621      -0.977207           1.413824   \n",
       "4              0.198792  -1.043621       1.632011          -0.037377   \n",
       "5              0.198792  -1.043621       0.327402           1.413824   \n",
       "\n",
       "   FINANZ_SPARER  FINANZ_VORSORGER  FINANZ_ANLEGER  FINANZ_UNAUFFAELLIGER  \\\n",
       "0      -1.151067          1.133958       -1.247921              -0.455771   \n",
       "2      -1.151067          1.133958       -1.247921               0.979056   \n",
       "3      -1.151067          1.133958       -0.569222              -1.173185   \n",
       "4      -1.151067          0.407527        0.788176               1.696469   \n",
       "5      -1.151067          1.133958       -1.247921              -0.455771   \n",
       "\n",
       "   FINANZ_HAUSBAUER  FINANZTYP           ...            PLZ8_ANTG4  PLZ8_HHZ  \\\n",
       "0         -0.816561  -0.868961           ...             -0.967252  1.432549   \n",
       "2          0.612841  -0.868961           ...              0.415570 -0.632911   \n",
       "3         -0.816561   1.045139           ...             -0.967252 -0.632911   \n",
       "4         -0.816561  -0.868961           ...              0.415570 -0.632911   \n",
       "5         -0.101860   0.566614           ...              0.415570  1.432549   \n",
       "\n",
       "   PLZ8_GBZ    ARBEIT  ORTSGR_KLS9  RELAT_AB  PRAEGENDE_JUGENDJAHRE_MOVEMENT  \\\n",
       "0  1.465017 -2.185898    -1.439242 -1.532842                       -1.712994   \n",
       "2 -1.248998 -0.174490    -0.132811 -0.057759                       -1.712994   \n",
       "3  0.560345 -2.185898    -1.003765 -1.532842                        0.583773   \n",
       "4 -0.344327 -0.174490    -0.132811 -1.532842                        0.583773   \n",
       "5  1.465017 -0.174490     0.738142  1.417324                       -1.712994   \n",
       "\n",
       "   PRAEGENDE_JUGENDJAHRE_AGE  CAMEO_INTL_2015_LIFE_STAGE  \\\n",
       "0                  -1.624951                   -0.192812   \n",
       "2                  -1.624951                    0.488853   \n",
       "3                  -2.307407                    0.488853   \n",
       "4                  -0.260039                   -1.556142   \n",
       "5                  -1.624951                    0.488853   \n",
       "\n",
       "   CAMEO_INTL_2015_WEALTH  \n",
       "0               -1.261688  \n",
       "2                0.078924  \n",
       "3               -0.591382  \n",
       "4                0.749231  \n",
       "5                0.078924  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply preprocessing, feature transformation, and clustering from the general\n",
    "# demographics onto the customer data, obtaining cluster predictions for the\n",
    "# customer demographics data.\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "feat_w_nan = final_customers.columns[final_customers.isna().any()].tolist()\n",
    "for feature in feat_w_nan:\n",
    "    final_customers[feature] = imr.transform(final_customers[[feature]])\n",
    "# Apply feature scaling to the general population demographics data\n",
    "final_customers[final_customers.columns] = scaler.transform(final_customers)\n",
    "final_customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers_pca = pca.transform(final_customers)\n",
    "predictions_customers = kmeans_clusters.predict(customers_pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "At this point, you have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, you will compare the two cluster distributions to see where the strongest customer base for the company is.\n",
    "\n",
    "Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.\n",
    "\n",
    "Take a look at the following points in this step:\n",
    "\n",
    "- Compute the proportion of data points in each cluster for the general population and the customer data. Visualizations will be useful here: both for the individual dataset proportions, but also to visualize the ratios in cluster representation between groups. Seaborn's [`countplot()`](https://seaborn.pydata.org/generated/seaborn.countplot.html) or [`barplot()`](https://seaborn.pydata.org/generated/seaborn.barplot.html) function could be handy.\n",
    "  - Recall the analysis you performed in step 1.1.3 of the project, where you separated out certain data points from the dataset if they had more than a specified threshold of missing values. If you found that this group was qualitatively different from the main bulk of the data, you should treat this as an additional data cluster in this analysis. Make sure that you account for the number of data points in this subset, for both the general population and customer datasets, when making your computations!\n",
    "- Which cluster or clusters are overrepresented in the customer dataset compared to the general population? Select at least one such cluster and infer what kind of people might be represented by that cluster. Use the principal component interpretations from step 2.3 or look at additional components to help you make this inference. Alternatively, you can use the `.inverse_transform()` method of the PCA and StandardScaler objects to transform centroids back to the original data space and interpret the retrieved values directly.\n",
    "- Perform a similar investigation for the underrepresented clusters. Which cluster or clusters are underrepresented in the customer dataset compared to the general population, and what kinds of people are typified by these clusters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compare the proportion of data in each cluster for the customer data to the\n",
    "# proportion of data in each cluster for the general population.\n",
    "azdias_clusters = []\n",
    "customers_clusters = []\n",
    "\n",
    "for i in range(0, 8):\n",
    "    azdias_clusters.append((predictions == i).sum()/len(predictions))\n",
    "    customers_clusters.append((predictions_customers == i).sum()/len(predictions_customers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4561acda58>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = list(range(0, 8)) \n",
    "width = 0.3 \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "\n",
    "plt.bar(pos, azdias_clusters, width) \n",
    "plt.bar([p + width for p in pos], customers_clusters, width) \n",
    "\n",
    "ax.set_ylabel('Proportion of persons in each cluster')\n",
    "ax.set_xlabel('Clusters')\n",
    "\n",
    "ax.set_title('General population vs Customers')\n",
    "\n",
    "# Adding the legend and showing the plot\n",
    "plt.legend(['General population', 'Customers'], loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What kinds of people are part of a cluster that is overrepresented in the\n",
    "# customer data compared to the general population?\n",
    "\n",
    "def plot_cluster_comparison(cluster_num):\n",
    "    \n",
    "    fig, ax = plt.subplots(6,2, figsize=(12,25))\n",
    "    \n",
    "    # Customers\n",
    "    pca_cluster = customers_pca[np.where(predictions_customers==cluster_num)]\n",
    "    customers_data = scaler.inverse_transform(pca.inverse_transform(pca_cluster))\n",
    "    features = pd.DataFrame(np.round(customers_data), columns = final_azdias.columns)\n",
    "    \n",
    "    sns.countplot(features['RETOURTYP_BK_S'], ax = ax[0,0])\n",
    "    sns.countplot(features['NATIONALITAET_KZ'], ax = ax[1,0])\n",
    "    sns.countplot(features['ALTERSKATEGORIE_GROB'], ax = ax[2,0])\n",
    "    sns.countplot(features['CJT_GESAMTTYP'], ax = ax[3,0])\n",
    "    sns.countplot(features['FINANZ_SPARER'], ax = ax[4,0])\n",
    "    sns.countplot(features['FINANZ_HAUSBAUER'], ax = ax[5,0])\n",
    "    \n",
    "    # General population\n",
    "    pca_cluster_gd = azdis_pca[np.where(predictions==cluster_num)]\n",
    "    gd_data = scaler.inverse_transform(pca.inverse_transform(pca_cluster_gd))\n",
    "    features = pd.DataFrame(np.round(gd_data), columns = final_azdias.columns)\n",
    "    \n",
    "    sns.countplot(features['RETOURTYP_BK_S'], ax = ax[0,1])\n",
    "    sns.countplot(features['NATIONALITAET_KZ'], ax = ax[1,1])\n",
    "    sns.countplot(features['ALTERSKATEGORIE_GROB'], ax = ax[2,1])\n",
    "    sns.countplot(features['CJT_GESAMTTYP'], ax = ax[3,1])\n",
    "    sns.countplot(features['FINANZ_SPARER'], ax = ax[4,1])\n",
    "    sns.countplot(features['FINANZ_HAUSBAUER'], ax = ax[5,1])\n",
    "    \n",
    "    plt.subplots_adjust(top=0.96)\n",
    "    fig.suptitle('Customer data vs General Population', fontsize=16)\n",
    "    plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overpresented:\n",
    "    RETOURTYP_BK_S: determined Minimal-Returner and conservative Low-Returner\n",
    "    NATIONALITAET_KZ: assimilated names\n",
    "    ALTERSKATEGORIE_GROB: 30 - 45 years old\n",
    "    CJT_GESAMTTYP: advertisinginterested Online-shopper\n",
    "    FINANZ_SPARER: low\n",
    "    FINANZ_HAUSBAUER: average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4561aa80f0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What kinds of people are part of a cluster that is underrepresented in the\n",
    "# customer data compared to the general population?\n",
    "\n",
    "plot_cluster_comparison(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Underpresented:\n",
    "    RETOURTYP_BK_S: influenceable Crazy-Shopper and demanding Heavy-Returner\n",
    "    NATIONALITAET_KZ: foreign-sounding\n",
    "    ALTERSKATEGORIE_GROB: < 30 years old\n",
    "    CJT_GESAMTTYP: Advertising- and Cross-Channel-Enthusiast\n",
    "    FINANZ_SPARER: very low\n",
    "    FINANZ_HAUSBAUER: very low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WtmkNcNL8hytJkiTN3Lwn3Um2A54CnAJQVT+pqjuA1cDprdrpwBHt/WrgjOp8Adg+ya7zHLYkSZI0Y+Po6f4lYAPwviRfTvLeJNsCu1TVTQDt9eGt/m7ADQPrr29l95FkTZK1SdZu2LCh3z2QJEmStsI4ku5lwP7ASVW1H/BD7h1KMkpGlNVGBVUnV9Wqqlq1fPmSur2tJEmSFrhxJN3rgfVVdUmbP5suCb95athIe71loP4eA+vvDtw4T7FKkiRJszbvD8epqu8kuSHJo6rqGuAQ4GttOhp4Y3s9p61yLvCSJGcCTwLunBqGIo1y4DsOHHcIs/a5l35u3CFI0th9+im/Me4Qfu43PvPpcYegCTeuJ1K+FPhAkgcA1wEvput1PyvJMcC3gOe2uucBhwPrgB+1upIkSdLEGEvSXVWXA6tGLDpkRN0Cju09KEmSJKknPpFSkiRJ6plJtyRJktQzk25JkiSpZybdkiRJUs9MuiVJkqSemXRLkiRJPTPpliRJknpm0i1JS0iSByX5YpKvJLkqyWta+V5JLklybZIPt4eXkeSBbX5dW75iYFuvbuXXJHnGQPlhrWxdkuPmex8laSEy6ZakpeUu4OCqejywL3BYkgOANwEnVtVK4HbgmFb/GOD2qtobOLHVI8k+wJHAY4HDgHcn2SbJNsC7gGcC+wDPb3UlaUkz6ZakJaQ6P2iz929TAQcDZ7fy04Ej2vvVbZ62/JAkaeVnVtVdVfVNYB3wxDatq6rrquonwJmtriQtaSbdkrTEtB7py4FbgAuAbwB3VNXdrcp6YLf2fjfgBoC2/E5gp8HyoXU2VS5JS5pJtyQtMVV1T1XtC+xO1zP9mFHV2ms2sWxryzeSZE2StUnWbtiwYcuBS9IEM+mWpCWqqu4ALgYOALZPsqwt2h24sb1fD+wB0JY/DLhtsHxonU2Vj/r8k6tqVVWtWr58+VzskiQtWCbdkrSEJFmeZPv2/sHA04CrgYuA57RqRwPntPfntnna8k9VVbXyI9vdTfYCVgJfBC4FVra7oTyA7mLLc/vfM0la2JZtuYokaRHZFTi93WXkfsBZVfXxJF8DzkzyeuDLwCmt/inA+5Oso+vhPhKgqq5KchbwNeBu4NiqugcgyUuA84FtgFOr6qr52z1JWphMuiVpCamqK4D9RpRfRze+e7j8x8BzN7GtNwBvGFF+HnDerIOVpEXE4SWSJElSz0y6JUmSpJ6ZdEuSJEk9M+mWJEmSembSLUmSJPXMpFuSJEnqmUm3JEmS1DOTbkmSJKlnJt2SJElSz8aSdCe5PslXk1yeZG0r2zHJBUmuba87tPIkeXuSdUmuSLL/OGKWJEmSZmqcPd1Prap9q2pVmz8OuLCqVgIXtnmAZwIr27QGOGneI5UkSZJmYSENL1kNnN7enw4cMVB+RnW+AGyfZNdxBChJkiTNxLiS7gI+meSyJGta2S5VdRNAe314K98NuGFg3fWtTJIkSZoIy8b0uQdW1Y1JHg5ckOTrm6mbEWW1UaUueV8DsOeee85NlJIkSdIcGEtPd1Xd2F5vAT4GPBG4eWrYSHu9pVVfD+wxsPruwI0jtnlyVa2qqlXLly/vM3xJkiRpq8x70p1k2yS/MPUeOBS4EjgXOLpVOxo4p70/Fziq3cXkAODOqWEokiRJ0iQYx/CSXYCPJZn6/A9W1SeSXAqcleQY4FvAc1v984DDgXXAj4AXz3/IkiRJ0szNe9JdVdcBjx9R/l3gkBHlBRw7D6FJkiRJvVhItwyUJEmSFiWTbkmSJKlnJt2SJElSz0y6JUmSpJ6ZdEuSJEk9M+mWpCUkyR5JLkpydZKrkryslZ+Q5NtJLm/T4QPrvDrJuiTXJHnGQPlhrWxdkuMGyvdKckmSa5N8OMkD5ncvJWnhMemWpKXlbuAVVfUY4ADg2CT7tGUnVtW+bToPoC07EngscBjw7iTbJNkGeBfwTGAf4PkD23lT29ZK4HbgmPnaOUlaqEy6JWkJqaqbqupL7f33gauB3TazymrgzKq6q6q+Sfegsie2aV1VXVdVPwHOBFane/LZwcDZbf3TgSP62RtJmhwm3ZK0RCVZAewHXNKKXpLkiiSnJtmhle0G3DCw2vpWtqnynYA7quruofJRn78mydokazds2DAHeyRJC5dJtyQtQUkeCvwj8PKq+h5wEvDLwL7ATcCbp6qOWL1mUL5xYdXJVbWqqlYtX758K/dAkibLvD8GXpI0XknuT5dwf6CqPgpQVTcPLH8P8PE2ux7YY2D13YEb2/tR5bcC2ydZ1nq7B+tL0pJlT7ckLSFtzPUpwNVV9ZaB8l0Hqv02cGV7fy5wZJIHJtkLWAl8EbgUWNnuVPIAuostz62qAi4CntPWPxo4p899kqRJYE+3JC0tBwIvBL6a5PJW9hd0dx/Zl24oyPXAHwJU1VVJzgK+Rnfnk2Or6h6AJC8Bzge2AU6tqqva9l4FnJnk9cCX6ZJ8SVrSTLolaQmpqs8yetz1eZtZ5w3AG0aUnzdqvaq6ju7uJpKkxuElkiRJUs9MuiVJkqSemXRLkiRJPTPpliRJknpm0i1JkiT1zKRbkiRJ6plJtyRJktQzk25JkiSpZybdkiRJUs9MuiVJkqSemXRLkiRJPTPpliRJkno2tqQ7yTZJvpzk421+rySXJLk2yYeTPKCVP7DNr2vLV4wrZkmSJGkmxtnT/TLg6oH5NwEnVtVK4HbgmFZ+DHB7Ve0NnNjqSZIkSRNjLEl3kt2BZwHvbfMBDgbOblVOB45o71e3edryQ1p9SZIkaSKMq6f7rcArgZ+1+Z2AO6rq7ja/Htitvd8NuAGgLb+z1b+PJGuSrE2ydsOGDX3GLkmSJG2VeU+6kzwbuKWqLhssHlG1prHs3oKqk6tqVVWtWr58+RxEKkmSJM2NZWP4zAOB30pyOPAgYDu6nu/tkyxrvdm7Aze2+uuBPYD1SZYBDwNum/+wJUmSpJmZ957uqnp1Ve1eVSuAI4FPVdXvAxcBz2nVjgbOae/PbfO05Z+qqo16uiVJkqSFaiHdp/tVwJ8lWUc3ZvuUVn4KsFMr/zPguDHFJ0mSJM3IOIaX/FxVXQxc3N5fBzxxRJ0fA8+d18AkSZKkObSQerolSZKkRcmkW5IkSerZWIeXSJLmV5I9gDOAR9A9K+Hkqnpbkh2BDwMrgOuB51XV7e1hZG8DDgd+BLyoqr7UtnU08Fdt06+vqtNb+ROA04AHA+cBL/MC+IXjwHccOO4Qfu5zL/3cuEOQ5o093ZK0tNwNvKKqHgMcABybZB+6i9QvrKqVwIXce9H6M4GVbVoDnATQkvTjgSfRXY9zfJId2jontbpT6x02D/slSQuaSbckLSFVddNUT3VVfR+4mu7Jv6uB01u104Ej2vvVwBnV+QLdMxV2BZ4BXFBVt1XV7cAFwGFt2XZV9fnWu33GwLYkaclyeIm0CHz6Kb8x7hBm7Tc+8+lxh7DkJFkB7AdcAuxSVTdBl5gneXirthtww8Bq61vZ5srXjygf9flr6HrE2XPPPWe3M5K0wNnTLUlLUJKHAv8IvLyqvre5qiPKagblGxdWnVxVq6pq1fLly7cUsiRNNJNuSVpiktyfLuH+QFV9tBXf3IaG0F5vaeXrgT0GVt8duHEL5buPKJekJc2kW5KWkHY3klOAq6vqLQOLzgWObu+PBs4ZKD8qnQOAO9swlPOBQ5Ps0C6gPBQ4vy37fpID2mcdNbAtSVqyHNMtSUvLgcALga8mubyV/QXwRuCsJMcA3+LeJwGfR3e7wHV0twx8MUBV3ZbkdcClrd5rq+q29v6PufeWgf/SJkla0ky6JWkJqarPMnrcNcAhI+oXcOwmtnUqcOqI8rXA42YRpiQtOg4vkSRJknpm0i1JkiT1zKRbkiRJ6tmsku4kF06nTJI092yDJWlyzOhCyiQPAh4C7NxuFTV1Uc52wCPnKDZJ0gi2wZI0eWZ695I/BF5O17hfxr0N/veAd81BXJKkTbMNlqQJM6Oku6reBrwtyUur6h1zHJMkaTNsgyVp8szqPt1V9Y4kvwasGNxWVZ0xy7gkSVtgGyxJk2NWSXeS9wO/DFwO3NOKC7DBl6Se2QZL0uSY7RMpVwH7tCeWSZLml22wJE2I2d6n+0rgEXMRiCRpq9kGS9KEmG1P987A15J8EbhrqrCqfmuW25UkbZltsCRNiNkm3SfMRRCSpBk5YdwBSJKmZ7Z3L/n0XAUiSdo6tsGSNDlm+xj47yf5Xpt+nOSeJN/bwjoPSvLFJF9JclWS17TyvZJckuTaJB9O8oBW/sA2v64tXzGbmCVpsZhJGyxJGo9ZJd1V9QtVtV2bHgT8LvDOLax2F3BwVT0e2Bc4LMkBwJuAE6tqJXA7cEyrfwxwe1XtDZzY6knSkjfDNliSNAazvXvJfVTVPwEHb6FOVdUP2uz921RtvbNb+enAEe396jZPW35IkqlHHkuSmum0wZKk8Zjtw3F+Z2D2fnT3jN3i/WKTbANcBuwNvAv4BnBHVd3dqqwHdmvvdwNuAKiqu5PcCewE3Dqb2CVp0s20DZYkzb/Z3r3kNwfe3w1cT9czvVlVdQ+wb5LtgY8BjxlVrb2O6tXe6I9KkjXAGoA999xzSyFI0mIwozZYkjT/Znv3khfPcv07klwMHABsn2RZ6+3eHbixVVsP7AGsT7IMeBhw24htnQycDLBq1Sp7eiQterNtgyVJ82e2dy/ZPcnHktyS5OYk/5hk9y2ss7z1cJPkwcDTgKuBi4DntGpHA+e09+e2edryT/nIY0maWRssSRqP2V5I+T66pPiRdGOv/7mVbc6uwEVJrgAuBS6oqo8DrwL+LMk6ujHbp7T6pwA7tfI/A46bZcyStFjMpA2WJI3BbJPu5VX1vqq6u02nAcs3t0JVXVFV+1XVr1bV46rqta38uqp6YlXtXVXPraq7WvmP2/zebfl1s4xZkhaLrW6Dk5zaesavHCg7Icm3k1zepsMHlr26PSfhmiTPGCg/rJWtS3LcQPnIZy5I0lI326T71iQvSLJNm14AfHcuApMkbdFM2uDTgMNGlJ9YVfu26TyAJPsARwKPbeu8e+qz6O489UxgH+D5rS5s+pkLkrSkzTbp/gPgecB3gJvoxlx7YY8kzY+tboOr6jOMuBh9E1YDZ1bVXVX1TWAd8MQ2rWtnKH8CnAmsbs9Q2NQzFyRpSZtt0v064OiqWl5VD6f7A3DCrKOSJE3HXLbBL0lyRRt+skMr+/lzEpqpZyhsqnwnNv3MBUla0mZ7n+5frarbp2aq6rYk+81ym5Kk6ZmrNvgkugS+2uub6RL4TT0nYVSHTW2m/kg+X0GLzTtf8c/jDuHnXvLm39xyJc2r2fZ032+gR4QkOzL7RF6SND1z0gZX1c1VdU9V/Qx4D93wEbj3OQlTpp6hsKnyW2nPXBgq39TnnlxVq6pq1fLlm73+U5Im3mwT5DcD/5bkbLrejOcBb5h1VJKk6ZiTNjjJrlV1U5v9bWDqzibnAh9M8ha62xKuBL5I16O9MslewLfpLrb8vaqqJFPPXDiT+z5zQZKWtNk+kfKMJGvpLpwJ8DtV9bU5iUyStFkzaYOTfAg4CNg5yXrgeOCgJPvSJe7XA3/Ytn9VkrOAr9E9Zv7YqrqnbeclwPnANsCpVXVV+4hXAWcmeT3wZe595oIkLWmzHgrSGngTbUkag61tg6vq+SOKN5kYV9UbGNF73m4reN6I8uu4d3iKJKmZ7ZhuSZIkSVtg0i1JkiT1zKRbkiRJ6plJtyRJktQzk25JkiSpZz7IZpH71mt/ZdwhzNqef/3VcYcgSZI0K/Z0S5IkST0z6ZYkSZJ6ZtItSZIk9cykW5IkSeqZSbckSZLUM5NuSZIkqWcm3ZIkSVLPTLolSZKknpl0S5IkST0z6ZYkSZJ6ZtItSZIk9cykW5IkSerZvCfdSfZIclGSq5NcleRlrXzHJBckuba97tDKk+TtSdYluSLJ/vMdsyRJkjQb4+jpvht4RVU9BjgAODbJPsBxwIVVtRK4sM0DPBNY2aY1wEnzH7IkSZI0c/OedFfVTVX1pfb++8DVwG7AauD0Vu104Ij2fjVwRnW+AGyfZNd5DluSJEmasbGO6U6yAtgPuATYpapugi5imO7mAAAgAElEQVQxBx7equ0G3DCw2vpWJkmSJE2EsSXdSR4K/CPw8qr63uaqjiirEdtbk2RtkrUbNmyYqzAlSZKkWRtL0p3k/nQJ9weq6qOt+OapYSPt9ZZWvh7YY2D13YEbh7dZVSdX1aqqWrV8+fL+gpekCZbk1CS3JLlyoGyrL2RPcnSrf22SowfKn5Dkq22dtycZ1XEiSUvOOO5eEuAU4OqqesvAonOBqYb7aOCcgfKjWuN/AHDn1DAUSdJWOw04bKhsqy5kT7IjcDzwJOCJwPFTiXqrs2ZgveHPkqQlaRw93QcCLwQOTnJ5mw4H3gg8Pcm1wNPbPMB5wHXAOuA9wH8fQ8yStChU1WeA24aKt/ZC9mcAF1TVbVV1O3ABcFhbtl1Vfb6qCjhjYFuStKQtm+8PrKrPMnqcNsAhI+oXcGyvQUnS0nafC9mTbOlC9s2Vrx9RLklLnk+klCRtyqYuZN/a8tEb9wJ4SUuISbckaWsvZN9c+e4jykfyAnhJS4lJtyRpay9kPx84NMkO7QLKQ4Hz27LvJzmgXTR/1MC2JGlJm/cx3ZKk8UnyIeAgYOck6+nuQvJG4KwkxwDfAp7bqp8HHE53IfuPgBcDVNVtSV4HXNrqvbaqpi7O/GO6O6Q8GPiXNknSkmfSLUlLSFU9fxOLtupC9qo6FTh1RPla4HGziVGSFiOHl0iSJEk9M+mWJEmSembSLUmSJPXMpFuSJEnqmUm3JEmS1DOTbkmSJKlnJt2SJElSz0y6JUmSpJ6ZdEuSJEk9M+mWJEmSembSLUmSJPXMpFuSJEnqmUm3JEmS1DOTbkmSJKlnJt2SJElSz0y6JUmSpJ6ZdEuSJEk9M+mWJEmSembSLUmSJPXMpFuSJEnq2ViS7iSnJrklyZUDZTsmuSDJte11h1aeJG9Psi7JFUn2H0fMkiRJ0kyNq6f7NOCwobLjgAuraiVwYZsHeCawsk1rgJPmKUZJkiRpTowl6a6qzwC3DRWvBk5v708HjhgoP6M6XwC2T7Lr/EQqSZIkzd5CGtO9S1XdBNBeH97KdwNuGKi3vpVJkuZQkuuTfDXJ5UnWtrKtHvqX5OhW/9okR49rfyRpIVlISfemZERZbVQpWZNkbZK1GzZsmIewJGlRempV7VtVq9r8Vg39S7IjcDzwJOCJwPFTibokLWULKem+eWrYSHu9pZWvB/YYqLc7cOPwylV1clWtqqpVy5cv7z1YSVoitnbo3zOAC6rqtqq6HbiAja/hkaQlZyEl3ecCU6chjwbOGSg/qp3KPAC4c2oYiiRpThXwySSXJVnTyrZ26J9DAiVphGXj+NAkHwIOAnZOsp7uVOQbgbOSHAN8C3huq34ecDiwDvgR8OJ5D1iSloYDq+rGJA8HLkjy9c3U3dTQv2kNCYRuWCDd0BT23HPPrY1VkibKWJLuqnr+JhYdMqJuAcf2G5EkqapubK+3JPkY3Zjsm5PsWlU3TXPo33q6TpXB8os38XknAycDrFq1amRiLkmLxUIaXiJJGpMk2yb5han3wKHAlWz90L/zgUOT7NAuoDy0lUnSkjaWnm5J0oKzC/CxJND9bfhgVX0iyaVsxdC/qrotyeuAS1u911bV8HMZJGnJMemWJFFV1wGPH1H+XbZy6F9VnQqcOtcxStIkc3iJJEmS1DOTbkmSJKlnJt2SJElSz0y6JUmSpJ6ZdEuSJEk9M+mWJEmSembSLUmSJPXMpFuSJEnqmQ/HkSRpFr712l8Zdwg/t+dff3XcIUjaBHu6JUmSpJ7Z0y1pIr3zFf887hBm7SVv/s1xhyBJmif2dEuSJEk9M+mWJEmSembSLUmSJPXMMd2SJElLzBte8Jxxh/Bzf/kPZ487hHlhT7ckSZLUM5NuSZIkqWcm3ZIkSVLPTLolSZKknpl0S5IkST0z6ZYkSZJ6ZtItSZIk9WxJ3af7CX9+xrhDmLXL/vaocYcgSZKkrTQxPd1JDktyTZJ1SY4bdzySpE2zzZak+5qIpDvJNsC7gGcC+wDPT7LPeKOSJI1imy1JG5uIpBt4IrCuqq6rqp8AZwKrxxyTJGk022xJGjIpY7p3A24YmF8PPGlMsUiSNm/WbfZCugbHa2kkzYVU1bhj2KIkzwWeUVX/tc2/EHhiVb10oM4aYE2bfRRwzbwH2tkZuHVMnz0u7vPit9T2F8a7z79YVcvH9NmzNp02u5X33W5P2nE7SfFOUqwwWfFOUqwwWfH2Feu02uxJ6eleD+wxML87cONghao6GTh5PoMaJcnaqlo17jjmk/u8+C21/YWluc9zaIttNvTfbk/az3CS4p2kWGGy4p2kWGGy4h13rJMypvtSYGWSvZI8ADgSOHfMMUmSRrPNlqQhE9HTXVV3J3kJcD6wDXBqVV015rAkSSPYZkvSxiYi6QaoqvOA88YdxzSMfYjLGLjPi99S219Ymvs8ZxZImz1pP8NJineSYoXJineSYoXJinessU7EhZSSJEnSJJuUMd2SJEnSxDLpnoEkpya5JcmVm1ieJG9vjz++Isn+8x3jXEqyR5KLklyd5KokLxtRZ7Ht84OSfDHJV9o+v2ZEnQcm+XDb50uSrJj/SOdekm2SfDnJx0csW3T7nOT6JF9NcnmStSOWL6pjezHa0iPnF9JxO0l/Pyat7Z/EdnuS2ttJaiuTbJ/k7CRfb8fvkxdCrCbdM3MacNhmlj8TWNmmNcBJ8xBTn+4GXlFVjwEOAI7Nxo90Xmz7fBdwcFU9HtgXOCzJAUN1jgFur6q9gROBN81zjH15GXD1JpYt1n1+alXtu4lbSS22Y3tRyfQeOb+QjtvTmJy/H5PW9k9iuz1p7e2ktJVvAz5RVY8GHs/G3/FYYjXpnoGq+gxw22aqrAbOqM4XgO2T7Do/0c29qrqpqr7U3n+f7uDdbajaYtvnqqoftNn7t2n4AojVwOnt/dnAIUkyTyH2IsnuwLOA926iyqLb52lYVMf2IjSdR84vmON2kv5+TFrbP2nt9iJsbxfEsZBkO+ApwCkAVfWTqrpjqNpYYjXp7seoRyAPN1QTqZ3e2g+4ZGjRotvndtrvcuAW4IKq2uQ+V9XdwJ3ATvMb5Zx7K/BK4GebWL4Y97mATya5LN0TEoctumN7kZnOz2eSjtsFebxNSts/Ye32pLW3k9JW/hKwAXhfG7rz3iTbDtUZS6wm3f0Y9Z/oxN8mJslDgX8EXl5V3xtePGKVid7nqrqnqvale5reE5M8bqjKotrnJM8GbqmqyzZXbUTZxO5zc2BV7U93uvHYJE8ZWr4Y93kxmc7PZ5J+hgsu1klq+yel3Z7Q9nZS2splwP7ASVW1H/BDYPhaj7HEatLdj2k9AnmSJLk/XaP7gar66Igqi26fp7TTUhez8TjMn+9zkmXAw9j8aeOF7kDgt5JcT3eK/uAk/zBUZ7HtM1V1Y3u9BfgY3XCFQYv22F4kpvPzmaTjdkEdb5Pa9k9Auz1x7e0EtZXrgfUDZznOpkvCh+vMe6wm3f04FziqXR17AHBnVd007qBmqo0hOwW4uqresolqi22flyfZvr1/MPA04OtD1c4Fjm7vnwN8qib4xvdV9eqq2r2qVtA9tvtTVfWCoWqLap+TbJvkF6beA4cCw3eVWFTH9iI0nUfOT9Jxu2COt0lr+yep3Z609naS2sqq+g5wQ5JHtaJDgK8NVRtLrBPzRMqFJMmHgIOAnZOsB46nu2CDqvo7uqewHQ6sA34EvHg8kc6ZA4EXAl9tY+UA/gLYExbtPu8KnN7ujHA/4Kyq+niS1wJrq+pcuj9G70+yjq734cjxhdufRb7PuwAfa9cmLQM+WFWfSPJHsGiP7UWlNvHI+YV63E7Y349Ja/snvt1ewLFOWlv5UuAD7R/x64AXL4RYfSKlJEmS1DOHl0iSJEk9M+mWJEmSembSLUmSJPXMpFuSJEnqmUm3JEmS1DOTbkmSJKlnJt1aEJLck+TyJFcm+eeBBxysSPIfbdnUdFSSS9r7byXZMLBsRZKHJTkjyTfadEaSh7XtHZTk40OffVqS57T3Fye5JslXklyaZN9WvqnP+2CSPx7Y1pOSXJFkWZLrk3y1beuTSR6xmf2fqnt5e109sOwHA+8PT3Jtkj03sZ1HtX24PMnVSU6e2U9EkjbNNts2WzNQVU5OY5+AHwy8Px34y/Z+BXDlZtZ7EfDOobKzgRMG5l8DfKS9Pwj4+FD904DntPcXA6va+xcDF2zu8+geGHAdsJzun9hLgV9vy64Hdm7v/wZ4+2b2Y7Duo4B/H/5u6J6q9Q3glzeznfOB1QPzvzLun62Tk9Pim2yzbbOdtn6yp1sL0eeB3WayYpK9gScArxsofi2wKskvz3UcVXUz8L+A/w/4I+CKqvrsiKqfAfae5uduB9w+WJDkPwPvAZ5VVd/YzLq7AusH4vvqND9TkmbKNts2W9PgY+C1oKR7fO8hdI+/nfLLufcRxAAvrap/3cQm9gEur6p7pgqq6p62/mOB721FOIcB/zSNen8HHE3XI7NqE3WeDWypMb0oSYBfAp43UP5A4BzgoKr6+ha2cSLwqST/BnwSeF9V3bGFdSRpRmyzbbM1fSbdWige3BrZFcBlwAUDy75RVftOczsBajPlo5YxVP6BJNsC2wD7b+kDq+pnSf6e7hTnd4cWX5TkHuAK4K+2sKmnVtWtrXfnwiQXV9UPgJ8C/wYcA7xsC7G8L8n5dH98VgN/mOTxVXXXlvZDkraCbbZttraSw0u0UPxHa6R/EXgAcOwMt3MVsF+Snx/b7f3jgauB7wI7DK2zI3DrwPzvA3sBHwTeNc3P/Vmbhj21qvatqqOm23vRTkXeTNcDNLXt5wH/KclfTGP9G6vq1KpaDdwNPG5aeyBJ02eb3dhma7pMurWgVNWdwJ8A/yPJ/Wew/jrgy9y3h+KvgC+1ZdcCj0zyGIAkv0jXuF8+tJ2ftvUOmKo7X5I8nO4PyL8PxPMjutOdv5/kmM2se9jU99auvN8J+Ha/EUtaqmyzbbM1fQ4v0YJTVV9O8hXgSOBf2Xh84KlV9fbNbOIY4B1J1tGdovx8K6Oq7kryAuB9SR5Edxrwv7Y/HMNx/EeSNwP/Y2r9nk2d1rw/cFy74GcwntuSHAZ8JsmtVXXOiG0cCrwtyY/b/J9X1Xf6DVvSUmabbZut6UnVpoZLSZIkSZoLDi+RJEmSeubwEmkeJbmE7nZSg164tfdmTfKXwHOHij9SVW+YTXySpHvZZmsuObxEkiRJ6pnDSyRJkqSemXRLkiRJPTPpliRJknpm0i1JkiT1zKRbkiRJ6plJtyRJktQzk25JkiSpZybdkiRJUs9MuiVJkqSeLcrHwO+88861YsWKcYchSTNy2WWX3VpVy8cdx3yy3ZY0qabbZi/KpHvFihWsXbt23GFI0owk+fdxxzDfbLclTarpttkOL5EkSZJ6ZtItSZIk9cykW5IkSeqZSbckSZLUM5NuSZIkqWcm3ZIkSVLPTLolSZKknpl0S5IkST1blA/Hma4n/PkZ4w5hUbjsb48adwiSlgjb7dmzzZbGw55uSZIkqWcm3ZIkSVLPTLolSZKknvWWdCc5NcktSa4cKNsxyQVJrm2vO7TyJHl7knVJrkiy/8A6R7f61yY5uq94JUmSpL702dN9GnDYUNlxwIVVtRK4sM0DPBNY2aY1wEnQJenA8cCTgCcCx08l6pIkSdKk6C3prqrPALcNFa8GTm/vTweOGCg/ozpfALZPsivwDOCCqrqtqm4HLmDjRF6StBWSXJ/kq0kuT7K2lc3ZmcgkT2jbX9fWzfzvpSQtLPM9pnuXqroJoL0+vJXvBtwwUG99K9tU+UaSrEmyNsnaDRs2zHngkrTIPLWq9q2qVW1+Ls9EntTqTq1nZ4mkJW+hXEg5qhekNlO+cWHVyVW1qqpWLV++fE6Dk6QlYE7ORLZl21XV56uqgDMGtiVJS9Z8J903twaZ9npLK18P7DFQb3fgxs2US5JmroBPJrksyZpWNldnIndr74fLN+IZSklLyXwn3ecCU+P+jgbOGSg/qo0dPAC4szX65wOHJtmhnbY8tJVJkmbuwKran27oyLFJnrKZult7JtIzlJI0Qp+3DPwQ8HngUUnWJzkGeCPw9CTXAk9v8wDnAdcB64D3AP8doKpuA14HXNqm17YySdIMVdWN7fUW4GN0Y7Ln6kzk+vZ+uFySlrRlfW24qp6/iUWHjKhbwLGb2M6pwKlzGJokLVlJtgXuV1Xfb+8PBV7LvWci38jGZyJfkuRMuosm76yqm5KcD/zNwMWThwKvrqrbkny/nbW8BDgKeMd87Z8kLVS9Jd2SpAVpF+Bj7S5+y4APVtUnklwKnNXOSn4LeG6rfx5wON2ZyB8BL4buTGSSqTORcN8zkX9M96yGBwP/0iZJWtJMuiVpCamq64DHjyj/LnN0JrKq1gKPm3WwkrSILJRbBkqSJEmLlkm3JEmS1DOTbkmSJKlnJt2SJElSz0y6JUmSpJ6ZdEuSJEk9M+mWJEmSembSLUmSJPXMpFuSJEnqmUm3JEmS1DOTbkmSJKlnJt2SJElSz0y6JUmSpJ6ZdEuSJEk9M+mWJEmSembSLUmSJPXMpFuSJEnqmUm3JEmS1DOTbkmSJKlnJt2SJElSz0y6JUmSpJ6ZdEuSJEk9M+mWJEmSembSLUmSJPXMpFuSJEnqmUm3JEmS1DOTbkmSJKlnJt2SJElSz8aSdCf50yRXJbkyyYeSPCjJXkkuSXJtkg8neUCr+8A2v64tXzGOmCVJkqSZmvekO8luwJ8Aq6rqccA2wJHAm4ATq2olcDtwTFvlGOD2qtobOLHVkyRJkibGuIaXLAMenGQZ8BDgJuBg4Oy2/HTgiPZ+dZunLT8kSeYxVkladJJsk+TLST7e5rf6bGOSV7fya5I8Y6D8sFa2Lslx871vkrQQzXvSXVXfBv4X8C26ZPtO4DLgjqq6u1VbD+zW3u8G3NDWvbvV32k+Y5akRehlwNUD81t1tjHJPnRnKR8LHAa8uyXy2wDvAp4J7AM8v9WVpCVtHMNLdqDrvd4LeCSwLV3jPKymVtnMssHtrkmyNsnaDRs2zFW4krToJNkdeBbw3jYftv5s42rgzKq6q6q+CawDntimdVV1XVX9BDiz1ZWkJW0cw0ueBnyzqjZU1U+BjwK/BmzfhpsA7A7c2N6vB/YAaMsfBtw2vNGqOrmqVlXVquXLl/e9D5I0yd4KvBL4WZvfia0/2/jz8qF1NlW+ETtLJC0l40i6vwUckOQhrbfkEOBrwEXAc1qdo4Fz2vtz2zxt+aeqaqOebknSliV5NnBLVV02WDyi6pbONm5t+caFdpZIWkKWbbnK3KqqS5KcDXwJuBv4MnAy8L+BM5O8vpWd0lY5BXh/knV0PdxHznfMkrSIHAj8VpLDgQcB29H1fG+fZFnrzR51tnH90NnGn5+FbAbX2VS5JC1Z8550A1TV8cDxQ8XX0Y0FHK77Y+C58xGXJC12VfVq4NUASQ4C/kdV/X6Sj9CdTTyT0WcbP8/A2cYk5wIfTPIWuutzVgJfpOvpXplkL+DbdB0lvzdPuydJC9ZYkm5J0oLzKrbibGNVXZXkLLrhgXcDx1bVPQBJXgKcT/cchlOr6qp53RNJWoBMuiVpiaqqi4GL2/utPttYVW8A3jCi/DzgvDkMVZIm3rgejiNJkiQtGSbdkiRJUs9MuiVJkqSemXRLkiRJPTPpliRJknpm0i1JkiT1zKRbkiRJ6plJtyRJktQzk25JkiSpZybdkiRJUs9MuiVJkqSemXRLkiRJPTPpliRJknpm0i1JkiT1zKRbkiRJ6plJtyRJktQzk25JkiSpZybdkiRJUs9MuiVJkqSemXRLkiRJPTPpliRJknpm0i1JkiT1zKRbkiRJ6plJtyRJktQzk25JkiSpZybdkiRJUs9MuiVJkqSemXRLkiRJPRtL0p1k+yRnJ/l6kquTPDnJjkkuSHJte92h1U2StydZl+SKJPuPI2ZJkiRppsbV0/024BNV9Wjg8cDVwHHAhVW1EriwzQM8E1jZpjXASfMfriRJkjRz8550J9kOeApwCkBV/aSq7gBWA6e3aqcDR7T3q4EzqvMFYPsku85z2JK0KCR5UJIvJvlKkquSvKaV75Xkkna28cNJHtDKH9jm17XlKwa29epWfk2SZwyUH9bK1iU5bjgGSVqKppV0J7lwOmXT9EvABuB9Sb6c5L1JtgV2qaqbANrrw1v93YAbBtZf38okaUmbYdt8F3BwVT0e2Bc4LMkBwJuAE9vZxtuBY1r9Y4Dbq2pv4MRWjyT7AEcCjwUOA96dZJsk2wDvojtLuQ/w/FZXkpa0zSbdrUdkR2DnJDu0cdc7tp6OR87wM5cB+wMnVdV+wA+5dyjJyDBGlNWIWNckWZtk7YYNG2YYmiQtfLNpm9tZwx+02fu3qYCDgbNb+fDZxqmzkGcDh+T/Z+/ew+8q6zvvvz8moFgrx2hpAg0d8liorYgZzJR5rANWA20N7WALVcnQ9EmnxVO1rdjnuQartdVpK4q19KFCSSwjUNSaWlom5aDVCho0cooOES2kUBINoJaKE/qdP/b9g82PneR3yNr7d3i/rmtfe6173Xvt7147+eabe99rrSSt/fKqeqSqvgpsBU5oj61VdVdVfRe4vPWVpHltbyPdvwzcDPxQex57fIzeSMZUbAO2VdVNbf0qekX4/WPTRtrz9r7+R/S9fglw7/idVtVFVbW8qpYvWrRoiqFJ0qwwrdzcRqQ308uzG4GvAA9W1a7Wpf8Xxcd+bWzbHwIOZfe/Qk7410kHSyTNJ3ssuqvqvVV1FPDrVfWDVXVUezyvqv5oKm9YVf8M3JPkOa3pZOAOYAOwurWtpvePB639rHYVkxXAQ2PTUCRpPppubq6qR6vqOHqDGCcAxwzq1p5392vjZNsHxeFgiaR5Y+FEOlXV+5L8GLC0/zVVtX6K7/ta4LJ2os5dwNn0/gNwZZI1wN3AK1rfq4FT6f10+XDrK0nz3nRzc1U9mOQGYAW9k9QXttHs/l8Ux35t3JZkIXAgsJM9/wq5118nJWm+mVDRneSDwL8DNgOPtuYCplR0V9VmYPmATScP6FvAOVN5H0may6aSm5MsAv53K7gPAF5C7+TI64HT6c3BHv9r42rgM237dVVVSTYA/yPJu+nNI18GfJbeSPeyJEcB/0TvZMtf2GcfWpJmqQkV3fQK5GNbASxJmhmmkpsPB9a1q4w8Bbiyqj6e5A7g8iS/A3yBdlnX9vzBJFvpjXCfAVBVtye5kt70wF3AOVX1KECS1wDXAAuAS6rq9ul+UEma7SZadN8GfB/gXGpJmjkmnZur6hbg+QPa76I3v3t8+3d4fLrf+G3vAN4xoP1qelMDJUnNRIvuw4A7knyW3jVeAaiql3cSlSRpIszNkjRLTLTofmuXQUiSpuStow5AkjQxE716ySe6DkSSNDnmZkmaPSZ69ZJv8fh1Vvendwezf6mqZ3YVmCRpz8zNkjR7THSk+3v715OcxoATbiRJw2NulqTZY2+3gR+oqv4SOGkfxyJJmgZzsyTNXBOdXvKzfatPoXdtWK/ZLUkjZG6WpNljolcv+em+5V3A14BV+zwaSdJkmJslaZaY6Jzus7sORJI0OeZmSZo9JjSnO8mSJB9Nsj3J/Uk+nGRJ18FJknbP3CxJs8dET6T8M2AD8P3AYuCvWpskaXTMzZI0S0y06F5UVX9WVbva41JgUYdxSZL2ztwsSbPERIvuryd5VZIF7fEq4BtdBiZJ2itzsyTNEhMtun8R+Dngn4H7gNMBT+CRpNEyN0vSLDHRSwa+HVhdVQ8AJDkE+AN6CV+SNBrmZkmaJSY60v2jY0kdoKp2As/vJiRJ0gSZmyVplpho0f2UJAePrbTRlImOkkuSumFulqRZYqLJ+Q+Bf0hyFb1bDP8c8I7OopIkTYS5WZJmiYnekXJ9kk3ASUCAn62qOzqNTJK0R+ZmSZo9JvwzZEvkJnNJmkHMzZI0O0x0TrckSZKkKbLoliRJkjpm0S1JkiR1zKJbkiRJ6phFtyRJktQxi25JkiSpYxbdkiRJUscsuiVJkqSOWXRLkiRJHRtZ0Z1kQZIvJPl4Wz8qyU1J7kxyRZL9W/tT2/rWtn3pqGKWJEmSpmKUI92vB7b0rb8LOL+qlgEPAGta+xrggao6Gji/9ZMkTUGSI5Jcn2RLktuTvL61H5JkYxv42Jjk4NaeJBe0gY9bkhzft6/Vrf+dSVb3tb8gya3tNRckyfA/qSTNLCMpupMsAX4S+EBbD3AScFXrsg44rS2vauu07SebwCVpynYBb6qqY4AVwDlJjgXOBa5tAx/XtnWAU4Bl7bEWuBB6RTpwHvBC4ATgvLFCvfVZ2/e6lUP4XJI0o41qpPs9wG8C/9bWDwUerKpdbX0bsLgtLwbuAWjbH2r9JUmTVFX3VdXn2/K36P3iuJgnDnCMH/hYXz03AgclORx4GbCxqnZW1QPARmBl2/bMqvpMVRWwvm9fkjRvDb3oTvJTwPaqurm/eUDXmsC2/v2uTbIpyaYdO3bsg0glaW5r58g8H7gJeHZV3Qe9whx4Vuv22MBHMzYosqf2bQPaB72/eVvSvDGKke4TgZcn+RpwOb1pJe+hN3qysPVZAtzblrcBRwC07QcCO8fvtKouqqrlVbV80aJF3X4CSZrlkjwD+DDwhqr65p66DmirKbQ/udG8LWkeGXrRXVVvqaolVbUUOAO4rqpeCVwPnN66rQY+1pY3tHXa9uvaT5aSpClIsh+9gvuyqvpIa76/TQ2hPW9v7Y8NfDRjgyJ7al8yoF2S5rWZdJ3uNwNvTLKV3pzti1v7xcChrf2NPH5yjyRpktqJ6BcDW6rq3X2b+gc4xg98nNWuYrICeKhNP7kGeGmSg9sJlC8FrmnbvpVkRXuvs/r2JUnz1sK9d+lOVd0A3NCW76J3Bvz4Pt8BXjHUwBgyMk4AACAASURBVCRp7joReDVwa5LNre23gHcCVyZZA9zN43n3auBUYCvwMHA2QFXtTPJ24HOt39uqamzq368AlwIHAH/THpI0r4206JYkDVdVfYrB864BTh7Qv4BzdrOvS4BLBrRvAp47jTAlac6ZSdNLJEmSpDnJoluSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxxaOOgBJkjR73f22Hxl1CLPekf/t1lGHoCFwpFuSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOjb0ojvJEUmuT7Ilye1JXt/aD0myMcmd7fng1p4kFyTZmuSWJMcPO2ZJkiRpOkYx0r0LeFNVHQOsAM5JcixwLnBtVS0Drm3rAKcAy9pjLXDh8EOWpLkhySVJtie5ra9t0oMeSVa3/ncmWd3X/oIkt7bXXJAkw/2EkjQzDb3orqr7qurzbflbwBZgMbAKWNe6rQNOa8urgPXVcyNwUJLDhxy2JM0VlwIrx7VNatAjySHAecALgROA88YK9dZnbd/rxr+XJM1LI53TnWQp8HzgJuDZVXUf9Apz4Fmt22Lgnr6XbWttkqRJqqpPAjvHNU920ONlwMaq2llVDwAbgZVt2zOr6jNVVcD6vn1J0rw2sqI7yTOADwNvqKpv7qnrgLYasL+1STYl2bRjx459FaYkzQeTHfTYU/u2Ae0DmbclzScjKbqT7Eev4L6sqj7Smu8fmzbSnre39m3AEX0vXwLcO36fVXVRVS2vquWLFi3qLnhJmj92N+gx2faBzNuS5pNRXL0kwMXAlqp6d9+mDcDYyTirgY/1tZ/VTuhZATw0NiIjSdonJjvosaf2JQPaJWneG8VI94nAq4GTkmxuj1OBdwI/keRO4CfaOsDVwF3AVuBPgV8dQcySNJdNdtDjGuClSQ5uJ1C+FLimbftWkhVtgOWsvn1J0ry2cNhvWFWfYvBPkAAnD+hfwDmdBiVJ80SSDwEvBg5Lso3eVUjeCVyZZA1wN/CK1v1q4FR6gx4PA2cDVNXOJG8HPtf6va2qxk7O/BV6V0g5APib9pCkeW/oRbckaXSq6szdbJrUoEdVXQJcMqB9E/Dc6cQoSXORt4GXJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbkmSJKljXr1EM87db/uRUYcw6x35324ddQiSJKmPI92SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSx7wjpSRJ0hxz4vtOHHUIs96nX/vpfbo/R7olSZKkjjnSLWmvHDHZN/b1qIkkafZwpFuSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSx2ZN0Z1kZZIvJ9ma5NxRxyNJ2j1ztiQ90awoupMsAN4PnAIcC5yZ5NjRRiVJGsScLUlPNiuKbuAEYGtV3VVV3wUuB1aNOCZJ0mDmbEkaJ1U16hj2KsnpwMqq+qW2/mrghVX1mr4+a4G1bfU5wJeHHmg3DgO+Puog9CR+LzPPXPpOfqCqFo06iKmaSM5u7XMxb8+lP4dzid/LzDOXvpMJ5eyFw4hkH8iAtif8b6GqLgIuGk44w5NkU1UtH3UceiK/l5nH72RG2WvOhrmZt/1zODP5vcw88/E7mS3TS7YBR/StLwHuHVEskqQ9M2dL0jizpej+HLAsyVFJ9gfOADaMOCZJ0mDmbEkaZ1ZML6mqXUleA1wDLAAuqarbRxzWsMypn17nEL+XmcfvZIYwZ2sG8nuZeebddzIrTqSUJEmSZrPZMr1EkiRJmrUsuiVJkqSOWXTPAEkuSbI9yW272Z4kF7TbKd+S5PhhxzjfJDkiyfVJtiS5PcnrB/TxexmyJE9L8tkkX2zfy28P6PPUJFe07+WmJEuHH6nmMnP2zGTennnM2U9k0T0zXAqs3MP2U4Bl7bEWuHAIMc13u4A3VdUxwArgnAG3sfZ7Gb5HgJOq6nnAccDKJCvG9VkDPFBVRwPnA+8acoya+y7FnD0TmbdnHnN2H4vuGaCqPgns3EOXVcD66rkROCjJ4cOJbn6qqvuq6vNt+VvAFmDxuG5+L0PWjvW32+p+7TH+bPBVwLq2fBVwcpJBN2uRpsScPTOZt2cec/YTWXTPDouBe/rWt/HkRKKOtJ+6ng/cNG6T38sIJFmQZDOwHdhYVbv9XqpqF/AQcOhwo9Q8Z24YMfP2zGHOfpxF9+wwoVsqa99L8gzgw8Abquqb4zcPeInfS8eq6tGqOo7eXQ5PSPLccV38XjRq/hkcIfP2zGLOfpxF9+zgLZVHIMl+9BL3ZVX1kQFd/F5GqKoeBG7gyXNrH/tekiwEDmTPUwGkfc3cMCLm7ZnLnG3RPVtsAM5qZ12vAB6qqvtGHdRc1uaTXQxsqap376ab38uQJVmU5KC2fADwEuBL47ptAFa35dOB68q7gGm4zA0jYN6eeczZTzQrbgM/1yX5EPBi4LAk24Dz6J1sQFX9CXA1cCqwFXgYOHs0kc4rJwKvBm5tc9EAfgs4EvxeRuhwYF2SBfQGDa6sqo8neRuwqao20PtH94NJttIbLTljdOFqLjJnz1jm7ZnHnN3H28BLkiRJHXN6iSRJktQxi25JkiSpYxbdkiRJUscsuiVJkqSOWXRLkiRJHbPo1kgkqSR/2Lf+60neOq7PF9ulucbW359kc5I7kvxrW96c5PQklyY5vfXbP8l7knwlyZ1JPpZkyXTeu7U99h59bUuT3JbkZX3xfDvJl9vy+r6+703yT0me0tf2X5Ls6Hvt5iTP61vemeSrbfnvdnMslya5rW/9/0ny+SSHjtvv5iRfT3LF7r8ZSXoyc/ZjbeZsTZnX6daoPAL8bJLfq6qvj9+Y5Bh6/yl8UZLvqap/qapz2ralwMfbbWXH+v9U38t/F/he4P+qqkeTnA18JMkL2wX3J/3ee/swVXUNcE17/Q3Ar1fVpr59PgX4GeAe4EX07so15oqqes24XR7XXndp+6xX7S2G1v/VwGuBk6rqG2P7adsOBz4LvH0i+5KkPubsx5mzNSWOdGtUdgEXAb+2m+2/AHwQ+J/Ayye60yRPp3ezg1+rqkcBqurP6CXtk7p87734T8BtwIXAmfton0+Q5OeAc4GXjv+HKUmAdcDvV9Vtg14vSXtgzt7HzNnzj0W3Run9wCuTHDhg288DVwAfYnIJ72jg7qr65rj2TcAPd/zee3Jm299HgZ9Ksl//+437OfGAKez/B4A/ope8/3nA9l+j9w/X+6awb0kCc/Zj72fO1lRYdGtkWpJdD7yuvz3Jvwd2VNU/AtcCxyc5eIK7DTDoNqtPaO/ovQcHlOxP77bDf9ne9ybgpX1drqiq4/oe/zqFt9kB3A383ID3fx7wBuDs8ha0kqbInP0Yc7amxKJbo/YeYA3wPX1tZwI/lORrwFeAZwL/eYL72wr8QJLvHdd+PHBHx++9OyuBA4Fb237/I/v+58qHgVOA/5rklWONbQTmMuBXq+r+ffyekuYfc/a+Yc6ehyy6NVJVtRO4kl4iHTt55RXAj1bV0qpaCqxiggmvnUCzDnh3kgVtn2cBTweu6/K99+BM4Jf69nkU8NI2l3Gfqaod9P6x+N0kL2vNfwB8oqo+vi/fS9L8ZM7ed8zZ849Ft2aCPwQOa8svAv6pqv6pb/sngWPbmdwT8RbgO8D/SnInvaT8M7v5mW6y7/3/J9nWHp/ZWyAtSb8M+OuxtvaPzKeAn25N4+cH/tgEP+eTVNVX6Z1IdEmSHwd+FThp3P4vm+r+JQlztjlbUxKnC0mSJEndcqRbkiRJ6pg3x5FmiSQ/Qu9atP0eqaoXjiIeSdLumbM1ntNLJEmSpI45vUSSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnq2Jy8Dfxhhx1WS5cuHXUYkjQlN99889eratGo4xgm87ak2WqiOXtOFt1Lly5l06ZNow5DkqYkyT+OOoZhM29Lmq0mmrOdXiJJkiR1zKJbkiRJ6phFtyRJktQxi25JkiSpYyMpupP8WpLbk9yW5ENJnpbkqCQ3JbkzyRVJ9m99n9rWt7btS0cRsyRJkjRVQy+6kywGXgcsr6rnAguAM4B3AedX1TLgAWBNe8ka4IGqOho4v/WTJEmSZo1RTS9ZCByQZCHwdOA+4CTgqrZ9HXBaW17V1mnbT06SIcYqSZIkTcvQi+6q+ifgD4C76RXbDwE3Aw9W1a7WbRuwuC0vBu5pr93V+h86zJglSZKk6Rj6zXGSHExv9Poo4EHgL4BTBnStsZfsYVv/ftcCawGOPPLIfRKreu5+24+MOoShO/K/3TrqECRpSszZ0sw0iuklLwG+WlU7qup/Ax8Bfgw4qE03AVgC3NuWtwFHALTtBwI7x++0qi6qquVVtXzRonl192RJkiTNcKMouu8GViR5epubfTJwB3A9cHrrsxr4WFve0NZp26+rqieNdEuSJEkz1SjmdN9E74TIzwO3thguAt4MvDHJVnpzti9uL7kYOLS1vxE4d9gxS5IkSdMx9DndAFV1HnDeuOa7gBMG9P0O8IphxCVJkiR1wTtSSpIkSR2z6JYkSZI6ZtEtSZIkdcyiW5IkSeqYRbckSZLUMYtuSZIkqWMW3ZIkSVLHLLolSZKkjll0S5IkSR2z6JYkSZI6ZtEtSZIkdcyiW5LmoSQLknwhycfb+lFJbkpyZ5Irkuzf2p/a1re27Uv79vGW1v7lJC/ra1/Z2rYmOXfYn02SZiKLbkman14PbOlbfxdwflUtAx4A1rT2NcADVXU0cH7rR5JjgTOAHwZWAn/cCvkFwPuBU4BjgTNbX0ma1yy6JWmeSbIE+EngA209wEnAVa3LOuC0tryqrdO2n9z6rwIur6pHquqrwFbghPbYWlV3VdV3gctbX0ma1yy6JWn+eQ/wm8C/tfVDgQeraldb3wYsbsuLgXsA2vaHWv/H2se9ZnftT5JkbZJNSTbt2LFjup9JkmY0i25JmkeS/BSwvapu7m8e0LX2sm2y7U9urLqoqpZX1fJFixbtIWpJmv0WjjoASdJQnQi8PMmpwNOAZ9Ib+T4oycI2mr0EuLf13wYcAWxLshA4ENjZ1z6m/zW7a5ekeWvoI91JnpNkc9/jm0nekOSQJBvbmfMbkxzc+ifJBe0s+FuSHD/smCVprqiqt1TVkqpaSu9EyOuq6pXA9cDprdtq4GNteUNbp22/rqqqtZ/Rrm5yFLAM+CzwOWBZuxrK/u09Ngzho0nSjDb0oruqvlxVx1XVccALgIeBjwLnAte2M+evbevQOwN+WXusBS4cdsySNA+8GXhjkq305mxf3NovBg5t7W+k5eaquh24ErgD+FvgnKp6tI2Uvwa4ht7VUa5sfSVpXhv19JKTga9U1T8mWQW8uLWvA26g94/AKmB9G1m5MclBSQ6vqvtGEbAkzRVVdQO9XEtV3UXvyiPj+3wHeMVuXv8O4B0D2q8Grt6HoUrSrDfqEynPAD7Ulp89Vki352e19gmdCe9Z8JIkSZqpRlZ0t7l+Lwf+Ym9dB7Q96Ux4z4KXJEnSTDXKke5TgM9X1f1t/f4khwO05+2tfU9nyEuSJEkz3iiL7jN5fGoJPPEM+fFnzp/VrmKyAnjI+dySJEmaTUZyImWSpwM/AfxyX/M7gSuTrAHu5vETd64GTqV3i+GHgbOHGKokSZI0bSMpuqvqYXqXpOpv+wa9q5mM71vAOUMKTZIkSdrnRn31EkmSJGnOs+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxyy6JUmSpI6NpOhOclCSq5J8KcmWJP8hySFJNia5sz0f3PomyQVJtia5Jcnxo4hZkiRJmqpRjXS/F/jbqvoh4HnAFuBc4NqqWgZc29YBTgGWtcda4MLhhytJkiRN3dCL7iTPBF4EXAxQVd+tqgeBVcC61m0dcFpbXgWsr54bgYOSHD7ksCVJkqQpG8VI9w8CO4A/S/KFJB9I8j3As6vqPoD2/KzWfzFwT9/rt7U2SZIkaVYYRdG9EDgeuLCqng/8C49PJRkkA9rqSZ2StUk2Jdm0Y8eOfROpJEmStA+MoujeBmyrqpva+lX0ivD7x6aNtOftff2P6Hv9EuDe8TutqouqanlVLV+0aFFnwUuSJEmTNfSiu6r+GbgnyXNa08nAHcAGYHVrWw18rC1vAM5qVzFZATw0Ng1FkiRJmg0Wjuh9XwtclmR/4C7gbHr/AbgyyRrgbuAVre/VwKnAVuDh1leSJEmaNUZSdFfVZmD5gE0nD+hbwDmdByVJkiR1xDtSSpIkSR2z6JYkSZI6ZtEtSZIkdcyiW5IkSeqYRbckSZLUMYtuSZIkqWMW3ZI0jyR5WpLPJvliktuT/HZrPyrJTUnuTHJFu48CSZ7a1re27Uv79vWW1v7lJC/ra1/Z2rYmOXfYn1GSZiKLbkmaXx4BTqqq5wHHASvb3X7fBZxfVcuAB4A1rf8a4IGqOho4v/UjybHAGcAPAyuBP06yIMkC4P3AKcCxwJmtryTNaxbdkjSPVM+32+p+7VHAScBVrX0dcFpbXtXWadtPTpLWfnlVPVJVX6V31+AT2mNrVd1VVd8FLm99JWles+iWpHmmjUhvBrYDG4GvAA9W1a7WZRuwuC0vBu4BaNsfAg7tbx/3mt21S9K8ZtEtSfNMVT1aVccBS+iNTB8zqFt7zm62Tbb9SZKsTbIpyaYdO3bsPXBJmsUsuiVpnqqqB4EbgBXAQUkWtk1LgHvb8jbgCIC2/UBgZ3/7uNfsrn3Q+19UVcuravmiRYv2xUeSpBnLoluS5pEki5Ic1JYPAF4CbAGuB05v3VYDH2vLG9o6bft1VVWt/Yx2dZOjgGXAZ4HPAcva1VD2p3ey5YbuP5kkzWwL995FkjSHHA6sa1cZeQpwZVV9PMkdwOVJfgf4AnBx638x8MEkW+mNcJ8BUFW3J7kSuAPYBZxTVY8CJHkNcA2wALikqm4f3seTpJnJoluS5pGqugV4/oD2u+jN7x7f/h3gFbvZ1zuAdwxovxq4etrBStIc4vQSSZIkqWMW3ZIkSVLHRlJ0J/lakluTbE6yqbUdkmRjuwXxxiQHt/YkuaDdTviWJMePImZJkiRpqkY50v2fquq4qlre1s8Frm23IL62rUPvVsLL2mMtcOHQI5UkSZKmYSZNL+m/1fD4WxCvb7cuvpHetWQPH0WAkiRJ0lSMqugu4H8muTnJ2tb27Kq6D6A9P6u1e0thSZIkzWqjumTgiVV1b5JnARuTfGkPfSd0S+FWvK8FOPLII/dNlJIkSdI+MJKR7qq6tz1vBz5K79qw949NG2nP21v3Cd1S2NsJS5IkaaYaetGd5HuSfO/YMvBS4DaeeKvh8bcgPqtdxWQF8NDYNBRJkiRpNhjF9JJnAx9NMvb+/6Oq/jbJ54Ark6wB7ubxO6BdDZwKbAUeBs4efsiSJEnS1A296G63Gn7egPZvACcPaC/gnCGEJkmSJHViJl0yUJIkSZqTLLolSZKkjll0S5IkSR2z6JYkSZI6Nq2iO8m1E2mTJO175mBJmj2mdPWSJE8Dng4cluRgHr9r5DOB799HsUmSBjAHS9LsM9VLBv4y8AZ6yf1mHk/43wTevw/ikiTtnjlYkmaZKRXdVfVe4L1JXltV79vHMUmS9sAcLEmzz7RujlNV70vyY8DS/n1V1fppxiVJ2gtzsCTNHtMqupN8EPh3wGbg0dZcgAlfkjpmDpak2WO6t4FfDhzbbtUuSRouc7AkzRLTvU73bcD37YtAJEmTZg6WpFliuiPdhwF3JPks8MhYY1W9fJr7lSTtnTlYkmaJ6Rbdb90XQUiSpuStow5AkjQx0716ySf2VSCSpMkxB0vS7DHdq5d8i96Z8gD7A/sB/1JVz5xuYJKkPTMHS9LsMd2R7u/tX09yGnDCtCKSJE2IOViSZo/pXr3kCarqL4GTJtI3yYIkX0jy8bZ+VJKbktyZ5Iok+7f2p7b1rW370n0ZsyTNFZPJwZKk4Zru9JKf7Vt9Cr1rxk70erGvB7YAYz+Dvgs4v6ouT/InwBrgwvb8QFUdneSM1u/npxO3JM0F08zBkqQhmu7VS366b3kX8DVg1d5elGQJ8JPAO4A3Jgm90ZlfaF3W0Tsr/8K2v7e29quAP0oSbwYhSVPLwZKk4ZvunO6zp/jS9wC/CYzNRzwUeLCqdrX1bcDitrwYuKe9364kD7X+X+/fYZK1wFqAI488cophSdLsMY0cLEkasmnN6U6yJMlHk2xPcn+SD7dR7D295qeA7VV1c3/zgK41gW2PN1RdVFXLq2r5okWLJvwZJGm2mkoOliSNxnRPpPwzYAPw/fRGpP+qte3JicDLk3wNuJzetJL3AAclGRt5XwLc25a3AUcAtO0HAjunGbckzQVTycGSpBGYbtG9qKr+rKp2tcelwB6HmavqLVW1pKqWAmcA11XVK4HrgdNbt9XAx9ryhrZO236d87klCZhCDpYkjcZ0i+6vJ3lVu/zfgiSvAr4xxX29md5JlVvpzdm+uLVfDBza2t8InDvNmCVprtiXOViS1KHpXr3kF4E/As6nN8/6H4AJn9hTVTcAN7TluxhwU4eq+g7wimnGKUlz0bRysCRpeKY70v12YHVVLaqqZ9H7B+Ct045KkjQRk87BSY5Icn2SLUluT/L61n5Iko3tBmUbkxzc2pPkgnaDsluSHN+3r9Wt/51JVve1vyDJre01F7TLwkrSvDbdovtHq+qBsZWq2gk8f5r7lCRNzFRy8C7gTVV1DLACOCfJsfSm7l1bVcuAa3l8Kt8pwLL2WEvv/gkkOQQ4D3ghvV8pzxsr1FuftX2vWznNzylJs950i+6n9CXZsSQ83SkrkqSJmXQOrqr7qurzbflb9O4MvJjeTXXWtW7rgNPa8ipgffXcSO9KU4cDLwM2VtXOVvhvBFa2bc+sqs+0k97X9+1Lkuat6RbIfwj8Q5Kr6M0n/Dl6d5mUJHVvWjk4yVJ6I+M3Ac+uqvugV5gneVbr9tgNypqxm5ftqX3bgHZJmteme0fK9Uk20bvWdoCfrao79klkkqQ9mk4OTvIM4MPAG6rqm3uYdr27G5RNtn1QDN5JWNK8Me2pIC3BW2hL0ghMJQcn2Y9ewX1ZVX2kNd+f5PA2yn04sL21P3aDsmbs5mXbgBePa7+htS8Z0H9Q7BcBFwEsX77c+y9ImtOmO6dbkjSLtCuJXAxsqap3923qvxHZ+BuUndWuYrICeKhNQ7kGeGmSg9u88pcC17Rt30qyor3XWX37kqR5y5MeJWl+ORF4NXBrks2t7beAdwJXJlkD3M3j90e4GjgV2Ao8TLsOeFXtTPJ24HOt39va1VMAfgW4FDgA+Jv2kKR5zaJbkuaRqvoUg+ddA5w8oH8B5+xmX5cAlwxo3wQ8dxphStKc4/QSSZIkqWMW3ZIkSVLHLLolSZKkjll0S5IkSR2z6JYkSZI6ZtEtSZIkdcyiW5IkSerY0IvuJE9L8tkkX0xye5Lfbu1HJbkpyZ1Jrkiyf2t/alvf2rYvHXbMkiRJ0nSMYqT7EeCkqnoecBywst1a+F3A+VW1DHgAWNP6rwEeqKqjgfNbP0mSJGnWGHrRXT3fbqv7tUcBJwFXtfZ1wGlteVVbp20/Ocnu7qYmSZIkzTgjmdOdZEGSzcB2YCPwFeDBqtrVumwDFrflxcA9AG37Q8Chw41YkiRJmrqFo3jTqnoUOC7JQcBHgWMGdWvPg0a1a3xDkrXAWoAjjzxyH0UqTd6J7ztx1CEM3adf++lRh6A57AW/sX7UIQzdzb9/1qhDkLSPjfTqJVX1IHADsAI4KMnYfwKWAPe25W3AEQBt+4HAzgH7uqiqllfV8kWLFnUduiRJkjRho7h6yaI2wk2SA4CXAFuA64HTW7fVwMfa8oa2Ttt+XVU9aaRbkiRJmqlGMb3kcGBdkgX0iv4rq+rjSe4ALk/yO8AXgItb/4uBDybZSm+E+4wRxCxJkiRN2dCL7qq6BXj+gPa7gBMGtH8HeMUQQpMkSZI64R0pJUmSpI5ZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxyy6JUmSpI4NvehOckSS65NsSXJ7kte39kOSbExyZ3s+uLUnyQVJtia5Jcnxw45ZkiRJmo5RjHTvAt5UVccAK4BzkhwLnAtcW1XLgGvbOsApwLL2WAtcOPyQJUmSpKlbOOw3rKr7gPva8reSbAEWA6uAF7du64AbgDe39vVVVcCNSQ5KcnjbjyRJ0pSd+L4TRx3C0H36tZ8edQjz0kjndCdZCjwfuAl49lgh3Z6f1botBu7pe9m21iZJkiTNCiMrupM8A/gw8Iaq+uaeug5oqwH7W5tkU5JNO3bs2FdhSpIkSdM2kqI7yX70Cu7Lquojrfn+JIe37YcD21v7NuCIvpcvAe4dv8+quqiqllfV8kWLFnUXvCTNYkkuSbI9yW19bZM+bys8kgAAIABJREFUkT3J6tb/ziSr+9pfkOTW9poLkgwaOJGkeWcUVy8JcDGwpare3bdpAzCWuFcDH+trP6sl/xXAQ87nlqQpuxRYOa5tUieyJzkEOA94IXACcN5Yod76rO173fj3kqR5aRQj3ScCrwZOSrK5PU4F3gn8RJI7gZ9o6wBXA3cBW4E/BX51BDFL0pxQVZ8Edo5rXkXvBHba82l97eur50bgoPZL5MuAjVW1s6oeADYCK9u2Z1bVZ9rJ7+v79iVJ89oorl7yKQbP0wY4eUD/As7pNChJmt+ecCJ7kr2dyL6n9m0D2gdKspbeqDhHHnnkND+CJM1s3pFSkrQ7uzuRfbLtA3kujqT5xKJbkjTZE9n31L5kQLskzXsW3ZKkyZ7Ifg3w0iQHtxMoXwpc07Z9K8mKdtL8WX37kqR5behzuiVJo5PkQ/Tu/ntYkm30rkLyTuDKJGuAu4FXtO5XA6fSO5H9YeBsgKrameTtwOdav7dV1djJmb9C7wopBwB/0x6SNO9ZdEvSPFJVZ+5m06ROZK+qS4BLBrRvAp47nRglaS5yeokkSZLUsXk50v2C31g/6hCG7ubfP2vUIUiSJM1bjnRLkiRJHbPoliRJkjpm0S1JkiR1zKJbkiRJ6phFtyRJktQxi25JkiSpYxbdkiRJUscsuiVJkqSOWXRLkiRJHbPoliRJkjo2kqI7ySVJtie5ra/tkCQbk9zZng9u7UlyQZKtSW5JcvwoYpYkSZKmalQj3ZcCK8e1nQtcW1XLgGvbOsApwLL2WAtcOKQYJUmSpH1iJEV3VX0S2DmueRWwri2vA07ra19fPTcCByU5fDiRSpIkSdM3k+Z0P7uq7gNoz89q7YuBe/r6bWttT5BkbZJNSTbt2LGj82AlSZKkiZpJRffuZEBbPamh6qKqWl5VyxctWjSEsCRJkqSJmUlF9/1j00ba8/bWvg04oq/fEuDeIccmSZIkTdlMKro3AKvb8mrgY33tZ7WrmKwAHhqbhiJJkiTNBgtH8aZJPgS8GDgsyTbgPOCdwJVJ1gB3A69o3a8GTgW2Ag8DZw89YEmSJGkaRlJ0V9WZu9l08oC+BZzTbUSSJElSd2bS9BJJkiRpTrLoliRJkjpm0S1JkiR1zKJbkiRJ6phFtyRJktQxi25JkiSpYxbdkiRJUscsuiVJkqSOWXRLkiRJHbPoliRJkjpm0S1JkiR1zKJbkiRJ6phFtyRJktQxi25JkiSpYxbdkiRJUscsuiVJkqSOLRx1ABOVZCXwXmAB8IGqeueIQ5K0D3ziRT8+6hCG7sc/+YlRh9A5c7YkPdGsGOlOsgB4P3AKcCxwZpJjRxuVJGkQc7YkPdlsGek+AdhaVXcBJLkcWAXcMdKoJEmDmLOlOcpfJ6duVox0A4uBe/rWt7U2SdLMY86WpHFmy0h3BrTVEzoka4G1bfXbSb7ceVSTdxjw9VG8cf5g9SjedrpGdrw4b9AfuVlhJMcsr/N4TUr2erx+YBhhdGivORvM23syS3M2jOrvlDl7UszZk7SPcvZsKbq3AUf0rS8B7u3vUFUXARcNM6jJSrKpqpaPOo7ZwuM1eR6zyfF4dWavORvM23ORx2tyPF6TM9uP12yZXvI5YFmSo5LsD5wBbBhxTJKkwczZkjTOrBjprqpdSV4DXEPv8lOXVNXtIw5LkjSAOVuSnmxWFN0AVXU1cPWo45imGf0z6gzk8Zo8j9nkeLw6MkdyNvhnZLI8XpPj8ZqcWX28UvWkc1skSZIk7UOzZU63JEmSNGtZdHcgySVJtie5bTfbk+SCJFuT3JLk+GHHOFMkOSLJ9Um2JLk9yesH9PF4NUmeluSzSb7YjtdvD+jz1CRXtON1U5Klw490ZkmyIMkXknx8wDaP1zxnzp4c8/bkmLcnb67mbIvublwKrNzD9lOAZe2xFrhwCDHNVLuAN1XVMcAK4JwBt4v2eD3uEeCkqnoecBywMsmKcX3WAA9U1dHA+cC7hhzjTPR6YMtutnm8dCnm7Mkwb0+OeXvy5mTOtujuQFV9Eti5hy6rgPXVcyNwUJLDhxPdzFJV91XV59vyt+j9JRt/5zqPV9OOwbfb6n7tMf7EjFXAurZ8FXBysvcr+89VSZYAPwl8YDddPF7znDl7cszbk2Penpy5nLMtukfDWyQP0H4iej5w07hNHq8+7We3zcB2YGNV7fZ4VdUu4CHg0OFGOaO8B/hN4N92s93jpb0xB+2GeXtizNuTMmdztkX3aEzoFsnzSZJnAB8G3lBV3xy/ecBL5u3xqqpHq+o4enf5OyHJc8d18Xg1SX4K2F5VN++p24C2eXm8tFv+GRnAvD1x5u2Jmes526J7NCZ0i+T5Isl+9BL3ZVX1kQFdPF4DVNWDwA08eS7qY8cryULgQPb80/lcdiLw8iRfAy4HTkry5+P6eLy0N+agcczbU2Pe3qs5nbMtukdjA3BWO7t7BfBQVd036qBGoc3DuhjYUlXv3k03j1eTZFGSg9ryAcBLgC+N67YBWN2WTweuq3l6Qf6qektVLamqpfRuRX5dVb1qXDePl/bGHNTHvD055u2Jm+s5e9bckXI2SfIh4MXAYUm2AefRO3GCqvoTendpOxXYCjwMnD2aSGeEE4FXA7e2+W4AvwUcCR6vAQ4H1iVZQO8/zVdW1ceTvA3YVFUb6P1j+MEkW+n97/+M0YU7M3m81M+cPWnm7ckxb0/TXDlW3pFSkiRJ6pjTSyRJkqSOWXRLkiRJHbPoliRJkjpm0S1JkiR1zKJbkiRJ6phFtyRJktQxi25NWZKfSVJJfqitL01y27g+f5Jkc5I7kvxrW97cXvvnSb7a1/b37TW/lGRHa/tSktf17e+YJJ9o27YkubC1vyTJX/b1+70kf51k/7b+7CS7kqzp67Op7efuvvfbnOSIJNuS3NrXdn57TZL8eovrliRfTPIH7fqrJDm4fa6tSb6S5NIkz2zbju47BlvatoXj4x/3+ccez9nD9/Cc9lm/kuTmJNcl+Y97O5Zt+6+09i8luSnJj/Vt+1SSL/d9f2vGv7ek2cOcbc7WiFWVDx9TegBXAn8PvLWtLwVu203fo4HN49r+HDhtQN9fAt7TlhfRu/j94W39WuAn23KAH2nLLwH+si2/Ffg74Gl9+3xdi/Xv9vR+fW3bgIMG9H0N8NfAgW39qfRuCvH0tv6XwP/X1/93gA+NPwb0bkz1CeDnB8T/pHj28B08Hbhz7Ji0th8FzprAsTwN+CxwSFtfDtwNLGrrnwKOa8uHAQ8AC0f9586HDx9Te5izzdk+RvtwpFtTkuQZ9O5KtoYO7wZVVTuAu+jd0Yv2vK1tq6q6dVxcbwZOAl5eVd/p23Qm8AbgB5N83zRC+i3gv1bVQy2GR6rqd6vq4Tay8Vzgd/v6vxU4McnScZ9rF/A5YPE0YoHeXeE+WVV/3bfvW6pq/fiOA47lm4Ffr6qdbfsm4DLgVwe8zzOAfwEenWa8kkbAnG3O1uhZdGuqTgP+tqr+F7AzyfFT3M/5fT/HPSnptMS3ABj7CfTdwCeTXJ3kDUkO7Ov+IuAX6Y0gPDxuHwdX1c3AVcDPTTC2v++L7XVJDgb2q6p7dtP/h4EvVNW/jTW0RP1F4Nhxn+sA4N8D1+xmX68c91Pl/nt4z89P5MMMOJbHAjeP67ap7XPMFUluAbbQGx3zFrbS7GTOfjJztobKoltTdSZweVu+vK1Pxa9V1XHtcVZf+yuT3A5sBd5dVd8FqKoP0Es8VwEnA5/pS253Avu19vGxXjGFWP/vvtguoPfT6GOSnNqS6z8mOaFtH5Tg+tufk2Qz8A1ga1Xdvpv3vqzvvY8b+/x7k2RDktuTXNnXPPBY7m4X4z7Dz1fVjwI/AJybZMlE4pA045izzdkaMYtuTVqSQ+n9HPiBJF8DfgP4ecYluGm6rKp+GHgx8N4kzxrbUFX/VFWXVNVP0/szfEzbdB9wKvBHSV7Ut68zgV9qsX4EeEGSoyYbUPtJb1eSI9v61VV1HL0Rhf2B24Hjkzz296qdrPMjrQ/Al9trjgZ+PMmpk41jnNuBx0asqurl9H4+PqSvz+6O5RbgBeP2dzxwx/g3qart9EZ/TphmvJKGzJxtztbMYNGtqTgdWF9VP1BVS6vqCOCrwD7/H3VVfQr4EPBagCQr8/jZ498PHAzc29f/S8B/Bj6U5EeTHAssqKrFLdalwO8z9TmNvwdcOPYTaZIAT+t77zuAc/v6nwfcWFVfG/e57gXe0h7T8UHgxUl+sq/t6YM6jj+WwH8H/nv7CZb2c/OrgAvHvzbJ9wDPA74yzXglDZ8525ytGWDhqAPQrHQm8M5xbR+md8LKc5Js62v/tar6iz3s6/wkb+1bH/+/eNp7fTbJO4FT6P3P/zv0flJ7Q1Xt6OXRnqq6KckvAX9F7+z3jw6IdR29ZLwnf59k7CSUL1TV2cD7gAOAz7UYvt3e44ut33+hN2ozlug+Dazdzf6vAt6a5D8M2PbKJC/uW//lqrppfKd2MtBPA3+Y5H3A/cA3eeKJQf0eO5ZV9ZEkhwM3Jqn2ul9oIyRjrkjyr/TO+P/TqvrigH1KmtnM2eZszQBxjr0kSZLULaeXSJIkSR1zeok0CyQ5Drh0XPPDVfVjA7pLkkbInK1BnF4iSZIkdczpJZIkSVLHLLolSZKkjll0S5IkSR2z6JYkSZI6ZtEtSZIkdcyiW5IkSeqYRbckSZLUMYtuSZIkqWMW3ZIkSVLH5uRt4A877LBaunTpqMOQpCm5+eabv15Vi0YdxzCZtyXNVhPN2XOy6F66dCmbNm0adRiSNCVJ/nHUMQybeVvSbDXRnO30EkmSJKljFt2SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1LE5eXMcaZj+6E1/NbL3fs0f/vTI3luzU5KnAZ8Enkrv34Crquq8JEcBlwOHAJ8HXl1V303yVGA98ALgG8DPV9XX2r7eAqwBHgVeV1XXtPaVwHuBBcAHquqdQ/yI0h6941Wnd7r///fPr+p0/5q9HOmWpPnlEeCkqnoecBywMskK4F3A+VW1DHiAXjFNe36gqo4Gzm/9SHIscAbww8BK4I+TLEiyAHg/cApwLHBm6ytJ85pFtyTNI9Xz7ba6X3sUcBIwNkS3DjitLa9q67TtJydJa7+8qh6pqq8CW4ET2mNrVd1VVd+lN3q+quOPJUkznkW3JM0zbUR6M7Ad2Ah8BXiwqna1LtuAxW15MXAPQNv+EHBof/u41+yuXZLmNYtuSZpnqurRqjoOWEJvZPqYQd3ac3az7f+wd/fhdtX1nfffH3mSVjEgB4sEBqqZVnSmAVPIlLnUQkVgWoO9oIVOJXWYSccBR6udiu19DVRlbr2nSkUpvahEQ8cxMqgldWJphge9qfIQEHlKLSlSSUlJbACx3sUGv/cf+3dge9gnD+ecdfY52e/Xde1rr/Vdv7XWd7OTX76s/Vu/tbvx50iyIsn6JOu3bt2688QlaR6z6JakEVVVjwM3AUuBBUnGb65fCDzSljcBhwO07S8CtvXHJ+wzWXzQ+a+oqiVVtWRsbGwmPpIkzVkW3ZI0QpKMJVnQlvcHfg7YANwIjE/rsBy4ti2vaeu07TdUVbX4WUn2azOfLAJuA24HFiU5Ksm+9G62XNP9J5Okuc0pAyVptBwKrGqzjDwPuLqqvpDkfmB1kvcDXwOubO2vBP44yUZ6V7jPAqiq+5JcDdwPbAfOq6qnAZKcD1xHb8rAlVV13+x9PEmamyy6JWmEVNXdwDED4g/SG989Mf6PwJmTHOti4OIB8bXA2mknK0l7EIeXSJIkSR2z6JYkSZI6ZtEtSZIkdcyiW5IkSeqYRbckSZLUMYtuSZIkqWMW3ZIkSVLHLLolSZKkjll0S5IkSR2z6JYkSZI6ZtEtSZIkdcyiW5IkSeqYRbckSZLUMYtuSZIkqWMW3ZIkSVLHLLolSZKkjll0S5IkSR2z6JYkSZI6ZtEtSZIkdWxoRXeSvZJ8LckX2vpRSW5N8kCSzyTZt8X3a+sb2/Yjh5WzJEmSNBXDvNL9dmBD3/oHgUuqahHwGHBui58LPFZVLwcuae0kSZKkeWMoRXeShcC/AT7e1gOcCFzTmqwCTm/Ly9o6bftJrb0kSZI0LwzrSvfvA78F/KCtvxh4vKq2t/VNwGFt+TDgYYC2/YnW/ockWZFkfZL1W7du7TJ3SZIkabfMetGd5OeBLVV1R394QNPahW3PBqquqKolVbVkbGxsBjKVJEmSZsYwrnSfALwxyUPAanrDSn4fWJBk79ZmIfBIW94EHA7Qtr8I2DabCUvSniLJ4UluTLIhyX1J3t7iFyX52yR3tddpffu8p93M/o0kb+iLn9JiG5Nc0BcfeGO8JI2yWS+6q+o9VbWwqo4EzgJuqKp/C9wInNGaLQeubctr2jpt+w1V9Zwr3ZKkXbIdeFdVvQJYCpyX5Oi27ZKqWtxeawHatrOAVwKnAH/QZp/aC7gMOBU4Gji77ziT3RgvSSNrLs3T/W7gnUk20huzfWWLXwm8uMXfCVwwyf6SpJ2oqs1VdWdbfpLeLFKH7WCXZcDqqnqqqr4JbASOa6+NVfVgVX2f3i+Xy3ZyY7wkjay9d96kO1V1E3BTW36QXic+sc0/AmfOamKSNALacw+OAW6lN/Tv/CTnAOvpXQ1/jF5Bfkvfbv03uj88IX48O74xXpJG1ly60i1JmiVJXgB8FnhHVX0HuBx4GbAY2Ax8aLzpgN1rCvFBOTjrlKSRYdEtSSMmyT70Cu5PVdXnAKrq0ap6uqp+APwRz/7y+MzN7M34je6Txb/N5DfG/xBnnZI0Siy6JWmEtDHXVwIbqurDffFD+5q9Cbi3La8BzkqyX5KjgEXAbcDtwKI2U8m+9G62XNNudJ/sxnhJGllDHdMtqTsX/+oZO2/Ukd/5H9fsvJGG5QTgzcA9Se5qsd+mN/vIYnpDQR4Cfh2gqu5LcjVwP72ZT86rqqcBkpwPXAfsBaysqvva8d4NrE7yfuBrPHtjvCSNLItuSRohVXUzg8ddr93BPhcDFw+Irx2032Q3xkvSKHN4iSRJktQxi25JkiSpYxbdkiRJUscsuiVJkqSOWXRLkiRJHbPoliRJkjpm0S1JkiR1zKJbkiRJ6phFtyRJktQxi25JkiSpYxbdkiRJUscsuiVJkqSOWXRLkiRJHbPoliRJkjpm0S1JkiR1zKJbkiRJ6phFtyRJktQxi25JkiSpYxbdkiRJUscsuiVJkqSOWXRLkiRJHbPoliRJkjpm0S1JkiR1zKJbkiRJ6phFtyRJktQxi25JGiFJDk9yY5INSe5L8vYWPyjJuiQPtPcDWzxJLk2yMcndSY7tO9by1v6BJMv74q9Ock/b59Ikmf1PKklzi0W3JI2W7cC7quoVwFLgvCRHAxcA11fVIuD6tg5wKrCovVYAl0OvSAcuBI4HjgMuHC/UW5sVffudMgufS5LmNItuSRohVbW5qu5sy08CG4DDgGXAqtZsFXB6W14GXFU9twALkhwKvAFYV1XbquoxYB1wStt2QFV9taoKuKrvWJI0siy6JWlEJTkSOAa4FXhJVW2GXmEOHNKaHQY83LfbphbbUXzTgLgkjbRZL7qTPD/JbUm+3sYT/m6LH5Xk1jY28DNJ9m3x/dr6xrb9yNnOWZL2NEleAHwWeEdVfWdHTQfEagrxQTmsSLI+yfqtW7fuLGVJmteGcaX7KeDEqvopYDG9nyOXAh8ELmnjCR8Dzm3tzwUeq6qXA5e0dpKkKUqyD72C+1NV9bkWfrQNDaG9b2nxTcDhfbsvBB7ZSXzhgPhzVNUVVbWkqpaMjY1N70NJ0hw360V3Gxf43ba6T3sVcCJwTYtPHE84Ps7wGuAk74SXpKlp/eeVwIaq+nDfpjXA+Awky4Fr++LntFlMlgJPtOEn1wEnJzmw3UB5MnBd2/ZkkqXtXOf0HUuSRtbewzhpkr2AO4CXA5cBfw08XlXbW5P+MYDPjBusqu1JngBeDHx7VpOWpD3DCcCbgXuS3NVivw18ALg6ybnAt4Az27a1wGnARuB7wFsAqmpbkvcBt7d2762qbW35rcAngf2BL7aXJI20oRTdVfU0sDjJAuDzwCsGNWvvuzQ+MMkKelNUccQRR8xQppK0Z6mqmxncrwKcNKB9AedNcqyVwMoB8fXAq6aRpiTtcYY6e0lVPQ7cRG+u2AVJxv8noH8M4DPjBtv2FwHbmMCxgZIkSZqrhjF7yVi7wk2S/YGfozdP7I3AGa3ZxPGE4+MMzwBuaFdeJEmSpHlhGMNLDgVWtXHdzwOurqovJLkfWJ3k/cDX6N3oQ3v/4yQb6V3hPmsIOUuSJElTNutFd1XdTe9hDBPjD9J7lPDE+D/y7A09kiRJ0rzjEyklSZKkjll0S5IkSR2z6JYkSZI6ZtEtSZIkdcyiW5IkSerYUJ5IKUmSRtfH3vWnnZ/j/A/9QufnkHbHtK50J7l+V2KSpJlnHyxJ88eUrnQneT7wI8DBSQ4E0jYdALx0hnKTJA1gHyxJ889Uh5f8OvAOep37HTzb4X8HuGwG8pIkTc4+WJLmmSkV3VX1EeAjSd5WVR+d4ZwkSTtgHyxJ88+0bqSsqo8m+RngyP5jVdVV08xLkrQT9sGSNH9Mq+hO8sfAy4C7gKdbuAA7fEnqmH2wJM0f050ycAlwdFXVTCQjSdot9sGSNE9M9+E49wI/NhOJSJJ2m32wJM0T073SfTBwf5LbgKfGg1X1xmkeV5K0c/bBkjRPTLfovmgmkpAkTclFw05AkrRrpjt7yZdmKhFJ0u6xD5bmjw0X39D5OV7xOyd2fg5N3XRnL3mS3p3yAPsC+wD/UFUHTDcxSdKO2QdL0vwx3SvdL+xfT3I6cNy0MpIk7RL7YEmaP6Y7e8kPqao/AfxtQ5KGwD5Ykuau6Q4v+cW+1efRmzPW+WIlaRZMpQ9OshL4eWBLVb2qxS4C/gOwtTX77apa27a9BziX3sN3/nNVXdfipwAfAfYCPl5VH2jxo4DVwEHAncCbq+r70/6wkjTPTXf2kl/oW94OPAQsm+YxJUm7Zip98CeBj/Hcp1ZeUlW/1x9IcjRwFvBK4KXA/0nyz9vmy4DXA5uA25Osqar7gQ+2Y61O8of0CvbLd/NzSdIeZ7pjut8yU4lIknbPVPrgqvpykiN3sfkyYHVVPQV8M8lGnh0zvrGqHgRIshpYlmQDveEtv9LarKI3raFFt6SRN60x3UkWJvl8ki1JHk3y2SQLZyo5SdLkZrgPPj/J3UlWJjmwxQ4DHu5rs6nFJou/GHi8qrZPiEvSyJvujZSfANbQ+9nxMOBPW0yS1L2Z6oMvB14GLAY2Ax9q8QxoW1OID5RkRZL1SdZv3bp1smaStEeYbtE9VlWfqKrt7fVJYGwG8pIk7dyM9MFV9WhVPV1VPwD+iGeHkGwCDu9ruhB4ZAfxbwMLkuw9IT7Zea+oqiVVtWRszH86JO3Zplt0fzvJrybZq71+Ffj7mUhMkrRTM9IHJzm0b/VNwL1teQ1wVpL92qwki4DbgNuBRUmOSrIvvZst11RVATcCZ7T9lwPXTumTSdIeZrqzl/w7enfBX0LvJ8SvAN5cKUmzY7f74CSfBl4HHJxkE3Ah8Loki9sxHgJ+HaCq7ktyNXA/vdlRzquqp9txzgeuozdl4Mqquq+d4t3A6iTvB74GXDlTH1aS5rPpFt3vA5ZX1WMASQ4Cfo/ePwSSpG7tdh9cVWcPCE9aGFfVxcDFA+JrgbUD4g/iUzEl6TmmO7zkX4539gBVtQ04ZprHlCTtGvtgSZonplt0P69vaqnxqyzTvXouSdo19sGSNE9Mt3P+EPCVJNfQGwv4Swz4GVKS1An7YEmaJ6b7RMqrkqyn9wSyAL/YHgMsSeqYfbAkzR/T/hmydfB28pI0BPbBkjQ/THdM925LcniSG5NsSHJfkre3+EFJ1iV5oL0f2OJJcmmSje0RxcfOds6SJEnSdMx60U1vrtd3VdUrgKXAeUmOBi4Arq+qRcD1bR3gVHoPZFgErKD3uGJJkiRp3pj1oruqNlfVnW35SWADcBiwDFjVmq0CTm/Ly4CrqucWeo8YPhRJkiRpnhjGle5nJDmS3pyytwIvqarN0CvMgUNas8OAh/t229RikiRJ0rwwtKI7yQuAzwLvqKrv7KjpgFgNON6KJOuTrN+6detMpSlJkiRN21CK7iT70Cu4P1VVn2vhR8eHjbT3LS2+CTi8b/eFwCMTj1lVV1TVkqpaMjY21l3ykiRJ0m4axuwlAa4ENlTVh/s2rQGWt+XlwLV98XPaLCZLgSfGh6FIkiRJ88EwHhd8AvBm4J4kd7XYbwMfAK5Oci7wLeDMtm0tcBqwEfge8JbZTVeSJEmanlkvuqvqZgaP0wY4aUD7As7rNClJkiSpQ8O40i1NyZde89qhnfu1X/7S0M4tSZLmv6FOGShJkiSNAotuSZIkqWMW3ZIkSVLHLLolSZKkjll0S5IkSR2z6JYkSZI6ZtEtSZIkdcyiW5IkSeqYRbckSZLUMYtuSZIkqWMW3ZI0QpKsTLIlyb19sYOSrEvyQHs/sMWT5NIkG5PcneTYvn2Wt/YPJFneF391knvaPpcmyex+Qkmamyy6JWm0fBI4ZULsAuD6qloEXN/WAU4FFrXXCuBy6BXpwIXA8cBxwIXjhXprs6Jvv4nnkqSRZNEtSSOkqr4MbJsQXgasasurgNP74ldVzy3AgiSHAm8A1lXVtqp6DFgHnNK2HVBVX62qAq7qO5YkjTSLbknSS6pqM0B7P6TFDwMe7mu3qcV2FN80IC5JI8+iW5I0mUHjsWsK8cEHT1YkWZ9k/datW6eYoiTNDxbdkqRH29AQ2vuWFt8EHN7XbiHwyE7iCwfEB6qqK6pqSVUtGRsbm/aHkKS5zKJbkrQGGJ+BZDlwbV/8nDaLyVIzvDksAAAgAElEQVTgiTb85Drg5CQHthsoTwaua9ueTLK0zVpyTt+xJGmk7T3sBCRJsyfJp4HXAQcn2URvFpIPAFcnORf4FnBma74WOA3YCHwPeAtAVW1L8j7g9tbuvVU1fnPmW+nNkLI/8MX2kqSRZ9EtSSOkqs6eZNNJA9oWcN4kx1kJrBwQXw+8ajo5StKeyOElkiRJUscsuiVJkqSOWXRLkiRJHXNMt6RZteHiG4Z27lf8zolDO7ckabR5pVuSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbkmSJKljQym6k6xMsiXJvX2xg5KsS/JAez+wxZPk0iQbk9yd5Nhh5CxJkiRN1bCudH8SOGVC7ALg+qpaBFzf1gFOBRa11wrg8lnKUZIkSZoRQym6q+rLwLYJ4WXAqra8Cji9L35V9dwCLEhy6OxkKkmSJE3fXBrT/ZKq2gzQ3g9p8cOAh/vabWqxH5JkRZL1SdZv3bq182QlSZKkXTWXiu7JZECsnhOouqKqllTVkrGxsVlIS5IkSdo1c6nofnR82Eh739Lim4DD+9otBB6Z5dwkSZKkKZtLRfcaYHlbXg5c2xc/p81ishR4YnwYiiRJkjQf7D2Mkyb5NPA64OAkm4ALgQ8AVyc5F/gWcGZrvhY4DdgIfA94y6wnLEmSJE3DUIruqjp7kk0nDWhbwHndZiRJkiR1Zy4NL5EkDVGSh5Lck+SuJOtbbLcfXJZkeWv/QJLlk51PkkaJRbckqd/PVtXiqlrS1nfrwWVJDqI3ZPB44DjgwvFCXZJGmUW3JGlHdvfBZW8A1lXVtqp6DFjHc59ALEkjx6JbkjSugD9PckeSFS22uw8u26UHmknSqBnKjZSSpDnphKp6JMkhwLokf7mDtpM9uGyXHmgGvScJ0xuawhFHHLG7uUrSvOKVbkkSAFX1SHvfAnye3pjs3X1w2S4/0MwnCUsaJRbdkiSS/GiSF44vAycD97L7Dy67Djg5yYHtBsqTW0ySRprDSyRJAC8BPp8Eev82/M+q+rMkt7MbDy6rqm1J3gfc3tq9t6q2zd7HkKS5yaJbkkRVPQj81ID437ObDy6rqpXAypnOUZLmM4tuPccJHz1haOf+i7f9xdDOLUmS1BXHdEuSJEkds+iWJEmSOubwEkmSpD3YRRddNK+Pv6fwSrckSZLUMYtuSZIkqWMW3ZIkSVLHLLolSZKkjnkjpSRJI+pLr3ltp8d/7Ze/1OnxpfnEK92SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOrb3sBMYZd96778Y2rmP+K/3DO3ckiRJo8Yr3ZIkSVLHLLolSZKkjll0S5IkSR2bN2O6k5wCfATYC/h4VX1gyClJ2oNcdNFFI3nurthn77oTPnpC5+f4i7f9RefnkLRj8+JKd5K9gMuAU4GjgbOTHD3crCRJg9hnS9JzzZcr3ccBG6vqQYAkq4FlwP1DzUqSNIh9tiSu/l/HdXr8Xzrztk6PP9PmS9F9GPBw3/om4Phd3fnV/+WqGU9oV93x388Z2rklaUim1WcPS9fTuDpVqzTaUlXDzmGnkpwJvKGq/n1bfzNwXFW9ra/NCmBFW/0J4BszdPqDgW/P0LFmknntnrmaF8zd3Mxr98xkXv+sqsZm6Fizblf67BafqX57WH8mhvln0c/seffEc8/Xz7xLffZ8udK9CTi8b30h8Eh/g6q6Arhipk+cZH1VLZnp406Xee2euZoXzN3czGv3zNW8hmSnfTbMXL89rP/2w/zO/cyed088957+mefFjZTA7cCiJEcl2Rc4C1gz5JwkSYPZZ0vSBPPiSndVbU9yPnAdvemnVlbVfUNOS5I0gH22JD3XvCi6AapqLbB2CKee8SErM8S8ds9czQvmbm7mtXvmal5DMct99rD+2w/zO/cze9498dx79GeeFzdSSpIkSfPZfBnTLUmSJM1bFt1Nkp9M8tUkTyX5zR20OyrJrUkeSPKZdpNQl3mtTLIlyb2TbE+SS5NsTHJ3kmO7zKed8/AkNybZkOS+JG+fI3k9P8ltSb7e8vrdAW32a9/bxvY9Htl1Xn3n3ivJ15J8Ya7kleShJPckuSvJ+gHbZ/17bOddkOSaJH/Z/pz9qzmS10+0/1bjr+8kecdcyG0UDKufTnJKkm+07/SCAds7+fs7rP5/WH38XOjDh9FPD7MfHkZfO8x+NMlvtD9b9yb5dJLnT9je7b/FVeWrN8TmEOCngYuB39xBu6uBs9ryHwJv7Tiv1wDHAvdOsv004ItAgKXArbPw3+pQ4Ni2/ELgr4Cj50BeAV7QlvcBbgWWTmjzn4A/bMtnAZ+ZxT9j7wT+J/CFAduGkhfwEHDwDrbP+vfYzrsK+PdteV9gwVzIa0IOewF/R29+1jmV2576GkY/3b7nvwZ+vP1Z/PqA/q6Tv7/D6v+H1cfPhT58GP30MPvhYfe1s9mP0nto1zeB/dv61cCvzeafL690N1W1papuB/5psjZJApwIXNNCq4DTO87ry8C2HTRZBlxVPbcAC5Ic2nFOm6vqzrb8JLCB3h/mYedVVfXdtrpPe028aWEZve8Net/jSe177VSShcC/AT4+SZOh5LULZv17THIAvWLjSoCq+n5VPT7svAY4CfjrqvqbOZjbHmlI/fQzj7Svqu8D44+079fJ399h9f/D6uOH3YfP4X66k+95jvS1s92P7g3sn2Rv4Ed47vMDOv2OLbp3z4uBx6tqe1vfxHM7otk26HHLs5ZT++nlGHpXJPoNJa/20+BdwBZgXVVNmlf7Hp+g97127feB3wJ+MMn2YeVVwJ8nuSO9pwNOmlczG9/jjwNbgU+0n3k/nuRH50BeE50FfHpAfC7kNspmup/ele9zWH9/O/+zNtt9/JD78GH108Pqh+dCXztr/WhV/S3we8C3gM3AE1X155Odt4u/yxbdu2fQ/+0Me/qXoeWU5AXAZ4F3VNV3Jm4esEvneVXV01W1mN4T8I5L8qph55Xk54EtVXXHjpoNiM3G93hCVR0LnAqcl+Q1E7YPI6+96f2kfnlVHQP8AzBxHO1Q/y6mN0b4jcD/GrR5QGzY/cQomen//rtyvGF9552edxh9/LD68CH308Pqh4fa1852P5rkQHpXso8CXgr8aJJf7fq8/Ua66E5yXt9A/pfuwi7fpvcTx/j85gMfbTzLdulxyzMtyT70OuNPVdXn5kpe49pPZDcBp0zY9Exe7Xt8ETv++XYmnAC8MclD9H6aPjHJ/5gDeVFVj7T3LcDn6f2UPjCvZja+x03Apr4rXNfQ+4dh2Hn1OxW4s6oeHbBt2LntUeZAP70r3+dQ/v7uYm5TMuw+fgh9+ND66SH2w8Pua2e7H/054JtVtbWq/gn4HPAzk523i7/LI110V9VlVbW4vXb6ZVZVATcCZ7TQcuDaLnPcBWuAc9qdvkvp/VyyucsTtvFNVwIbqurDcyivsSQL2vL+9P6C/eWAvJa35TOAG9r32pmqek9VLayqI+n9lHZDVU38v+tZzyvJjyZ54fgycDIwcZaEWf8eq+rvgIeT/EQLnQTcP+y8JjibwT+JwvBz26PMgX56Vx5pP+t/f/vOO+N/1obVxw+zDx9WPz3MfngO9LWz3Y9+C1ia5Efan/GT6N2vMPG83f1drhm8K3M+v4Afo/d/ON8BHm/LB7Rta4GXtuUfB24DNtL7SWS/jvP6NL2xR//UcjoX+I/Af2zbA1xG7+76e4Als/Df6l/T+7nlbuCu9jptDuT1L4GvtbzuBf5ri78XeGNbfn773ja27/HHZ/nP2etod8UPO6/2Z/nr7XUf8DstPtTvsZ13MbC+fZd/Ahw4F/Jq5/4R4O+BF/XF5kRue/prWP1069/+qn2n439POv/7y5D6f4bUxzNH+nBmsZ9myP0wQ+prGVI/Cvwuvf+Ruxf4Y2C/2fzz5RMpJUmSpI6N9PASSZIkaTZYdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbs1ZSX4syeokf53k/iRrk/zzJPcmeUPfU+q+m+QbbfmqHRzvuCQ3JXkgyZ1J/neSf9G2XZTkb/uOeVeSBW0S/U8luaed9+b0Ho08fsw3JakkP9kXO7LF3tcXOzjJPyX5WJLf6TvH033LF04S/89Jvtom8yfJXi3+MxPyvjfJG7v5NiRpx+yz7bO1Y87TrTmpdVZfAVZV1R+22GLghcDlVfWqvrY3Ab9ZVet3cLyXALcCv1JVX2mxfw0cXFV/kuQi4LtV9XsT9nsPMFZV72zrPwE8VFVPtfWrgUOB66vqohY7Erge+E5VHdNibwV+Hbi5qs7vO/53q+qZfxAmiydZDfyfqvp4kncAr6yq/9Cfd5JXAP8vcEhV/WDy/7qSNLPss+2ztXN7DzsBaRI/C/zTeOcNUFV3tc5xKs6n94/BV/qOd/Mu7Hco8Dd9+3xjfLldPTmh5boGuKhvv/8P2JBkSfuH5ZeBq4GXTjH/3wBuTvLV9lmOm9igqjYk2Q4cDGyZ4nkkaSrss3+Yfbaew+ElmqteBdwxg8d7JXDnTtr8Rt/Pgze22Erg3e2nwvcnWdTX/nTgz6rqr4BtSY6dcLzVwFlJFgJPA49MNfmq2gz8PvBV4P1VtW1imyTHAz8Atk71PJI0RfbZfeyzNYhFt0ZSkluTbEjykb7wJVW1uL1+FnpXaoAfB/47cBBwe/tJEOBsep007f3sCaf5M+D1Lf6ZGUj7MmCvqvrkhPhvJLkL+D3gl8sxY5L2MPbZ2hM4vERz1X3AGTN8vGOBawGq6vgkZwA/v7Mdq+q7wOeAzyX5AXBaki3AicCrkhSwF1BJfqtvv+8nuQN4F72rNr8wnQ9QVT9o55rokonjGiVpltlnPzcP+2z9EK90a666AdgvyX8YDyT5aeCfTfF4lwG/luRn+mI/srOdkpyQ5MC2vC9wNL3xgmcAV1XVP6uqI6vqcOCbwL+ecIgPAe+uqr+fYt6SNB/YZ0s7YdGtOan93PYm4PXpTT91H72bXh4BnprC8f6O3o0x/3eSjUm+Qq8T/lhfs/7xgeM3AL0M+FKSe4CvAeuBz9L7+fHzE07zWeBXJpz3vqpatbv5StJ8Yp8t7ZxTBmpeSbIM+LdV9UvDzkWStGP22dKzHNOteSPJe4FlwK8NORVJ0k7YZ0s/zCvd2qMkeQPwwQnhb1bVm4aRjyRpcvbZGiUW3ZIkSVLHvJFSkiRJ6phFtyRJktQxi25JkiSpYxbdkiRJUscsuiVJkqSOWXRLkiRJHbPoliRJkjpm0S1JkiR1zKJbkiRJ6tjew06gCwcffHAdeeSRw05Dkqbkjjvu+HZVjQ07j9lkvy1pvtrVPnuPLLqPPPJI1q9fP+w0JGlKkvzNsHOYbfbbkuarXe2zHV4iSZIkdcyiW5IkSeqYRbckSZLUMYtuSZIkqWMW3ZIkSVLHLLolSZKkjg2l6E6yIMk1Sf4yyYYk/yrJQUnWJXmgvR/Y2ibJpUk2Jrk7ybHDyFmSJEmaqmFd6f4I8GdV9ZPATwEbgAuA66tqEXB9Wwc4FVjUXiuAy2c/XUmSJGnqZv3hOEkOAF4D/BpAVX0f+H6SZcDrWrNVwE3Au4FlwFVVVcAt7Sr5oVW1eZZTl3boS6957bBT2KHXfvlLw05BktSBj73rT4edwg6d/6FfGHYKc8IwrnT/OLAV+ESSryX5eJIfBV4yXki390Na+8OAh/v239RikiRJ0rwwjKJ7b+BY4PKqOgb4B54dSjJIBsTqOY2SFUnWJ1m/devWmclUkiRJmgHDKLo3AZuq6ta2fg29IvzRJIcCtPctfe0P79t/IfDIxINW1RVVtaSqloyNjXWWvCRJkrS7Zr3orqq/Ax5O8hMtdBJwP7AGWN5iy4Fr2/Ia4Jw2i8lS4AnHc0uSJGk+GdbsJW8DPpXkbmAx8N+ADwCvT/IA8Pq2DrAWeBDYCPwR8J9mP11J2jMkeX6S25J8Pcl9SX63xT+Z5JtJ7mqvxS0+6bStSZa3aV4fSLK8L/7qJPe0fS5NMmiYoCSNlFmfvQSgqu4ClgzYdNKAtgWc13lSkjQangJOrKrvJtkHuDnJF9u2/1JV10xo3z9t6/H0pm09PslBwIX0+vIC7kiypqoea21WALfQu3ByCvBFJGmE+URKSRoh1fPdtrpPez3n5vQ+z0zbWlW3AAvafTdvANZV1bZWaK8DTmnbDqiqr7aLJlcBp3f2gSRpnrDolqQRk2SvJHfRu2F9Xd+N7Re3ISSXJNmvxSabtnVH8U0D4oPycNYpSSPDoluSRkxVPV1Vi+nNBnVcklcB7wF+Evhp4CB6DyeDyadt3d34oDycdUrSyLDolqQRVVWP03v67ylVtbkNIXkK+ARwXGs22bStO4ovHBCXpJFm0S1JIyTJWJIFbXl/4OeAv+x7TkLojcG+t+0y2bSt1wEnJzkwyYHAycB1bduTSZa2Y53Ds1PAStLIGsrsJZKkoTkUWJVkL3oXXq6uqi8kuSHJGL3hIXcB/7G1XwucRm/a1u8BbwGoqm1J3gfc3tq9t6q2teW3Ap8E9qc3a4kzl0gaeRbdkjRCqupu4JgB8RMnaT/ptK1VtRJYOSC+HnjV9DKVpD2Lw0skSZKkjll0S5IkSR2z6JYkSZI6ZtEtSZIkdcyiW5IkSeqYRbckSZLUMYtuSZIkqWMW3ZIkSVLHLLolSZKkjll0S5IkSR2z6JYkSZI6ZtEtSZIkdcyiW5IkSeqYRbckSZLUMYtuSZIkqWMW3ZIkSVLHLLolSZKkjll0S5IkSR0bStGd5KEk9yS5K8n6FjsoybokD7T3A1s8SS5NsjHJ3UmOHUbOkiRJ0lQN80r3z1bV4qpa0tYvAK6vqkXA9W0d4FRgUXutAC6f9UwlaQ+R5PlJbkvy9ST3JfndFj8qya3twsdnkuzb4vu19Y1t+5F9x3pPi38jyRv64qe02MYkF0zMQZJG0VwaXrIMWNWWVwGn98Wvqp5bgAVJDh1GgpK0B3gKOLGqfgpYDJySZCnwQeCSduHjMeDc1v5c4LGqejlwSWtHkqOBs4BXAqcAf5BkryR7AZfRu2ByNHB2aytJI21YRXcBf57kjiQrWuwlVbUZoL0f0uKHAQ/37bupxSRJu6ldwPhuW92nvQo4EbimxSde+Bi/IHINcFKStPjqqnqqqr4JbASOa6+NVfVgVX0fWN3aStJI23tI5z2hqh5JcgiwLslf7qBtBsTqOY16xfsKgCOOOGJmspSkPVC7Gn0H8HJ6V6X/Gni8qra3Jv0XN5658FFV25M8Aby4xW/pO2z/PhMvlBw/SR7225JGxlCudFfVI+19C/B5eldGHh0fNtLet7Tmm4DD+3ZfCDwy4JhXVNWSqloyNjbWZfqSNK9V1dNVtZhef3oc8IpBzdr7ZBc+djc+KA/7bUkjY9aL7iQ/muSF48vAycC9wBpgeWu2HLi2La8BzmmzmCwFnhgfhiJJmrqqehy4CVhK736Z8V8/+y9uPHPho21/EbCNyS+I7NKFEkkaNcO40v0S4OYkXwduA/53Vf0Z8AHg9UkeAF7f1gHWAg/SGy/4R8B/mv2UJWnPkGQsyYK2vD/wc8AG4EbgjNZs4oWP8QsiZwA3VFW1+FltdpOj6M0wdRtwO7CozYayL72bLdd0/8kkaW6b9THdVfUg8FMD4n8PnDQgXsB5s5CaJI2CQ4FVbVz384Crq+oLSe4HVid5P/A14MrW/krgj5NspHeF+yyAqrovydXA/cB24LyqehogyfnAdcBewMqqum/2Pp4kzU3DupFSkjQEVXU3cMyA+IP0xndPjP8jcOYkx7oYuHhAfC29XyklSc1cmqdbkiRJ2iNZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnqmEW3JEmS1DGLbkmSJKljztMtSZJGzpde89php7BDr/3yl4adgmaYV7olSZKkjll0S5IkSR2z6JYkSZI6ZtEtSZIkdcyiW5IkSeqYRbckSZLUMYtuSZIkqWMW3ZIkSVLHLLolSZKkjll0S5IkSR2z6JYkSZI6ZtEtSSMkyeFJbkyyIcl9Sd7e4hcl+dskd7XXaX37vCfJxiTfSPKGvvgpLbYxyQV98aOS3JrkgSSfSbLv7H5KSZp7LLolabRsB95VVa8AlgLnJTm6bbukqha311qAtu0s4JXAKcAfJNkryV7AZcCpwNHA2X3H+WA71iLgMeDc2fpwkjRXWXRL0gipqs1VdWdbfhLYABy2g12WAaur6qmq+iawETiuvTZW1YNV9X1gNbAsSYATgWva/quA07v5NJI0f1h0S9KISnIkcAxwawudn+TuJCuTHNhihwEP9+22qcUmi78YeLyqtk+IDzr/iiTrk6zfunXrDHwiSZq7LLolaQQleQHwWeAdVfUd4HLgZcBiYDPwofGmA3avKcSfG6y6oqqWVNWSsbGx3fwEkjS/DK3obmMCv5bkC2194I03SfZr6xvb9iOHlbMk7QmS7EOv4P5UVX0OoKoeraqnq+oHwB/RGz4CvSvVh/ftvhB4ZAfxbwMLkuw9IS5JI22YV7rfTm8s4bjJbrw5F3isql4OXNLaSZKmoI25vhLYUFUf7osf2tfsTcC9bXkNcFa7AHIUsAi4DbgdWNQumOxL72bLNVVVwI3AGW3/5cC1XX4mSZoPhlJ0J1kI/Bvg4219RzfeLGvrtO0ntfaSpN13AvBm4MQJ0wP+P0nuSXI38LPAbwBU1X3A1cD9wJ8B57Ur4tuB84Hr6F1Aubq1BXg38M4kG+mN8b5yFj+fJM1Je++8SSd+H/gt4IVtfUc33jxzs05VbU/yRGv/7f4DJlkBrAA44ogjOk1ekuarqrqZweOu1+5gn4uBiwfE1w7ar6oe5NnhKZIkhnClO8nPA1uq6o7+8ICmtQvbng14Q44kSZLmqGFc6T4BeGP7OfP5wAH0rnwvSLJ3u9rdf+PN+M06m9qNOS8Cts1+2pIkSdLUzPqV7qp6T1UtrKoj6d14c0NV/Vsmv/FmTVunbb+h3agjSZIkzQtzaZ7uyW68uRJ4cYu/E7hgSPlJkiRJUzKsGykBqKqbgJva8sAbb6rqH4EzZzUxSZIkaQbNpSvdkiRJ0h7JoluSJEnqmEW3JEmS1DGLbkmSJKljFt2SJElSxyy6JUmSpI5ZdEuSJEkds+iWJEmSOmbRLUmSJHXMoluSJEnq2LSK7iTX70pMkjTz7IMlaf7Yeyo7JXk+8CPAwUkOBNI2HQC8dIZykyQNYB8sSfPPlIpu4NeBd9Dr3O/g2Q7/O8BlM5CXJGly9sGSNM9Mqeiuqo8AH0nytqr66AznJEnaAftgSZp/pnqlG4Cq+miSnwGO7D9WVV01zbwkSTsxlT44yeHAVcCPAT8ArqiqjyQ5CPhMO9ZDwC9V1WNJAnwEOA34HvBrVXVnO9Zy4P9qh35/Va1q8VcDnwT2B9YCb6+qmplPLUnz07SK7iR/DLwMuAt4uoWLXocuSerQFPvg7cC7qurOJC8E7kiyDvg14Pqq+kCSC4ALgHcDpwKL2ut44HLg+FakXwgsaee8I8maqnqstVkB3EKv6D4F+OKMfXBJmoemVXTT62yP9gqGJA3FbvfBVbUZ2NyWn0yyATgMWAa8rjVbBdxEr+heBlzVznFLkgVJDm1t11XVNoBWuJ+S5CbggKr6aotfBZyORbekETfdebrvpfcTpSRp9k2rD05yJHAMcCvwklaQjxfmh7RmhwEP9+22qcV2FN80ID7o/CuSrE+yfuvWrVP9GJI0L0z3SvfBwP1JbgOeGg9W1RuneVxJ0s5NuQ9O8gLgs8A7quo7vaHbg5sOiNUU4s8NVl0BXAGwZMkSfzGVtEebbtF90UwkIUmakoumslOSfegV3J+qqs+18KNJDq2qzW34yJYW3wQc3rf7QuCRFn/dhPhNLb5wQHtJGmnTnb3kSzOViCRp90ylD26zkVwJbKiqD/dtWgMsBz7Q3q/ti5+fZDW9GymfaIX5dcB/aw/nATgZeE9VbUvyZJKl9IatnAM4raGkkTfd2Uue5NmfDfcF9gH+oaoOmG5ikqQdm2IffALwZuCeJHe12G/TK7avTnIu8C3gzLZtLb3pAjfSmzLwLQCtuH4fcHtr997xmyqBt/LslIFfxJsoJWnaV7pf2L+e5HTguGllJEnaJVPpg6vqZgaPuwY4aUD7As6b5FgrgZUD4uuBV+0oD0kaNdOdveSHVNWfACfO5DElSbvGPliS5q7pDi/5xb7V5/HsQxJ2tM/zgS8D+7XzX1NVFyY5ClgNHATcCby5qr6fZD96D3p4NfD3wC9X1UPTyVuS9gRT6YMlScMx3dlLfqFveTu9Rwcv28k+TwEnVtV32x30Nyf5IvBO4JKqWp3kD4Fz6T3V7Fzgsap6eZKzgA8CvzzNvCVpTzCVPliSNATTHdP9linsU8B32+o+7VX0fhL9lRZfRW8qrMvp/QNyUYtfA3wsSXwKpqRRN5U+WJI0HNMa051kYZLPJ9mS5NEkn02ycBf226vdNb8FWAf8NfB4VW1vTfqfYPbMU8/a9ieAF08nb0naE0y1D5Ykzb7pDi/5BPA/eXZqqV9tsdfvaKeqehpYnGQB8HngFYOatfdderpZkhXACoAjjjhiV3LXHHDCR08Ydgo79Bdv+4thpyDtyJT6YEnS7Jvu7CVjVfWJqtreXp8ExnZ156p6nN4TzJYCC5KM/09A/xPMnnkaWtv+ImAbE1TVFVW1pKqWjI3tcgqSNJ9Nqw+WJM2e6V7p/naSXwU+3dbPpjfDyKSSjAH/VFWPJ9kf+Dl6N0feCJxBbwaTiU9DWw58tW2/wfHcUnc+9rZTStgAACAASURBVK4/HXYKO3X+h35h541Gw273wZKk4Zjule5/B/wS8HfAZnpF8c5u7DkUuDHJ3fSeZLauqr4AvBt4Z5KN9MZsX9naXwm8uMXfCVwwzZwlaU8xlT5YkjQE073S/T5geVU9BpDkIOD36P1DMFBV3Q0cMyD+IAOepFZV/8iz4xUlSc/a7T5YkjQc073S/S/HO3uAqtrGgIJaktQJ+2BJmiemW3Q/L8mB4yvtKst0r55LknaNfbAkzRPT7Zw/BHwlyTX0pvH7JeDiaWclSdoV9sGSNE9M94mUVyVZT+9pkgF+sarun5HMJEk7ZB8sSfPHtH+GbB28nbwkDYF9sCTND9Md0y1JkiRpJyy6JUmSpI5ZdEuSJEkds+iWJEmSOmbRLUkjJMnKJFuS3NsXuyjJ3ya5q71O69v2niQbk3wjyRv64qe02MYkF/TFj0pya5IHknwmyb6z9+kkae6y6Jak0fJJ4JQB8UuqanF7rQVIcjRwFvDKts8fJNkryV7AZcCpwNHA2a0twAfbsRYBjwHndvppJGmesOiWpBFSVV8Gtu1i82XA6qp6qqq+CWwEjmuvjVX1YFV9H1gNLEsSenOGX9P2XwWcPqMfQJLmKYtuSRLA+UnubsNPxh8tfxjwcF+bTS02WfzFwONVtX1CfKAkK5KsT7J+69atM/U5JGlOsuiWJF0OvAxYDGym93h56D3lcqKaQnygqrqiqpZU1ZKxsbHdy1iS5plpP5FSkjS/VdWj48tJ/gj4QlvdBBze13Qh8EhbHhT/NrAgyd7tand/e0kaaV7plqQRl+TQvtU3AeMzm6wBzkqyX5KjgEXAbcDtwKI2U8m+9G62XFNVBdwInNH2Xw5cOxufQZLmOq90S9IISfJp4HXAwUk2ARcCr0uymN5QkIeAXweoqvuSXA3cD2wHzquqp9txzgeuA/YCVlbVfe0U7wZWJ3k/8DXgyln6aJI0p1l0S9IIqaqzB4QnLYyr6mLg4gHxtcDaAfEH6c1uIknq4/ASSZIkqWMW3ZIkSVLHLLolSZKkjll0S5IkSR2z6JYkSZI6ZtEtSZIkdcyiW5IkSerYrBfdSQ5PcmOSDUnuS/L2Fj8oybokD7T3A1s8SS5NsjHJ3UmOne2cJUmSpOkYxpXu7cC7quoVwFLgvCRHAxcA11fVIuD6tg5wKr1HDy8CVgCXz37KkiRJ0tTNetFdVZur6s62/CSwATgMWAasas1WAae35WXAVdVzC7AgyaGznLYkSZI0ZUMd053kSOAY4FbgJVW1GXqFOXBIa3YY8HDfbptaTJIkSZoXhlZ0J3kB8FngHVX1nR01HRCrAcdbkWR9kvVbt26dqTQlSZKkaRtK0Z1kH3oF96eq6nMt/Oj4sJH2vqXFNwGH9+2+EHhk4jGr6oqqWlJVS8bGxrpLXpIkSdpNw5i9JMCVwIaq+nDfpjXA8ra8HLi2L35Om8VkKfDE+DAUSZIkaT7YewjnPAF4M3BPkrta7LeBDwBXJzkX+BZwZtu2FjgN2Ah8D3jL7KYrSZIkTc+sF91VdTODx2kDnDSgfQHndZqUJEmS1CGfSClJkiR1zKJbkiRJ6phFtyRJktQxi25JGiFJVibZkuTevthBSdYleaC9H9jiSXJpko1J7k5ybN8+y1v7B5Is74u/Osk9bZ9L24xVkjTyLLolabR8EjhlQuwC4PqqWgRc39YBTgUWtdcK4HLoFenAhcDxwHHAheOFemuzom+/ieeSpJFk0S1JI6SqvgxsmxBeBqxqy6uA0/viV1XPLcCC9vCyNwDrqmpbVT0GrANOadsOqKqvtpmnruo7liSNNItuSdJLxh861t4PafHDgIf72m1qsR3FNw2ID5RkRZL1SdZv3bp12h9CkuYyi25J0mQGjceuKcQHqqorqmpJVS0ZGxubYoqSND9YdEuSHm1DQ2jvW1p8E3B4X7uFwCM7iS8cEJekkWfRLUlaA4zPQLIcuLYvfk6bxWQp8EQbfnIdcHKSA9sNlCcD17VtTyZZ2mYtOafvWJI00mb9MfCSpOFJ8mngdcDBSTbRm4XkA8DVSc4FvgWc2ZqvBU4DNgLfA94CUFXbkrwPuL21e29Vjd+c+VZ6M6TsD3yxvSRp5Fl06/9v7+6jLavKO99/fwFRr2+gFITwYmmstkUTAWsgnUobFMOLecEkmECiloR02d1oqzFGjD2CIbGvdqIohiaXCFIYFbko1wqDSLgoGk1ACkVeNZRISwWkSkHQGPWCz/1jz4PbzT51TtWpdfZe53w/Y+yx15prrrWfydk198Nac80laRmpqhNm2XTEmLoFnDzLcc4Fzh1TvhF41kJilKSlyOElkiRJUsdMuiVJkqSOmXRLkiRJHTPpliRJkjpm0i1JkiR1zKRbkiRJ6phJtyRJktQxk25JkiSpYybdkiRJUsdMuiVJkqSO+Rh4SZI0L2ves2bSIczps6/+7KRDkMbyTLckSZLUMZNuSZIkqWMTSbqTnJtkS5Ibh8qemOTyJLe29z1aeZKckWRTkuuTHDKJmCVJkqQdNakz3ecBR4+UnQJcUVWrgCvaOsAxwKr2WgectUgxSpIkSTvFRJLuqvo0cM9I8bHA+ra8HnjxUPn5NXAVsHuSfRYnUkmSJGnhpmlM995VdRdAe9+rle8L3DFUb3Mr+zFJ1iXZmGTj1q1bOw9WkiRJmq9pSrpnkzFl9bCCqrOranVVrV6xYsUihCVJkiTNzzQl3XfPDBtp71ta+WZg/6F6+wF3LnJskiRJ0g6bpqR7A7C2La8FPjZU/vI2i8lhwH0zw1AkSTtPktuT3JDkuiQbW9l2zyyVZG2rf2uStbN9niQtJ5OaMvBDwD8BT0+yOclJwNuAX0xyK/CLbR3gUuA2YBPw18B/nUDIkrRcPL+qDqqq1W19u2aWSvJE4FTgucChwKkzibokLWcTeQx8VZ0wy6YjxtQt4ORuI5IkzeJY4PC2vB64EngjQzNLAVclmZlZ6nDg8qq6ByDJ5QymiP3Q4oYtSdNlmoaXSJImq4C/T3JtknWtbHtnlprXjFPgrFOSlpeJnOmWJE2lNVV1Z5K9gMuTfGkbdWebWWpeM07BYNYp4GyA1atXj60jSUuFZ7olSQBU1Z3tfQtwMYMx2ds7s5QzTknSGCbdkiSSPCbJ42aWgSOBG9n+maUuA45Mske7gfLIViZJy5rDSyRJAHsDFyeBwW/DB6vq40muAS5ss0x9DXhJq38p8CIGM0t9FzgRoKruSfKnwDWt3mkzN1VK0nJm0i1JoqpuA549pvybbOfMUlV1LnDuzo5RkvrMpLuHvnbaz0w6hDkd8Mc3TDoESZKkqeGYbkmSJKljJt2SJElSx0y6JUmSpI6ZdEuSJEkdM+mWJEmSOmbSLUmSJHXMpFuSJEnqmEm3JEmS1DGTbkmSJKljJt2SJElSx0y6JUmSpI6ZdEuSJEkdM+mWJEmSOmbSLUmSJHXMpFuSJEnqmEm3JEmS1LFdJx2AJElL2ddO+5lJhzCnA/74hkmHIC15numWJEmSOtabpDvJ0Um+nGRTklMmHY8kaXb22ZL043qRdCfZBTgTOAY4EDghyYGTjUqSNI59tiQ9XF/GdB8KbKqq2wCSXAAcC9w83wM85w3ndxTaznPtn7980iFI0s6w4D5b0vLz1pceN+kQ5vTmv7loh/dNVe3EULqR5Djg6Kr6vbb+MuC5VfWqoTrrgHVt9enAlxchtD2BbyzC53RtqbQDbMu0WiptWax2PLmqVizC53RiPn12K1/sfnupfA9h6bRlqbQDbMu0Woy2zKvP7suZ7owp+7H/W6iqs4GzFyecgSQbq2r1Yn5mF5ZKO8C2TKul0pal0o5FMGefDYvfby+lv99SactSaQfYlmk1TW3pxZhuYDOw/9D6fsCdE4pFkrRt9tmSNKIvSfc1wKokT0myG3A8sGHCMUmSxrPPlqQRvRheUlUPJHkVcBmwC3BuVd004bBgkYezdGiptANsy7RaKm1ZKu3olH32olgqbVkq7QDbMq2mpi29uJFSkiRJ6rO+DC+RJEmSesukW5IkSeqYSfcckpybZEuSG2fZniRntEcdX5/kkMWOcT6S7J/kk0luSXJTkteMqdOXtjwqyeeSfLG15U/G1Hlkkg+3tlydZOXiRzo/SXZJ8oUkl4zZ1qd23J7khiTXJdk4Znsvvl8ASXZPclGSL7V/M/9hZHtv2rLc2GdPn6XWZ4P99rTpS59t0j2384Cjt7H9GGBVe60DzlqEmHbEA8Drq+oZwGHAyXn4Y5n70pbvAy+oqmcDBwFHJzlspM5JwL1V9TTgdODtixzj9ngNcMss2/rUDoDnV9VBs8yJ2pfvF8C7gY9X1b8Hns3D/z59astycx722dNmqfXZYL89bXrRZ5t0z6GqPg3cs40qxwLn18BVwO5J9lmc6Oavqu6qqs+35W8z+ELuO1KtL22pqvpOW31Ee43eEXwssL4tXwQckWTcAzsmKsl+wC8B752lSi/aMU+9+H4leTzwPOAcgKr6QVV9a6RaL9qyHNlnT2VblkyfDfbb0/Yd61OfbdK9cPsCdwytb+bhHeNUaZe6DgauHtnUm7a0S3vXAVuAy6tq1rZU1QPAfcCTFjfKeXkX8IfAD2fZ3pd2wOBH9O+TXJvB471H9eX79VRgK/C+dvn4vUkeM1KnL23Rw/Xub2efPXXst6dLb/psk+6Fm9fjjqdFkscCHwFeW1X3j24es8tUtqWqHqyqgxg86e7QJM8aqTL1bUnyy8CWqrp2W9XGlE1VO4asqapDGFzGOznJ80a296UtuwKHAGdV1cHAvwKnjNTpS1v0cL3629lnTxf7bWD62tKbPtuke+F687jjJI9g0Hl/oKo+OqZKb9oyo11CupKHj+F8qC1JdgWewLYvOU/CGuBXk9wOXAC8IMnfjNTpQzsAqKo72/sW4GLg0JEqffl+bQY2D52Ju4hBhz5apw9t0cP15m9nnz2VfZ399vR9x3rTZ5t0L9wG4OXtztjDgPuq6q5JBzWqjSc7B7ilqt45S7W+tGVFkt3b8qOBFwJfGqm2AVjblo8DPlFT9iSoqnpTVe1XVSsZPCb7E1X10pFqU98OgCSPSfK4mWXgSGB09ohefL+q6uvAHUme3oqOAG4eqdaLtmisXvzt7LOns6+z356+71if+uxePAZ+kpJ8CDgc2DPJZuBUBjeBUFV/BVwKvAjYBHwXOHEykc5pDfAy4IY2rg7gj4ADoHdt2QdYn2QXBv/jeGFVXZLkNGBjVW1g8GP1/iSbGJxhOH5y4W6fnrZjb+Didq/QrsAHq+rjSf4z9O77BfBq4ANJdgNuA07scVuWFfvsqbSk+2yw354CveizfQy8JEmS1DGHl0iSJEkdM+mWJEmSOmbSLUmSJHXMpFuSJEnqmEm3JEmS1DGTbk2lJA8muW7otTLJ4UkuadtfkeSHSX52aJ8b2+OSZ9YPTlJJjho5diV5x9D6HyR5S1u+bORz70wy+rji4WMdluTqVveWoeO8IsnWVn5zkv80st/HkvzTSNlbkvzL0D4nDG07L8lXh+L6xzGf86Ukr5v/f2VJ2jnss+2zNTeTbk2rf6uqg4Zet4+psxl48zaOcQLwmfY+7PvAryfZc3SHqjpq5jMZzJN7P/Dft/EZ64F1rf6zgAuHtn24lR8O/I8kewNk8JCIQ4Ddkzxl5Hint32OBf6vDJ5IN+MNQ/89fm7M56wB3pxk+KlbkrQY7LPtszUHk2712SXAM/Ojp1A9JIPZ/o8DXgEcmeRRQ5sfAM4G5jrD8G7g0qq6fBt19gLuAqiqB6tq9ClYM4/X/Qrw5Fb0G8DfMniE8NiHJlTVrQwm8N9jjhiH9/kmg4n/95nvPpK0iOyzf3wf++xlxqRb0+rRQ5flLp6lzg+B/8ngKW2j1gBfraqvAFcyeBLVsDOB30nyhHEHTvJrwGrgTXPEeTrw5SQXJ3nlyA/FzLGeCjyVQecKg7M4H2qv0TM6M/scAtzaOv8Zfz703+QDY/Y5AHgUcP0cMUvSzmafbZ+tOfgYeE2rf2uX3+byQQaX50Yv+Z3A4KwE7f1lwEdnNlbV/UnOB/4b8G/DOybZFzgDOKqqvr+tD6+q01pneiTw2+1zD2+bfyvJzzO4NPrKqrqnXa58GvCZqqokDyR5VlXd2PZ5XRtL+FTg6JGPe0NVXTQmjN9K8nzg6cB/qqrvbStmSeqAfbZ9tubgmW71WlU9ALwDeONMWZJdGFwO/OMktwPvAY5J8riR3d8FnAQ8ZmjfMBjz97Zxlx1nieErVXUWcATw7CRPaps+3MbyPbeqZs78/BaDy49fbbGt5McvV55eVU9v9c4fdxZmjA9X1TOB/wi8I8lPziduSVps9tkPfY599jJk0q2l4DzghcCKtv5C4ItVtX9VrayqJwMfAV48vFNV3cPgJpqThor/APheVZ05nw9O8kut0wdYBTwIfGsbu5wAHN3iWgk8hzFjBKvqo8BGYO184mj7/BPwfuA1891HkibgPOyz7bOXIZNu9V5V/YDBpcW9WtEJwOiYwo8wuJQ46h3A8B3xfwY8Y2QKqk9u4+NfxmB84HUMOs/fqaoHx1XMYGqsA4CrhmL/KnB/kueO2eU04PeTzPw7/fORuHYbs8/bgRPHnCGSpKlgn/1j7LOXkVTVpGOQJEmSljTPdEuSJEkdc/YSaR6SnMlgSqth766q900iHknS7OyzNY0cXiJJkiR1zOElkiRJUsdMuiVJkqSOmXRLkiRJHTPpliRJkjpm0i1JkiR1zKRbkiRJ6phJtyQtI0keleRzSb6Y5KYkf9LKn5Lk6iS3JvnwzCOrkzyyrW9q21cOHetNrfzLSY4aKj+6lW1Kcspit1GSppFJtyQtL98HXlBVzwYOAo5OchjwduD0qloF3Auc1OqfBNxbVU8DTm/1SHIgcDzwTOBo4H8l2SXJLsCZwDHAgcAJra4kLWsm3ZK0jNTAd9rqI9qrgBcAF7Xy9cCL2/KxbZ22/YgkaeUXVNX3q+qrwCbg0PbaVFW3VdUPgAtaXUla1pbkY+D33HPPWrly5aTDkKQdcu21136jqlZ0dfx2Nvpa4GkMzkp/BfhWVT3QqmwG9m3L+wJ3AFTVA0nuA57Uyq8aOuzwPneMlD93rpjstyX11Xz77CWZdK9cuZKNGzdOOgxJ2iFJ/neXx6+qB4GDkuwOXAw8Y1y1mXBm2TZb+bgrqDWmjCTrgHUABxxwgP22pF6ab5/t8BJJWqaq6lvAlcBhwO5JZk7E7Afc2ZY3A/sDtO1PAO4ZLh/ZZ7bycZ9/dlWtrqrVK1Z0dmJfkqaCSbckLSNJVrQz3CR5NPBC4Bbgk8Bxrdpa4GNteUNbp23/RFVVKz++zW7yFGAV8DngGmBVmw1lNwY3W27ovmWSNN2W5PASSdKs9gHWt3HdPwFcWFWXJLkZuCDJnwFfAM5p9c8B3p9kE4Mz3McDVNVNSS4EbgYeAE5uw1ZI8irgMmAX4NyqumnxmidJ08mkW5KWkaq6Hjh4TPltDGYeGS3/HvCSWY71VuCtY8ovBS5dcLCStIQ4vESSJEnqmEm3JEmS1DGTbkmSJKljJt2SJElSx7yRUhO15j1rJh3CNn321Z+ddAiSpJ74y9f/7aRDeMir3vErkw5BIzzTLUmSJHVsIme624MZ3gs8i8HjgX8X+DLwYWAlcDvwm1V1b5IA7wZeBHwXeEVVfX4CYUuSpEX0qef9wqRDeMgvfPpTkw5BPTepM93vBj5eVf8eeDaDp6GdAlxRVauAK9o6wDEMnnS2ClgHnLX44UqSJEk7btGT7iSPB55He9pZVf2gqr4FHAusb9XWAy9uy8cC59fAVcDuSfZZ5LAlSZKkHTaJM91PBbYC70vyhSTvTfIYYO+qugugve/V6u8L3DG0/+ZWJkmSJPXCJJLuXYFDgLOq6mDgX/nRUJJxMqasHlYpWZdkY5KNW7du3TmRSpIkSTvBJJLuzcDmqrq6rV/EIAm/e2bYSHvfMlR//6H99wPuHD1oVZ1dVauravWKFSs6C16SJEnaXouedFfV14E7kjy9FR0B3AxsANa2srXAx9ryBuDlGTgMuG9mGIokSZLUB5N6OM6rgQ8k2Q24DTiRwf8AXJjkJOBrwEta3UsZTBe4icGUgScufriSJEnSjptI0l1V1wGrx2w6YkzdAk7uPChJkiSpIz6RUpIkSeqYSbckSZLUMZNuSZIkqWMm3ZIkSVLHTLolSZKkjpl0S5IkSR0z6ZYkSZI6ZtItSZIkdcykW5IkSeqYSbckSZLUMZNuSZIkqWMm3ZIkSVLHTLolaRlJsn+STya5JclNSV7Tyt+S5F+SXNdeLxra501JNiX5cpKjhsqPbmWbkpwyVP6UJFcnuTXJh5PstritlKTpY9ItScvLA8Drq+oZwGHAyUkObNtOr6qD2utSgLbteOCZwNHA/0qyS5JdgDOBY4ADgROGjvP2dqxVwL3ASYvVOEmaVibdkrSMVNVdVfX5tvxt4BZg323scixwQVV9v6q+CmwCDm2vTVV1W1X9ALgAODZJgBcAF7X91wMv7qY1ktQfJt2StEwlWQkcDFzdil6V5Pok5ybZo5XtC9wxtNvmVjZb+ZOAb1XVAyPlkrSsmXRL0jKU5LHAR4DXVtX9wFnATwMHAXcB75ipOmb32oHycTGsS7IxycatW7duZwskqV9MuiVpmUnyCAYJ9weq6qMAVXV3VT1YVT8E/prB8BEYnKnef2j3/YA7t1H+DWD3JLuOlD9MVZ1dVauravWKFSt2TuMkaUqZdEvSMtLGXJ8D3FJV7xwq32eo2q8BN7blDcDxSR6Z5CnAKuBzwDXAqjZTyW4MbrbcUFUFfBI4ru2/FvhYl22SpD7Yde4qkqQlZA3wMuCGJNe1sj9iMPvIQQyGgtwOvBKgqm5KciFwM4OZT06uqgcBkrwKuAzYBTi3qm5qx3sjcEGSPwO+wCDJl6RlzaRbkpaRqvoM48ddX7qNfd4KvHVM+aXj9quq2/jR8BRJEg4vkSRJkjpn0i1JkiR1zKRbkiRJ6thEku4ktye5Icl1STa2sicmuTzJre19j1aeJGck2dQe2nDIJGKWJEmSdtQkz3Q/v6oOqqrVbf0U4IqqWgVc0dYBjmEwRdUqYB2DBzhIkiRJvTFNw0uOBda35fXAi4fKz6+Bqxg8dGGfcQeQJEmSptGkku4C/j7JtUnWtbK9q+ougPa+VyvfF7hjaN/NrUySJEnqhUnN072mqu5MshdweZIvbaPuuPlk62GVBsn7OoADDjhg50QpSZIk7QQTOdNdVXe29y3AxQweonD3zLCR9r6lVd8M7D+0+37AnWOOeXZVra6q1StWrOgyfEmSJGm7LHrSneQxSR43swwcCdwIbADWtmprgY+15Q3Ay9ssJocB980MQ5EkSZL6YBLDS/YGLk4y8/kfrKqPJ7kGuDDJScDXgJe0+pcCLwI2Ad8FTlz8kCVJkqQdt+hJd1XdBjx7TPk3gSPGlBdw8iKEJkmSJHVimqYMlCRJkpYkk25JkiSpYybdkiRJUsdMuiVJkqSOmXRLkiRJHTPpliRJkjpm0i1JkiR1zKRbkiRJ6phJtyRJktQxk25JkiSpYybdkiRJUsdMuiVJkqSOmXRLkiRJHTPpliRJkjpm0i1JkiR1zKRbkpaRJPsn+WSSW5LclOQ1rfyJSS5Pcmt736OVJ8kZSTYluT7JIUPHWtvq35pk7VD5c5Lc0PY5I0kWv6WSNF1MuiVpeXkAeH1VPQM4DDg5yYHAKcAVVbUKuKKtAxwDrGqvdcBZMEjSgVOB5wKHAqfOJOqtzrqh/Y5ehHZJ0lQz6ZakZaSq7qqqz7flbwO3APsCxwLrW7X1wIvb8rHA+TVwFbB7kn2Ao4DLq+qeqroXuBw4um17fFX9U1UVcP7QsSRp2TLplqRlKslK4GDgamDvqroLBok5sFerti9wx9Bum1vZtso3jykf9/nrkmxMsnHr1q0LbY4kTTWTbklahpI8FvgI8Nqqun9bVceU1Q6UP7yw6uyqWl1Vq1esWDFXyJLUaybdkrTMJHkEg4T7A1X10VZ8dxsaQnvf0so3A/sP7b4fcOcc5fuNKZekZc2kW5KWkTaTyDnALVX1zqFNG4CZGUjWAh8bKn95m8XkMOC+NvzkMuDIJHu0GyiPBC5r276d5LD2WS8fOpYkLVu7TjoASdKiWgO8DLghyXWt7I+AtwEXJjkJ+BrwkrbtUuBFwCbgu8CJAFV1T5I/Ba5p9U6rqnva8n8BzgMeDfxde0nSsmbSLe0kn3reL0w6hG36hU9/atIhaApU1WcYP+4a4Igx9Qs4eZZjnQucO6Z8I/CsBYQpSUvOxIaXJNklyReSXNLWn5Lk6vaQhQ8n2a2VP7Ktb2rbV04qZkmSJGlHTHJM92sYzA874+3A6e3BDPcCJ7Xyk4B7q+ppwOmtniRJktQbE0m6k+wH/BLw3rYe4AXARa3K6IMZZh7YcBFwhI8UliRJUp9M6kz3u4A/BH7Y1p8EfKuqHmjrww9TeOgBDG37fa2+JEmS1AuLnnQn+WVgS1VdO1w8pmrNY9vwcX2ymSRJkqbSJM50rwF+NcntwAUMhpW8C9g9ycxsKsMPU3joAQxt+xOAexjhk80kSZI0rRY96a6qN1XVflW1Ejge+ERV/Q7wSeC4Vm30wQwzD2w4rtUf+0hhSZIkaRpN0xMp3wj8fpJNDMZsn9PKzwGe1Mp/HzhlQvFJkiRJO2SiD8epqiuBK9vybcChY+p8jx89GU2SJEnqnWk60y1JkiQtSSbdkiRJUsdMuiVJkqSOmXRLkiRJHTPpliRJkjpm0i1JkiR1zKRbkiRJ6phJtyRJktQxk25JkiSpYwtKupNcMZ8ySdLOZx8sSf2xQ4+BT/Io4P8A9kyyB5C26fHAT+2k2CRJY9gHS1L/7FDSDbwSeC2Dzv1aftTh3w+cPnhWiAAAFpNJREFUuRPikiTNzj5Yknpmh5Luqno38O4kr66q9+zkmCRJ22AfLEn9s6NnugGoqvck+Tlg5fCxqur8BcYlSZqDfbAk9ceCku4k7wd+GrgOeLAVF2CHL0kdsw+WpP5YUNINrAYOrKraGcFIkraLfbAk9cRC5+m+EfjJnRGIJGm72QdLUk8sNOneE7g5yWVJNsy8dkZgkqQ5bXcfnOTcJFuS3DhU9pYk/5LkuvZ60dC2NyXZlOTLSY4aKj+6lW1KcspQ+VOSXJ3k1iQfTrLbTm6zJPXSQoeXvGVnBCFJ2iFv2YF9zgP+koeP+z69qv5iuCDJgcDxwDMZTE/4/yb5d23zmcAvApuBa5JsqKqbgbe3Y12Q5K+Ak4CzdiBOSVpSFjp7yad2ViCSpO2zI31wVX06ycp5Vj8WuKCqvg98Nckm4NC2bVNV3QaQ5ALg2CS3AC8AfrvVWc/gfwxMuiUtewt9DPy3k9zfXt9L8mCS+3dWcJKk2e3kPvhVSa5vw0/2aGX7AncM1dncymYrfxLwrap6YKRckpa9BSXdVfW4qnp8ez0K+A0Gly0lSR3biX3wWQymHjwIuAt4RyvPmLq1A+VjJVmXZGOSjVu3bt2+iCWpZxZ6I+WPqar/h8GlRUnSItvRPriq7q6qB6vqh8Bf86MhJJuB/Yeq7gfcuY3ybwC7J9l1pHy2zz27qlZX1eoVK1Zsb9iS1CsLfTjOrw+t/gSDOWOdL1aSFsHO6oOT7FNVd7XVX2MwFSHABuCDSd7J4EbKVcDnGJzRXpXkKcC/MLjZ8rerqpJ8EjgOuABYC3xsuxsmSUvQQmcv+ZWh5QeA2xnceCNJ6t5298FJPgQcDuyZZDNwKnB4koMYJOy3A68EqKqbklwI3NyOf3JVPdiO8yrgMmAX4Nyquql9xBuBC5L8GfAF4JwFt1KSloCFzl5y4vbuk+RRwKeBR7bPv6iqTm1nTC4Angh8HnhZVf0gySMZTG31HOCbwG9V1e0LiVuSloId6YOr6oQxxbMmxlX1VuCtY8ovBS4dU34bPxqeIklqFjp7yX5JLm4PWrg7yUeS7DfHbt8HXlBVz2Zw087RSQ7jR3O7rgLuZTC3K+393qp6GnB6qydJy94O9sGSpAlY6I2U72Mw5u+nGEwL9betbFY18J22+oj2KgY3/1zUytcDL27Lx7Z12vYjkoy7Q16Slpvt7oMlSZOx0KR7RVW9r6oeaK/zgDlvQU+yS5LrgC3A5cBXmH1u14fmg23b72MwF+zoMZ16StJys0N9sCRp8S006f5Gkpe2JHqXJC9lMO56m9rUVAcxmE7qUOAZ46q193nN++rUU5KWoR3qgyVJi2+hSffvAr8JfJ3BAxWOA+Z9Y09VfQu4EjiM2ed2fWg+2Lb9CcA9C4xbkpaCBfXBkqTFs9Ck+0+BtVW1oqr2YvAD8JZt7ZBkRZLd2/KjgRcCtwAzc7vCj8/tuqGt07Z/oqqcC1ySdqAPliRNxkLn6f7Zqrp3ZqWq7kly8Bz77AOsT7ILg6T/wqq6JMnNjJ/b9Rzg/Uk2MTjDffwCY5akpWJH+mBJ0gQsNOn+iSR7zHT6SZ441zGr6nrgYT8Ks83tWlXfA16ywDglaSna7j5YkjQZC+2c3wH8Y5KLGNzc+JuMeYiCJKkT9sGS1BMLfSLl+Uk2MphjO8CvV9XNOyUySdI22QdLUn8s+DJk6+Dt5CVpAuyDJakfFjp7iSRJkqQ5mHRLkiRJHTPpliRJkjpm0i1JkiR1zKRbkiRJ6phJtyRJktQxk25JkiSpYybdkiRJUsdMuiVJkqSOmXRLkiRJHTPpliRJkjpm0i1JkiR1zKRbkiRJ6phJtyRJktQxk25JkiSpYybdkiRJUsdMuiVpGUlybpItSW4cKntiksuT3Nre92jlSXJGkk1Jrk9yyNA+a1v9W5OsHSp/TpIb2j5nJMnitlCSppNJtyQtL+cBR4+UnQJcUVWrgCvaOsAxwKr2WgecBYMkHTgVeC5wKHDqTKLe6qwb2m/0syRpWTLplqRlpKo+DdwzUnwssL4trwdePFR+fg1cBeyeZB/gKODyqrqnqu4FLgeObtseX1X/VFUFnD90LEla1ky6JUl7V9VdAO19r1a+L3DHUL3NrWxb5ZvHlEvSsrfrpAOQJE2tceOxawfKxx88WcdgKAoHHHDAjsQnaQe99aXHTTqEh7z5by6adAiLYtHPdCfZP8knk9yS5KYkr2nl230jjyRpp7i7DQ2hvW9p5ZuB/Yfq7QfcOUf5fmPKx6qqs6tqdVWtXrFixYIbIUnTbBLDSx4AXl9VzwAOA05OciDbeSOPJGmn2QDMzECyFvjYUPnL28mPw4D72vCTy4Ajk+zRTpAcCVzWtn07yWFt1pKXDx1Lkpa1RR9e0jrlmbGD305yC4Mxf8cCh7dq64ErgTcydCMPcFWS3ZPsMzP+UJI0f0k+xKCv3TPJZgazkLwNuDDJScDXgJe06pcCLwI2Ad8FTgSoqnuS/ClwTat3WlXN3Jz5XxjMkPJo4O/aS5KWvYmO6U6yEjgYuJqRG3mSzHUjj0m3JG2nqjphlk1HjKlbwMmzHOdc4Nwx5RuBZy0kRklaiiY2e0mSxwIfAV5bVfdvq+qYsofdmJNkXZKNSTZu3bp1Z4UpSZIkLdhEku4kj2CQcH+gqj7airf3Rp4f4w05kiRJmlaTmL0kwDnALVX1zqFN23sjjyRJktQLkxjTvQZ4GXBDkuta2R+xnTfySJIkSX0xidlLPsP4cdqwnTfySJIkSX3gY+AlSZKkjpl0S5IkSR0z6ZYkSZI6ZtItSZIkdcykW5IkSeqYSbckSZLUMZNuSZIkqWMm3ZIkSVLHTLolSZKkjpl0S5IkSR0z6ZYkSZI6ZtItSZIkdcykW5IkSeqYSbckSZLUMZNuSZIkqWMm3ZIkSVLHTLolSZKkjpl0S5IkSR0z6ZYkSZI6ZtItSZIkdWzXSQeg7fe1035m0iHM6YA/vmHSIUiSJE0Nz3RLkiRJHTPpliQBkOT2JDckuS7Jxlb2xCSXJ7m1ve/RypPkjCSbklyf5JCh46xt9W9NsnZS7ZGkaWLSLUka9vyqOqiqVrf1U4ArqmoVcEVbBzgGWNVe64CzYJCkA6cCzwUOBU6dSdQlaTmbSNKd5NwkW5LcOFS23WdTJEmdOxZY35bXAy8eKj+/Bq4Cdk+yD3AUcHlV3VNV9wKXA0cvdtCSNG0mdab7PB7eCW/X2RRJ0k5XwN8nuTbJula2d1XdBdDe92rl+wJ3DO27uZXNVv4wSdYl2Zhk49atW3diMyRp+kwk6a6qTwP3jBRv79kUSdLOtaaqDmFwsuPkJM/bRt2MKattlD+8sOrsqlpdVatXrFix/dFKUo9M05ju7T2bIknaiarqzva+BbiYwZjsu2dOdLT3La36ZmD/od33A+7cRrkkLWt9mKd7XmdN2qXQdQAHHHBA1zFJ0pKS5DHAT1TVt9vykcBpwAZgLfC29v6xtssG4FVJLmBw0+R9VXVXksuA/zF08+SRwJsWsSmaw5r3rJl0CA/57Ks/O+kQpEUzTUn33Un2aZ32fM6m/JiqOhs4G2D16tVjL2VKmttfvv5vJx3CnF71jl+ZdAhL0d7AxUlg8Nvwwar6eJJrgAuTnAR8DXhJq38p8CJgE/Bd4ESAqronyZ8C17R6p1XV6HBCSVp2pinp3q6zKZMJUZKWpqq6DXj2mPJvAkeMKS/g5FmOdS5w7s6OUZL6bCJJd5IPAYcDeybZzGBO17exHWdTJEmSpL6YSNJdVSfMsmm7zqZIkiRJfTBNs5dIkiRJS5JJtyRJktQxk25JkiSpYybdkiRJUsdMuiVJkqSOmXRLkiRJHTPpliRJkjpm0i1JkiR1zKRbkiRJ6phJtyRJktQxk25JkiSpYybdkiRJUsdMuiVJkqSOmXRLkiRJHTPpliRJkjpm0i1JkiR1zKRbkiRJ6phJtyRJktQxk25JkiSpYybdkiRJUsdMuiVJkqSOmXRLkiRJHTPpliRJkjpm0i1JkiR1bNdJBzBfSY4G3g3sAry3qt62Pfs/5w3ndxLXznTtn7980iFI0k6x0D67T7522s9MOoSHHPDHN0w6BEmz6MWZ7iS7AGcCxwAHAickOXCyUUmSxrHPlqSH68uZ7kOBTVV1G0CSC4BjgZsnGpUkaRz7bEk71S1v/cSkQ3jIM978gh3ary9J977AHUPrm4HnTigWST3x1pceN+kQ5vTmv7lo0iF0wT5bkkakqiYdw5ySvAQ4qqp+r62/DDi0ql49VGcdsK6tPh348iKEtifwjUX4nK4tlXaAbZlWS6Uti9WOJ1fVikX4nE7Mp89u5V3323373vUp3j7FCv2Kt0+xQr/i7SrWefXZfTnTvRnYf2h9P+DO4QpVdTZw9mIGlWRjVa1ezM/swlJpB9iWabVU2rJU2rEI5uyzoft+u29/rz7F26dYoV/x9ilW6Fe8k461FzdSAtcAq5I8JcluwPHAhgnHJEkazz5bkkb04kx3VT2Q5FXAZQymnzq3qm6acFiSpDHssyXp4XqRdANU1aXApZOOY8SiDmfp0FJpB9iWabVU2rJU2tG5Kemz+/b36lO8fYoV+hVvn2KFfsU70Vh7cSOlJEmS1Gd9GdMtSZIk9ZZJ9xySnJtkS5IbZ9meJGck2ZTk+iSHLHaM85Fk/ySfTHJLkpuSvGZMnb605VFJPpfki60tfzKmziOTfLi15eokKxc/0vlJskuSLyS5ZMy2PrXj9iQ3JLkuycYx23vx/QJIsnuSi5J8qf2b+Q8j23vTluUgydFJvtz+HqeM2T41/4769JvSt9+NPv429Kn/71MfP7V9eFX52sYLeB5wCHDjLNtfBPwdEOAw4OpJxzxLnPsAh7TlxwH/DBzY07YEeGxbfgRwNXDYSJ3/CvxVWz4e+PCk495Ge34f+CBwyZhtfWrH7cCe29jei+9Xi3U98HtteTdg9762Zam/GNyo+RXgqe1v9cUxfdvU/Dvq029K3343+vjb0Kf+v099/LT24Z7pnkNVfRq4ZxtVjgXOr4GrgN2T7LM40c1fVd1VVZ9vy98GbmHw1LhhfWlLVdV32uoj2mv05oRjGfyjA7gIOCJJFinEeUuyH/BLwHtnqdKLdsxTL75fSR7PIDE6B6CqflBV3xqp1ou2LBMPPXK+qn4AzDxyftjU/Dvq029K3343+vbbsAT7/6n4LkxzH27SvXDjHnc82ilNlXaJ6mAGZwGG9aYt7ZLcdcAW4PKqmrUtVfUAcB/wpMWNcl7eBfwh8MNZtvelHTD4cfv7JNdm8KTBUX35fj0V2Aq8r132fW+Sx4zU6UtbloP5/C369O9oKr9bffnd6NlvQ9/6/7708VPbh5t0L9y4/+uc2ilhkjwW+Ajw2qq6f3TzmF2msi1V9WBVHcTgSXeHJnnWSJWpb0uSXwa2VNW126o2pmyq2jFkTVUdAhwDnJzkeSPb+9KWXRlc/j+rqg4G/hUYHSfcl7YsB/P5W/Tp7zV1sfbpd6Mvvw097f/70sdPbR9u0r1w83rc8TRI8ggGHecHquqjY6r0pi0z2iWjK4GjRzY91JYkuwJPYNuXdCdhDfCrSW5ncEn8BUn+ZqROH9oBQFXd2d63ABczuOw/rC/fr83A5qEzZBcx6MBH6/ShLcvBfP4Wvfl3xJR9t/r6u9GD34be9f896uOntg836V64DcDL252whwH3VdVdkw5qVBsHdg5wS1W9c5ZqfWnLiiS7t+VHAy8EvjRSbQOwti0fB3yi2t0T06Kq3lRV+1XVSgY3yXyiql46Um3q2wGQ5DFJHjezDBwJjM7O0IvvV1V9HbgjydNb0RHAzSPVetGWZWI+j5zvxb+jZmq+W3373ejTb0Pf+v8+9fHT3If35omUk5LkQ8DhwJ5JNgOnMrg5g6r6KwZPXHsRsAn4LnDiZCKd0xrgZcANbbwbwB8BB0Dv2rIPsD7JLgz+x/HCqrokyWnAxqrawOCH4v1JNjE4M3D85MLdPj1tx97Axe0en12BD1bVx5P8Z+jd9wvg1cAHWhJ3G3Bij9uypNUsj5yf1n9HPftN6dvvRu9/G6Y41r718VPZh/tESkmSJKljDi+RJEmSOmbSLUmSJHXMpFuSJEnqmEm3JEmS1DGTbkmSJKljJt2SJElSx0y6tSiSPJjkuqHXyiSHJ7mkbX9Fkh8m+dmhfW5MsnJo/eAkleSokWNXkncMrf9Bkre05ctGPvfOJFcziyTnJTlupOw7I+uvS/K9JE8YKntFkr8cqXdlktVt+XeT3JDk+tauY4c+76stti8lOXXkGCuS/H9JXjlHTA99fpK3JPmXkXbv3v5735fkC+2z/mK2/w6Sljf7bPts7Xwm3Vos/1ZVBw29bh9TZzPw5m0c4wTgM+192PeBX0+y5+gOVXXUzGcyeNDD/cB/36EW/Hgc1wC/Np/KSfZj0K6fr6qfBQ4Drh+q8oYW30HA2iRPGdr2EuAqHt7muZw+8t/7W638H6rqYOBg4JeTrNnO40paHuyz7bO1k5l0a5pcAjwzP3p060OShMFjcF8BHJnkUUObHwDOBl43x/HfDVxaVZfvaIBJfhp4LIMfgfl2qnsB3wa+A1BV36mqr46pN9Omfx0qOwF4PbBfkn13KOgxqurfgOuAnXZMScuOffaAfbbmxaRbi+XRQ5fNLp6lzg+B/8ngMcOj1gBfraqvAFcyeHzrsDOB3xm+fDgsya8Bq4E3zSPWPx++zDey7QTgQ8A/AE9Pstc8jvdF4G7gq0nel+RXxn0eg7NGF1TVlhbz/sBPVtXngAuB35rHZ8143VAbPjm6MckewCrg09txTEnLh322fbZ2MpNuLZbhS5XbusT3QeCwkct1MOg4L2jLFzByxqKq7gfOB/7b6AHb2YYzgN+uqu/PI9Y3DF/mG9l2PINO9ofARxlcSgSoWY5VVfUgcDSDsz7/DJw+M35x+POAnwSOSPJzQ591YVt+WJvHfdbQ8vClyucPlf/HJNcDXwcuqaqvz3FMScuTfbZ9tnayXScdgDSsqh5oN9i8caYsyS7AbwC/muTNQIAnJXlcVX17aPd3AZ8H3je0b4D1wNuq6uaFxNZuGFoFXD44LLsBtzE4Y/NNYI+RXZ4IfKO1q4DPAZ9LcnmL8S0jbf9OkiuBnwf+kUGHvXeS32lVfirJqqq6Ffi3JLtV1Q9GP2sO/1BVv5zk3wGfSXJxVY2eGZKkebHPts/W/HmmW9PoPOCFwIq2/kLgi1W1f1WtrKonAx8BXjy8U1Xdw+Asw0lDxX8AfK+qztwJcZ0AvKXFsLKqfgrYN8mTGdyksybJTwK0O+AfCdyR5KeSHDJ0nIOA/z168CS7As8FvtLGSD6mqvad+Tzg/2RwJgXgU8BL236PBn4TeNglydlU1T+3471xrrqSNIfzsM+2z9acTLo1ddqZgDMY3MwCg45zdEzhR4DfHrP7O4DhO+L/DHjGyFRM8+7oRhw/Jo6LgeOr6m7gNcClbazfu4AT2iXNRwB/0aZ8uo7BOL/XDB1jZnzg9cANDC6BztbmmcuVr2Fw9/91DO6U/7+ranis3+tG2rxyTHv+CnjemMvCkjRv9tn22ZqfDK6gSJIkSeqKZ7olSZKkjnkjpZalJGcymNJq2Lur6n3j6kuSJsc+W0uBw0skSZKkjjm8RJIkSeqYSbckSZLUMZNuSZIkqWMm3ZIkSVLHTLolSZKkjv3/cq0NpkJifW0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4561a6be80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cluster_comparison(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion 3.3: Compare Customer Data to Demographics Data\n",
    "\n",
    "(Double-click this cell and replace this text with your own text, reporting findings and conclusions from the clustering analysis. Can we describe segments of the population that are relatively popular with the mail-order company, or relatively unpopular with the company?)\n",
    "\n",
    "*We could say that our research proves that most of the features we are relatively popular with mail-company.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Congratulations on making it this far in the project! Before you finish, make sure to check through the entire notebook from top to bottom to make sure that your analysis follows a logical flow and all of your findings are documented in **Discussion** cells. Once you've checked over all of your work, you should export the notebook as an HTML document to submit for evaluation. You can do this from the menu, navigating to **File -> Download as -> HTML (.html)**. You will submit both that document and this notebook for your project submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}