customer_segments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Engineer Nanodegree\n",
    "## Unsupervised Learning\n",
    "## Project 3: 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",
    "import renders as rs\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Show matplotlib plots inline (nicely formatted in the notebook)\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>3366</td>\n",
       "      <td>5403</td>\n",
       "      <td>12974</td>\n",
       "      <td>4400</td>\n",
       "      <td>5977</td>\n",
       "      <td>1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10253</td>\n",
       "      <td>1114</td>\n",
       "      <td>3821</td>\n",
       "      <td>397</td>\n",
       "      <td>964</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0   3366  5403    12974    4400              5977          1744\n",
       "1  10253  1114     3821     397               964           412\n",
       "2  17546  4519     4602    1066              2259          2124"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Select three indices of your choice you wish to sample from the dataset\n",
    "indices = [10, 15, 20]\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:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
      "0 -8634.0  -393.0   5023.0  1328.0            3096.0         219.0\n",
      "1 -1747.0 -4682.0  -4130.0 -2675.0           -1917.0       -1113.0\n",
      "2  5546.0 -1277.0  -3349.0 -2006.0            -622.0         599.0\n",
      "    Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
      "10   26.9  64.5     82.0    79.5              84.3          73.2\n",
      "15   57.3  16.4     44.1    13.4              54.8          25.7\n",
      "20   76.8  57.7     48.6    38.4              65.5          79.8\n"
     ]
    }
   ],
   "source": [
    "print samples - np.mean(data).round()\n",
    "print 100. * data.rank(axis=0, pct=True).iloc[indices].round(decimals=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. grocery - above average consumption of Grocery, Frozen, and Detergents and Paper products\n",
    "2. small restaurant - below average consumption  \n",
    "3. market - above average consumption of Fresh"
   ]
  },
  {
   "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": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "feature_to_score = {}\n",
    "\n",
    "for column in data.columns:\n",
    "    new_data = data.drop(column, axis = 1)\n",
    "    y_all = data[column]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(new_data, y_all, test_size=0.25, random_state=42)\n",
    "    regressor = DecisionTreeRegressor(random_state=0)\n",
    "    regressor.fit(X_train, y_train)\n",
    "    feature_to_score[column] = regressor.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "*Which feature did you attempt to predict? What was the reported prediction score? Is this feature relevant for identifying a specific customer?*  \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:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Which feature did you attempt to predict?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grocery\n",
      "Frozen\n",
      "Delicatessen\n",
      "Detergents_Paper\n",
      "Fresh\n",
      "Milk\n"
     ]
    }
   ],
   "source": [
    "for key in feature_to_score:\n",
    "    print key"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*What was the reported prediction score?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The feature Grocery has a reported prediction score: 0.699248196675\n",
      "The feature Frozen has a reported prediction score: -0.278249148824\n",
      "The feature Delicatessen has a reported prediction score: -11.0236279005\n",
      "The feature Detergents_Paper has a reported prediction score: 0.348777454691\n",
      "The feature Fresh has a reported prediction score: -0.333070533605\n",
      "The feature Milk has a reported prediction score: 0.173438009379\n"
     ]
    }
   ],
   "source": [
    "for key, value in feature_to_score.iteritems():\n",
    "    print \"The feature {} has a reported prediction score: {}\".format(key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Is this feature relevant for identifying a specific customer?* **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": [
    "Detergents_Paper and Milk are relevant features because they had low R^2 score.\n",
    "\n",
    "Grocery has a high R^2 score which implies information contained in this feature is found in the remaining features.  Therefore it is not a relevant feature.   \n",
    "\n",
    "Frozen, Delicatssen, and Fresh all had negative R^2 scores which means the model failed to fit the data for those features. "
   ]
  },
  {
   "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": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OEKX//OfvKC+/1Jnz6fM9Ma5AMOA67IMeB6Nh8DMQaAEAJlBb+wGffZaKzVZP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eUurzGL1MKBCLEQHvVeDu3hGtdwkMDCQpKZSMjN24XMfRuhSHIxit83A4fsPDD8fxi1+8zPHj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\nNy7+WPF7swAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3f46278>"
      ]
     },
     "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:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Are there any pairs of features which exhibit some degree of correlation?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Milk, Grocery: 0.728335  \n",
    "Milk, Detergents_Paper: 0.661816                                                                                                  \n",
    "Grocery, Detergents_Paper: 0.924641     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     Fresh      Milk   Grocery    Frozen  Detergents_Paper  \\\n",
      "Fresh             1.000000  0.100510 -0.011854  0.345881         -0.101953   \n",
      "Milk              0.100510  1.000000  0.728335  0.123994          0.661816   \n",
      "Grocery          -0.011854  0.728335  1.000000 -0.040193          0.924641   \n",
      "Frozen            0.345881  0.123994 -0.040193  1.000000         -0.131525   \n",
      "Detergents_Paper -0.101953  0.661816  0.924641 -0.131525          1.000000   \n",
      "Delicatessen      0.244690  0.406368  0.205497  0.390947          0.069291   \n",
      "\n",
      "                  Delicatessen  \n",
      "Fresh                 0.244690  \n",
      "Milk                  0.406368  \n",
      "Grocery               0.205497  \n",
      "Frozen                0.390947  \n",
      "Detergents_Paper      0.069291  \n",
      "Delicatessen          1.000000  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jlee1\\Anaconda2\\lib\\site-packages\\seaborn\\linearmodels.py:1285: UserWarning: The `corrplot` function has been deprecated in favor of `heatmap` and will be removed in a forthcoming release. Please update your code.\n",
      "  warnings.warn((\"The `corrplot` function has been deprecated in favor \"\n",
      "C:\\Users\\jlee1\\Anaconda2\\lib\\site-packages\\seaborn\\linearmodels.py:1351: UserWarning: The `symmatplot` function has been deprecated in favor of `heatmap` and will be removed in a forthcoming release. Please update your code.\n",
      "  warnings.warn((\"The `symmatplot` function has been deprecated in favor \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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YixfP06/fQDQaDWYmctalEE9UDJeYaNOmDW3atMn3XO/evR/bNiQkRC/HlL9U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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe8ddeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print data.corr(method='pearson')\n",
    "import seaborn as sns\n",
    "_ = sns.corrplot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Does this confirm or deny your suspicions about the relevance of the feature you attempted to predict?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It confirms my suspicions that Grocery might be an reduclant feature since it is highly correlated with two other featuers Milk and Detergents_Paper."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*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": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grocery does not follow a normal distrubtion\n",
      "Detergents_Paper does not follow a normal distrubtion\n",
      "Milk does not follow a normal distrubtion\n"
     ]
    },
    {
     "data": {
      "image/png": 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srq4u5efnx3BcAABwNSI+o1+5cqX27dunvr4+3X333Xr44Ye1b98+HThwQElJScrOztbT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\nESk4/D8FZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe46c208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "selected_features = [\"Grocery\", \"Detergents_Paper\", \"Milk\"]\n",
    "for feature in selected_features:\n",
    "    x = data[feature]\n",
    "    x.hist()\n",
    "    z,pvalue = stats.normaltest(x)\n",
    "    if(pvalue < 0.055):\n",
    "        print \"{} does not follow a normal distrubtion\".format(feature)\n",
    "    else:\n",
    "        print \"{} does follow a normal distrubtion\".format(feature)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram shows most of data points grouped near zero, and are positively (right) skewed."
   ]
  },
  {
   "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 a logarithm scaling. Use the `np.log` function for this.\n",
    " - Assign a copy of the sample data to `log_samples` after applying a logrithm scaling. Again, use `np.log`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UGpSrNQRBxWp1IuhNZPIlEokYi9NDpPMVmpiJRiOYrE5S2QIeu4TdrEfQ2kyP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\nWs8EJXtq29vRiklLgVlhvn9dU3MbzUYN/a16mu3t+GPKTeu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CFUhISEhISEhISGyKulns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\n7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KooFAqcCA7vUNIGD9fjAQ/lQg4ZuZ9vNZhnsZec7v3msRn63ryeh6cXAdD72/3677gd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\n6+nStex2v3qgxc5zzz3HK6+8gkKhwOPxIJfvT+6b1sXOTlbr26nE/Tweb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TgzaOfVO0He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mZVAiSYJEKkuzxYDZWL7P5bTX9ccRQWV2nto5T6VerNYexqeXCCWKXJvwYDcbqvfV+gO2NFsp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uhChDpFFqNWyYwvgkGryekXKf96hyXnyx3w+yte6241yvnsVLTZeeaZZ/it3/otJicnMZvN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\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oTwfdvcdpbTKTz5WYWIiRyhRQKaC7o7le3u3o0LXeW/b7Cv26FINOQTAYblguDXodnjk/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\n9u1cfvnlANhsNpKTk9Fqtfz9738PTmB37tzJ008/zYsvvsi+ffv44IM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      "text/plain": [
       "<matplotlib.figure.Figure at 0xec14438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Scale the data using the natural logarithm\n",
    "log_data = data.apply(np.log)\n",
    "\n",
    "# TODO: Scale the sample data using the natural logarithm\n",
    "log_samples = samples.apply(np.log)\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": 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>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>8.121480</td>\n",
       "      <td>8.594710</td>\n",
       "      <td>9.470703</td>\n",
       "      <td>8.389360</td>\n",
       "      <td>8.695674</td>\n",
       "      <td>7.463937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.235326</td>\n",
       "      <td>7.015712</td>\n",
       "      <td>8.248267</td>\n",
       "      <td>5.983936</td>\n",
       "      <td>6.871091</td>\n",
       "      <td>6.021023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "0  8.121480  8.594710  9.470703  8.389360          8.695674      7.463937\n",
       "1  9.235326  7.015712  8.248267  5.983936          6.871091      6.021023\n",
       "2  9.772581  8.416046  8.434246  6.971669          7.722678      7.661056"
      ]
     },
     "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": 13,
   "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"
    }
   ],
   "source": [
    "# For each feature find the data points with extreme high or low values\n",
    "index_to_outliners = {}\n",
    "for feature in log_data.keys():\n",
    "    \n",
    "    # TODO: Calculate Q1 (25th percentile of the data) for the given feature\n",
    "    Q1 = log_data[feature].quantile(0.25)\n",
    "    \n",
    "    # TODO: Calculate Q3 (75th percentile of the data) for the given feature\n",
    "    Q3 = log_data[feature].quantile(0.75)\n",
    "    \n",
    "    # TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)\n",
    "    step = 1.5 * (Q3 - Q1)\n",
    "    \n",
    "    # Display the outliers\n",
    "    print \"Data points considered outliers for the feature '{}':\".format(feature)\n",
    "    outliers = log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))] \n",
    "    display(outliers)\n",
    "    \n",
    "    for i in outliers.index.tolist():\n",
    "        if i in index_to_outliners:\n",
    "            index_to_outliners[i] += 1\n",
    "        else:\n",
    "            index_to_outliners[i] = 1\n",
    "\n",
    "filtered_dict = {k:v for k,v in index_to_outliners.iteritems() if v > 1}\n",
    "\n",
    "# OPTIONAL: Select the indices for data points you wish to remove\n",
    "outliers  = filtered_dict.keys()\n",
    "\n",
    "# Remove the outliers, if any were specified\n",
    "good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 4\n",
    "*Are there any data points considered outliers for more than one feature? 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:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Are there any data points considered outliers for more than one feature?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The datapoint 128 has outliers for 2 features\n",
      "The datapoint 65 has outliers for 2 features\n",
      "The datapoint 154 has outliers for 3 features\n",
      "The datapoint 75 has outliers for 2 features\n",
      "The datapoint 66 has outliers for 2 features\n"
     ]
    }
   ],
   "source": [
    "for key, value in filtered_dict.iteritems():\n",
    "    print \"The datapoint {} has outliers for {} features\".format(key, value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*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": [
    "The data points had more than one outlier per feature were removed.  Those data points are more likely to be irregularities since there were outliers found in multiple features.  They should be removed, because Gaussian Mixture Models assumes that the data is normally distributed and is sensitive to outliers.   "
   ]
  },
  {
   "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. \n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Import `sklearn.preprocessing.PCA` and assign the results of fitting PCA in six dimensions with `good_data` to `pca`.\n",
    " - Apply a PCA transformation of the sample log-data `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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CQvTSSy8pMjLS6YsdwnXOdw5ycnJkGIb9P4iYlzF9+nTFxcWVWdbf//53+/VWUlNT9Z//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\nlTYOunBxOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xebe8978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# TODO: Apply PCA to the good data with the same number of dimensions as features\n",
    "pca = PCA(n_components = good_data.shape[1]).fit(good_data)\n",
    "\n",
    "# TODO: Apply a PCA transformation to the sample log-data\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# Generate PCA results plot\n",
    "pca_results = rs.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:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb496e80>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/QTF7oL/tOamapi7l3jhOk8fHmh0HAOBGFLOHKatq1bb9FUqKDdWKuXaz4wAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xba4fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = np.arange(6)\n",
    "cumsum_explained_vr = np.cumsum(pca.explained_variance_ratio_)\n",
    "plt.plot(n, cumsum_explained_vr, '-o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*How much variance in the data is explained* ***in total*** *by the first and second principal component? *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.706817230807\n"
     ]
    }
   ],
   "source": [
    "print cumsum_explained_vr[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*What about the first four principal components?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.931090109951\n"
     ]
    }
   ],
   "source": [
    "print cumsum_explained_vr[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*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": {
    "collapsed": true
   },
   "source": [
    "1st principal component \n",
    "* strong increased spending for Milk, Grocery, and Detergents_Paper\n",
    "\n",
    "2nd principal component \n",
    "* strong increased spending for Fresh, Frozen, and Delicatssen  \n",
    "\n",
    "3rd principal component\n",
    "* strong increased spending for Delicatssen\n",
    "* strong decreased spending for Fresh\n",
    "\n",
    "4th principal component\n",
    "* strong increased spending for Frozen \n",
    "* strong decreased spending for Delicatssen"
   ]
  },
  {
   "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": 19,
   "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>2.0887</td>\n",
       "      <td>0.7006</td>\n",
       "      <td>0.8537</td>\n",
       "      <td>1.0105</td>\n",
       "      <td>0.5587</td>\n",
       "      <td>-0.2495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.4258</td>\n",
       "      <td>-0.8803</td>\n",
       "      <td>-1.2189</td>\n",
       "      <td>-0.7945</td>\n",
       "      <td>0.7319</td>\n",
       "      <td>-0.3868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2  Dimension 3  Dimension 4  Dimension 5  \\\n",
       "0       2.0887       0.7006       0.8537       1.0105       0.5587   \n",
       "1      -0.4258      -0.8803      -1.2189      -0.7945       0.7319   \n",
       "2       0.8375       1.0765      -0.3684      -0.8111       0.5111   \n",
       "\n",
       "   Dimension 6  \n",
       "0      -0.2495  \n",
       "1      -0.3868  \n",
       "2       0.3040  "
      ]
     },
     "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 reuslts to `reduced_data`.\n",
    " - Apply a PCA transformation of the sample log-data `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# TODO: Fit PCA to the good data using only two dimensions\n",
    "pca = PCA(n_components = 2).fit(good_data)\n",
    "\n",
    "# TODO: Apply a PCA transformation the good data\n",
    "reduced_data = pca.transform(good_data)\n",
    "\n",
    "# TODO: Apply a PCA transformation to the sample log-data\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": 21,
   "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>2.0887</td>\n",
       "      <td>0.7006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.4258</td>\n",
       "      <td>-0.8803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.8375</td>\n",
       "      <td>1.0765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2\n",
       "0       2.0887       0.7006\n",
       "1      -0.4258      -0.8803\n",
       "2       0.8375       1.0765"
      ]
     },
     "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": [
    "## 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:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*What are the advantages to using a K-Means clustering algorithm?* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "* Faster than Gaussian Mixture Model\n",
    "* Simple to implement\n",
    "* Flexible to feature / distance choices\n",
    "* Naturally handles multi-class cases\n",
    "* Can do well in practice with enough representative data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*What are the advantages to using a Gaussian Mixture Model clustering algorithm?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "* Fastest algorithm for learning mixture models\n",
    "* Monotonically non-decreasing likelihood\n",
    "* Cannnot diverge\n",
    "* Create soft clusters which contains more structural information (K-Means can only create hard clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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a2opBgwZh7ty5uPfee/HEE08AAP7xj38woFPMU3t80N/qba++9VdZ33eoqsaz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd6af1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reduced_data.plot(kind='scatter', x='Dimension 1', y='Dimension 2');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reduced data scatter plot doesn't show distinct and obvious clusters.  There are many data points that could belong to multiple clusters.  The Gaussian Mixture Model clustering algorithm would be better here for its ability to create soft clusters.  "
   ]
  },
  {
   "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": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.411818864386\n"
     ]
    }
   ],
   "source": [
    "from sklearn.mixture import GMM \n",
    "from sklearn.metrics import silhouette_score, make_scorer\n",
    "\n",
    "# TODO: Apply your clustering algorithm of choice to the reduced data \n",
    "clusterer = GMM(n_components=2).fit(reduced_data)\n",
    "\n",
    "# TODO: Predict the cluster for each data point\n",
    "preds = clusterer.predict(reduced_data)\n",
    "probs = clusterer.predict_proba(reduced_data)\n",
    "\n",
    "# TODO: Find the cluster centers\n",
    "centers = clusterer.means_\n",
    "\n",
    "# TODO: Predict the cluster for each transformed sample data point\n",
    "sample_preds = clusterer.predict(pca_samples)\n",
    "sample_probs = clusterer.predict_proba(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 score"
   ]
  },
  {
   "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:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Report the silhouette score for several cluster numbers you tried.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the score for 2 clusters is 0.411818864386\n",
      "the score for 3 clusters is 0.376166165091\n",
      "the score for 4 clusters is 0.332444904187\n",
      "the score for 5 clusters is 0.280983407051\n",
      "the score for 6 clusters is 0.271589693558\n"
     ]
    }
   ],
   "source": [
    "from sklearn import grid_search\n",
    "\n",
    "silhouette_scorer = make_scorer(silhouette_score)\n",
    "\n",
    "parameters = {'n_components': range(2, 10)}\n",
    "\n",
    "for i in range(2, 7):\n",
    "    gmm = GMM(n_components = i).fit(reduced_data)\n",
    "    predictions = gmm.predict(reduced_data)\n",
    "    score = silhouette_score(reduced_data, predictions)\n",
    "    print \"the score for {} clusters is {}\".format(i, score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Of these, which number of clusters has the best silhouette score?* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of clusters with the  best silhouette score is 2 clusters."
   ]
  },
  {
   "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": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Pzw+HDx/GvHnzYGZmhpEjR6JPnz4YNGgQOnfujMePH2vdTkSkCwZDRFTraIYA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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcb031d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the results of the clustering from implementation\n",
    "rs.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": 26,
   "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>8812.0</td>\n",
       "      <td>2052.0</td>\n",
       "      <td>2689.0</td>\n",
       "      <td>2058.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>4316.0</td>\n",
       "      <td>6347.0</td>\n",
       "      <td>9555.0</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>3046.0</td>\n",
       "      <td>945.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Segment 0  8812.0  2052.0   2689.0  2058.0             337.0         712.0\n",
       "Segment 1  4316.0  6347.0   9555.0  1036.0            3046.0         945.0"
      ]
     },
     "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)"
   ]
  },
  {
   "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:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*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": "code",
   "execution_count": 27,
   "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>-3188.0</td>\n",
       "      <td>-3744.0</td>\n",
       "      <td>-5262.0</td>\n",
       "      <td>-1013.0</td>\n",
       "      <td>-2544.0</td>\n",
       "      <td>-812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>-7684.0</td>\n",
       "      <td>551.0</td>\n",
       "      <td>1604.0</td>\n",
       "      <td>-2035.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>-579.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Segment 0 -3188.0 -3744.0  -5262.0 -1013.0           -2544.0        -812.0\n",
       "Segment 1 -7684.0   551.0   1604.0 -2035.0             165.0        -579.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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oNzdXRUVFioqKkiQNHjzYsQ4AAKhere8zP3jwoIKCgpScnKydO3eqU6dOmjJl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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcf2e748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc22d390>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fvpG65ZbbtHXrZnl7e+vpp59TUFBQje5vXUOoAwCqrbS0RGPG3C673a7mzVso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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6d1898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(true_centers-data.mean().astype(int))\n",
    "true_centers.plot(kind = 'bar')\n",
    "plt.show()\n",
    "(true_centers-data.mean()).plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Segment 0 \n",
    "* Spending significantly concentrated in Fresh\n",
    "* Compared to the original dataset\n",
    "     * Spends less on every product category\n",
    "     * Spends significantly less on grocery\n",
    "* Could represent a restaurant\n",
    "    * Restaurants are usually smaller which explain the lower spending\n",
    "    * Restaurant tend to mostly use fresh ingredients \n",
    "    \n",
    "Segment 1\n",
    "* Spending significantly concentrated in Grocery\n",
    "* Spending moderately concentrated in Milk and Fresh\n",
    "* Compared to the original dataset\n",
    "    * Spends significantly less on Fresh \n",
    "    * Spends moderately more on Grocery\n",
    "    * Spends moderately less on Frozen \n",
    "* Could represent a grocery store\n",
    "    * Grocery, Milk, Fresh are the most commonly found items inside a grocery store    "
   ]
  },
  {
   "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": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample point 0 predicted to be in Segment 1\n",
      "Probability of data in each segment: [ 0.14374761  0.85625239]\n"
     ]
    },
    {
     "data": {
      "image/png": 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X6sILL+xMvK/MbuVgtzySNHToxfr5/FsitrzExDgFAkc7vZyhQy+OQBoA6N46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\ngCH+H/1FM8iRBmoFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcdcb160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Sample point 1 predicted to be in Segment 0\n",
      "Probability of data in each segment: [ 0.75817183  0.24182817]\n"
     ]
    },
    {
     "data": {
      "image/png": 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YqHvuuUf33Xef2rZtW+u1t956S48++qhFyeylZ8+eOnnypJo2baqmTZvK5/PJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcfe5a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Sample point 2 predicted to be in Segment 1\n",
      "Probability of data in each segment: [ 0.44496826  0.55503174]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd8de400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Display the predictions\n",
    "for i, pred in enumerate(sample_preds):\n",
    "    print \"Sample point\", i, \"predicted to be in Segment\", pred\n",
    "    print \"Probability of data in each segment: {}\".format(sample_probs[i])\n",
    "    (samples.iloc[i] - true_centers.iloc[pred]).plot(kind = 'bar') \n",
    "    plt.show()\n",
    "    print \"\\n\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "**Answer:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sample Point 0\n",
    "* High probability assigned to predicted cluster  \n",
    "* Compared to the prediction cluster\n",
    "    * Spends significantly more on Grocery, Frozen, Detegregents_Paper\n",
    "* Could represent a bigger than average grocery store\n",
    "\n",
    "Sample Point 1\n",
    "* High probability assigned to predicted cluster  \n",
    "* Compared to the prediction cluster\n",
    "    * Spends significantly less on Frozen\n",
    "    * Spends moderately more on Fresh and Grocery\n",
    "* Could represent a restaurant doesn't like to use frozen food in their ingredients  \n",
    "\n",
    "Sample Point 2\n",
    "* Low probability assigned to predicted cluster \n",
    "* Compared to the prediction cluster\n",
    "    * Spends significantly more on Fresh\n",
    "* Could represent a grocery store with an inhouse restaurant "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Question 10\n",
    "*Companies often run [A/B tests](https://en.wikipedia.org/wiki/A/B_testing) when making small changes to their products or services. If the wholesale distributor wanted to change its delivery service from 5 days a week to 3 days a week, how would you use the structure of the data to help them decide on a group of customers to test?*  \n",
    "**Hint:** Would such a change in the delivery service affect all customers equally? How could the distributor identify who it affects the most?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "You can test the new delivery service on a subset of the customers that belong to one cluster.  Then you can repeat the same test for another cluster.  With that information, the distributor identify who was most affected by it.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 11\n",
    "*Assume the wholesale distributor wanted to predict some other feature for each customer based on the purchasing information available. How could the wholesale distributor use the structure of the data to assist a supervised learning analysis?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use information from cluster analysis as new features\n",
    "* Predicted cluster \n",
    "* Probability of each cluster\n",
    "* Distance from center \n",
    "\n",
    "You can also engineer more features from the cluster analysis to help make better predictions.  Perhaps it would be better to find what percentage each business spends on each product category.  It normalizes the data for business of different size.  This could increase the optimal number of clusters.  Ideally, the model would create 4 clusters - Retailer, Hotel, Restaurant, and Cafe.             "
   ]
  },
  {
   "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 on 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": 29,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0BQBs3/4D2rVrj7lzFyA9PR2TJo2Bp2d7/Pe/B/HeeyPQp89bkMvlUCqVmDp1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\n2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3u7J+lyitybLs4wzRylxMYqFbrfVZlrWahLBiQRhgCgG7piAYglkXhNC4SX/xilIUdH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\nYai8vFzLly/Xnj17t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      "text/plain": [
       "<matplotlib.figure.Figure at 0xcb03390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the clustering results based on 'Channel' data\n",
    "rs.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:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage classified as Cluster 0: 0.555647702545\n",
      "Percentage classified as Cluster 1: 0.444352297455\n"
     ]
    }
   ],
   "source": [
    "print \"Percentage classified as Cluster 0: {}\".format(clusterer.weights_[0])\n",
    "print \"Percentage classified as Cluster 1: {}\".format(clusterer.weights_[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The underlying distribution of Hotel/Restaurant/Cafe customers to Retailer customers looks very similar to the distribution that clustering algorithm came up with.  It seems slightly more than half of the data points were classified in the 1st cluster for both distributions."
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
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    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
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 },
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