Customer Segments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unsupervised Learning\n",
    "## Creating Customer Segments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "In this project, we 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
    "\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, we will begin exploring the data through visualizations and code to understand how each feature is related to the others. We will observe a statistical description of the dataset, consider the relevance of each feature, and select a few sample data points from the dataset to be tracked through the course of this project.\n",
    "\n",
    "The dataset is composed of six important product categories: **'Fresh'**, **'Milk'**, **'Grocery'**, **'Frozen'**, **'Detergents_Paper'**, and **'Delicatessen'**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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, **three** indices are added to sample from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0   7057  9810     9568    1762              3293          1776\n",
       "1   6353  8808     7684    2405              3516          7844\n",
       "2  13265  1196     4221    6404               507          1788"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4943.0</td>\n",
       "      <td>4014.0</td>\n",
       "      <td>1617.0</td>\n",
       "      <td>-1310.0</td>\n",
       "      <td>412.0</td>\n",
       "      <td>251.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-5647.0</td>\n",
       "      <td>3012.0</td>\n",
       "      <td>-267.0</td>\n",
       "      <td>-667.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>6319.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1265.0</td>\n",
       "      <td>-4600.0</td>\n",
       "      <td>-3730.0</td>\n",
       "      <td>3332.0</td>\n",
       "      <td>-2374.0</td>\n",
       "      <td>263.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0 -4943.0  4014.0   1617.0 -1310.0             412.0         251.0\n",
       "1 -5647.0  3012.0   -267.0  -667.0             635.0        6319.0\n",
       "2  1265.0 -4600.0  -3730.0  3332.0           -2374.0         263.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1447.0</td>\n",
       "      <td>6183.0</td>\n",
       "      <td>4812.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>2477.0</td>\n",
       "      <td>810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2151.0</td>\n",
       "      <td>5181.0</td>\n",
       "      <td>2928.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>6878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4761.0</td>\n",
       "      <td>-2431.0</td>\n",
       "      <td>-535.0</td>\n",
       "      <td>4878.0</td>\n",
       "      <td>-309.0</td>\n",
       "      <td>822.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0 -1447.0  6183.0   4812.0   236.0            2477.0         810.0\n",
       "1 -2151.0  5181.0   2928.0   879.0            2700.0        6878.0\n",
       "2  4761.0 -2431.0   -535.0  4878.0            -309.0         822.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select three indices of your choice you wish to sample from the dataset\n",
    "indices = [1,2,3]\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)\n",
    "display(samples - data.mean().round())\n",
    "display(samples - data.median().round())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Given the data of the total purchase cost of each product category and the statistical description of the dataset above for your sample customers each customer could be grouped into some establishment. For instance:-"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first 2 datapoints can be grouped into the category 'retailers and last one can be grouped into 'cafe/restaurant'.This is evident from the significantly higher grocery,milk and detergents_paper spending for the first 2 customers as compared to the last one.After looking at the mean and median values,the first 2 samples have a high spending in mean and median for all categories except 'Fresh' and 'Frozen' .\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Feature Relevance\n",
    "\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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.252469807688\n"
     ]
    }
   ],
   "source": [
    "# Make a copy of the DataFrame, using the 'drop' function to drop the given feature\n",
    "new_data = data.drop('Fresh', axis = 1)\n",
    "data2=data.drop(['Grocery', 'Milk','Frozen','Detergents_Paper','Delicatessen'], axis = 1)\n",
    "# Split the data into training and testing sets using the given feature as the target\n",
    "#print new_data\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(new_data, data2, test_size=0.25,random_state=0 )\n",
    "\n",
    "# Create a decision tree regressor and fit it to the training set\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor = DecisionTreeRegressor(random_state=0)\n",
    "regressor=regressor.fit(X_train,y_train)\n",
    "# Report the score of the prediction using the testing set\n",
    "score = regressor.score(X_test,y_test)\n",
    "print score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature that was attempted to predict is 'Fresh'. The reported prediction score(R2 Score) is -0.25. This indicates that the model fails to fit the data. Hence this feature is necessary for identifying customer's spending habits."
   ]
  },
  {
   "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 we 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 we believed 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ppzk5lJJ+gQC2nmziv85n7WtTSv1d6qNHX7jeH6a1rmqtS1prD/g6cAlomf92C5ClHjxt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\nso7LRKa8eJ3TTo1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0OB2xEFU/oD0a4vJMkbLrMZWvYpuKqufzpZev0RarJylamEq23vld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e+ZRbgBTBQblOou8bBO4W6H999zgC+dm6KnI8qhbIxnhnO8NlXZdKc0iu3DnhNSR7sTpKIGlnu9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1W1YymM7z4L6Bk8ZqLXQe1dP+Yap1s+PgEHc+1XfCtu4A6YLD0eEMDXF3nEzvSIbBVJ4v/uoZ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\n0igiX/e2+8dE5A+95V8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ba846d790>"
      ]
     },
     "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": [
    "   There are pairs of features which exhibit degree of correlation . For e.g. Grocery exhibits correlation with Detergents_Paper and Milk and likewise Milk and Detergents_Paper exhibits correlation with the other two. However it confirms the aforementioned assertion about 'Fresh'. By a visual guess it has a negative R2 score with all of the other features if judged as a pair. This is because the data for those points are all over the place. A proper hypothesis cant be made and hence the feature 'Fresh' is relevant.\n",
    "   The features themselves are skewed to the right or positively 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",
    "We 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": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6sTeD1vVYdDbfab0X8u9eX6IXREQD63nT22dpXKE0bUIBXiyJkuxnx9DQNSg7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/PmWG26xEJwfDxP2wuYq1sDg3pupUnTCmjYPm0vJI4FxydypDSFtpNwe7teyIWVFoeHs1xc66Ar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U1oGJrAjzJ0ITA1jX6YMKMpEcJjMxUKjkGjF3J4uoQuBF98YQU/ybhc67N/osh0xebT981y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\nlVjqPZZllYBSVZ/QHcA35//9DHbmaEs7P1uhB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1T8bidT2RKXZrLE8ypvTmfxuZykZJVX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fe20OW0qefHPeVQ5ZyLOQK7lhVcBwVwRdFcxkSnSHPAQ9GgFdo2SaGJbEdizGF4ukiyam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\nhvApMagcfeedLt0woeUnuLahK5OxfrHlVkjONjk8Xril8vtRcWB3O7w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vf+PMCmMFeyQa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\njWbUuQakeoKO69nh0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Wh5XVlMVts4MZulHje021vj2SEi/wZsBpwYVgIAV/NEWjQGlFJv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\nH41Go9FoNBqNRrMq+P8BgiKWD2uC/EAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ba800c410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scale the data using the natural logarithm\n",
    "log_data = np.log(data)\n",
    "\n",
    "# Scale the sample data using the natural logarithm\n",
    "log_samples = np.log(samples)\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 we may have identified earlier as being correlated, we see here whether that correlation is still present (and whether it is now stronger or weaker than before).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>8.861775</td>\n",
       "      <td>9.191158</td>\n",
       "      <td>9.166179</td>\n",
       "      <td>7.474205</td>\n",
       "      <td>8.099554</td>\n",
       "      <td>7.482119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.756682</td>\n",
       "      <td>9.083416</td>\n",
       "      <td>8.946896</td>\n",
       "      <td>7.785305</td>\n",
       "      <td>8.165079</td>\n",
       "      <td>8.967504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.492884</td>\n",
       "      <td>7.086738</td>\n",
       "      <td>8.347827</td>\n",
       "      <td>8.764678</td>\n",
       "      <td>6.228511</td>\n",
       "      <td>7.488853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "0  8.861775  9.191158  9.166179  7.474205          8.099554      7.482119\n",
       "1  8.756682  9.083416  8.946896  7.785305          8.165079      8.967504\n",
       "2  9.492884  7.086738  8.347827  8.764678          6.228511      7.488853"
      ]
     },
     "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",
    "We 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
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       "  </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",
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      "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": {
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       "      <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",
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       "      <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",
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       "      <th>161</th>\n",
       "      <td>9.428190</td>\n",
       "      <td>6.291569</td>\n",
       "      <td>5.645447</td>\n",
       "      <td>6.995766</td>\n",
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       "        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"
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     "text": [
      "Data points considered outliers for the feature 'Delicatessen':\n"
     ]
    },
    {
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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      "text/plain": [
       "         Fresh       Milk    Grocery     Frozen  Detergents_Paper  \\\n",
       "66    2.197225   7.335634   8.911530   5.164786          8.151333   \n",
       "109   7.248504   9.724899  10.274568   6.511745          6.728629   \n",
       "128   4.941642   9.087834   8.248791   4.955827          6.967909   \n",
       "137   8.034955   8.997147   9.021840   6.493754          6.580639   \n",
       "142  10.519646   8.875147   9.018332   8.004700          2.995732   \n",
       "154   6.432940   4.007333   4.919981   4.317488          1.945910   \n",
       "183  10.514529  10.690808   9.911952  10.505999          5.476464   \n",
       "184   5.789960   6.822197   8.457443   4.304065          5.811141   \n",
       "187   7.798933   8.987447   9.192075   8.743372          8.148735   \n",
       "203   6.368187   6.529419   7.703459   6.150603          6.860664   \n",
       "233   6.871091   8.513988   8.106515   6.842683          6.013715   \n",
       "285  10.602965   6.461468   8.188689   6.948897          6.077642   \n",
       "289  10.663966   5.655992   6.154858   7.235619          3.465736   \n",
       "343   7.431892   8.848509  10.177932   7.283448          9.646593   \n",
       "\n",
       "     Delicatessen  \n",
       "66       3.295837  \n",
       "109      1.098612  \n",
       "128      1.098612  \n",
       "137      3.583519  \n",
       "142      1.098612  \n",
       "154      2.079442  \n",
       "183     10.777768  \n",
       "184      2.397895  \n",
       "187      1.098612  \n",
       "203      2.890372  \n",
       "233      1.945910  \n",
       "285      2.890372  \n",
       "289      3.091042  \n",
       "343      3.610918  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[128, 154, 65, 66, 75]\n"
     ]
    }
   ],
   "source": [
    "# For each feature find the data points with extreme high or low values\n",
    "from collections import Counter\n",
    "\n",
    "c = Counter()\n",
    "\n",
    "for feature in log_data.keys():\n",
    "    \n",
    "    # Calculate Q1 (25th percentile of the data) for the given feature\n",
    "    Q1 = np.percentile(log_data[feature],25)\n",
    "    \n",
    "    # Calculate Q3 (75th percentile of the data) for the given feature\n",
    "    Q3 =np.percentile(log_data[feature],75)\n",
    "    \n",
    "    # Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)\n",
    "    step = 1.5*(Q3-Q1)\n",
    "    c.update(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))].index.values)\n",
    "    # Display the outliers\n",
    "    print \"Data points considered outliers for the feature '{}':\".format(feature)\n",
    "    display(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))])\n",
    "print [o for o in c.keys() if c[o]>1]    \n",
    "# Select the indices for data points you wish to remove\n",
    "outliers  = [128, 154, 65, 66, 75]\n",
    "\n",
    "# Remove the outliers, if any were specified\n",
    "good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The indices selected for outliers are [66,154,75,65,128]. Yes these are those data points that have been considered as outliers for more than one feature and is the one of the reasons behind which they were removed from the dataset.Another reason is the sensitivity of K-means to the outliers in the data set. Since it takes into account distance or \"sum of squares\" therefore a large deviation gets a lot of weight. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Transformation\n",
    "In this section we will use principal component analysis (PCA) to draw conclusions about the underlying structure of the wholesale customer data. Since using PCA on a dataset calculates the dimensions which best maximize variance, we will find which compound combinations of features best describe customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Implementation: PCA\n",
    "\n",
    "Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the `good_data` to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the *explained variance ratio* of each dimension — how much variance within the data is explained by that dimension alone. Note that a component (dimension) from PCA can be considered a new \"feature\" of the space, however it is a composition of the original features present in the data.\n",
    "\n",
    "In the code block below, we implement the following:\n",
    " - Import `sklearn.decomposition.PCA` and assign the results of fitting PCA in six dimensions with `good_data` to `pca`.\n",
    " - Apply a PCA transformation of 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,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension 1    0.4430\n",
      "Dimension 2    0.7068\n",
      "Dimension 3    0.8299\n",
      "Dimension 4    0.9311\n",
      "Dimension 5    0.9796\n",
      "Dimension 6    1.0000\n",
      "Name: Explained Variance, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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ffjsAs2fPZt68eXTp0oX777+fRYsW8cwzz5BSorKykkcffZTly5ez2267cc89uTNjK1as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\nNKsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ba6ebd110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply PCA by fitting the good data with the same number of dimensions as features\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=6)\n",
    "pca.fit(good_data)  \n",
    "\n",
    "# Transform the sample log-data using the PCA fit above\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# Generate PCA results plot\n",
    "pca_results = rs.pca_results(good_data, pca)\n",
    "print pca_results['Explained Variance'].cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A total variance of 0.7068 is explained by the first and second principal component and a total variance of 0.9311 is explained by the first four principal componenents. The first principal component is made up of large positive weights in Detergents_Paper, Grocery & Milk. It also correlates with a decrease in Fresh and Frozen. This pattern might represent spending in household staples products that are purchased together.The second principal component is made up of large weights in Fresh and Frozen and very small positive weights in Grocery,Milk and Detergents Paper. Again this represents that one category of customer spend more in Fresh and Frozen and spend less in Grocery Milk and Detergents Paper.Therefore combining this information from the first principal componenet gives us an idea about the customer segments and their relative spending.The third dimension has large spending in Fresh and Detergents Paper with large decrease in Delicatessen while the fourth dimension has positive spending in Frozen and Detergents Paper with reduced spending in Fresh and Delicatessen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "We will 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. We will also consider if this is consistent with our initial interpretation of the sample points."
   ]
  },
  {
   "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",
       "      <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>-1.7887</td>\n",
       "      <td>-0.8123</td>\n",
       "      <td>0.2315</td>\n",
       "      <td>-0.0036</td>\n",
       "      <td>0.1194</td>\n",
       "      <td>-0.2106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.8834</td>\n",
       "      <td>-1.5991</td>\n",
       "      <td>1.3204</td>\n",
       "      <td>-0.5432</td>\n",
       "      <td>-0.3934</td>\n",
       "      <td>-0.3117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.1553</td>\n",
       "      <td>-1.4052</td>\n",
       "      <td>0.5422</td>\n",
       "      <td>0.4127</td>\n",
       "      <td>-0.6865</td>\n",
       "      <td>0.6409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2  Dimension 3  Dimension 4  Dimension 5  \\\n",
       "0      -1.7887      -0.8123       0.2315      -0.0036       0.1194   \n",
       "1      -1.8834      -1.5991       1.3204      -0.5432      -0.3934   \n",
       "2       1.1553      -1.4052       0.5422       0.4127      -0.6865   \n",
       "\n",
       "   Dimension 6  \n",
       "0      -0.2106  \n",
       "1      -0.3117  \n",
       "2       0.6409  "
      ]
     },
     "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",
    "We 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": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Apply PCA by fitting the good data with only two dimensions\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(good_data)\n",
    "\n",
    "# Transform the good data using the PCA fit above\n",
    "reduced_data = pca.transform(good_data)\n",
    "\n",
    "# Transform the sample log-data using the PCA fit above\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# Create a DataFrame for the reduced data\n",
    "reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "We now see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions. Another critical observation is how the values for the first two dimensions remains unchanged when compared to a PCA transformation in six dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>-1.7887</td>\n",
       "      <td>-0.8123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.8834</td>\n",
       "      <td>-1.5991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.1553</td>\n",
       "      <td>-1.4052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2\n",
       "0      -1.7887      -0.8123\n",
       "1      -1.8834      -1.5991\n",
       "2       1.1553      -1.4052"
      ]
     },
     "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, we 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. We 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": [
    "#### Given our observations about the wholesale customer data so far,we will decide which of the two algorithms to use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**The advantage of GMM is that it is a probabilistic model and gives us what may be called 'soft clusters' . It simply states the probability for each point lying in each of the clusters and the particular point can be considered assigned to one cluster if its particular probability for that cluster is high enough while it may be considered to be coming from a mix of clusters if it has a fair amount of probability for each of those clusters. On the other hand K-Means os a 0/1 version of GMM and makes clear distinctions. From the distinction made earlier on the sample points regarding the class of customer as being 'cafe' or 'retailers' , it is clear that these two customer segments are not closely spaced and can be and must be distinguished from each other to apply different market strategies etc. GMM is more useful in cases where data points are more closely related . Hence K means is a better match here."
   ]
  },
  {
   "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",
    "We 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": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.426281015469\n"
     ]
    }
   ],
   "source": [
    "# Apply your clustering algorithm of choice to the reduced data \n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import mixture\n",
    "clusterer = KMeans(n_clusters=2,random_state=0).fit(reduced_data)\n",
    "#clusterer2= mixture.GaussianMixture(n_components=2).fit(reduced_data)\n",
    "# Predict the cluster for each data point\n",
    "preds = clusterer.predict(reduced_data)\n",
    "\n",
    "# Find the cluster centers\n",
    "centers = clusterer.cluster_centers_\n",
    "#centers = clusterer2.means_\n",
    "# Predict the cluster for each transformed sample data point\n",
    "sample_preds = clusterer.predict(pca_samples)\n",
    "\n",
    "# Calculate the mean silhouette coefficient for the number of clusters chosen\n",
    "from sklearn import metrics\n",
    "score =  metrics.silhouette_score(reduced_data,preds)\n",
    "print score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reporting the silhouette score for several cluster numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Silhoutte score for clusters=2 :0.426\n",
    "\n",
    "Silhoutte score for clusters=3:0.399\n",
    "\n",
    "Silhoutte score for clusters=4:0.335\n",
    "\n",
    "Number of clusters=2 has the best score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cluster Visualization\n",
    "Once we've chosen the optimal number of clusters for your clustering algorithm using the scoring metric above, we can now visualize the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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+AT/h/zV88YbZwXNv4ws3zMants3Cf3uez5HBvf46fg7WTvjJ/CuD5wvda4uBQ4LfiZud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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ba850abd0>"
      ]
     },
     "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>8867.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>2477.0</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>4005.0</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>12104.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>4561.0</td>\n",
       "      <td>1036.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Segment 0  8867.0  1897.0   2477.0  2088.0             294.0         681.0\n",
       "Segment 1  4005.0  7900.0  12104.0   952.0            4561.0        1036.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
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       "      <th>Segment 1</th>\n",
       "      <td>-4499.0</td>\n",
       "      <td>4273.0</td>\n",
       "      <td>7348.0</td>\n",
       "      <td>-574.0</td>\n",
       "      <td>3745.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Segment 0   363.0 -1730.0  -2279.0   562.0            -522.0        -285.0\n",
       "Segment 1 -4499.0  4273.0   7348.0  -574.0            3745.0          70.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inverse transform the centers\n",
    "log_centers = pca.inverse_transform(centers)\n",
    "\n",
    "# Exponentiate the centers\n",
    "true_centers = np.exp(log_centers)\n",
    "\n",
    "# Display the true centers\n",
    "segments = ['Segment {}'.format(i) for i in range(0,len(centers))]\n",
    "true_centers = pd.DataFrame(np.round(true_centers), columns = data.keys())\n",
    "true_centers.index = segments\n",
    "display(true_centers)\n",
    "display(true_centers - data.mean().round())\n",
    "display(true_centers - data.median().round())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The right side cluster center is higher than average in Grocery , Milk and Detergents_Paper.Segment 0 should ideally correspond to Retailer. Given that Segment 1 has lower than average statistices for the above mentioned departments and therefore correspond to a more outgoing person who might be labelled as Hotel/Cafe ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample point 0 predicted to be in Cluster 1\n",
      "Sample point 1 predicted to be in Cluster 1\n",
      "Sample point 2 predicted to be in Cluster 0\n"
     ]
    }
   ],
   "source": [
    "# Display the predictions\n",
    "for i, pred in enumerate(sample_preds):\n",
    "    print \"Sample point\", i, \"predicted to be in Cluster\", pred\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Sample point 1 and 2 represent Cluster 0 while Sample point 3 represents Cluster 1. Yes the predictions for each sample point are consistent with this. This is because for sample 0 and and sample 1 the values for Grocery , Milk and Detergent Paper are above average. This mirrors the category spending for Segment 0 and therefore is aligned with the predictions. Similar argument can be made for Sample point 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this final section, we will investigate ways that you can make use of the clustered data. First, we will consider how the different groups of customers, the ***customer segments***, may be affected differently by a specific delivery scheme. Next, we will consider how giving a label to each customer (which *segment* that customer belongs to) can provide for additional features about the customer data. Finally, we will compare the ***customer segments*** to a hidden variable present in the data, to see whether the clustering identified certain relationships."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##### Companies will often run [A/B tests](https://en.wikipedia.org/wiki/A/B_testing) when making small changes to their products or services to determine whether making that change will affect its customers positively or negatively. If the wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week then he/she will only make this change in delivery service for customers that react positively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the A/B test is run on Segment 0 then we will see negative changes since Segment 0 people are ones who are more into buying groceries,milk etc. Now if another A/B test is run on Segment 1 he wouldn't lose customers from Segment 1 because they were not into it in the first place. Therefore the A/B Tests will help us conclude which segments prefer the change and which don't prefer the change."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Additional structure is derived from originally unlabeled data when using clustering techniques. Since each customer has a ***customer segment*** it best identifies with (depending on the clustering algorithm applied), we can consider *'customer segment'* as an **engineered feature** for the data. Knowing this, the wholesale distributor can now label the new incoming customers using only their estimated product spending and the* ***customer segment*** *data?*  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A supervised learning algorithm can be used to train the data obtained from clustering by allocating Segment 0 to a particular set of customers and Segment 1 to another set of customers as determined by unsupervised learner.Therefore it is a simple supervised binary classification problem with target variable as customer segment and the distributor could consider using Logistic Regression , SVM or ensemble methods etc. for the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Underlying Distributions\n",
    "\n",
    "At the beginning of this project, it was discussed that the `'Channel'` and `'Region'` features would be excluded from the dataset so that the customer product categories were emphasized in the analysis. By reintroducing the `'Channel'` feature to the dataset, an interesting structure emerges when considering the same PCA dimensionality reduction applied earlier to the original dataset.\n",
    "\n",
    "We will see how each data point is labeled either `'HoReCa'` (Hotel/Restaurant/Cafe) or `'Retail'` the reduced space.The sampled points will be circled based on their labeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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CpUBIRKRwnnbOPeac2+Wce9c5t9A51+qc2+Gcewm4Dfhwwmt+4Jzb6JxbBcwDRvqPdwFD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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ba7f05d90>"
      ]
     },
     "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": [
    "The clustering algorithm does a decent job of distributing the customers into 2 segments.This classification is also consistent with my previous definition of customer segments."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}