customer_segments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Engineer Nanodegree\n",
    "## Unsupervised Learning\n",
    "## Project: Creating Customer Segments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to the third project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!\n",
    "\n",
    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.  \n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "In this project, you will analyze a dataset containing data on various customers' annual spending amounts (reported in *monetary units*) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.\n",
    "\n",
    "The dataset for this project can be found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers). For the purposes of this project, the features `'Channel'` and `'Region'` will be excluded in the analysis — with focus instead on the six product categories recorded for customers.\n",
    "\n",
    "Run the code block below to load the wholesale customers dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wholesale customers dataset has 440 samples with 6 features each.\n"
     ]
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Import supplementary visualizations code visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the wholesale customers dataset\n",
    "try:\n",
    "    data = pd.read_csv(\"customers.csv\")\n",
    "    data.drop(['Region', 'Channel'], axis = 1, inplace = True)\n",
    "    print \"Wholesale customers dataset has {} samples with {} features each.\".format(*data.shape)\n",
    "except:\n",
    "    print \"Dataset could not be loaded. Is the dataset missing?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "In this section, you will begin exploring the data through visualizations and code to understand how each feature is related to the others. You will observe a statistical description of the dataset, consider the relevance of each feature, and select a few sample data points from the dataset which you will track through the course of this project.\n",
    "\n",
    "Run the code block below to observe a statistical description of the dataset. Note that the dataset is composed of six important product categories: **'Fresh'**, **'Milk'**, **'Grocery'**, **'Frozen'**, **'Detergents_Paper'**, and **'Delicatessen'**. Consider what each category represents in terms of products you could purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000    440.000000    440.000000    440.000000   \n",
       "mean    12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std     12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         3.000000     55.000000      3.000000     25.000000   \n",
       "25%      3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%      8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%     16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max    112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "\n",
       "       Detergents_Paper  Delicatessen  \n",
       "count        440.000000    440.000000  \n",
       "mean        2881.493182   1524.870455  \n",
       "std         4767.854448   2820.105937  \n",
       "min            3.000000      3.000000  \n",
       "25%          256.750000    408.250000  \n",
       "50%          816.500000    965.500000  \n",
       "75%         3922.000000   1820.250000  \n",
       "max        40827.000000  47943.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display a description of the dataset\n",
    "display(data.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Selecting Samples\n",
    "To get a better understanding of the customers and how their data will transform through the analysis, it would be best to select a few sample data points and explore them in more detail. In the code block below, add **three** indices of your choice to the `indices` list which will represent the customers to track. It is suggested to try different sets of samples until you obtain customers that vary significantly from one another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen samples of wholesale customers dataset:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17546</td>\n",
       "      <td>4519</td>\n",
       "      <td>4602</td>\n",
       "      <td>1066</td>\n",
       "      <td>2259</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3067</td>\n",
       "      <td>13240</td>\n",
       "      <td>23127</td>\n",
       "      <td>3941</td>\n",
       "      <td>9959</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4446</td>\n",
       "      <td>906</td>\n",
       "      <td>1238</td>\n",
       "      <td>3576</td>\n",
       "      <td>153</td>\n",
       "      <td>1014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh   Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0  17546   4519     4602    1066              2259          2124\n",
       "1   3067  13240    23127    3941              9959           731\n",
       "2   4446    906     1238    3576               153          1014"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Select three indices of your choice you wish to sample from the dataset\n",
    "indices = [20,200,400]\n",
    "\n",
    "# Create a DataFrame of the chosen samples\n",
    "samples = pd.DataFrame(data.loc[indices], columns = data.keys()).reset_index(drop = True)\n",
    "print \"Chosen samples of wholesale customers dataset:\"\n",
    "display(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "Consider the total purchase cost of each product category and the statistical description of the dataset above for your sample customers.  \n",
    "*What kind of establishment (customer) could each of the three samples you've chosen represent?*  \n",
    "**Hint:** Examples of establishments include places like markets, cafes, and retailers, among many others. Avoid using names for establishments, such as saying *\"McDonalds\"* when describing a sample customer as a restaurant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "Customer 0 spends more money on fresh produce and delicastessen when compared to mean of money spent by all the customers. Spends less money on all other categories. This suggests that customers bussiness is around fresh produce and delicatessen. So the customers bussiness might be a fresh produce market or may be a farmers market.\n",
    "\n",
    "Customer 1 is among top 25% of customers spending money on grocery, milk, frozen and detergent_paper categories, this suggests that customers bussiness could be grocery stores.\n",
    "\n",
    "Customer 2 spends less than average in all categories which suggests that the customers bussiness might be a smaller sandwich place. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Feature Relevance\n",
    "One interesting thought to consider is if one (or more) of the six product categories is actually relevant for understanding customer purchasing. That is to say, is it possible to determine whether customers purchasing some amount of one category of products will necessarily purchase some proportional amount of another category of products? We can make this determination quite easily by training a supervised regression learner on a subset of the data with one feature removed, and then score how well that model can predict the removed feature.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Assign `new_data` a copy of the data by removing a feature of your choice using the `DataFrame.drop` function.\n",
    " - Use `sklearn.cross_validation.train_test_split` to split the dataset into training and testing sets.\n",
    "   - Use the removed feature as your target label. Set a `test_size` of `0.25` and set a `random_state`.\n",
    " - Import a decision tree regressor, set a `random_state`, and fit the learner to the training data.\n",
    " - Report the prediction score of the testing set using the regressor's `score` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction scores using each column as target column\n",
      "_____________________________________\n",
      "Column              Prediction score\n",
      "_____________________________________\n",
      "Fresh            : -0.693188852626\n",
      "Milk             :  0.512237492459\n",
      "Grocery          :  0.731639331883\n",
      "Frozen           : -0.829065561976\n",
      "Detergents_Paper :  0.819052813511\n",
      "Delicatessen     :  -1.00963476397\n",
      "_____________________________________\n"
     ]
    }
   ],
   "source": [
    "#from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature\n",
    "\"\"\"\n",
    "# Implementation as suggested in project questionnaire using 'drop' function\n",
    "new_data = data.copy(deep=True)\n",
    "new_data.drop(['Detergents_Paper'], axis = 1, inplace = True)\n",
    "target_column = data.copy(deep=True)\n",
    "target_column.drop(['Fresh','Milk','Grocery','Frozen','Delicatessen'],\n",
    "                        axis = 1, inplace = True)\n",
    "\n",
    "\n",
    "# TODO: Split the data into training and testing sets using the given feature as the target\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "                new_data,target_column, test_size=0.25, random_state=11)\n",
    "\n",
    "# TODO: Create a decision tree regressor and fit it to the training set\n",
    "regressor = DecisionTreeRegressor(random_state=0)\n",
    "regressor.fit(X_train,y_train)\n",
    "    \n",
    "# TODO: Report the score of the prediction using the testing set\n",
    "score = regressor.score(X_test,y_test)\n",
    "\n",
    "print \"Detergents_Paper   : {}\".format(score)\n",
    "\n",
    "\"\"\"\n",
    "new_data = data.copy(deep=True)\n",
    "data_columns = new_data.columns\n",
    "\n",
    "# Formatting the result\n",
    "print \"Prediction scores using each column as target column\"\n",
    "row_format =\"{:<17}: {:>15}\" \n",
    "print \"{}\".format(\"_\"*37)\n",
    "print \"{:<17}   {:>15}\".format(\"Column\", \"Prediction score\")\n",
    "print \"{}\".format(\"_\"*37)\n",
    "\n",
    "for column in data_columns :\n",
    "    train_columns = [x for x in data_columns if x is not column]\n",
    "    X_all = new_data[train_columns]\n",
    "    y_all = new_data[column]\n",
    "\n",
    "    # TODO: Split the data into training and testing sets using the given feature as the target\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=0.25, random_state=11)\n",
    "\n",
    "    # TODO: Create a decision tree regressor and fit it to the training set\n",
    "    regressor = DecisionTreeRegressor(random_state=0)\n",
    "    regressor.fit(X_train,y_train)\n",
    "\n",
    "    # TODO: Report the score of the prediction using the testing set\n",
    "    score = regressor.score(X_test,y_test)\n",
    "    \n",
    "    print row_format.format(column,score)\n",
    "    \n",
    "print \"{}\".format(\"_\"*37)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "*Which feature did you attempt to predict? What was the reported prediction score? Is this feature is necessary for identifying customers' spending habits?*  \n",
    "**Hint:** The coefficient of determination, `R^2`, is scored between 0 and 1, with 1 being a perfect fit. A negative `R^2` implies the model fails to fit the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "My first guess for the feature that can be estimated using other features is \"Detergents_Paper\". Every other feature is related to food and cleaning is necessary after preparing/consuming food.I wanted to see how well other features can also be predicted, so impleted a loop. And after observing the results my first guess was right. The reported prediction score while using detergent_paper as target column was 0.819052813511. As it has a prediction score of over 81% which means it can be predicted using other features, this feature is not as important as the others"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Feature Distributions\n",
    "To get a better understanding of the dataset, we can construct a scatter matrix of each of the six product features present in the data. If you found that the feature you attempted to predict above is relevant for identifying a specific customer, then the scatter matrix below may not show any correlation between that feature and the others. Conversely, if you believe that feature is not relevant for identifying a specific customer, the scatter matrix might show a correlation between that feature and another feature in the data. Run the code block below to produce a scatter matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2YTaXU1AQhcFwvPOeJiVFUVJyEnCQlJTS7w6DaofRR0pJQEAAc+aEUVr6ZwIC\n9Lzxxn8ze3YA1dUW6utLCQ01YrUuo6Iij9LSI6SlRVJevpP588Ove6bT05eRkqJNYKxWa7fP3Tle\n3n33N50RuPqKvKZyrwwfva0o+/r6MmNGFL6+57HZ3kevn055eQsOR22f7XTnnUs5fvx1fvGLAt58\nM4t//uc13HPPSvVMewFlZRU0Nkbi6+vAbDbT0vIRvr4T+OCD15g1azb+/rWsWZNMcHAwmZlLMJkq\nSUpa2s3cfrjNE9V4MvIohWkI8feH//1f+NrX4Nw5UDlPPRM/Pz9mzhzHT3/6VyCNK1eOkJGRxl//\n+msaGy1MnRpPU1MgO3ZkcfFiE+PHL2Px4kji4ipwOBxs2vQxcXGhSCn58MPTSGln7drFrFnzYGfe\nlq6MtpnCjUyFxjJWq5WtW09QVNRAW1s9gYElTJo0g4oKP2bOjGXqVD1z55axaFEmDoeDggJt1yk1\n9Wk2bPghM2YsoKTkJCkpSfj5+XVMmJN6bXPVDp6HxWLh1Ve3cPnyBBoaykhIeBidrpZPPjmE0RhF\nUNBcGhrOAR9QVlZFbGwSZWXt/PCHKxg/fnzn77gnR76+vhw4cJzt23PpaZb5aQSuz1FTk9U5mepL\nBlQY65GjtbWVqiojUn4eeBOn8yphYZmUl9uxWq34+fld1w5tbW0cP16Hr+8qLlyoYePGgwih84r0\nEwqor2+kstKBlJno9dux29Px92+nvb2O9PQZlJZayco6Rnr6MtLS7J1+idpzbhh2ZUaNJyPPiJvk\nCSGWCSEOCyH2CyF+3lH2LSHEQSHEn4UQ+o6ybw62bDS5/35YvBheemm0a6LoCykld9yRgMHQgsNR\ngctVhcm0DYNhHiEhcRQXX8TXN47CwmYcjhqKi49QX7+ZhIQICgrqiY6+h9Onyzhz5hpm81Islpmc\nP9/Qq7LkCXhD9KJboS+zBXf51avVtLX54efXis1WQ2HhEazWRCoqGpg4MZDPfS6F5csXU1TUwtSp\n65DSzuXL2wgLk/j41COlvdNkSgjR6+QKbq0dul6DMsMYOux2OzU1Nuz22bhcOi5e3ExZ2SFCQiYh\nZRg1NYfJyPgMqakLSU1NZNKk5Qjhe91K8v79x3n99b38/Oev89prhykujqW9fQY5OZWd0dW6RuAa\nUEQmL/EHuR0IDg5m5kyJ3f4eEAuE4nDUYTIVs3fvYVwu13XPXHBwMMuXR2Ox7EWvP8zixav6jKR6\nKwzn865LOY32AAAgAElEQVT6kusRQhAZGU17ux8OhwuXqwQp7bhcx5g6dRwzZgQSEBB7nbmsy+Vi\nz56DbNiwkz17DvVpujtUjPVxfTToN0resJxQiIlAo5TSJoT4M7AReElKuU4I8W2gGNgPvHmTZd8C\nLkkp3+txvhGJkteV+npYuFCLmLd27YieWtEHXSPgZGUd429/O8D77x/A4ZjNtGnVLF48me3br2I2\nV7NkyVKsVjvz5oXg4xNISMgKLlzYQ3JyGGVlNsrLrxATM5Vp08Zx+XIbQjhZvz5lxOyWB8PtHL3o\nVujNbEGn03VGRzt1qoz339/M+fNNtLdbGD/eF5crCoejnUmTXKSlzaC5OZTk5HDi42dw/nwjLlc9\nen0YTmcdQoQgZQN6fVivURJ7q8/NtkPXa4iL0wJPFBTUKzOMW0QIgdPpJDPzOY4cqcRma2HixPux\n2/fg4xOF03mNoKAYQkKMJCTMZvr08fj6RhIfH9YtWqbZbObVV7czZcoDvPvub4iLS2b//o+YPz+a\nmTNDMRoju0VXG4vPoSfjloMf/egV/uM/tgCTgQvExk4lLi6Z4GA7y5aF4O8/iTlzxnfbQXI6nWzf\nvo9du86h18PatYu5996VwND0ucNpdqVMurrTdY7wX//1Cj/60VYsllCgmlmz/HjqqXvYtesEVVX+\nxMfrufvutcydOwGj0UheXi3t7WVs23aRoKB0Zswo5YEHEikqaiU+PmzY5gZjdVwfTvqLkjfiO0xS\nymoppVvddgIJQFbH+71AKrBsEGUfAynDVvGbIDQU/vxneOYZKC4e7dooumKz2Thz5hpZWdeorZ1C\nff0n5OY2sGXLCT7zmd8we/Y8pk4dR2NjFbm57ezYsYu//vVVxo2L5cSJRpKTnwJCWbHiGQyGifzw\nh0/yk58859HKEozd1eq+nOetViubN5/EZAqjoKCUpiZ/7PYwgoLCCA9vJy4unGXLpnLiRDs6XRpb\ntuSxeXM2588Xc+lSA7Gxa9Hrw/iHf1hOaWkLeXk6fvvbzf3m2xrs4Nb1GkymKkymKhUMYIhoaWmh\nudmPefMewWZrp7KygPJyG3Z7IpWVOpzOeykubqS4WPDhhyba269SUFDf2cYul4sNG97ho48O8vbb\nP2DJkvFERLTy0ksP8aMffRmjMbJbW43V59DTaWlpYceOEnS6eUAtYKGhoZxTp45gNI7j2LF6Kir8\n2bDhQLcE5na7nUuX2li16l9wOp2cP99AVtYxXC7XkOTkGc7gHyqwSO9YrVauXGnEYrECTUAbTU2t\nXLrUQkWFjvj4X3H5sg/h4Q7On29g8+ZsIiJWcexYPQEBkTQ0XMXptA55AIi+Ei6r/mTkGDUfJiFE\nIhAGNKIpTqBJZwgwHmgeZNl1vPzyy52vMzIyyMjIGJqL6If0dPj+9+GBB+DIEU2JUowcWVlZZGVl\nXVduNBqZMSOAmpoyXK4EIAa7/XFqav6PjRsfwt9fj8USTkREOufO2XG5Ipg2bTptbVdJTg6jsfEQ\ny5dHUVOTxfz54d38GBSeR3/O8+XlV7h8uZLGRh2wBthMSUkFc+cuJC3t8xw//j5G43yOHfs98+YF\n4nTGU1Zmo6VlP2Vl32PKlIkcPx6IlPqOKImOjiiJWTcMKX0zq7ldryEpKQpABQMYIjS/AwdlZc04\nnQE4nWG4XBWUlTUBdoqL/4S/fytXrgQgRADvvHOcpUsXdfqtWa1WDh26Rnj45zl79lWioiaQmTmP\n9PRlGI1GZs4M5Pz5rSQlRam28mCMRmOHeXYtEAfE0NpazowZd2E2NxEYWM0bb7yGwbCM3/1uB4mJ\ns5kwYQLZ2TmUlJRRWPgb9HojU6euIz9/F4sXtw6Jf8lwBv9QgUV6x+VycerUebS1/HIgjdra8xQX\nR9PWdpg9ex7HaKzhxz9u5ZFHvoWUV6mo2M3y5VFcvtyE02nnkUdWDGm+RbUb6BmMisIkhAgBfgM8\nCtwBxHR8FAw0oClRkwdR1tjb+boqTCPJV74CRUXw4IOwcycEBY1KNcYkPRXjH/zgB52v77//Lnx8\n/h/QCpQBbwABOBx22tqmYjJdwNf3fXx9rRgM06ivP8l//MdXefDB1Z0OnVar1aOTTA41t/P19eY8\nL4QgJmYqLtckTp36CNgJXMHlCqewsIg33vg1vr41TJ4MAQFWfHwiyc//OyUlVmJjp1Bb20xi4n28\n+eYRkpND8fWtYcGC+b36p0gpaWlpuaUJVNdrkFIqB99B0JsMa5ENZ6LX11Na2g6cAq6iGV/MwekE\np7OUhoaPCAlJor29HSGqAAcAp07lc+HCcUpKThEWFoDdPpetW09y4UIjTmcdly834XLpSEyMREqp\nJjkeitPp5MqVBrTdpYuAGXBite4jIWEF775bR2OjDX//Qs6cucpjj/2UsDAX0dGTWL36Ra5e3U5c\nXGhnDp7g4GDi4kIxmbaSmBh5S3UbzuAfYzmwSG/9gZSSvXsPU1R0BbgEGIFCpISDB9/GaDQgZSA+\nPmFUV7vYs+dVvvvdx8jISOk2Lxjq+6kCPHgGoxH0QQ+8BXxLSlkDnADSOz5eDWQDJ4GVgyzzKH76\nU1iwQAsG0eH7qxhFXC4X//Vfr1BdHQ4kAZHANCABnS4El+seYAEOx904nRMIDU1m3Ljx10U+y87O\n6TS36Mv8wlscat2rW7dqXjJa9DaAGY1GHnxwEc3N24EJwHRgPvAQDoc/Ntt0DIYEysvLcDgmUVmZ\nBISRkPAIvr7TCQnxZ//+LVit8ykrs/PFL97Fiy/+43UmGO5799Zbh7Baqygv3zng1dyu8tP1GpQZ\nxs3TlwwbDAZiYgIoKDgF3A08DswAKoAaXK4zREQsxWIRtLfHEhDgx7x5TjIzkzvMe8sJDk4mOvoR\nzGYnFRW70euNTJp0L0eOVNHePhW7PaXXnFwKz6GhoYGmJisQBfwDEIQQaVgseo4eraK1dTK+vqmY\nzWUIEYTR+AyFhQHk5l7hk09+S2JiZGc+toyM5G5jwNmzBbz++t5B9539Pe+3OsaM1b6kr/5Ai5p6\nhrY2fyAReB4tCMh8YA1Wqw27vYGGhjKCg+NISppDRoaWPqK/gD+3igrw4BmMRuLaR4GlwE+EEJ+g\njU4HhBAH0WawmzsUqYODKRuF6+kXnQ5+9zuIi9MCQDT2ugemGCmam5vZvDkfKSejrSRagStADomJ\ngfj5vQPk4+NjIiDAit3ejMvVyp/+dKBTOWpubiYnp5Lo6HvIyamktbW1W0JKm802bErGaChh3mjr\nLqWkra2V4uIWNIX5EFAK7AbaMZtz0OlKSU9/lObmQlpbT6HXNzB9+lUWLrTw1a8+SHx8JJMmCYRw\nYjQau61Wutup670zGCby5JN33tCmXUqJxWIZlPx4i5I+1PTny1ZS0khdXRnaetsf0cxw/IEF6PXB\nGAzR+PjAzJkTmTAhjKefzgDgjTf2odO1MHFiNULsY+3adaxadTcPPJBETc0nLF8eRWDgVfz9TyqT\nPA8nKioKg6EabSx4HyhGiJM0NjZw5UoNLS3n8Pc/SmbmwyxePBmz+XV8fK5wzz1PMHv2dNLSFnX7\nPbe8TZyYTnZ2DRMn3j2ovrO/5/lWxpix3k/01R8IIXC5LGhu9heB3wOXgQvAYcCMn18oEyaE4HSa\nmDLFD4NhZBIX95ZwWTGyjLhJnpTyHeCdHsXHgJ/2+N7/AP8zmDJPQ6eDjRvhhRc036YdO2DSpNGu\n1dilquoK0A4EomXotgDVNDVNIiEhnICA1Vgsxej1YVRXh1FZqWPPHhuFhQexWCxs3XqWioqrSHmY\n6OhoTp2KxGqt6paQ0mq1kpNT2WnTnppq60hcef1W+kDN3UbLjtkbbd0tFgt/+MM+6ur0QDSaGVY0\nUAm0I6XEYICysgM4nS6czmk4na3MnBnLggUTsdsd+PoGYLfn89BDK8nOzuHcuRqmTw/g/vszOHDg\nRGc7zZsXQm6u5scSHBzcZ53ck5js7BxyciopKSkjPf1Z8vN3D8gEQ9m5901/MlxcfBWHIwLNj20r\nsBioxtd3P1OnzmbChNOsXJnE+fMHmDhxHNnZOezenY/Nloyv7yXeeOP7HDx4kh07crl6Vc/ixTE8\n88wqjEbjDU10PN3U1dPrN1SUl5fT3h6LZqY9Hs0Uywcpl1JWVkxUVApG40XKy3OIjw8iOXkBDkcN\nAQE1JCVFYzAYyMo6hslURVJSFHfeuZT29nL27/89oaHNVFd/THx82HXn7e/+3uh5HqyZluon+u4P\njEYj9967iE2b3kGbGwR1HAJtx6kQs7kGh+MOwsMb8PWd2Nl+w/2sjNXdQE9CJa4dIXQ6+M1v4Mc/\nhuXLNZ+muXNHu1ZjDyEEPp1SHwGcByQguHJlHK2tVTz4oAUhppOdfZjKSjMWi44jR7ZgMrVx7twF\npFzN+PFOmpouEBKSxt/+doA5c2bzyCOPU1OT1TnpLSm5TEnJBjIzl/SZyK6/watnB9ybEjZSHai3\n2brbbDauXClDrw8A3kUzy7Oh7TYZcLkaKCy00dISgM3mQ3X1IVpazLS0zGHDhr9w+XIbkyYlM25c\nK+3t7ZSUtHP6tC+bNm3h9OlcJkyYQVTU3eTl7WP27E+dF/vyY3HLgaYoXSY9/V8oKfktpaXbSEqK\n6lTC+7v/ys69f/qSYSHsaKvIO9B8kyYCxdjtvpSWjqOy8iohIQ7Gj59CcPAUfvrTrTQ21hAQUIa/\nv+TYMRO5uec5evQSS5YsIi+vlrQ00Wmi0xc9n/2VK+/wqFxuY2li7ePjg8NxCc3g5Srgg8tVjdl8\nFpdrHNeuHWf+/GRmzJhJTU0RkZFR7N9/lPh4O4mJkTQ1NbFlyynM5qVcunSC1tYWTpxowN9/MnV1\nLmJjDdjtNjZt+rizrW02G9nZOTdUiDRLhm3Xye5gF7JGcxzxJHrrD1wuF+fPXwIMaG7xdYC949MK\nNNf5eoRooaKiFLP5GgaDoTNdiVth9uZnZSwzGiZ5YxYh4N//Hb73PbjzTtjscQaE3o/RaCQqKgit\nQ/RF82FaDATjctXT1CS4dq2CtrYYampc6HShuFwSi6UOne7zXL5cR1BQDdeunaCtzUl+fjMFBdXY\n7dWUl+8mPj4MIQT5+XWkp/8L06dPJi1tUZ8mAH2V9zS3cLlcnUpYVtYG4uJCR3SQ87bVLT8/P2Jj\no2lqMqNNksYD8WgmOfVoATcnUlm5B6MxAoNhKZMmJXLixLtculSOj08KJ05sZ968ZEpLrUye7MOh\nQ3+nrS2WP/3pDCbTIf7+91doaysjP7++Y3LSt0mOWw6mTl0H+FBauo3MzGSee+4+0tOXDcj0xlPt\n3D3F/Kc3GRZCEBERgbZo0oI2JGajKU4+2GyFtLYKDh2qxmyewK5du6mrm4+/fwLt7S0kJz/Khx+e\n5u9/v0BY2N2cPm1i6tT+lST3vej67Ofl1bJ372GP8hP0RlPcvmhqagKmAqsAP7TAvRHY7WUYDFEI\n0URzcx4GQyHJyeGcObMPX98pWCzL+Pvfj7Jp017Kyq4iZTVOp5Xi4jbi4+8nN/c4c+YsYvduExs3\nHqK6ehLnztWwd+9hNm7cxebNJ4mOvqfX+2s0GomLC2X//t9SUlLG0aNnr5OLmzXTklKO6jgyktyo\n3+mtP2hpaeHtt4+gzQ8i0axQotFkogoYB0h8fCqZM+dBxo2bjM1m60xTkZsbyubNJz2iv1MMPUph\nGgW++EX46CP413+FZ5+FmprRrtHYQUqJ0xkALACK0JSmMLQBshm7PYWDB8+zZ08WdXXNNDefQa8v\nQqezotfvZupUf+66K5Tly5eRmfkk7e3HWbnyAS5fbu7sJH19fZk1K4iKit2dvgt9TWa7lsfFhfbq\n+5KfX0dra+t1Sphi8BgMBubPD0HKemAFmhxMAwLQVhLrO8oCaG8/h5R7KS/PJy/vBFVVNVRXbyco\nyEpj4xnmzQth1ao04uKCcTrNhIRMoqFhAuvXP0d5uZkLFy6xb9//MnNmYOdOUU+6ykFm5hKee+4+\n7rorpdM3quvEuqWf6DGeZufu6QFDfHx8KC29gBZsdRqaLLhNs0CbNLVhsTRz9apESjvjxl3Eas3H\n4Sjn3Xd/x7lzeSQk3EdDw34ee2w2QUFBvP76XnbvPtjN+b+nX5rBYOhs89mzgykqavEo5cRTFfDh\nYObMmWg+KweAhWjBe7VJs8tlx+HQU1FRy5Ej5zl/voiLF49x5MhBtmx5iYMHj9PUFMrEiWHMnWvl\nkUdWkJQURXh4KcuWBXHhwmnKyyvx9w/mww/fpKWllMLC5o7FEQelpdv6vL8pKUnExsaQkfFcr3Jx\nswtZ7t0lbx9HBtvvaP1tDdrU2IG2iNKI1g+YgUkYDBOJjTWTkmIjLi60s08vK7tMfv5Bysou91of\npUTd/iiTvFEiORnOnNFyNcXFwec/D48/DkuXwgj5EI5JrFYrdXXVaIOjf8eRg+boPQ8pdbS12XA6\nJU7nSvz9o7HZtrN06RJqaq4QFhaGEM2Ag9zc3cTGSnJyPqKkpJHLl2dx9Wo2FouFCxcamTdvAosW\nzes8d08TALfJ3cqVd7BwYTPHjpl46aWNgA+ZmUuIiwuloODTMLWa+cWnSthY8S8YDqxWKxUVTjSl\nWQKFaErzRLRIWQFoE+cptLZeRAh/rNYYDIZKLBYzen05CQkP4HQK2tvb+L//O0x6+gImTWrAYAgk\nOtpIdfUnlJXVERq6lsrKDzGZKjGZfk9AQAzz54eTnr6ssy5CCO68cynz59cTHh7ebWLknrjm5e3E\nZqvmrbcO9Wki5Wk7gZ5uJlhbW8v58y60nOf70ZTlO4AatB1HC9BMcPBcKioOsnRpArW1dRiNDior\np+Hrm8jVq9sICzvDo4/O4fnn/4G33jpEQ0MImzYdAmD16uUcOHCiV7+0rn2CwXCM/Hxt4cRT8DZT\n3L44d+4cbnNcOI5memVAiOU4nbnodAaczhTq6+Gvfz2MxeKHTvdldLp38PEJ5q23/sCCBRFkZiZ3\nPteLF7fw5psu/PwW8uab3+PKlWoWLFhFYGAoU6YYuXRpK5mZyaSlLerTh+nYMROlpZWUlPyKhx9e\njsFgwGKxAAN/1t3jhMFg6MwbVVLyWzIzk722XW+l39FyK9aj7SoFoPk7zwZygUKmTUtn3jwXq1bN\npqCgnry8N5FyPFVVLTidi5HS3O33xpJpq7fjFQqTEOIXaJH3Tkkp/3W06zNQJkyAX/9a22l64w14\n/nkoLobERFi0CBYv1v7On6+UqKGkrc2FtqIchGajvAJtslwHHAEasFguAAWYzRKdroGLF8fh5zeX\nqiof/vznT1i06LMUFZ1iwYK15OZup6XFQFnZTi5fbuHq1XIiIj7De+/9mfDwg6SmTuQb3/gndDpd\nN2Vp//7j5OXVYrVWUVzcxLlzVwgKWsCkSQswmap49tl7SUv7dFDsmYtHdcKDRwiB2VyPlgLuIWAL\nWsTEKDR/ljloplktSGnAbBbY7UVYrTrAhd0+kXPnPiQ0dD6vv95GXNxSoqLC+bd/u5PNm/dx7Fgt\nS5dKYmJCaWuroKTkGmbzEoqLj/HYY88hZRWLFjVz+nQBW7acwuWyYbc3UlcXQHi4mfj4pSxYENHZ\nrunpy1i8uIW33jrUOQlISRmenB9DiacHDNHr9dTV5aGtJFsBPZrSBNqiShUQREPDNfz8mjl/3p/I\nyBVYLCcwGutpbT2Hnx/Ex99HZeUx3nhjH2ZzOSZTHomJ6ygqukxKSmunuWVJySudfmldd5lBe75T\nUqydKQs84bn2dPkaKiZPnoy2qzgOzWclEShCymacznb8/e/E4ThOa6smIy5XIy7XK1itl7l2bSYw\njUuXnHzwwRFWrFhCW1sbAQEB7Nz5AceOvYXFUkZU1ENcurSf5ubF7Nljx26XJCZG4uvri8Viue5e\n22w28vJqCQ+/i5ycD7HZbHzyyVHef/8IVVW1xMRMZv36pWRkJPcpI13HiVmzgigsbCY9/VlKS7d5\n7e4S3Fq/o9f7oy2cTUbLekPHX80s7/LlIwQHT+CPf2wmNHQ9ly4dJDX1bi5dqmTChCI0X9hP8fRF\nI8XAue0VJiHEIiBASrlSCPGqEGKJlPLUaNfrZpg2DX7wA+1obtZ2nk6fhqws+MUv4NIlmDcPVq+G\nxx6DJUs0fyjFzSOEwGDQo02E9GhK0o6Ov75o2+/T0PLy1AMtuFxBNDTYgEO0tY1Dr5eEhV2murqN\nw4ebsdsdBAUl09Jylra2qZw8eZ74+JOUl9cTHf0If/nLh8yatZv16+/tHNjcg2FISBrvv7+B8eNX\nEhwsaWoyMW1aG0lJKdc5jHcdUFUnfGsYDAbi4mKBg2jJSqvQdht3oUVEcqLJgg9wL3b7JaABIWYh\nZQHwPGbzzygqaqapqZ09e14nPLyS115bQENDIfPnv8Du3ft48cW1FBTUUlMTRl1dJXq9g48+eoPU\n1BB+/3szly9fw2ZLxmot5cyZi6xe/T9kZX2X5OSl5Oef7mxXIUSXXUZtF6I/h3FPwpN3KcrLy9FM\ncqeg7Sq50BSlQLS2DwX+CfgTVmsADQ2t1NcfxGAoYdKkWMaPb+GOO9Zx4cJeIiN9aW2diMl0kUWL\nAgkOLiE+Prxbu/W3oyCE6PR/VM/1yKLt8Fmx2erRlKYSND/GEqRsJjy8nqioGO644zH+8pffYLPN\nwOUKw+l00NoqsNkkVusVcnKcPPbY1ygoaMfpbKCy0g8fn8dwON7Fbm8iISEQozGSS5cqaWiIBI5g\ntVrZufMsbsuCrua0s2cH8/rrW1mwIJWCgnouXLhATo6D5mYIDl6IyVRFWlrvEVh7JswuKnKbfu4e\nE2HuB9PvOJ1OiosvokVJ/BhtjrAebXw4AUzBZovi2rXxlJdfIikpl/HjJfv3b8ZgmAlUERU1vVtf\n7OmLRoqBc9srTEAqsLfj9V4024rbSmHqSnCwFno8Pf3TMrMZcnJg61bNdM/hgM98Btavh7Q00OtH\nr763G76+vgQE2NAmxRFoylEu2oqSHW2VMRYtJ880NNOMaWiTqCiam6cRGFjMqVNZWCxN+PtXYjA0\nMXNmKb6+ZnQ6HwICpmI2nycqKojTp//AzJn38s47JsaNG8fq1cux2+0dA1w1mzf/nvDwNozGMwQG\n6li79hEyMpK77UT1ZnanOuFbw2KxcPp0BdqOUi2agpwEHAWa0GzW3X5uB4EW/P39sVpzkNIJ/Aqo\noLjYAdQTEJBOefkhoqPvoqamjNLSMubPH4fBYODy5QqktBMcXEhjo5mIiESOHTuD3b6U+voSIiNt\ntLVVM2GC4PTp/2DyZAvbt/+pM0R9V9yTAIBNmz4eVEjhkTbj9PxdCveOM2hBP8LQ+oN6tDDzbwL+\n6HQWrFYHwcFP4HK9zeLFX2DChAJmzpxAYqIW8nTTpkMkJq4jOLiEJ5+8szOMfH+Tt65top7r0cPH\nJwrNEjYWTQaagCsIEYaUdcybN419+95ACCsGw3ksFj0ulw9mcwsQQGtrC21tfpw9ew69fhUWSxUu\nVyM22/9hNJayevUdfO5z92Oz2fjwwywcDjN5eWU4nWaczvn4+ERjMlWSmqrtMubl1TJzZiB33DGB\nkyePs3TpBAyGcYSFxWA2bycg4CRJSam9RmAFOsvcCbPdZsDp6bf2/N8upuCD6Xdqa2tpbAwAlqDt\nOJ8AdqJNlQPQzHTPUlcnCA4eR2PjWe6/P46PPy4jKioag6GMhx5a1umz1Jt1iOL2xRsUpglosxrQ\nerj4UazLsODvDykp2vGf/wkmkxZh7+tfh7IyTWlauBBiYyE0FIKCYNy47kd4OPj6jvaVjC5SSvbu\nPURFhQMoQ1s9cq8shwFr0TrHc0Bbx381oG3FW9FCkNfR2lqHTvdlXK4dtLaeICwsBh8fyYoVyVRX\nVzBpUgxCGFi16nl27vwV9fVXSUjIoLCwAThMUVELs2YF4eMTTnz8EvLzd/LUU/NYvXpFt12lG5nd\nqU548FitVvLzL6L5L7kVphNoGd3PAnvQ2j0UzdlXYDSuQKc7Slubo+N/MtC6nDzM5lKkrKKi4gCh\noS7mzavhmWdWsXXrSXbtaqCp6QoBAXYcDh/q6krR6RopL7dTVdVAREQ0UVGTeeKJr1BU9AFGo5GY\nmPuoqfnkuolJ10nAzU6slRnn9Wj+IBVoiYub0Mx016IpzteYNSsWi0VHY2MKPj4lBAWV4HD8FWjE\nZjvMrFlz8PHxxWAwsHLlHQAUFV3u3Fly09fkrbc2Uc/1yGO1WmlvL0LzVYlG82NqBAIJCJjGvHmh\nzJ69kOzsNmABRmMu7e2VwBPABuAsLpeOkhI/bDZffH1L0enO43RagBiczmCiovRs23aKsrIKHA4b\ntbWnmTUrlePHTUh5gVmzppCYuA4hBHl5tTQ0hPD661lERY3j4Ye/Rm3tPtauDeT8+Qbi4p4kPT25\nM9dXz11JoLOsvHxnN+X9VpUlb+5DdDodUtYAZ9CsDHw7jjlAPtocwIzReCfh4YGEhlZgMpmZMWM1\nLS1n+Md/fACDwdDNF9ltMqme59sfb1CYGtEC5tPxt7HnF15++eXO1xkZGWRkZIxEvYYFISApSTu+\n/30oLYXjx+HsWTh6FOrqoKUF2tq0o71d+9vUBLNnw4IFEB+vBZqIi4OoKG1Xy8cHXC6w2bTDbtcO\nh+PTv0Jo3+vt8Pf3nJ0uKWHPniz27cuiax9ls9koKKjHxycGqzUabWUZtGg47Wid4Th8fduRMgqH\n424gD7hKYOA6WlvPo9OtwuV6C4PhPFZrE35+LiIiptHc3MyTTz5PRcVuvvjFuzh9uoD8/D088cSd\n2O12iooamTXr00hYRUW7mD49gEOHdpKYuILS0sbrBp4bmd2pTnjw6HQ6AgJC8fGZgcOxEy0qUiUg\n0eli0etrsdttwCLATEBADkFBjQQEJGG3T+Xy5Y9wucqAmfj7lxEQoEOIuQjhx5w50/nyl+9kzZoV\nbFD0LswAACAASURBVN9uwmIJR8pobLYAhPBlwoTFGAwt+PjkMHnyUnS6ZcAhysp2kJw8DYD8/E9u\nqAjd7MRamXFeT0lJCdoa21NoBgo5aIsm9SxZksHjj6cxd24I27blIMQsHnro85hMFUyfvp6rV7fj\n4+PTJZ+NnTVrVtzUCn5fbTLW22WkKSwsBOLQgj6Y0JTo6fj5tXLnneP5zncex2az8be/7cPfPwAh\nbP+fvTMPi/K6F//nsMwAAiKgCC64YQVUVFTAqOCaJhq1adPb3nRJtb82TXuT9va2Tbfc5Pbe9Lnt\nvUnTNr1JW5Pc2O02xkRjjLuIGiVugAJGUTZZZF8EZoaZOb8/XgbZl2FgYOZ8nmceZs68w3ve9/2e\n7/t+z/ku+PlZMZkuYTZ74uGxBH//ZlpbrxAdvYjq6lI+//mNvPLKeUymb+Dh8TcyMuoIC1tEebkH\nUi5h9uxiysuvExExj4iIBGJiKtrdNaOiAtm58zSLFm2jqupIe8bV5OTlpKQMrB6TrS02NrTPgtmD\nwdV1SFlZGVqJiVi01eZPA/+LJg8tgGTmzBQ8PIpJSppNQMA8QkMXk5V1mq98JYH16+/jlVcO0tIy\nC5hEVlY5K1a41jlyZ8RoS/M6WNpimL4mpfyGEOJl4HUp5YUO38uxfoyOoKUFcnPh6lXtr+1VUaEZ\nWAAWC+j12kqUTqcZQt7e94wi0Aynrq/WVu3/2wwnPz/tb9eXIwwqKTWDzmjs/DIYtD40N2t/dTpt\nRS41VfudrUhsamo6P/3pHzhz5jxSBuDhYWbZsimUlRmorGwmNDSQb397C9nZ1zlxooxJkwTQxM2b\ngpaWInx8pjFpUiPBwfOwWKoJCwtl6tQZzJ49Hr0+jJiYEFJSEjq5LXR8n5qa3ml27siR0+TlNbb/\nrisdtx8tqaLHOjZZeOGFnbz++llu3EjHaAzB3x8SE6e0GThN1NRUUF5uRqfzJCpqAqGhk/D09EJK\nL65du0pNTSszZoTx8MMJnDlTQH5+ETNnTuOJJzaxceMqAE6cOMdLL+3l1q0igoK0Gh5CBLB163zm\nz4/iwIFL2GYibQ9Lw+nyouTpHjY5EGIOmtttDWDAx6eVxx57HL3ei23bEklOXt6eybDrGAaGfD7V\nNXEuNjmYMGERdXX+QAPe3nrmzv0E//iPiXz3u/+vfVwePpzGlSuVSFnD3r2nuHnTSEiIgcDAMLy8\nfAgMNODrO5slSwL44Q+/yZYtX+PDD+uIiLDyla9sorCwgdu3y5DSm6lTQ5g61Y+SkhbAqz2BA2gr\nObZ7Q3R0cK9xbzZ60hnDpUdcVV5tcjBp0jIqKyWagRQO3MbbeyKRkf6sWjWfu3dDWbo0iKeeeoyz\nZzPIzq4iKiqwXeenpqbz7rvn6HpNFWODNjnocdl0zBtMAEKIX6FVH82QUj7Z5buxf4AKhUKhUCgU\nCoViWOnNYBpxlzwhxP3A020fPwE8juYguhUtn+9jUkqLEOJfBtEW39bWY0rxkTIKXd2/dyxjmz1S\nKMaaLCi9Mjx0lAN1jt2XsaYPHIWS+c50lQN1ftyTvq6xxwj2AwAp5SEp5Rop5Ro0J9GLQIqUchVa\nurJtQohQO9qy0PI/Oo3O/r2jo1q7QqEY2yi9Mvyoc6xwN5TM9406P4qujLjBZEMIMRMtuX0ckNrW\nfBQtTfhyO9qOoaUUdxq24MvSUpUSVqFQOAalV4YfdY4V7oaS+b5R50fRFafFMAkhvouWm7cV8JdS\n/l4IMRv4EZpBFGBH2w+llF/tsp8RTfowVmoUuBvu6nah6M5YlAWlVxxPTy446hy7H2NRHzgKJfP3\n6EkO1PlxP/pK+uDMtOIPAZ9CWymKaGsLRCt8U8e9SoKDaeuWUhxGNq24SvU8OkhNTSXVliJPoRjj\nKL0y/KhzrHA3lMz3jTo/io44ZYVJCBEGvCmlvF8IMRF4TUr5kBDie0A+cNLeNinl7i77UmnFFW49\ni6jojJIFBSg5UGgoOVCAkgOFRl8rTM6KYdoK7AWQWlnlU0KIU2jxTO8Opc0Jx6JQKBQKhUKhUChc\nFJeow9QXaoVJAWr2SHEPJQsKUHKg0FByoAAlBwqN0bjCpFAoFAqFQqFQKBSjHmUwKdwasxmOHweD\nwdk9USgUCoVCoVCMRpxiMAkhviiEOCqEOC6ECBdCvCCESBNCvNhhG7vbFIqB8uMfw9at8Oijzu6J\nQqFQKBQKhWI0MuIGkxAiAkiWUq6XUq4FJgN+UsrVgE4IES+EWGxHm14IET/Sx6MYuzQ0wKuvwtWr\nkJ4OGRnO7pFCoVAoFAqFYrThjDpM9wOeQoijQA5wDa0ALcAxtLpMVjvajgKJwMVh7r/CRTh4EFas\ngMhI2LEDdu2CRYuc3SuFQqFQKBQKxWjCGS55YYC3lHI90AyMBxravqsHJgyxTaEYEKmpsG6d9v6R\nR2D3blBJchQKhUKhUCgUHXHGClM9WsFZgOPAUiCw7XMgUAtY7Gyr62mHzz77bPv7lJQUUlJShnwQ\nitFNamoqqampfW6Tlgbbt2vvY2PBaoUbN2Du3OHvn0KhUCgUCoVibDDidZiEEHHAV6WU/ySE+D7a\nKleklPIbQoiXgdfRDKGv2dMmpbzQZX+qDpOiW42FlhYIDob6etDptLYvf1lz0fv6153UScWIoOpt\nKEDJgUJDyYEClBwoNEZVHSYpZSZgEEKcQFtd+i/AKIRIAyxSygtSysv2to308SjGJteuwZw594wl\ngDVrtBTjCoVCoVAoFAqFjRFfYRppXGmFSUqJyWRCr9c7uytjjq6zR7t2wYED8Ne/3tsmP19bYSot\nBdHj/ILCFVAziYPDVfWOo+TAVc+Pu6D0weBwVXl3pBy46jlyB/paYXJGDJPCDqSUnDz5ETk51cTE\nhJCcvByhnurt5upVmD+/c9uMGVrSh+JimD7dKd1SKEYVSu/0jTo/CndCyXv/qHPkujilcK1i8JhM\nJnJyqomIuJ+cnGpMJpOzuzSm6clgEgISErSaTAqFQumd/lDnR+FOKHnvH3WOXBdnFK6NFEKUCyGO\nCyEOtrV9TwhxSgixSwjh2db2L/a2uSJ6vZ6YmBBKSw8RExOilnqHSG/Z8BIT4dy5ke+PQjEaUXqn\nb9T5UbgTSt77R50j18UZWfIigZ9JKb/U9jkUeENKubkta95NtLTjg237HnBLSvl2l/2pGCZFJ/9k\niwXGjYPaWvD17bzd8ePwzDNw+rQTOqkYEVTMwuBwVb2jYpgUoPTBYHFVeVcxTAoYZVny2lgrhDgp\nhPg2sBxIbWs/CiTZ2XYMSBzebjsXIYQagA6gtFRLKd7VWAJYtgwyMkCtoisUGkrv9I06Pwp3Qsl7\n/6hz5Jo4I+lDKRAFGIF9QABwp+27emACMB5osLOtG6pwrfvRV+Ha/HyYObPn3wUEaN9lZcHSpcPX\nP4VCoVAoFArF2GDEDSYpZSvQCiCE2I9m6Exp+zoQqAXqgKl2tNX1tM+OBpPCPehqGD/33HPt7/Pz\nYdas3n9ri2NSBpNCoVAoFAqFwhlJH/w7fLwPyAOS2z6vB84BF4DVdrYNK1JKjEbjcO9GMYz0tcIE\nmsGkMuUpFK5Ff7pb6XaFwn2wd7wrPeG+OMMlb5UQ4meAATgtpTzfluXuFFAIvCilNNvbNpwdV/n1\nXYNbt2DNmt6/T0yEn/985PqjUCiGl550d3/fK92uULgm/emDwfxO6Qn3YcRXmKSUH0gpl0opV0op\nn25r+4WUcpWU8gtSSvNQ24aL4c6vr2YuRob+XPKio6GqCiorR65PCtdGjW3n0p/uVrVT+scdZNgd\njlFh/3hXesL1GMyYd8YK05jFll8/J8fx+fXVzMXI0Z9LnocHLF+uueVt3jxy/VK4JmpsO5/+dPdw\n6nZXwB1k2B2OUaFh73hXesK1GOxKo90GkxDi36SUz3T47Am8KaV8dIC//2fgU1LKVUKIF4ClwEUp\n5Xfavre7bThJTl5OUpLj8+t3nrk4NCz7UIDZDD4+MGVK39slJsLZs8pgUgwdNbZHB/3p7uHS7a6A\nO8iwOxyj4h72jnelJ1yHnsZ8XwzFJW+6EOKHAEIIPfAOcGMgPxRC6ICFgBRCLAb8pJSrAZ0QIt7O\nNr0QIn4IxzMghiu/vqoOPTJ4eUFeHnh69r1dUpKWKU+hGCpqbI8O+tPdqnZK77iDDLvDMSruYe94\nV3rCdRjsmBf2VjYW2lr1n4ErwBrgAynlgJIuCCGeAHKAnwF/ASqllLuFEA8DEYAVqLCjLVxK+XKX\nfcmxUsVbVYcePgZbxbu6WnPbq63t37hSjC0cWdF9oKixPfpwhhyMZVxVhjvKgaseo6J/lD5wT7qO\n+TY56NEXd9ArTEKIJUKIJcBi4CXgH9BWlk62tff3ey9gtZQyta0pCPuL1A6ocO1YQCnq0UVICISH\nQ06Os3uicAVcbVbSHYLj3eEYB4OryXBPuMMxDieuPGZc+djcmcGMeXtimP67y+daIKatXQJr+/n9\nF9FWlWzUoRWdhXsFaS12tvVbuLZrQdPRgAo2dTypqamkpqYO6X/Y4pgWLHBMnxQKV8Ad9JU7HKNC\n4Uhcecy48rEpBs6gDSYpZR8VbAbEJ4A4IcQ30AytULR4pt1oxWdfRzOEvmZnWzc6GkwjyUBXjVSw\nqePpahg/99xzg/4ftjimr33NgR1TuDWusJLsDvqqp2PU6XRj/topHIsrjGdH4cp6YbQem5K/kWXQ\nBlNbdrtekVK+0M/3T3f4X2lSyp8JIX4lhEgDMqSUF9q+M9rbNhoYzIyESlU5OklMhJdecnYvFK6C\nq8xSuoO+6nqMOp3OJa6dwnG4ynh2FK6sF0bjsSn5G3kGnfRBCPGvfX0vpRz8VP4w4qykD0ajkZ07\njxERcT+lpYfYsWNdn4NMzRQML/YEdFosWizT9eswadIwdUwx4jgruHewOmE04wr6qj856HiMrnTt\nFJ2xVx8omejOWNYLg9EHowElf8NDX0kf7HHJG1UG0WhlsDMSKth09OHpCatWQWoqfPazzu6NYqwz\nGmcp7cUd9FXHY3Sla6dwDEomuuPKemG0HZuSv5HHnhWm70spfyGE+A1akodOSCmfdFTnHIEz04qP\nthkJd8beWcQXX4SPP4ZXXhmGTimcgjPTxyqdMHoYrByoa+eaDEUfKJlwHcZiWnElf47HoStMQG7b\nX7vihYQQscDvATOQJ6XcIYR4AVgKXJRSfqdtO7vbRgujbUZCMXjWrFHGksJxKJ0wdlHXTtEVJRMK\nZ6Lkb2SxxyXvvba//2vnPq9JKe8DEELsFEIsB/yklKuFEC8LIeLRCtIOtu13Qoh4KeVFO/ulUHRj\n4UKoqoLSUoiIcHZvFAqFQqFQKBQjjT1Z8vb19b2Ucks/31s6fDQB64CjbZ+PAUlohtBg244CicCA\nDSa1nOm6OOraenhAcjKcOAGPPuqgzincgsHKoNJHowdnXAt1/ccuo/najea+jQUcdf7UdRj72OOS\nlwQUA38F0oFB5zEUQjwEPA98DJQCDW1f1QOxtLnr2dEWM9A+DDQloxLysUdP13YorF0Lx44pg0kx\ncAab8tVRKWKVvho6vV2L4Ty3KkXw2GU0XztnyLIr0duzxGDP3WiWEcXA8bDjN5OBHwHzgZeADUCV\nlPKklPLkQP6BlPI9KeUCNGPJDAS2fRUI1AJ1drbV9bS/Z599tv2VmpoKdC1EVo3JZLL1DaPR2P7+\n5MmP2LnzGKmp6WMuINBdMZlMvPvuCdLSzvHLX/6an/70p0P6f5/8JBw8CFargzqocHl60y/2bt9R\nL/WG0leOwXYtwsM3kplZjslkGvZzO1h5UYweRvO166lvVquVI0dOKz0xALqeP6PR2Kse6EtHj2YZ\nUQycQRtMUkqLlPKglPLLaC5weUCqEOKfBvJ7IYSuw8cGtEx769o+rwfOtb3sbetGR4MpJSUFuJeS\nsbT0XkrGrjdFo9GohHwMotfr2bZtDatXJ/K97z3Jv//7vw/p/82ZAwEBcPmygzqocHl60i/2bj/Q\nh3V1U3YMer2e6OhgTp58mfz825w9mzHs94LByoti9DCar13Xvul0Oo4ePcPOnWepqZlBdnaV0hN9\n0PX8CSF6nWjvS0ePZhlRDBx7XPIQQuiBTcDngRnAr4E9A/z5J4UQ/4xmKN2QUv5ECPErIUQakCGl\nvNC2D6O9bb3RdRk6OXk5SUn3PneeWXyfpKRFKs/9GKXrtR0qmzfD/v0QH++Qf6dwcaSUJCbGkZQk\n0Ol0GI3GfmWxN5ntbAgd6lWuVV0O+7HNDtuyTiUlLSIr6w6RkZvbzrkY9nPraJ2lGDkGe+0G4xI3\nVPe5jn0zGo3k5TWyYMFKrlzZz44dSUre+mH16mUsWXKXwEDNmaknPdBRR2dnH2TJksb27W2o8T32\nsacO0/+iueN9APxNSnl1ODrmKGx1mAbqQ3rixDn27k0HvNi2bSmrVy+jtbVVCfkYZ6g1Fk6cgO9/\nH86fd2CnFE5huOttdNQ10dHBAOTm1gzJdz01Nb1dd6WkJPS5bxWbMDBsciClJDU1vV3vb90aT0pK\nQqf7RUpKgjq3LspI198ZTDzLcMS+pKamk51dRVRUIBs3rhrS/3IlepKDwcQw2c6ryVSBXh+mYpXG\nKH3VYbInhumLwFzgKeBDIURD26tRCNHQz2+dRl/uKlarlYYGretJSYuYOXMGKSmPk5NTrYwlBQAr\nV0JeHpSXO7snitFGV9/1jromK+sOWVl3huzKlZy8nB071pGcvLzPWCZVl2PwmEwmsrLu0NIyi5aW\npWRl3cFkMrWfc5uB2vXc9hWzMJCYM8XoZTiv32BcZx3tZmtb+f7qV9crY2kA9HT+bXqgq4ysXr2M\nz352OXp9mHKLdlHsqcNkj5HVTlvdpRfRkj1ckFJ+VwjxPWALUAA8JqW0CCH+BdhqT1tP++3NXcVi\nsfDCCzu5cKGOxMSJPPXUl4mLm0x29kGiogLVw4cCAG9veOAB2LMHnnjC2b1RjBa6riYlJS3Cx8en\nXdfExU0GICfnUPtqkz0Iobn2qUxLjkev1xMXN5n8/HNIWUh0dFy73u/NnbKvmX+VEWvsYnsIPncu\n02HXr+vK5GBcZx3pZqvkcvD0dv67nstVq5Zy9OgZrl2rxWqtoaTkILGxoer50cUYtEvekHcoxCSg\nTkppEkLsAl4FnpZSbhZCfB+4CZwE3hhk2/eAW1LKt7vsr90lr6OPOmhCv3//MZ555j2io7+N2fwW\nf/zj4/j7+3P06Bny8hqVYnERHOF28d578ItfwKlTDuqUwik40gXHaDSyc+cxwsM3cvLky8ycOYO4\nuMmdXHkH+xDWm+uXbV8REfdTWnqIHTvWDfqGrNzK7tHRJc9oNCKl5NSpC9y40UBUVCDr199HWtr5\nHq9ZX9fCEddJMXJ0lIOTJz8iM7Oc/PwCkpO/SVnZ4SFdP0ek9e647VDGr6Pk0lV1SG8ueQaDgdbW\n1k4xSR3PZUnJQaZP1/PCC/sZN24Bs2Y18LOfbWf8+PEjfQgKB+Bol7whIaWskFLa1iktwAIgte3z\nUbQ6T8vtaDuGlrWvp322ZzA5efKj9kFhMBg4dCgTHx9Pzp//d5YtG09gYCCtra3k5TWqZVVFJ+6/\nH3JzobDQ2T1RjBZsGdVu3XoX8GpLEtDZlVcI0Wt2JRh4KYOhZlpSace7Yzsnr712nFOnznP1aiWV\nlePYufMsBw6cIDu7qsdr1te1UBmxxiY296vIyM2AF0VF7w/5+hmNRjIzyzvJ0GANjo4uYEMZv46Q\nS3fSIVarlcOHT/HDH/6eZ57ZxYkT59qPt+O5jIoK5NatJsaNm011tT9Wqwc+Pj5O7r1iOLArS54j\nEEIsBELQaifZ3OjqgQnAeDoXsx1MWzdMJhPZ2VXU1ATx+99rpaLWr7+PU6cukJ1dRnDwauLibvDk\nk48BKtuUomd0OvjMZ+Cvf4Wnn3Z2bxSOYigzprYbqJeXN5GRfr26YgzUtSMxMa7PjHhDybQ00Gx7\n7kTHzKjvv/8K+fk3yM1tZtWqdRQWGoiKCiQvr/s1s8U49XYOVUassYdtjGZnH+TBBxeQnJwwZBe4\nc+cyyc+/TX7+b9m6NWFIbrWOGL9DlUt30SFSSo4ePcMf/nAGo1FPeHgCWVl3WLGic5blxETNa8nb\n+zJpaR+i0+Uze/YCdDpdP3tQjEWcYjAJISagpSJ/BFgGTGn7qmNB2ql2tPVYuPbnP/85eXmFnD6d\nS3j4Kg4cuERCwkLy8hpZu/ZTZGam8fDDKzsN/K6pJBVji9TU1PYixY7k0Ufha1+DH/wAlJfm2Geo\nldxNJhO5uTVERm6mpOQgX/ziql51Rk8PK90fQPpOXz2UpA5qIqg7tnOSmfk+FouRyZPXYzYbaWi4\nQVTUSjZsWElysqk9lqmnB96eUMk3xiarVy/DZNLc8XW6jCFlybWN7eTkr1NU9D4rViweksHhiPE7\nVLl0Fx1iMpnIy2tk0aKtHDv2GjrdOeLikjpNmnR0s54zx5+VKxOZOvV+KitTXdJlUeGcGCZPYB/w\nrJTyvBBiIvCalPKhtjikfLTYJLvapJS7u+xP2vxQ/+VfXsVkSkCnS+f557/CpUu5PabXHO7gyJ7i\nqRTDi6PiVqSEhQvhV7+Cdev6314x+ugoC139+rdvX8u5c5ntemHDhpX9jv0TJ86RlXWHuLjJfab8\n7o2uKcOtVit37/Y/WWPPypirxh/Yg00OrFYrjY2NpKdnceDAJaT0ZNOmuPZ7Qsf7wZw5Ady40cCU\nKZ90SHySuh4jT9dz3lEfGAwGXn31ENOnb6KwcD9z5gRQVGS0+zmgp3IAAy0R0BMD1Q3DiavKbMdY\nNpPJxIcfXiYzs5w5c/zZsGFVN68ALdbtNsnJX6es7HC7blDp2sc2fcUwOWOF6RFgKfCfbcrnh0Ca\nEOIUUAi8KKU0CyFO2dPW2059fHx4+OHl7NmTTnn5bZ55Zhdbtizm0Ufvw9fXt9O2w7nsfK/mx0XA\nzNatCaSkJKikEmMEIeBb34Lf/lYZTK5A1xlTIQTZ2VXU1k5g587TAH0aTT0FCcPAV6jg3sqTTqfD\nYDAMKDmEvZM6apKmMzZ9vGfPR3h6Sh54YDEpKQntRT71en2n+0Fe3qEe3fTs3bfKWjay9LaibPvu\n3LlMbt0qJi3tx1gsRvbu1bF27Vays6vseg7oyVPFXrc4KSVpaecHNZkzHLiyDrHJx5UrFTQ0FFJa\naqCgwJdx4/zbn9M6xrrl5/+WoqL32xP9gG11Ml2NZxfEGUkf/ialDJNSrm17pUspfymlXCWl/IKU\n0ty23S/sbeuNFSsWM2NGBEFBSTQ3x7Nnz3l+8pOdfP/7f+DQobRuAX0lJQeZMyegx5z79nKv5sdS\nWlpmtdf8UIwdvvAFLVPezZvO7onCEXSst6PX64mKCuTKldMsWLCZvLzGXsenlJLGxsZ2l7ycnGqM\nRmOPQdF96Q9byvDU1HRefnk/7757jvDwjWRnV9HY2Njjbxxdn8VdMRqN7NnzEZcuhZGb68HVqxVI\nKTly5HT7NdTpdJ2C5TdsWNmpPpO9qGs48vR1zm3f3XffDqQMIDR0Nf7+SWRmpvVYYqS/ZwKbgfOn\nP53upAu6Zurt73/YvrfFYtfWzmTnzrMcOXIaq9Wq6n05EJPJxNWrlVy4UMvvfneW8+ctNDTEkZlZ\n3q6LbYl+Cgv3s3VrAo8//klSUhJUsjA3wGlJH5yBXq/H0/MuN29mExQkCAsLprV1LhUVDfzhD2cQ\nQrTP2nT0Zfb2PgdAbm7NkGcC79X8uACYiYtLdNnZGldl3DhtlelnP4M33nB2bxRDpeuM6YYNKwHI\nyyvodRWh40y10XinPdlD52x4h9pXjnqq1WT7P7bMWXv3ptPSMovq6jIKCt4D6vjTn073qHPcJZZg\nuBFC4OFhpbb2A7Kz62hqCsFkqiAjo4mFC1dy9WolS5Y0dlsVcMT5Vtdw5OnrnN/77gSrVk2hsLCY\nceM82bRpZTcXq4GsDtoMnEmT1pCTk9ptVam//9HT91FRgezcuZ/58+8jJ6cS24qGWqF0DHq9nshI\nH1566TDjxkVx8+YhvL3L0en8eP110WElScM22WX7rRrPro1bGUxGoxEPj2Aef/zfSEv7A8XF+eTn\nX0NKwcMPf5vc3EKSkzWlZpstCA/fyKVL77ZlwdpMdvZBlixptNuHWLZV2k5MjHPppW1X5zvfgTlz\n4Pp1mDvX2b1ROBIhBOvX30diYmdXmo6++x1nqrsme4iODiYraz9xcZPR6XQ0NDSQmVlOePhG3n13\nJ5cu3WbJkqkkJsaRnp7VHhsjpScwiSlTpvLII8vYty+rT7dglYlt6Oj1ejZsWMDJk3nMmPEw1dWZ\n/O1vOcTGriMzM40lSwLYtesUUVEBrF7dc4KHoaCu4cjT2zm33ZuTkkR7ko/e7tH3jKF15OQc7/H/\n6XQ6TKYK3nrrNyxdGtS+GmTzWGlsbGzP0JiZ+X6PCWGys6uYOHEtmZmHSUoysWHDSqSUfPDBBe7c\nkRQUlJCc/HVycg4rOXIQGzasJDb2GEVFM/D0vEVCwkPs3fsmRUUWbtw4x+LF8zp4FXTWzWo8uzbO\nSPoQDuwHogF/KaVVCPECWlzTRSnld9q2s7uty/6kLZAvNTWdt98+g8UiKCsrprw8lJqaEhobC/D1\n9WPx4k/wrW89RHLyclpbW/nww8vtsUaRkYF4e0+ktbUSvT7MruKTVquVo0fPtAcGOssH2R1xZLFS\nG88/D5cvw1tvOfTfKoaZ/mShtziHEyfOcflyCfHx01i5Mp733jtKWZm1U/C2xWLhgw9OcPNmEwsX\nhrWtHF3k8uXzSBmKEFV84hOfpqYmjWXLllBeXtshaNif3FytUrxeH4bReAedbhKxsaFDdv9SdKdj\nAdHNm3dw+nQNFksDkyb5YbWGERsrCQiIZvz4Wdy6dZHY2DC2bFnOxo2r7NbbrhowP5YRQmC1J0st\nbAAAIABJREFUWjl58iOuXq1kxgxfNm1a2+e93Wg08rvf/ZnTp8tZtSqCp576crdsekajkT/84QiV\nlQGkpu4hKMiLGTNmsnVrfPtKdHPzbW7fbkUIC9u2JXZ6ppBS8uKLr/PeezmEhHjxzW9uIyUlAaPR\nyKuvHiIycjOpqb9tL5atdMTQ6JgE5oknnuHvf7+MwdCMlCY8PHzx95/FrFnw7LNfQAjIy7vbZ+IO\nNdbHJqMt6UM1sBZ4B0AIsRjwk1KuFkK8LISIB6x2tP1OCBEvpbzY006NRiPvvHOeGzem0NBwiqAg\nSWtrHcXFtwkKmkddnSdNTUvIyCijqekERUVGZs0ax5QpE5kxYzMVFSd45JFl/PWvZ/tNBmF74OoY\nnAlw9OgZ/vjHswQETOfkyatA3wHlitHNd74DsbFw+DBs3Ojs3igcgZSyfUWo4wyixWLhN7/ZQ2Xl\nDM6cSefNN3dz8aKB+Hg9X//6fwJgNpt5/vmXeeedWyxenERrazEgqK9fSGtrI1FRiygsPEZr63hu\n3aonKMiHmpo8Cgv3s2hReFtdj0b+9KfTPa5cKRyPxWLhG9/4CadPN9DaGo+3dzWVlYUsWJDM5ctH\niYwMoq7uMHPmLKeoqLmb6/ZgUEkeRi+22JXLl+/w2mtZXLnyMT/4weMIITqtNNmuYUZGGe+8cw6z\nOZnTp68RHX2KW7eaiI4OJiFhIWazGX9/f8LDBYcPH8dgmExWViutrVO4dKkET09P6utnc+VKJiEh\nkgce+EGnVSLbCpSHRzCzZ6/A03MKWVnlrFhhwsfHh7i4yeTkHGLr1gRWrFisHsodSENDA5cv13D3\n7hLM5mDgEB4eibS0HMLbex7/8R+vs23bQ8yfP7HXsgJqrLsmI24wSSlNgKmD8CQBR9veH2v7bLWj\n7SiQCPRoMEkp+eijC1y+bMZqrcPf3xu93kBAQAw1NRcJDAylrKwGgyGeXbtuMH/+fVy79hHFxRXs\n23eKzZtjyc4OIT+/gPz8V9i6Nb7HJX1boN/Vq5VUVQWSlnYW0LLlaL7Gn2TfvtfYsmUbeXmN7S6A\nirGHry/8+tdaPNOVK6Au49jmXgbLdIqLq5g6NZ9t2xLx9vbmwIHjXL1ahb9/Ctevn6GsrBWYx40b\n2cyY8Ru+850d/PKXr/K7311Ar1/KoUN70eni8PBoISPjNo2N1URENLFlyzwMhmIaG4OpqPCisdGT\nOXMC2m+ogYGB7X7wsbGhylgaZqqqqvjgg2sYDEFI+V7bA1IZFy/uxscnnLt3PyY62p+pU5u4dq2M\nhITt5OUV2KW33aXo51hEr9czY4Yvr72WRXDwNt56ay8LFhzHz28c+/ZdAsxs2bKc+PgYsrOrCA5e\nTXn5XiIjfamsbODy5RKmTXuA3btf56WX/o+aGg+CghooKDDS2GjAaKzHy2sKJSWFLFjwJbKyPmbf\nvpOEhc2muroQne63fPrTiZ2MspycaqSsxc+vDqu1qFMdIOX6NTxIKTl7NoMbNy7T2uoFtAJWLJYj\nQBClpRuor/8zjz2WQF5eZjc90PEZUI1112M0xDAFAXlt7+uBWMBsZ1tMbztpaWnh+vXrmEwCGIeU\niRiNl5g2bTUGw3WmTJmBv38TJSWtzJ+/iYyMvQQHe2EyJVBd/SH791+nqOgu69Y9RXHxAVasWNzp\n/9setrKy7rBwYRjNzSXs23eURYse4MaNBpKTtYKUUhbz+c/HMG5cswoMdAE2b4bXX4ef/AR++Utn\n90ZhL7YZ3czMcu7enUFISALTp5ewfPkC3nrrPd56K4egoFBKSv5CUJAVIeZRW3sVP794/u//rpCW\n9iTnz9dgsQRiNH7E9OkeJCd/k7feeokFC9ag001n3rxyrNZaLl8uZuJELyoqrrB+/cMUFdV1ct3o\n72FIuXo4Di8vLyora5FyMXAHeAA4i5TFmM1WKiv9SE6O4b/+63FSU9MpKMhnzpzuGdMGggoKH908\n+OAaLl++yp4977J4cRIff1wP1NHSshQpK3j77bN89FE+RUXXqaq6QlQU+PpeY+LEQPbte5+iorfQ\n6byQ0hdv721kZ+9iwoQkhJiC1foOer0HYKG1tRVf3wgefHAWBw7s56GHthEcXN/+TNHRsL59+wM2\nbJjG9ev1gDb2hRAq/nmYMJlMZGSUYDZPBhYCpcB44ApQi5Rv0dJSwc6dP+fTn16CTqdr18ddi1pH\nRweTm6uNdVtMnLpmY5vRYDDVAbZp1ECgFrDY2VbX0w7+9V//lZycj6mtLQDmA+G0tBzF1xcKCv7I\nxIn+VFSUERe3Cimb8Pe/ztKlQRQWNpCf/xYwkfHjV2O1FlBcfIC4uMndBN9gMPD222dobZ3LjRtn\nmD17Flu2zCM3N52oKG1myFaTISAgoP2BRz38DA+pqamkpqaOyL5efRUWLYL774f160dklwoHYpvs\nyMws5/r1y1y/XkNIiD+f+tT9fPe7z/P227fQ6xvx8NDz2c8+yt69fwAKEeImOp2O0tJCyso+gdm8\nCCHqmTq1gi984T5qatKYPNlMTs5lAgMvsm3bJv70pwKior7LzZu/4otfXEBpaW23B/C+HoaUq4dj\nqaysxGyuA7KAIuA9YDowGykriIn5f9TWXuHEiXMUFLTQ0lLK9esSKU+SmLiI8ePHD2p/amVgdGLT\nAWVlkvBwM1VVlygsDGfqVD+8vIoxmw1kZuZw+XI0jY0fM29ePMXFOrZs8ebSpVI+/jgQD49QAgKi\naWg4hp9fNpMne1BWtofm5jCCgqqYMCGJkJBIPv64jgULwhCigc9/PgY/vyaiooI7ZWCMiQkhO/sg\nkZE+HDv2MS0tSykuvkBiYlx7hk2F49Ey3tVhMBSgGUtegAmYBMQD1/HxWci0aZvw9DRhNBrb6+Z1\nLGqdnX2QL3xhJStW+HQzpJTOHruMeB2mDoi211nAVgJ0PXCu7WVvWzeefvpp6uv9gThgI1CCySRp\nbd0CQFlZDV5egkuX0mhuNtHcXML+/dc4caKU8vK73L17nfr6I3h7w+zZ41i+fAENDQ1IKTEYDBgM\nBtLSznPlShklJa1I6UFsbCgTJ95l+/ZEVq9e1r7Nrl2nOHLkdPvMRMeaLQOtqeComlCuTEpKCs8+\n+2z7azgJDdVWmb78ZSgtHdZdKRyMlJL6+np27z5NRoaZ06fz8fSM5OOPy/jb347xwQe38PX9LhUV\njVRXm9m//xWKi6uprW3AYvHCam3GYglGr4/DYEjH0/MKc+b44e8/nchIH6Kj44mMXEtxsZXc3JvE\nxwdy69avSEqayNat97cVQW3kxIlzGAyGfvuravc4Fg8PD8APmAUsAMqBDHx8PsbT08TFi/9NQEAV\nN2/eJSxsLefP11FRMY2nn97Fli3P8d//vZO6uroB6+TBrAwoPT9yGI1Gdu8+x+HDcP58I2lpmeTm\n1vPee1nk598mP/8meXkmmppiaWw0cPDgMUpKTLzyyjucO3cHk2k8d+/mMGHCeZ58ci0rV3qSmLgU\nCCYoaDtm8yQmTLhGWdl+zpzJwGq1smPHOr773a+264COtZpWr15GVFQgt241cft2AVJWAOZ+E1Eo\nhkZLSwu7d6fS2irRVpYS0SZQwoBrQCVNTRns2fMihw/vo6WlpUNR60aiogIpKTmIyVTBn/98hrNn\nMzAajUpnuwgjvsIkhPACPkBb7zwI/AgwCiHSgAwp5YW27exu64rJZGqLOZiENoNYCwTQ3PwWYCA4\neAstLf4YDB9y7JieDz54H09PHyorPfDxmYvVasBisRIUFM9//df7PP/8Lry9g4iJGY+XVxAmk5k7\nd2rw85tHYeFeZsxYgre3N9u3r+XcuUyefvpVzGbw8LAyceJGdu58H9CUom0gZWcfbK/71NcshJph\nHp1s2ABPPAFbtkBaGvj5ObtHiv6wzSrv3n2aDz64hJeXN/X15RQVNdLcbKGoKIempgBgOxCKh0cy\nN2/uA/RAMFBBa2sInp43MJvPoddb8fZexaVLWXzuc6spKjrD1KnevP76QebN+wwffPA2mzat5Etf\nmsuDD67BZDK1ly7Yu/dlsrLuEBc3uc8xrdy6HIu2QmQCrqI5LOgBfwyGZuBB4BinT+fg5TWBGzcK\nWLZsPGfPvkdrqx863Q7efPNFTp0qISzMSHT0svbkHYPJntrbdkrPjxwWi4UDB/ZRWOgJBODr68Op\nUycJCVlKXZ0PUuZhsTRSX/8qU6d6otNFU1NTihYV0ARcZNy4QECHxdLKnTutNDb6UFfXhKfnHkJC\nWli5ciUFBeOpqKjlj3/8EIvFwrp1KzoUO70X62IraxIZuZlbt4qZPt1IfHzPNRuVrDiO+vp6rl7N\nB0LRnhOz0Fx1PQEdsAG4TmtrPCdOnOR733uVmJiJFBS8R1zcZJKSFpGYaGpP3KNdU6F0tovgjKQP\nZjSp68j5Hrb7tr1tXfHx8WHOnCnk5pZhseiBKCAEzS/Vj5qaY3h5CTw8TJjNt2hsNDFxYgSenjU0\nNWXj6bkEKW9z5copmprmcPOmB+HhoRw/ns2MGXqKi+upr88nPNxCS4skJCSFzMx85s+f3RYTMQ1P\nzykIcZoLF3azbNkG8vLq2uOacnIOtc8y9RYkOJhgwo435aG4/Cl3wcHxox/BtWvw+c9rqcbb6tkp\nRikmk4msrDs0NU2noeEETU0fAiaknI3ZfJvWVh9gNiCBYIzGQ2hJPoOABkDS3Czx8RlPaKiehgYL\nZrM/Ol0rt28fICFhFsnJy8nLKyI9/RTBwQHMmfMpCgsPtY+rmJgQMjPfB7x6rOvRE2PNrWs06xEv\nLy80Q8kMNKIFeX8C+Bg4iYfHXMrLi8jLC8dguMDmzZ9lwYJ5/M//vEtFxasI0Yq392d4++1niI9f\nxLVrp4mLm4unp2e3hB0DfbDtWKNHBY2PDLW1tdTVTUBbVZhAS8tJwJuysgyktODtvQCrtRad7hOU\nlp5Eyg/RdIMP4A1Mo6npBtev+7BzZxrjx88kJ+cyXl6zEaKBmTNDWb58JnfunKex8Q4TJ67jzTcv\n4O3t3SnWpatbXk7OIbZtW9pnJjyVTKRn7NE7FosFKUMBfzQHKC8040kANUAmUAVMwWAwUVgYgU5X\nw/TpvmRk5JKdXcXcueO7XdOh6uzRrEPdiRGvwzTS2Oow/eUv7/DYY7+ltXUhcAqYAMwBWgAdgYHl\nNDY2IuV0vLymEx5+lZYWA1brVKzWFqKjA/jkJ+fx9ttXuHGjiJaWJjw8BP7+RiIithEYGEFz83li\nY+dQXl5CUJCViIhIWlvvcP26ieBgDyZP9iUnp4bQ0Al885sPsnZtUqeBkJqaTk5ONdHRwSQlLbL1\nv5sPrJSS3Nya9hoAXQ0k27bR0cFIKblypaLfmeuuDOTmPpYG8XDUYeoJoxE+9zkwmWD3bi2TnmJ0\nYZMFKSWHDqXx4x//hkuXKtFukpVos4nzgMloN8h6NCMpBG3W0QA8CryLl5eBwMC1eHkV4etbxMSJ\nq/D1zWHr1s3Ex09l9eplGI1GTCYT6elZ7SvIttodtjF09mxG+1hzpXoqo3n2WwjBtWvXmDfvK2ju\n2qVos8mz0a79aXS6CQQG1uLrOxOj0Z+kpAC++tXN/M//vENdnSA4uJns7CaamswIMQkhSgkJsRIa\nOo9Nm2bzz//8VcxmM3q9HqPRyM6dx4iIuJ/S0kPs2LGu26SYLSYiO7uK5uYS/PymqDpcw4wQAovF\nQlRUCrdutQC+aGN+FZpBdAYfHy/AjNU6Bz+/Vu7ebcLTcxlG40k0Y/thYB+TJ2+mtfUIM2cuo6Cg\nBoOhgaCgaDZv1vHii08AcOzYh7z55gUWLFhJcHAd27evbe9HT5OkA7m/2p4detItY+H+7Gjs0Ts2\nOZgzJ5n8fIlW3jMTTe8vB04CRrQVRS0Gbfr0ABYunEFw8Epu3jzB6tXbyMvLYPt2raaWI+LNRrMO\ndUVGWx2mEcdqtXLrVjFmM0ABms96A5r7xcfAOBoa6tGWXHMxm+toaqolMDCIwsIAhMhj3LhkPD1D\niYubRHFxFS0tk7FarTQ0FBIaeoKlSzdTVNTKtWsF+PlVYzIt5/33DTQ1FbBwYTJWaznZ2ZUsW/Y8\nhYUvs3RpLNBZSa5evYzFixu5dCmXH/zgj5SU3GbKlMk8+OASbtxoICxsPTk5x9m+fS0rVmiGlMFg\naA86jIkJITExrn22KStrPzdu5NPamkR+/uACRvubtVKDuGf0evj73+ErX4H77tOMplmznN0rRVds\n8nv+fAGXLuWhrToXAeFoD84VQD7a6sNUtFnFm2iBv9cQYhe+vncRQo/JdBloZOPGBcyaNYXycj9m\nz97W7mZ740YDJlMF3t4TmTt3fKfaHbbxP9ZWjQbKaJ/91vpSB2SjTaDVoMlBNuCFv38oen0QNTWV\neHquJiPjBLt2HeDixbt4e8dSW3uViRODGT9+IoWFV4mI+Czl5Yfx9n6Qv/zlHczmVwgMnEFsbCjJ\nycuJjg4mK2t/e+Kgrhm2MjPLuXWrmNDQNVy5ks2XvjS711ovI4G7PHTX19dTXx+A5rY/Echo++Yj\nwIjB0IKnZwgWSxYmUygeHtFYremEhlppbjbh53cNq9VEUFA9JpM/4eF1TJ0agNks8PKqR6+fzLlz\nmSQnL+fBB9fg7e1NXl5d+wpET/fSwcS7ddUf7n5/tlfvNDQ04OExAe0ecAHtWfEa2uqzCS2m6Q6Q\nj4/PfPz97yKl4MKFTGpqblJa+meSkx8gL6+RlBTHnO/RrkPdCWcmfXAYQogXhBBpQogXe/q+oaGB\n//u/TKRchuZysR6YiZaRvBVtNsmEVtYpHNhITY0nBQVXkfImVmsTH354ljff3M3Zs9doaqoEItt+\nO4GyMh+mTtV+u2bNc3h6hlFdnYXBcANv7/FcupSKn18wUpo4fvynTJxo6LbaYbVaOXr0DG+8kcrb\nb6fT2LiI0tLJNDVNJze3hpaWUnbv/jVG4x30en37DfbVVw/x7rsXCA/fSE5ONUJobn6lpYeIjg7G\ny0ugPfz1HjDaEzaXgNLSnv1uVfB573h7w65d8NhjkJAAL71Em7GuGC1o6WPLeO21Y2jzRpFowb1m\ntFnleWjqMaGtLQrQI8Q1goNjWbJkJklJmxk3LhFfX0l8/D8SG5vIE09sZtu2pZSW3nOznTRpHefO\nVTJp0hpycqppbGzs1h9XTRPcnx5xNlrSB/+2VziaS9YatBWm9dTU3ObOnas0NRlpbr5MYeFt9uw5\nT1VVBOXl6dy+XYyvrw81NTl4edVQWPhnPDxyuXv3zyxcGMPFiw1MmrSG7OwqGhoaOu3barW2J/05\ncuQ02dlVREZuxmIxkpm5j4ULV1JUZHSabu2alMiVvVEMBgPV1dfRXG7D0Z4FLqFNrCYBK7BY7kfL\nshuIh0clVquBlpZwwIO4uLncd188c+cKHnvsKdauXcMbb/yAXbv+jZUr40lJ+Vb7fdJW+HjHjnWk\npCQ45F7aVX+4+/3ZXr0jhKCxUaLdC8ajRY9MBxaj3Q+OoHkZ3MXHp4HISF8iIqbh5xdNcPBS5szZ\nRENDPlFR9pUe6OtYSkoOMmdOwKjToe7EmF9hEkIsBvyklKuFEL8TQsRLKbsVr62szEdbXSpGm1EM\nQpspaEQ7DTPQArkzgD1og2IatlnH5uZCbt5chMXihfZAZUWbhbDQ0uLPj360Fw8PE35+Z1i4cAoL\nF0ZQWPghtbUhhIaGk519ncjIQB5//Bn27Pkljz32EqtXT+Opp76MEIKjR8+wc+dZFizYjIfHLWpr\n99LcXEd1dSHz5m0jN1eydesj1NWd7RTLFBm5mfz831JU9H77rGXH2SYvLy8uXy7pNWC0L/qa9VbB\n530jBDz5pJYM4sknNaPpW9+CRx6BadOc3TuFl5cXb731JgUFuWjuuUfQdIBNJ9xCW1XKAMqAJry8\nktHrLzN3rpEtWxZx8OAtZs2Ko7m5irlzK4iLSyIwMLDTuNHp0snJOU5CQihnzrxOSUkhBQUlbN0a\nT0pKglvM+o7m1TM/Pz+0h+RxaAmBZqK53tSh3QO8MJu1hK5mcw7QgtkcBJxECInROIXTp08g5QKs\nVh+CgsYhRBDx8d6MH2/i7t1qUlNfZfbs8bzxRir5+bdJTv46OTmHWbLkLjk51YSHbyQ3933mzZvA\ntWv7+fSn72sL+q9zqm51p5nt6mqbDOjRclLZkjlMAtLRnhc+Roh6hAjFy2s6VmsTVmscPj5mmptv\n8NRTn0Kv9yEvr56YmHACAwM5efIjiovvUFz8W7ZuTWg/fx0NnOG4l6r7s316x8vLi4aGfLRnQT2w\nH+2e0Io2ieKF9hxpIjS0npkzl2C11uDjcxsfn3rmzQtn06aVbNy4yqHHsnr1svakYDpdututGI4W\nxrzBhDb9c7Tt/VG0NdNOBpMQAg8PP7QHIl+0IM0FaEkfbKWbVqC55wWj1cSta3vfiKY8J2CxBKKt\nSt1By1PRAiwDCoFVWK0etLaW4es7lfLyZqZP38bkyRFUVn5AbOwiystLOHnyZfLy8gkL+wf27TvO\n9u2N+Pj4kJfXyIIFK7lyZT9f+MISbt6cycKFM8nOPkBW1jXS029RU3OGhx6KQafTta8k5eQcYuvW\nhE5BoTZlbCty5+XlDdwrejdQ+pv1Hs0PQqOF6Gg4cgTS0+F3v4Of/1zLoDd7NkyZAmFh2mvy5Hvv\nw8Jg4kTw9HR2712X4uJiPvzwBtrK0Tbg72hxCMfQHpSi0WYYC4Ap6HTjESKD2Fgfnn32c+h0Om7f\nNiBEHQ899GVSUhJ7fBiyjREpJS+/vB+DIZKWlklkZZWzYoV7jJ3RvHpWWlqKllL8n4HfAEuANGAR\nWorxhUAu2grUNDT9/zjwK8aNAyE+TUvLX5FyMx4ef6WxsY7p0z/JqVOZxMV9TELC58jMTEUIK+vW\nfYn8/JfbJ7cCAwOJjg5m795XkbIVi6UaD49ghBCsX38fycmtTj1v7vTQffDgQTSjaDna/X06mivW\nIuA9AgIkEREbKCk5z+LFi7h58wYTJiyioOA8M2fOISzMzK1bzSxcGMD27Wvx8fFpTyednPx1iore\n71bsviPDcS919/uzPXqnvLwcg0EPrEQzmE6gxav+DS1k49PAcXx9y9DrQwgJWUROzlm+/vWnqKxM\nY/v2td2SvTgCW9ZEd5i8GM24gsEUhHYXA823LqbrBr/85S/R6UrQZo2noc0cnkMbEGuA48CHbe13\n0E5LONrssgfaDXUyWurZ8cB9aC58BUAeHh63kfIuHh6CsLBoLJZyVq+OIT39FlVVpSxbFk5DQytr\n134FP79rVFQ0UlVlICwsAB8fnw6F6qrYsSOJjRtXcfjwKXbuPMT8+ffx0UenCQpKICgoDE/Pqnaf\n8v4UoslkIje3ZsDZtwbLaH4QGsnCtQMhIUF7Wa2Qnw+3bmk1m+7cgbIyyMjQ3t+5A+XlUF8P4eHa\natSUKTBpkmZE9fQKDlbG1WC5du0amn/6HbQZ5NvA62iTIy1oK8g1eHsbmDlzM2ZzNo8+uowf//if\nANi58xjr13+boqL3OxlLXek4RuLjp1FcfA4oIi5u6agdO+7EuHHj0BJ9/B4tZs2DezFrVcB1tCQQ\neWj6vxgPj5eYMuUuS5YsJT//OI2Nd2loeIvAQCNz5kwmN/cK8+Zto6npJJmZqSxZ8jBVVScoKnq/\n2+RWUtIisrLuEBFxP7t3/5rPfOZz5OSkkpTkXGPJhrs8dGtusiVoq4se3FthriY8fC7BwRbmzDGy\natVcFi1aQlaWkZqaABITZxMdvZTi4jtERm4mN/cQK1Zok5L3DM7DPRa778hw3EtH8/15tKLJQQOa\noRSEJgOFaM+E5cDf8fNrYeXKh9DpPkF29ockJIRQX3+ORYvCh8VYAveavBjNjPkseUKIJ4AKKeVu\nIcSngClSyt92+F5KKTl+/CybN3+HlhYf4A4hIZMxmy2YTL60tJSizR544u9vQAhfLJZJBAbewWAI\npKXFjE6nJzjYiMGgp7XVi7CwQFasiGXDhoWsX38f1dXVZGffpLDQSExMCBs2rMRgMNDa2kpAQABH\njpxuz5BltVq5fLmU+PipvWa0kVK2/8ZovENhYQPgxbZtSweVMamn7DnuyEhlyXMURiOUlEBxsfa3\nsrL3V0ODtio1dapmYAUFaQaUhwdYLNDaqsVQmc2d34OWpEKnu/fXw0Mz6mwvi6XzZym1zH/jxmkr\nZX5+9957dZl+6bqY2fGzEFofvbzuvWx9tu2n66u39o7fmUxgMGivlhbtr9EIL7xwb/82WdDr52Ey\nSbTJkTK09OFBQB3BwTOJj5/Jhg2L8fYOIyoqkE2b1rb3355xZcuCph5kRgc2OUhI+BQffVSB9sA8\nDogAbnBvci0Ync7AunUJfOUrG4iNnUN0dDQGgwGTyYS3tzeNjY34+vri7+/Pf/7n/5CR0URCQijz\n50eRl9dIdHRwr6mhbbKkxaeGub2uHmlsciBEBNok6jSgBk/PVqKiFjJ58hQeemgBX/3qIwQEBLRf\n87t377Z/7i3LpbskzXAFbHIwffp9FBfno8Wx3gIm4+trIjh4MrGxkaxfv5iSkhak9GTTpjg2bFg5\nItdYydLI0FeWPFcwmBYDX5NSfkMI8TLwescCtkKIsX2ACoVCoVAoFAqFYthx2bTiUsrLQgijECIN\nyOhoLHXYxlH7cutUnWOZsbbCNJpwNbnvTRZMJi3D4Rg+NMUg6CgHribjioGj7g3uR0/j3cPDo185\nUHrC9enrerpEWnEp5bellKullE8O537cPVWnwj1xB7kvKNBiwf7hH5zdE4UzcAcZVygUGvaOd6Un\n3BuXMJhGitFeU0ShGA7cQe5fegkefxzOnYOL3YoSKFwdd5BxhUKhYe94V3rCvRnzMUz9YUv64ChU\n4N3YRLldDA1XkvuusiAlzJgBBw7A3/+uJYv4xS+c1z/FyNBdDlxHxhUDR90b3JOu432wUVl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m4gOTmCgoJqkpMjroh42Rmu1qZ6qk13hf7eH7hcLn7zm5fZtOksmZkJPPbYN9rNv6Xd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\nGIZhtMH/B0njs2B97bJBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e2a3b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Produce a scatter matrix for each pair of features in the data\n",
    "pd.scatter_matrix(data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "*Are there any pairs of features which exhibit some degree of correlation? Does this confirm or deny your suspicions about the relevance of the feature you attempted to predict? How is the data for those features distributed?*  \n",
    "**Hint:** Is the data normally distributed? Where do most of the data points lie? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "3 feature pairs exhibit some degree of correlation. They are as follows\n",
    " - Detergent-paper and Grocery :   \n",
    "   Linear relation can be clearly observed, strong positive correlation\n",
    " - Detergent-paper and Milk :   \n",
    "   Some what linear relationship can be observed, positively correlated but not as strong as first pair\n",
    " - Milk and Grocery :   \n",
    "   This pair is also similar to second pair\n",
    "    \n",
    "The feature that I attempted to predict in previous step is Detergent_paper and yes this does confirm my suspicions about relavance of the feature I attempted to predict. Degetergent paper feature has positive correlation with Grocery (stronger) and milk, this is what made it more predictable than any other features. The data is not normally distributed.\n",
    "    \n",
    "There are other pairs which have some what linear relationship with either a slope of 0 or inifity which means the correlation is almost zero. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "In this section, you will preprocess the data to create a better representation of customers by performing a scaling on the data and detecting (and optionally removing) outliers. Preprocessing data is often times a critical step in assuring that results you obtain from your analysis are significant and meaningful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Feature Scaling\n",
    "If data is not normally distributed, especially if the mean and median vary significantly (indicating a large skew), it is most [often appropriate](http://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics) to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a [Box-Cox test](http://scipy.github.io/devdocs/generated/scipy.stats.boxcox.html), which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Assign a copy of the data to `log_data` after applying logarithmic scaling. Use the `np.log` function for this.\n",
    " - Assign a copy of the sample data to `log_samples` after applying logarithmic scaling. Again, use `np.log`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RQcYDD6Q6PMqLFnnzj/+4Bs/plasu816n5or5Mlt/X46bruSIomgBLNM6bhWQe+HPuUAG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vRFWwu26uojFwXA2LCwmSDAupgUJrfevxxzGvHxXtTW5iGVijz55Bbi45dZDBwz8vMrqK2to6rq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QhLloNGrk8g0MDeXi5rYCQeggNTWcN974Jbt3Z6PTBbFrVw4A69Yt5/z5YoqKBnBzW4wgtJGf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wAOn1CaxfL0Gh8OPcuSPEx9tRXt7C/PkbiI5W8swzDwHw8svvsHfvh0RHhyOX9zM8PIha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\niznPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111655850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Scale the data using the natural logarithm\n",
    "log_data = np.log(data.copy(deep=True))\n",
    "\n",
    "# TODO: Scale the sample data using the natural logarithm\n",
    "log_samples = np.log(data.copy(deep=True).loc[indices])\n",
    "\n",
    "# Produce a scatter matrix for each pair of newly-transformed features\n",
    "pd.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "After applying a natural logarithm scaling to the data, the distribution of each feature should appear much more normal. For any pairs of features you may have identified earlier as being correlated, observe here whether that correlation is still present (and whether it is now stronger or weaker than before).\n",
    "\n",
    "Run the code below to see how the sample data has changed after having the natural logarithm applied to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>9.772581</td>\n",
       "      <td>8.416046</td>\n",
       "      <td>8.434246</td>\n",
       "      <td>6.971669</td>\n",
       "      <td>7.722678</td>\n",
       "      <td>7.661056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>8.028455</td>\n",
       "      <td>9.490998</td>\n",
       "      <td>10.048756</td>\n",
       "      <td>8.279190</td>\n",
       "      <td>9.206232</td>\n",
       "      <td>6.594413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>8.399760</td>\n",
       "      <td>6.809039</td>\n",
       "      <td>7.121252</td>\n",
       "      <td>8.182000</td>\n",
       "      <td>5.030438</td>\n",
       "      <td>6.921658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh      Milk    Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "20   9.772581  8.416046   8.434246  6.971669          7.722678      7.661056\n",
       "200  8.028455  9.490998  10.048756  8.279190          9.206232      6.594413\n",
       "400  8.399760  6.809039   7.121252  8.182000          5.030438      6.921658"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the log-transformed sample data\n",
    "display(log_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Outlier Detection\n",
    "Detecting outliers in the data is extremely important in the data preprocessing step of any analysis. The presence of outliers can often skew results which take into consideration these data points. There are many \"rules of thumb\" for what constitutes an outlier in a dataset. Here, we will use [Tukey's Method for identfying outliers](http://datapigtechnologies.com/blog/index.php/highlighting-outliers-in-your-data-with-the-tukey-method/): An *outlier step* is calculated as 1.5 times the interquartile range (IQR). A data point with a feature that is beyond an outlier step outside of the IQR for that feature is considered abnormal.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Assign the value of the 25th percentile for the given feature to `Q1`. Use `np.percentile` for this.\n",
    " - Assign the value of the 75th percentile for the given feature to `Q3`. Again, use `np.percentile`.\n",
    " - Assign the calculation of an outlier step for the given feature to `step`.\n",
    " - Optionally remove data points from the dataset by adding indices to the `outliers` list.\n",
    "\n",
    "**NOTE:** If you choose to remove any outliers, ensure that the sample data does not contain any of these points!  \n",
    "Once you have performed this implementation, the dataset will be stored in the variable `good_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Fresh':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.950323</td>\n",
       "      <td>10.732651</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>10.095388</td>\n",
       "      <td>7.260523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.335634</td>\n",
       "      <td>8.911530</td>\n",
       "      <td>5.164786</td>\n",
       "      <td>8.151333</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5.389072</td>\n",
       "      <td>9.163249</td>\n",
       "      <td>9.575192</td>\n",
       "      <td>5.645447</td>\n",
       "      <td>8.964184</td>\n",
       "      <td>5.049856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.979339</td>\n",
       "      <td>8.740657</td>\n",
       "      <td>6.086775</td>\n",
       "      <td>5.407172</td>\n",
       "      <td>6.563856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>3.135494</td>\n",
       "      <td>7.869402</td>\n",
       "      <td>9.001839</td>\n",
       "      <td>4.976734</td>\n",
       "      <td>8.262043</td>\n",
       "      <td>5.379897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4.941642</td>\n",
       "      <td>9.087834</td>\n",
       "      <td>8.248791</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>6.967909</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>5.298317</td>\n",
       "      <td>10.160530</td>\n",
       "      <td>9.894245</td>\n",
       "      <td>6.478510</td>\n",
       "      <td>9.079434</td>\n",
       "      <td>8.740337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>5.192957</td>\n",
       "      <td>8.156223</td>\n",
       "      <td>9.917982</td>\n",
       "      <td>6.865891</td>\n",
       "      <td>8.633731</td>\n",
       "      <td>6.501290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>2.890372</td>\n",
       "      <td>8.923191</td>\n",
       "      <td>9.629380</td>\n",
       "      <td>7.158514</td>\n",
       "      <td>8.475746</td>\n",
       "      <td>8.759669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>5.081404</td>\n",
       "      <td>8.917311</td>\n",
       "      <td>10.117510</td>\n",
       "      <td>6.424869</td>\n",
       "      <td>9.374413</td>\n",
       "      <td>7.787382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>5.493061</td>\n",
       "      <td>9.468001</td>\n",
       "      <td>9.088399</td>\n",
       "      <td>6.683361</td>\n",
       "      <td>8.271037</td>\n",
       "      <td>5.351858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>1.098612</td>\n",
       "      <td>5.808142</td>\n",
       "      <td>8.856661</td>\n",
       "      <td>9.655090</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>6.309918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>4.762174</td>\n",
       "      <td>8.742574</td>\n",
       "      <td>9.961898</td>\n",
       "      <td>5.429346</td>\n",
       "      <td>9.069007</td>\n",
       "      <td>7.013016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>5.247024</td>\n",
       "      <td>6.588926</td>\n",
       "      <td>7.606885</td>\n",
       "      <td>5.501258</td>\n",
       "      <td>5.214936</td>\n",
       "      <td>4.844187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>3.610918</td>\n",
       "      <td>7.150701</td>\n",
       "      <td>10.011086</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>8.816853</td>\n",
       "      <td>4.700480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>4.574711</td>\n",
       "      <td>8.190077</td>\n",
       "      <td>9.425452</td>\n",
       "      <td>4.584967</td>\n",
       "      <td>7.996317</td>\n",
       "      <td>4.127134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh       Milk    Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "65   4.442651   9.950323  10.732651  3.583519         10.095388      7.260523\n",
       "66   2.197225   7.335634   8.911530  5.164786          8.151333      3.295837\n",
       "81   5.389072   9.163249   9.575192  5.645447          8.964184      5.049856\n",
       "95   1.098612   7.979339   8.740657  6.086775          5.407172      6.563856\n",
       "96   3.135494   7.869402   9.001839  4.976734          8.262043      5.379897\n",
       "128  4.941642   9.087834   8.248791  4.955827          6.967909      1.098612\n",
       "171  5.298317  10.160530   9.894245  6.478510          9.079434      8.740337\n",
       "193  5.192957   8.156223   9.917982  6.865891          8.633731      6.501290\n",
       "218  2.890372   8.923191   9.629380  7.158514          8.475746      8.759669\n",
       "304  5.081404   8.917311  10.117510  6.424869          9.374413      7.787382\n",
       "305  5.493061   9.468001   9.088399  6.683361          8.271037      5.351858\n",
       "338  1.098612   5.808142   8.856661  9.655090          2.708050      6.309918\n",
       "353  4.762174   8.742574   9.961898  5.429346          9.069007      7.013016\n",
       "355  5.247024   6.588926   7.606885  5.501258          5.214936      4.844187\n",
       "357  3.610918   7.150701  10.011086  4.919981          8.816853      4.700480\n",
       "412  4.574711   8.190077   9.425452  4.584967          7.996317      4.127134"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Milk':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>10.039983</td>\n",
       "      <td>11.205013</td>\n",
       "      <td>10.377047</td>\n",
       "      <td>6.894670</td>\n",
       "      <td>9.906981</td>\n",
       "      <td>6.805723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>6.220590</td>\n",
       "      <td>4.718499</td>\n",
       "      <td>6.656727</td>\n",
       "      <td>6.796824</td>\n",
       "      <td>4.025352</td>\n",
       "      <td>4.882802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>10.029503</td>\n",
       "      <td>4.897840</td>\n",
       "      <td>5.384495</td>\n",
       "      <td>8.057377</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>6.306275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fresh       Milk    Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "86   10.039983  11.205013  10.377047  6.894670          9.906981      6.805723\n",
       "98    6.220590   4.718499   6.656727  6.796824          4.025352      4.882802\n",
       "154   6.432940   4.007333   4.919981  4.317488          1.945910      2.079442\n",
       "356  10.029503   4.897840   5.384495  8.057377          2.197225      6.306275"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Grocery':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>9.923192</td>\n",
       "      <td>7.036148</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>8.390949</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>6.882437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "75   9.923192  7.036148  1.098612  8.390949          1.098612      6.882437\n",
       "154  6.432940  4.007333  4.919981  4.317488          1.945910      2.079442"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Frozen':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>8.431853</td>\n",
       "      <td>9.663261</td>\n",
       "      <td>9.723703</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>8.847360</td>\n",
       "      <td>6.070738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>8.597297</td>\n",
       "      <td>9.203618</td>\n",
       "      <td>9.257892</td>\n",
       "      <td>3.637586</td>\n",
       "      <td>8.932213</td>\n",
       "      <td>7.156177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>4.442651</td>\n",
       "      <td>9.950323</td>\n",
       "      <td>10.732651</td>\n",
       "      <td>3.583519</td>\n",
       "      <td>10.095388</td>\n",
       "      <td>7.260523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>10.000569</td>\n",
       "      <td>9.034080</td>\n",
       "      <td>10.457143</td>\n",
       "      <td>3.737670</td>\n",
       "      <td>9.440738</td>\n",
       "      <td>8.396155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>7.759187</td>\n",
       "      <td>8.967632</td>\n",
       "      <td>9.382106</td>\n",
       "      <td>3.951244</td>\n",
       "      <td>8.341887</td>\n",
       "      <td>7.436617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>6.978214</td>\n",
       "      <td>9.177714</td>\n",
       "      <td>9.645041</td>\n",
       "      <td>4.110874</td>\n",
       "      <td>8.696176</td>\n",
       "      <td>7.142827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>10.395650</td>\n",
       "      <td>9.728181</td>\n",
       "      <td>9.519735</td>\n",
       "      <td>11.016479</td>\n",
       "      <td>7.148346</td>\n",
       "      <td>8.632128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>8.402007</td>\n",
       "      <td>8.569026</td>\n",
       "      <td>9.490015</td>\n",
       "      <td>3.218876</td>\n",
       "      <td>8.827321</td>\n",
       "      <td>7.239215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>9.060331</td>\n",
       "      <td>7.467371</td>\n",
       "      <td>8.183118</td>\n",
       "      <td>3.850148</td>\n",
       "      <td>4.430817</td>\n",
       "      <td>7.824446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>7.932721</td>\n",
       "      <td>7.437206</td>\n",
       "      <td>7.828038</td>\n",
       "      <td>4.174387</td>\n",
       "      <td>6.167516</td>\n",
       "      <td>3.951244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fresh      Milk    Grocery     Frozen  Detergents_Paper  Delicatessen\n",
       "38    8.431853  9.663261   9.723703   3.496508          8.847360      6.070738\n",
       "57    8.597297  9.203618   9.257892   3.637586          8.932213      7.156177\n",
       "65    4.442651  9.950323  10.732651   3.583519         10.095388      7.260523\n",
       "145  10.000569  9.034080  10.457143   3.737670          9.440738      8.396155\n",
       "175   7.759187  8.967632   9.382106   3.951244          8.341887      7.436617\n",
       "264   6.978214  9.177714   9.645041   4.110874          8.696176      7.142827\n",
       "325  10.395650  9.728181   9.519735  11.016479          7.148346      8.632128\n",
       "420   8.402007  8.569026   9.490015   3.218876          8.827321      7.239215\n",
       "429   9.060331  7.467371   8.183118   3.850148          4.430817      7.824446\n",
       "439   7.932721  7.437206   7.828038   4.174387          6.167516      3.951244"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Detergents_Paper':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>9.923192</td>\n",
       "      <td>7.036148</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>8.390949</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>6.882437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>9.428190</td>\n",
       "      <td>6.291569</td>\n",
       "      <td>5.645447</td>\n",
       "      <td>6.995766</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.711101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "75   9.923192  7.036148  1.098612  8.390949          1.098612      6.882437\n",
       "161  9.428190  6.291569  5.645447  6.995766          1.098612      7.711101"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Delicatessen':\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.335634</td>\n",
       "      <td>8.911530</td>\n",
       "      <td>5.164786</td>\n",
       "      <td>8.151333</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.248504</td>\n",
       "      <td>9.724899</td>\n",
       "      <td>10.274568</td>\n",
       "      <td>6.511745</td>\n",
       "      <td>6.728629</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4.941642</td>\n",
       "      <td>9.087834</td>\n",
       "      <td>8.248791</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>6.967909</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8.034955</td>\n",
       "      <td>8.997147</td>\n",
       "      <td>9.021840</td>\n",
       "      <td>6.493754</td>\n",
       "      <td>6.580639</td>\n",
       "      <td>3.583519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>10.519646</td>\n",
       "      <td>8.875147</td>\n",
       "      <td>9.018332</td>\n",
       "      <td>8.004700</td>\n",
       "      <td>2.995732</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>10.514529</td>\n",
       "      <td>10.690808</td>\n",
       "      <td>9.911952</td>\n",
       "      <td>10.505999</td>\n",
       "      <td>5.476464</td>\n",
       "      <td>10.777768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>5.789960</td>\n",
       "      <td>6.822197</td>\n",
       "      <td>8.457443</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>5.811141</td>\n",
       "      <td>2.397895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>7.798933</td>\n",
       "      <td>8.987447</td>\n",
       "      <td>9.192075</td>\n",
       "      <td>8.743372</td>\n",
       "      <td>8.148735</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>6.368187</td>\n",
       "      <td>6.529419</td>\n",
       "      <td>7.703459</td>\n",
       "      <td>6.150603</td>\n",
       "      <td>6.860664</td>\n",
       "      <td>2.890372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>6.871091</td>\n",
       "      <td>8.513988</td>\n",
       "      <td>8.106515</td>\n",
       "      <td>6.842683</td>\n",
       "      <td>6.013715</td>\n",
       "      <td>1.945910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>10.602965</td>\n",
       "      <td>6.461468</td>\n",
       "      <td>8.188689</td>\n",
       "      <td>6.948897</td>\n",
       "      <td>6.077642</td>\n",
       "      <td>2.890372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>10.663966</td>\n",
       "      <td>5.655992</td>\n",
       "      <td>6.154858</td>\n",
       "      <td>7.235619</td>\n",
       "      <td>3.465736</td>\n",
       "      <td>3.091042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>7.431892</td>\n",
       "      <td>8.848509</td>\n",
       "      <td>10.177932</td>\n",
       "      <td>7.283448</td>\n",
       "      <td>9.646593</td>\n",
       "      <td>3.610918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fresh       Milk    Grocery     Frozen  Detergents_Paper  \\\n",
       "66    2.197225   7.335634   8.911530   5.164786          8.151333   \n",
       "109   7.248504   9.724899  10.274568   6.511745          6.728629   \n",
       "128   4.941642   9.087834   8.248791   4.955827          6.967909   \n",
       "137   8.034955   8.997147   9.021840   6.493754          6.580639   \n",
       "142  10.519646   8.875147   9.018332   8.004700          2.995732   \n",
       "154   6.432940   4.007333   4.919981   4.317488          1.945910   \n",
       "183  10.514529  10.690808   9.911952  10.505999          5.476464   \n",
       "184   5.789960   6.822197   8.457443   4.304065          5.811141   \n",
       "187   7.798933   8.987447   9.192075   8.743372          8.148735   \n",
       "203   6.368187   6.529419   7.703459   6.150603          6.860664   \n",
       "233   6.871091   8.513988   8.106515   6.842683          6.013715   \n",
       "285  10.602965   6.461468   8.188689   6.948897          6.077642   \n",
       "289  10.663966   5.655992   6.154858   7.235619          3.465736   \n",
       "343   7.431892   8.848509  10.177932   7.283448          9.646593   \n",
       "\n",
       "     Delicatessen  \n",
       "66       3.295837  \n",
       "109      1.098612  \n",
       "128      1.098612  \n",
       "137      3.583519  \n",
       "142      1.098612  \n",
       "154      2.079442  \n",
       "183     10.777768  \n",
       "184      2.397895  \n",
       "187      1.098612  \n",
       "203      2.890372  \n",
       "233      1.945910  \n",
       "285      2.890372  \n",
       "289      3.091042  \n",
       "343      3.610918  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlier indices:\n",
      "\n",
      "[128, 193, 264, 137, 142, 145, 154, 412, 285, 161, 420, 38, 171, 429, 175, 304, 305, 439, 184, 57, 187, 65, 66, 203, 325, 289, 75, 81, 338, 86, 343, 218, 95, 96, 353, 98, 355, 356, 357, 233, 109, 183]\n",
      "\n",
      "Repeated indices(more than one case):\n",
      "\n",
      "[128, 154, 65, 66, 75]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "outlier_indices = set()\n",
    "outlier_indices_count = Counter()\n",
    "\n",
    "# For each feature find the data points with extreme high or low values\n",
    "for feature in log_data.keys():\n",
    "    \n",
    "    # TODO: Calculate Q1 (25th percentile of the data) for the given feature\n",
    "    Q1 = np.percentile(log_data[feature],q=25.)\n",
    "    \n",
    "    # TODO: Calculate Q3 (75th percentile of the data) for the given feature\n",
    "    Q3 = np.percentile(log_data[feature],q=75.)\n",
    "    \n",
    "    # TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)\n",
    "    step = (Q3-Q1)*1.5\n",
    "    \n",
    "    # Display the outliers\n",
    "    print \"Data points considered outliers for the feature '{}':\".format(feature)\n",
    "    \n",
    "    current_outlier_indices = set([i for i, x in enumerate(~((log_data[feature] >= Q1 - step) \\\n",
    "                                                & (log_data[feature] <= Q3 + step))) if x])\n",
    "    # collecting the indices of the outliers\n",
    "    outlier_indices = outlier_indices | current_outlier_indices\n",
    "    \n",
    "    outlier_indices_count.update(current_outlier_indices)\n",
    "    \n",
    "    display(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))])\n",
    "    \n",
    "# OPTIONAL: Select the indices for data points you wish to remove\n",
    "outliers = [key for key,value in outlier_indices_count.iteritems() if value > 1]\n",
    "\n",
    "print \"Outlier indices:\\n\\n{}\\n\".format(list(outlier_indices))\n",
    "print \"Repeated indices(more than one case):\\n\\n{}\\n\".format(outliers)\n",
    "\n",
    "# Remove the outliers, if any were specified\n",
    "good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 4\n",
    "*Are there any data points considered outliers for more than one feature based on the definition above? Should these data points be removed from the dataset? If any data points were added to the `outliers` list to be removed, explain why.* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "Outliers : \n",
    "\n",
    "Outlier is an anomaly in data which might have been caused by data entry error or sensor malfunction or some other reason. These points lie at an abnormal distance from other points in a random sample from a population. Often outliers are bad data points, but in some scenarios these points may contain valuable information. \n",
    "\n",
    "Five outliers appeared in more than one case. These data points should be removed from the dataset as outliers were found in more than one column. Outliers are never good when we are trying to identify patterns in data. In machine learning methods like Kmeans clustering, centroids of the data points are calculated for identifying the clusters. Calculating centroid is nothing but taking a mean of all the points (seperately for x and y coordinates) and outliers have great impact on means. So removing these points is necessary. \n",
    "\n",
    "One can argue that the other outliers should also be removed, but the dataset we are dealing with is small and it makes sense to remove the outliers which can have greater impact.\n",
    "\n",
    "Ref: http://www.itl.nist.gov/div898/handbook/prc/section1/prc16.htm\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Transformation\n",
    "In this section you will use principal component analysis (PCA) to draw conclusions about the underlying structure of the wholesale customer data. Since using PCA on a dataset calculates the dimensions which best maximize variance, we will find which compound combinations of features best describe customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Implementation: PCA\n",
    "\n",
    "Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the `good_data` to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the *explained variance ratio* of each dimension — how much variance within the data is explained by that dimension alone. Note that a component (dimension) from PCA can be considered a new \"feature\" of the space, however it is a composition of the original features present in the data.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Import `sklearn.decomposition.PCA` and assign the results of fitting PCA in six dimensions with `good_data` to `pca`.\n",
    " - Apply a PCA transformation of `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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hO++8MwCf/exn2X777QE4+eST6dmzJ0VFRZx//vl89NFHzJs3r8Ht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\ng90AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111246f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# TODO: Apply PCA by fitting the good data with the same number of dimensions as features\n",
    "pca_obj = PCA(n_components=6,copy=True,random_state=10)\n",
    "pca = pca_obj.fit(good_data)\n",
    "\n",
    "# TODO: Transform log_samples using the PCA fit above\n",
    "pca_samples = pca_obj.transform(log_samples)\n",
    "\n",
    "# Generate PCA results plot\n",
    "pca_results = vs.pca_results(good_data, pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 5\n",
    "*How much variance in the data is explained* ***in total*** *by the first and second principal component? What about the first four principal components? Using the visualization provided above, discuss what the first four dimensions best represent in terms of customer spending.*  \n",
    "**Hint:** A positive increase in a specific dimension corresponds with an *increase* of the *positive-weighted* features and a *decrease* of the *negative-weighted* features. The rate of increase or decrease is based on the indivdual feature weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "Variance in the data explained in total by the first and second principal component is 0.7068. Which is more than 70% cutomers can be found in these two segments itself.\n",
    "\n",
    "Variance in the data explained in total by first four principal components is 0.9311. Which is more than 93% cutomers can be found in these four segments itself.\n",
    "    \n",
    "From the visulalizations it can be observed that :\n",
    "\n",
    "Detergent_paper, grocery and milk have high infulence on first dimension, which suggests that these products have higher infulence on money spent by customes in this segment. Fresh,frozen and delicatessen have higher influence on second dimension, so customer spending in this segment is around these products. Similarly fresh and delicatessen products influence third dimension while frozen and delicatessen influence the fourth dimention.\n",
    "    \n",
    "It is intresting to observe that the three products in first dimention are same as the products in strongly correlated pairs which we observed earlier. Similar pattern can also be observed in 5th dimention. In both these dimensions these products have higher influence. This means these products not only have higher infulence on almost 45% of cutomers but also are strongly correlated. \n",
    "\n",
    "If we observe 2nd and 3rd dimension similar relationship can be observed between fresh, frozen and delicatessen. This suggests that there is a pattern in customer spending, that is one segment is influenced more by detergent_paper, grocery and milk products while another segment is influenced by fresh,frozen and delicatessen products.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it in six dimensions. Observe the numerical value for the first four dimensions of the sample points. Consider if this is consistent with your initial interpretation of the sample points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "      <th>Dimension 3</th>\n",
       "      <th>Dimension 4</th>\n",
       "      <th>Dimension 5</th>\n",
       "      <th>Dimension 6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.8375</td>\n",
       "      <td>-1.0765</td>\n",
       "      <td>-0.3684</td>\n",
       "      <td>-0.8111</td>\n",
       "      <td>-0.5111</td>\n",
       "      <td>-0.3040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.9903</td>\n",
       "      <td>-0.3645</td>\n",
       "      <td>0.2521</td>\n",
       "      <td>1.5653</td>\n",
       "      <td>0.1922</td>\n",
       "      <td>0.1244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.5025</td>\n",
       "      <td>0.1006</td>\n",
       "      <td>1.0091</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>-0.5126</td>\n",
       "      <td>-0.0520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2  Dimension 3  Dimension 4  Dimension 5  \\\n",
       "0      -0.8375      -1.0765      -0.3684      -0.8111      -0.5111   \n",
       "1      -2.9903      -0.3645       0.2521       1.5653       0.1922   \n",
       "2       2.5025       0.1006       1.0091       0.1630      -0.5126   \n",
       "\n",
       "   Dimension 6  \n",
       "0      -0.3040  \n",
       "1       0.1244  \n",
       "2      -0.0520  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sample log-data after having a PCA transformation applied\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Dimensionality Reduction\n",
    "When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the *cumulative explained variance ratio* is extremely important for knowing how many dimensions are necessary for the problem. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Assign the results of fitting PCA in two dimensions with `good_data` to `pca`.\n",
    " - Apply a PCA transformation of `good_data` using `pca.transform`, and assign the results to `reduced_data`.\n",
    " - Apply a PCA transformation of `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO: Apply PCA by fitting the good data with only two dimensions\n",
    "pca = PCA(n_components=2,copy=True,random_state=10)\n",
    "\n",
    "pca.fit(good_data)\n",
    "# TODO: Transform the good data using the PCA fit above\n",
    "reduced_data = pca.transform(good_data)\n",
    "\n",
    "# TODO: Transform log_samples using the PCA fit above\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# Create a DataFrame for the reduced data\n",
    "reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions. Observe how the values for the first two dimensions remains unchanged when compared to a PCA transformation in six dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.8375</td>\n",
       "      <td>-1.0765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.9903</td>\n",
       "      <td>-0.3645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.5025</td>\n",
       "      <td>0.1006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2\n",
       "0      -0.8375      -1.0765\n",
       "1      -2.9903      -0.3645\n",
       "2       2.5025       0.1006"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sample log-data after applying PCA transformation in two dimensions\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing a Biplot\n",
    "A biplot is a scatterplot where each data point is represented by its scores along the principal components. The axes are the principal components (in this case `Dimension 1` and `Dimension 2`). In addition, the biplot shows the projection of the original features along the components. A biplot can help us interpret the reduced dimensions of the data, and discover relationships between the principal components and original features.\n",
    "\n",
    "Run the code cell below to produce a biplot of the reduced-dimension data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110934d90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ggedN0Mq4u6Ani8a12xjzGRHpzW7r1dB99V1o9Ut3W4sdo/8DXf7gZujQ4QPQLPulxhg7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\n1e8pii+/AVwih1AaYx4UkbkAZhhjDjq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PIIyMaCd1+XLv7Qy6/LmfrJ2dS7Rtm86ROvts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\n8njQXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11122af90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a biplot\n",
    "vs.biplot(good_data, reduced_data, pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "Once we have the original feature projections (in red), it is easier to interpret the relative position of each data point in the scatterplot. For instance, a point the lower right corner of the figure will likely correspond to a customer that spends a lot on `'Milk'`, `'Grocery'` and `'Detergents_Paper'`, but not so much on the other product categories. \n",
    "\n",
    "From the biplot, which of the original features are most strongly correlated with the first component? What about those that are associated with the second component? Do these observations agree with the pca_results plot you obtained earlier?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "\n",
    "In this section, you will choose to use either a K-Means clustering algorithm or a Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data. You will then recover specific data points from the clusters to understand their significance by transforming them back into their original dimension and scale. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "*What are the advantages to using a K-Means clustering algorithm? What are the advantages to using a Gaussian Mixture Model clustering algorithm? Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**\n",
    "\n",
    "KMeans advantages:\n",
    "   - Scalable with number of samples and number of clusters\n",
    "   - Goto clustering algomrithm when structure of data is unknown. It can also be used as initial step before applying complex clustering algorithms.\n",
    "\n",
    "Gaussian Mixture Model:\n",
    "   - Soft clustering, a data point can belong to many clusters with different probablity\n",
    "   - GMM clustering can accommodate clusters that have different sizes and correlation structures within them\n",
    "\n",
    "As we are not sure about the structure of the data I would like to use KMeans clustering.\n",
    "\n",
    "Ref: \n",
    "\n",
    "http://scikit-learn.org  \n",
    "https://www.quora.com/What-are-the-advantages-to-using-a-Gaussian-Mixture-Model-clustering-algorithm  \n",
    "https://www.mathworks.com/help/stats/clustering-using-gaussian-mixture-models.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Creating Clusters\n",
    "Depending on the problem, the number of clusters that you expect to be in the data may already be known. When the number of clusters is not known *a priori*, there is no guarantee that a given number of clusters best segments the data, since it is unclear what structure exists in the data — if any. However, we can quantify the \"goodness\" of a clustering by calculating each data point's *silhouette coefficient*. The [silhouette coefficient](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html) for a data point measures how similar it is to its assigned cluster from -1 (dissimilar) to 1 (similar). Calculating the *mean* silhouette coefficient provides for a simple scoring method of a given clustering.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Fit a clustering algorithm to the `reduced_data` and assign it to `clusterer`.\n",
    " - Predict the cluster for each data point in `reduced_data` using `clusterer.predict` and assign them to `preds`.\n",
    " - Find the cluster centers using the algorithm's respective attribute and assign them to `centers`.\n",
    " - Predict the cluster for each sample data point in `pca_samples` and assign them `sample_preds`.\n",
    " - Import `sklearn.metrics.silhouette_score` and calculate the silhouette score of `reduced_data` against `preds`.\n",
    "   - Assign the silhouette score to `score` and print the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_clusters        score\n",
      "2              : 0.426281015469\n",
      "3              :  0.39689092645\n",
      "4              : 0.331402447987\n",
      "5              : 0.341831480482\n",
      "6              : 0.362647634178\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "print \"{:<15} {:>7}\".format('n_clusters','score') \n",
    "print_format = \"{:<15}:{:>15}\"\n",
    "for i in xrange(2,7):\n",
    "    # TODO: Apply your clustering algorithm of choice to the reduced data \n",
    "    clusterer = KMeans(n_clusters=i, random_state=8).fit(reduced_data)\n",
    "\n",
    "    # TODO: Predict the cluster for each data point\n",
    "    preds = clusterer.predict(reduced_data)\n",
    "\n",
    "    # TODO: Find the cluster centers\n",
    "    centers = clusterer.cluster_centers_\n",
    "\n",
    "    # TODO: Predict the cluster for each transformed sample data point\n",
    "    sample_preds = clusterer.predict(pca_samples)\n",
    "\n",
    "    # TODO: Calculate the mean silhouette coefficient for the number of clusters chosen\n",
    "    score = silhouette_score(reduced_data,preds)\n",
    "    print print_format.format(i,score)\n",
    "\n",
    "# Implementing the clustering using n_clusters as 2\n",
    "clusterer = KMeans(n_clusters=2, random_state=8).fit(reduced_data)\n",
    "preds = clusterer.predict(reduced_data)\n",
    "centers = clusterer.cluster_centers_\n",
    "sample_preds = clusterer.predict(pca_samples)\n",
    "score = silhouette_score(reduced_data,preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "*Report the silhouette score for several cluster numbers you tried. Of these, which number of clusters has the best silhouette score?* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**  \n",
    "silhouette score for several cluster numbers:  \n",
    "\n",
    "|n_clusters|score|\n",
    "|----------|-----|\n",
    "|2 | 0.426281015469|\n",
    "|3| 0.39689092645|\n",
    "|4 | 0.331402447987|\n",
    "|5 |0.341831480482|\n",
    "|6  |0.362647634178|\n",
    "\n",
    "With n_clusters as 2 we have best silhouette score of 0.426"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster Visualization\n",
    "Once you've chosen the optimal number of clusters for your clustering algorithm using the scoring metric above, you can now visualize the results by executing the code block below. Note that, for experimentation purposes, you are welcome to adjust the number of clusters for your clustering algorithm to see various visualizations. The final visualization provided should, however, correspond with the optimal number of clusters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bIFYjWxER6VyZrR4g7WZmdQQdEiTqbEBEJGdlNAFyzr1rZvcQdEP5MfCM82NJ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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11099d050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the results of the clustering from implementation\n",
    "vs.cluster_results(reduced_data, preds, centers, pca_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Data Recovery\n",
    "Each cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the *averages* of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster's center point corresponds to *the average customer of that segment*. Since the data is currently reduced in dimension and scaled by a logarithm, we can recover the representative customer spending from these data points by applying the inverse transformations.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Apply the inverse transform to `centers` using `pca.inverse_transform` and assign the new centers to `log_centers`.\n",
    " - Apply the inverse function of `np.log` to `log_centers` using `np.exp` and assign the true centers to `true_centers`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Segment 0</th>\n",
       "      <td>4005.0</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>12104.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>4561.0</td>\n",
       "      <td>1036.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>8867.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>2477.0</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>681.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Segment 0  4005.0  7900.0  12104.0   952.0            4561.0        1036.0\n",
       "Segment 1  8867.0  1897.0   2477.0  2088.0             294.0         681.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000    440.000000    440.000000    440.000000   \n",
       "mean    12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std     12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         3.000000     55.000000      3.000000     25.000000   \n",
       "25%      3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%      8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%     16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max    112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "\n",
       "       Detergents_Paper  Delicatessen  \n",
       "count        440.000000    440.000000  \n",
       "mean        2881.493182   1524.870455  \n",
       "std         4767.854448   2820.105937  \n",
       "min            3.000000      3.000000  \n",
       "25%          256.750000    408.250000  \n",
       "50%          816.500000    965.500000  \n",
       "75%         3922.000000   1820.250000  \n",
       "max        40827.000000  47943.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Inverse transform the centers\n",
    "log_centers = pca.inverse_transform(centers)\n",
    "\n",
    "# TODO: Exponentiate the centers\n",
    "true_centers = np.exp(log_centers)\n",
    "\n",
    "# Display the true centers\n",
    "segments = ['Segment {}'.format(i) for i in range(0,len(centers))]\n",
    "true_centers = pd.DataFrame(np.round(true_centers), columns = data.keys())\n",
    "true_centers.index = segments\n",
    "display(true_centers)\n",
    "\n",
    "# describe data \n",
    "display(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OPfZYBg4cSO/evenXrx/veMc72vzdxSNlkydPZvXq1ey777706dOHo48+mhdf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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1101760d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import colors \n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "color = ['c','m','y','r','g','b']\n",
    "data_description = data.describe()\n",
    "ind = range(6)\n",
    "width = 0.5\n",
    "plots = []\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "def plot_bar_chart(row):\n",
    "    columns = [row['Fresh'],\n",
    "               row['Milk'],\n",
    "               row['Grocery'],\n",
    "               row['Frozen'],\n",
    "               row['Detergents_Paper'],\n",
    "               row['Delicatessen']] \n",
    "    return plt.bar(ind, columns, width=width,align='center', color=color[i])\n",
    "\n",
    "\n",
    "for i,item in enumerate(['75%','50%','25%']):\n",
    "    plots.append(plot_bar_chart(data_description.loc[item,:]))\n",
    "    \n",
    "for i,item in enumerate(true_centers.loc['Segment 0',:]): \n",
    "    plt.axhline(y=item, xmin=0.09 + i*0.143, xmax=0.19 + i*0.143,color='b',linewidth=2)\n",
    "    \n",
    "for i,item in enumerate(true_centers.loc['Segment 1',:]): \n",
    "    plt.axhline(y=item, xmin=0.09 + i*0.143, xmax=0.19 + i*0.143,color='k',linewidth=2)\n",
    "\n",
    "blue_line = mlines.Line2D([], [], color='b', \n",
    "                          markersize=15,linewidth=2)\n",
    "black_line = mlines.Line2D([], [], color='k', \n",
    "                          markersize=15,linewidth=2)\n",
    "\n",
    "plots.extend([blue_line,black_line])\n",
    "plt.ylabel('Totals')\n",
    "plt.title('Totals by products and percentiles')\n",
    "plt.xticks([x for x in ind],data_description.columns ) \n",
    "plt.legend(plots, ['75%','50%','25%','segment 0','segment 1'])#,handles=[plots[0],blue_line]) #,'segment 0','segment 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 8\n",
    "Consider the total purchase cost of each product category for the representative data points above, and reference the statistical description of the dataset at the beginning of this project. *What set of establishments could each of the customer segments represent?*  \n",
    "**Hint:** A customer who is assigned to `'Cluster X'` should best identify with the establishments represented by the feature set of `'Segment X'`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**  \n",
    "\n",
    "Total spending on milk, grocery and detergent_paper for segment 0 falls in top 25% range of customer spending for those products respectively. Similar to my prediction earlier a company with such spending might be a grocery store.\n",
    "\n",
    "Total spending on fresh and frozen for segment 1 falls in top 50% range of customer spending for those products respectively. And the spending for rest of products is in 25% to 50% range. Which means the companies in this range doesn't look like retailer but a consumer of these products. I would assume the company to be a restaurant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 9\n",
    "*For each sample point, which customer segment from* ***Question 8*** *best represents it? Are the predictions for each sample point consistent with this?*\n",
    "\n",
    "Run the code block below to find which cluster each sample point is predicted to be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f+AIbN26ktLSUXr16MWHCBI455hjefPNNAO6//37uvvtu+vXrx3HHHcesWbNY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\nGiAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11067a850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample point 0 predicted to be in Cluster 0\n",
      "Sample point 1 predicted to be in Cluster 0\n",
      "Sample point 2 predicted to be in Cluster 1\n"
     ]
    }
   ],
   "source": [
    "plot_list = []\n",
    "plt.figure(figsize=(12,9))\n",
    "_ind = [1,2,3,4,5,6]\n",
    "plot_list.extend([plt.plot(_ind,samples.loc[0,:],marker='o',ls='',color='y',label='sample 0'),\n",
    "                plt.plot(_ind,samples.loc[1,:],marker='o',ls='',color='c',label='sample 1'),\n",
    "                plt.plot(_ind,samples.loc[2,:],marker='o',ls='',color='m',label='sample 2')])\n",
    "plt.axis([0,7,-1000,25000])\n",
    "\n",
    "for i,item in enumerate(true_centers.loc['Segment 0',:]): \n",
    "    plt.axhline(y=item, xmin=0.09 + i*0.143, xmax=0.19 + i*0.143,color='b',linewidth=2,alpha=0.5,label='sample 0')\n",
    "    \n",
    "for i,item in enumerate(true_centers.loc['Segment 1',:]): \n",
    "    plt.axhline(y=item, xmin=0.09 + i*0.143, xmax=0.19 + i*0.143,color='k',linewidth=2,alpha=0.5)\n",
    "\n",
    "_plot_handles = [plot[0] for plot in plot_list]\n",
    "_plot_handles.extend([blue_line,black_line])\n",
    "plt.legend(_plot_handles,['Sample 0','Sample 1','Sample 2','Segment 0','Segment 1'])\n",
    "plt.show()\n",
    "\n",
    "for i, pred in enumerate(sample_preds):\n",
    "    print \"Sample point\", i, \"predicted to be in Cluster\", pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**Answer:**  \n",
    "Manual prediction:\n",
    "\n",
    "- 2 features of sample point 0 are closer to features of segment 0, 2 other features are closer to that of segment 1 and 2 other points are almost at the same distance from both segments. Manually determing the cluster to which the data point belongs to is difficult, but the features that are closer to segment 1 are more closer. So may be the data point belongs to Cluster 0\n",
    "- 4 out of 6 features of sample point 1 are closer to features of segment 0 so this data point might belong to cluster 0\n",
    "- 4 out of 6 features of sample point 2 are closer to features of segment 1 so this data point might belong to cluster 1\n",
    "\n",
    "After prediction using the cluster we learnt earlier: \n",
    "\n",
    "- Sample point 0 predicted to be in Cluster 0\n",
    "- Sample point 1 predicted to be in Cluster 0\n",
    "- Sample point 2 predicted to be in Cluster 1\n",
    "\n",
    "Manual prediction is same as cluster prediction. From previous question cluster 0 is grocery store and cluster 1 is restaurant. Here is how the comparision for predictions from question 1 and cluster prediction would like \n",
    "\n",
    "|Data point|Cluster prediction|Prediction from Q1|\n",
    "|----------------|---------------|-------------|\n",
    "|Sample point 0 | farmers market| grocery store|  \n",
    "|Sample point 1 | grocery store| grocery store|\n",
    "|Sample point 2 | sandwich place| restaurant|\n",
    "\n",
    "Both the predictions were pretty similar.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this final section, you will investigate ways that you can make use of the clustered data. First, you will consider how the different groups of customers, the ***customer segments***, may be affected differently by a specific delivery scheme. Next, you will consider how giving a label to each customer (which *segment* that customer belongs to) can provide for additional features about the customer data. Finally, you will compare the ***customer segments*** to a hidden variable present in the data, to see whether the clustering identified certain relationships."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Question 10\n",
    "Companies will often run [A/B tests](https://en.wikipedia.org/wiki/A/B_testing) when making small changes to their products or services to determine whether making that change will affect its customers positively or negatively. The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. *How can the wholesale distributor use the customer segments to determine which customers, if any, would react positively to the change in delivery service?*  \n",
    "**Hint:** Can we assume the change affects all customers equally? How can we determine which group of customers it affects the most?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**  \n",
    "A/B testing (also known as split testing or bucket testing) is a method of comparing two versions of a product/service against each other to determine which one performs better. AB testing is essentially an experiment where two or more variants of a products/services are provided to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal.\n",
    "\n",
    "One way of determining the cutomers who would react positively is through conducting A/B testing on each segment. After dividing the cutomers into two segments using the clusters we learned earlier, to find how a segment of customers would react to the change we can choose a sample customers at random from that segment and perform A/B testing. We repeat the same with the other segment and then we can see which segment reacts better to the change.\n",
    "\n",
    "Ref:  \n",
    "https://www.optimizely.com/ab-testing/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 11\n",
    "Additional structure is derived from originally unlabeled data when using clustering techniques. Since each customer has a ***customer segment*** it best identifies with (depending on the clustering algorithm applied), we can consider *'customer segment'* as an **engineered feature** for the data. Assume the wholesale distributor recently acquired ten new customers and each provided estimates for anticipated annual spending of each product category. Knowing these estimates, the wholesale distributor wants to classify each new customer to a ***customer segment*** to determine the most appropriate delivery service.  \n",
    "*How can the wholesale distributor label the new customers using only their estimated product spending and the* ***customer segment*** *data?*  \n",
    "**Hint:** A supervised learner could be used to train on the original customers. What would be the target variable?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**  \n",
    "Supervised learning algorithms are good at making instant decisions on which segment the customer belongs to. Now that we have two segments we can train a supervised learner with the six features as training variables and the customer segment lables from our clustering as target variable. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Underlying Distributions\n",
    "\n",
    "At the beginning of this project, it was discussed that the `'Channel'` and `'Region'` features would be excluded from the dataset so that the customer product categories were emphasized in the analysis. By reintroducing the `'Channel'` feature to the dataset, an interesting structure emerges when considering the same PCA dimensionality reduction applied earlier to the original dataset.\n",
    "\n",
    "Run the code block below to see how each data point is labeled either `'HoReCa'` (Hotel/Restaurant/Cafe) or `'Retail'` the reduced space. In addition, you will find the sample points are circled in the plot, which will identify their labeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hQ4G9S8VSU1MZOXJkvWBq27Ztdf2GQm3atIlNmzYxbty4vdpiZlx11VUkJCRw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MltNbhLJzedOsEbxhMe2xOxHiIrP5f3jK2YMbHUNpC6Y2cxlGRGjh7lc99A/Wj8SUuHg9Zjei94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\n/GnHeuvnM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1112ff850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the clustering results based on 'Channel' data\n",
    "vs.channel_results(reduced_data, outliers, pca_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 12\n",
    "*How well does the clustering algorithm and number of clusters you've chosen compare to this underlying distribution of Hotel/Restaurant/Cafe customers to Retailer customers? Are there customer segments that would be classified as purely 'Retailers' or 'Hotels/Restaurants/Cafes' by this distribution? Would you consider these classifications as consistent with your previous definition of the customer segments?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**  \n",
    "\n",
    "The number of clusters and the segments we identified from the clustering are very similar to the underlying distribution of Hotel/Restaurant/Cafe cutomers to retailer customers. There is no clear distinction between the segments from the distribution so identifying the segment to which the customers who fall in the middle of the plot is not possible. The classifications are consistant, but there can be errors while identifying some customers (who fall in the middle of cart show above) when compared to the undelying distribution. These errors are acceptable as the segments atleast give a pattern to start with and based on more information collected in coming time the warehouse company can optimize the classification.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \n",
    "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
  }
 ],
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