customer_segments.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Engineer Nanodegree\n",
    "## Unsupervised Learning\n",
    "## Project: Creating Customer Segments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to the third project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!\n",
    "\n",
    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.  \n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "In this project, you will analyze a dataset containing data on various customers' annual spending amounts (reported in *monetary units*) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.\n",
    "\n",
    "The dataset for this project can be found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers). For the purposes of this project, the features `'Channel'` and `'Region'` will be excluded in the analysis — with focus instead on the six product categories recorded for customers.\n",
    "\n",
    "Run the code block below to load the wholesale customers dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wholesale customers dataset has 440 samples with 6 features each.\n"
     ]
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Import supplementary visualizations code visuals.py\n",
    "import visuals as vs\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the wholesale customers dataset\n",
    "try:\n",
    "    data = pd.read_csv(\"customers.csv\")\n",
    "    data.drop(['Region', 'Channel'], axis = 1, inplace = True)\n",
    "    print \"Wholesale customers dataset has {} samples with {} features each.\".format(*data.shape)\n",
    "except:\n",
    "    print \"Dataset could not be loaded. Is the dataset missing?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "In this section, you will begin exploring the data through visualizations and code to understand how each feature is related to the others. You will observe a statistical description of the dataset, consider the relevance of each feature, and select a few sample data points from the dataset which you will track through the course of this project.\n",
    "\n",
    "Run the code block below to observe a statistical description of the dataset. Note that the dataset is composed of six important product categories: **'Fresh'**, **'Milk'**, **'Grocery'**, **'Frozen'**, **'Detergents_Paper'**, and **'Delicatessen'**. Consider what each category represents in terms of products you could purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "  <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",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
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       "      <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",
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      ],
      "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"
     ]
    },
    {
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       "<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>6269</td>\n",
       "      <td>1095</td>\n",
       "      <td>1980</td>\n",
       "      <td>3860</td>\n",
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       "      <td>2162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11594</td>\n",
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       "      <td>12144</td>\n",
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      "text/plain": [
       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0   3366  5403    12974    4400              5977          1744\n",
       "1   6269  1095     1980    3860               609          2162\n",
       "2  11594  7779    12144    3252              8035          3029"
      ]
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   ],
   "source": [
    "# TODO: Select three indices of your choice you wish to sample from the dataset\n",
    "indices = [10, 50, 100]\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:** The first customer's purchases are nearly in the top 75% for all categories except Fresh (25%) and Milk(25-50%). The customer seems to be a kind of warehouse or store that specializes in items that are not easily presishable.\n",
    "\n",
    "The second customer's purchases are mostly in the Delicatessen and Frozen category (75%) with other category purchases  at the 25-50% margins. It seems to be a deli shop.\n",
    "\n",
    "The third customer's purchases are in the top 75% for all categories. It seems to be a large chain-based retail store."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Feature Relevance\n",
    "One interesting thought to consider is if one (or more) of the six product categories is actually relevant for understanding customer purchasing. That is to say, is it possible to determine whether customers purchasing some amount of one category of products will necessarily purchase some proportional amount of another category of products? We can make this determination quite easily by training a supervised regression learner on a subset of the data with one feature removed, and then score how well that model can predict the removed feature.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Assign `new_data` a copy of the data by removing a feature of your choice using the `DataFrame.drop` function.\n",
    " - Use `sklearn.cross_validation.train_test_split` to split the dataset into training and testing sets.\n",
    "   - Use the removed feature as your target label. Set a `test_size` of `0.25` and set a `random_state`.\n",
    " - Import a decision tree regressor, set a `random_state`, and fit the learner to the training data.\n",
    " - Report the prediction score of the testing set using the regressor's `score` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score =  -0.4594691373\n"
     ]
    }
   ],
   "source": [
    "# TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature\n",
    "new_data = data.drop('Fresh', axis=1)\n",
    "\n",
    "# TODO: Split the data into training and testing sets using the given feature as the target\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(new_data, data['Fresh'], test_size=0.25, random_state=32)\n",
    "\n",
    "# TODO: Create a decision tree regressor and fit it to the training set\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "regressor = DecisionTreeRegressor(random_state=17)\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "# TODO: Report the score of the prediction using the testing set\n",
    "score = regressor.score(X_test, y_test)\n",
    "print \"Score = \", score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "*Which feature did you attempt to predict? What was the reported prediction score? Is this feature necessary for identifying customers' spending habits?*  \n",
    "**Hint:** The coefficient of determination, `R^2`, is scored between 0 and 1, with 1 being a perfect fit. A negative `R^2` implies the model fails to fit the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** I tried to predict the feature \"Fresh\". The coefficient of determination, R^2 for the Decision Tree Regressor is negative, indicating that the model failed to fit the data. It seems that this feature is not correlated to any of the other features and may be independent of them. The feature might be necessary to predict a customer's spending habits, since it seems to be independent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Feature Distributions\n",
    "To get a better understanding of the dataset, we can construct a scatter matrix of each of the six product features present in the data. If you found that the feature you attempted to predict above is relevant for identifying a specific customer, then the scatter matrix below may not show any correlation between that feature and the others. Conversely, if you believe that feature is not relevant for identifying a specific customer, the scatter matrix might show a correlation between that feature and another feature in the data. Run the code block below to produce a scatter matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "image/png": 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cVN9+m89vuCG4lZFz5xhWUF2t+268UVnJsLw33qCnYf9+WhBnzKCnMDOTQtOJ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\n6+ztPfjeWTzSs2ki8gc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d4fa650>"
      ]
     },
     "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:** Two pairs show some degree of correlation - (Grocery, Detergents_Paper) and (Milk, Grocery). The feature I attempted to predict (Fresh) does not show correlation with any of the other features and seems to be relevant.\n",
    "\n",
    "Upon observing the density plot of features along the diagonal of the scatter matrix plot, it can be clearly seen that the data is skewed. The tail of the distribution on the right side is longer for all features, which indicates a positive skew. The data needs to be normalised to make the features normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "In this section, you will preprocess the data to create a better representation of customers by performing a scaling on the data and detecting (and optionally removing) outliers. Preprocessing data is often times a critical step in assuring that results you obtain from your analysis are significant and meaningful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Feature Scaling\n",
    "If data is not normally distributed, especially if the mean and median vary significantly (indicating a large skew), it is most [often appropriate](http://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics) to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a [Box-Cox test](http://scipy.github.io/devdocs/generated/scipy.stats.boxcox.html), which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Assign a copy of the data to `log_data` after applying logarithmic scaling. Use the `np.log` function for this.\n",
    " - Assign a copy of the sample data to `log_samples` after applying logarithmic scaling. Again, use `np.log`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:8: FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead\n"
     ]
    },
    {
     "data": {
      "image/png": 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noY2KCkLUiouh1aYm6D41FWX95k0Uj5ERPrfbuU9CAgpwby/0OjGBMiMC/VVX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mciIphbVQQmMpI8ob4+ne4QEsJ5t28z3/39zOWjj2rFp7VVl5i+dg36CA/HYLJ69dTKoyp+ITK9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Dxr68IFeJrifyI8m6ysqQY1VYlI5Upu3Ihn5e23WYPj4whHLhchIArj46y5N9/kPlVV8AXV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j7ulxsxN2d5M2srLoKdHbS6PXoUP8fOmlbukHrxe4/36O4cUXc44uXEiecugQx9BkXD15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\nuh4P3aD6+gLvttTU0H0hFf0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j9t/w+bw9Du0/CeCrAC4CMA3AXOf7U6n07A0tAFgJIMvn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110024890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Scale the data using the natural logarithm\n",
    "log_data = np.log(data)\n",
    "\n",
    "# TODO: 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 you may have identified earlier as being correlated, observe here whether that correlation is still present (and whether it is now stronger or weaker than before).\n",
    "\n",
    "Run the code below to see how the sample data has changed after having the natural logarithm applied to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <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>8.743372</td>\n",
       "      <td>6.998510</td>\n",
       "      <td>7.590852</td>\n",
       "      <td>8.258422</td>\n",
       "      <td>6.411818</td>\n",
       "      <td>7.678789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.358243</td>\n",
       "      <td>8.959183</td>\n",
       "      <td>9.404590</td>\n",
       "      <td>8.087025</td>\n",
       "      <td>8.991562</td>\n",
       "      <td>8.015988</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  8.743372  6.998510  7.590852  8.258422          6.411818      7.678789\n",
       "2  9.358243  8.959183  9.404590  8.087025          8.991562      8.015988"
      ]
     },
     "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": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Fresh':\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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       "  <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",
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       "    <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",
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       "    <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",
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       "  </tbody>\n",
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      "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"
     ]
    },
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       "      <th>86</th>\n",
       "      <td>10.039983</td>\n",
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       "      <td>10.377047</td>\n",
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       "      <td>9.906981</td>\n",
       "      <td>6.805723</td>\n",
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       "      <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",
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       "      <td>1.945910</td>\n",
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       "      <th>356</th>\n",
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       "      <td>4.897840</td>\n",
       "      <td>5.384495</td>\n",
       "      <td>8.057377</td>\n",
       "      <td>2.197225</td>\n",
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       "         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"
      ]
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    {
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     "text": [
      "Data points considered outliers for the feature 'Grocery':\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",
       "      <td>6.882437</td>\n",
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       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</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",
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     "text": [
      "Data points considered outliers for the feature 'Frozen':\n"
     ]
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       "      <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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",
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       "    <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"
     ]
    },
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       "      <th>75</th>\n",
       "      <td>9.923192</td>\n",
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       "      <td>8.390949</td>\n",
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       "      <th>161</th>\n",
       "      <td>9.428190</td>\n",
       "      <td>6.291569</td>\n",
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       "      <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",
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      "Data points considered outliers for the feature 'Delicatessen':\n"
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       "    <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",
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       "      <th>109</th>\n",
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       "      <td>6.728629</td>\n",
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       "      <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",
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       "    <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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       "      <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",
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       "    <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": [
      "Duplicate outliers -\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[128, 154, 65, 66, 75]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outliers  = []\n",
    "# For each feature find the data points with extreme high or low values\n",
    "for feature in log_data.keys():\n",
    "    \n",
    "    # TODO: Calculate Q1 (25th percentile of the data) for the given feature\n",
    "    Q1 = np.percentile(log_data[feature], 25)\n",
    "    \n",
    "    # TODO: Calculate Q3 (75th percentile of the data) for the given feature\n",
    "    Q3 = np.percentile(log_data[feature], 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",
    "    feature_outliers = log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))]\n",
    "    display(feature_outliers)\n",
    "    outliers = outliers + feature_outliers.index.tolist()\n",
    "\n",
    "from collections import Counter\n",
    "cnt = Counter()\n",
    "for idx in (outliers):\n",
    "    cnt[idx] += 1\n",
    "outliers_remove = [o for o in cnt if cnt[o] > 1]\n",
    "print \"Duplicate outliers -\"\n",
    "display(outliers_remove)\n",
    "\n",
    "# Remove the outliers, if any were specified\n",
    "good_data = log_data.drop(log_data.index[outliers_remove]).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "*Are there any data points considered outliers for more than one feature based on the definition above? Should these data points be removed from the dataset? If any data points were added to the `outliers` list to be removed, explain why.* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** Yes, data points like 154 (Delicatessen, Grocery and Milk), 75 (Detergents_Paper and Grocery), 65 (Frozen and Fresh), 66 (Delicatessen and Fresh), 128 (Delicatessen and Fresh) are considered outliers for multiple features. It seems that since these points have been qualified as outliers for more than one feature, there is a stronger reason for removing them.\n",
    "\n",
    "Having these outliers present in the data can alter the results of PCA by increasing the variance for particular features and K-Means by changing the optimal number of clusters by forming new clusters at extreme ends and changing the silheoutte score."
   ]
  },
  {
   "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": {},
   "source": [
    "### Implementation: PCA\n",
    "\n",
    "Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the `good_data` to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the *explained variance ratio* of each dimension — how much variance within the data is explained by that dimension alone. Note that a component (dimension) from PCA can be considered a new \"feature\" of the space, however it is a composition of the original features present in the data.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Import `sklearn.decomposition.PCA` and assign the results of fitting PCA in six dimensions with `good_data` to `pca`.\n",
    " - Apply a PCA transformation of `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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LX65vU1dX95lD93r16kVJyaf1cWlpKe+9996G3UFJkiSpnbMwagcan2O0WkTU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\n+44jrRIAAFh61d2Ht/Oq25OsbF2VpJPckORnu/t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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f3731d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# TODO: Apply PCA by fitting the good data with the same number of dimensions as features\n",
    "pca = PCA(n_components=6)\n",
    "pca.fit(good_data)\n",
    "\n",
    "# TODO: Transform log_samples using the PCA fit above\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# Generate PCA results plot\n",
    "pca_results = vs.pca_results(good_data, pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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 individual feature weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** The first and second component in total have an expected variance of 0.714 and the first 4 components have a total expected variance of 0.9341.\n",
    "\n",
    "Dimension 1 assigns high negative weights to Milk, Grocery, Delicatessen and Detergents_Paper, capturing the variance of these 4 features collectively. It can help in differentiating customers on the basis of their purchases in these 4 categories combined i.e. customers who purchase more items in Milk, Grocery, Delicatessen and Detergents_Paper and customers who purchase less (like the Deli customer from Question 1). Fresh and Frozen categories have small positive weights which means rate of increase for these categories will be small and in the opposite direction. \n",
    "\n",
    "Similarly, dimension 2 assigns higher negative weights to Fresh, Frozen and Delicatessen, capturing customers expenditure variance for these 3 combined. Additionally all weights are negative so customers spending less on all categories (like small retail shops) may have higher value.\n",
    "\n",
    "Dimension 3 gives high weights to Fresh and Delicatessen but in opposite directions. Customers spending less on one category and more on the other will get extreme values for this dimension (like Deli shops and Grocery stores). Customers spending same amount on both will be near the origin. Similarly, dimension 4 tries to capture a combination of Frozen and Delicatessen features in opposite directions (again Deli shops)."
   ]
  },
  {
   "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": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<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>1.1404</td>\n",
       "      <td>-0.6710</td>\n",
       "      <td>0.9823</td>\n",
       "      <td>0.0603</td>\n",
       "      <td>-1.1423</td>\n",
       "      <td>0.0055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.3579</td>\n",
       "      <td>-1.7393</td>\n",
       "      <td>0.2210</td>\n",
       "      <td>0.2840</td>\n",
       "      <td>-0.5939</td>\n",
       "      <td>-0.0148</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       1.1404      -0.6710       0.9823       0.0603      -1.1423   \n",
       "2      -2.3579      -1.7393       0.2210       0.2840      -0.5939   \n",
       "\n",
       "   Dimension 6  \n",
       "0       0.2495  \n",
       "1       0.0055  \n",
       "2      -0.0148  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sample log-data after having a PCA transformation applied\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Dimensionality Reduction\n",
    "When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the *cumulative explained variance ratio* is extremely important for knowing how many dimensions are necessary for the problem. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Assign the results of fitting PCA in two dimensions with `good_data` to `pca`.\n",
    " - Apply a PCA transformation of `good_data` using `pca.transform`, and assign the results to `reduced_data`.\n",
    " - Apply a PCA transformation of `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# TODO: Apply PCA by fitting the good data with only two dimensions\n",
    "pca = PCA(n_components=2)\n",
    "pca.fit(good_data)\n",
    "\n",
    "# TODO: Transform the good data using the PCA fit above\n",
    "reduced_data = pca.transform(good_data)\n",
    "\n",
    "# TODO: Transform log_samples using the PCA fit above\n",
    "pca_samples = pca.transform(log_samples)\n",
    "\n",
    "# Create a DataFrame for the reduced data\n",
    "reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions. Observe how the values for the first two dimensions remains unchanged when compared to a PCA transformation in six dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>1.1404</td>\n",
       "      <td>-0.6710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.3579</td>\n",
       "      <td>-1.7393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2\n",
       "0      -2.0887      -0.7006\n",
       "1       1.1404      -0.6710\n",
       "2      -2.3579      -1.7393"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sample log-data after applying PCA transformation in two dimensions\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing a Biplot\n",
    "A biplot is a scatterplot where each data point is represented by its scores along the principal components. The axes are the principal components (in this case `Dimension 1` and `Dimension 2`). In addition, the biplot shows the projection of the original features along the components. A biplot can help us interpret the reduced dimensions of the data, and discover relationships between the principal components and original features.\n",
    "\n",
    "Run the code cell below to produce a biplot of the reduced-dimension data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fd58390>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QgOdqaMYqBQ1wf5xtB7L7aBV0H38a+l7eC30/2fme/wwNPG+BVsbdhVwpf/ex/ZyI9Ga3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\na/f665oBmjlTsyfuDJvftercQ5psmflVq8b+fbF/sLbNLS2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cOfrctCkdr29fW89DXTEo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\nuqg7jsOZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f373110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a biplot\n",
    "vs.biplot(good_data, reduced_data, pca)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "\n",
    "Once we have the original feature projections (in red), it is easier to interpret the relative position of each data point in the scatterplot. For instance, a point the lower right corner of the figure will likely correspond to a customer that spends a lot on `'Milk'`, `'Grocery'` and `'Detergents_Paper'`, but not so much on the other product categories. \n",
    "\n",
    "From the biplot, which of the original features are most strongly correlated with the first component? What about those that are associated with the second component? Do these observations agree with the pca_results plot you obtained earlier?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "\n",
    "In this section, you will choose to use either a K-Means clustering algorithm or a Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data. You will then recover specific data points from the clusters to understand their significance by transforming them back into their original dimension and scale. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "*What are the advantages to using a K-Means clustering algorithm? What are the advantages to using a Gaussian Mixture Model clustering algorithm? Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** Advantages of K-Means clustering are that it is fast, robust and its result are easier to interpret. It is  also guaranteed to converge.\n",
    "\n",
    "Advantages of Gaussian Mixture Model is that it assigns probabilities to points (soft assignment) instead of assigning them completely to one cluster.\n",
    "\n",
    "I will use K-Means clustering as it seems that clusters formed by customers would indicate the type of shop/restaurant and it seems unlikely that a customer can belong to different clusters simultaneously."
   ]
  },
  {
   "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": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score =  0.426281015469\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "# TODO: Apply your clustering algorithm of choice to the reduced data \n",
    "clusterer = KMeans(n_clusters=2).fit(reduced_data)\n",
    "\n",
    "# TODO: Predict the cluster for each data point\n",
    "preds = clusterer.predict(reduced_data)\n",
    "\n",
    "# TODO: Find the cluster centers\n",
    "centers = clusterer.cluster_centers_\n",
    "\n",
    "# TODO: Predict the cluster for each transformed sample data point\n",
    "sample_preds = clusterer.predict(pca_samples)\n",
    "\n",
    "from sklearn.metrics import silhouette_score\n",
    "# TODO: Calculate the mean silhouette coefficient for the number of clusters chosen\n",
    "score = silhouette_score(reduced_data, preds)\n",
    "print \"Score = \", 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:** Silhouette score for various number of clusters - \n",
    " \n",
    "|n_cluster   |score   |\n",
    "|---|---|\n",
    "|2   | 0.42  |\n",
    "| 3  | 0.39  |\n",
    "|  4 | 0.33  |\n",
    "| 5  | 0.34  |\n",
    "| 6  | 0.36  |\n",
    "| 7  | 0.36  |\n",
    "|  8 | 0.36  |\n",
    "\n",
    " The highest score was achieved when number of clusters were 2."
   ]
  },
  {
   "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": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BZtZIMLdAvE62IiLSubLbPEDazczqCP5vTTbYgIhI3spqAuSc22ZmdxAMQ/kh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\nJKk0/h8Jh2VmAxAwuQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110134f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the results of the clustering from implementation\n",
    "vs.cluster_results(reduced_data, preds, centers, pca_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Data Recovery\n",
    "Each cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the *averages* of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster's center point corresponds to *the average customer of that segment*. Since the data is currently reduced in dimension and scaled by a logarithm, we can recover the representative customer spending from these data points by applying the inverse transformations.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Apply the inverse transform to `centers` using `pca.inverse_transform` and assign the new centers to `log_centers`.\n",
    " - Apply the inverse function of `np.log` to `log_centers` using `np.exp` and assign the true centers to `true_centers`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Segment 0</th>\n",
       "      <td>4005.0</td>\n",
       "      <td>7900.0</td>\n",
       "      <td>12104.0</td>\n",
       "      <td>952.0</td>\n",
       "      <td>4561.0</td>\n",
       "      <td>1036.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>8867.0</td>\n",
       "      <td>1897.0</td>\n",
       "      <td>2477.0</td>\n",
       "      <td>2088.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>681.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Segment 0  4005.0  7900.0  12104.0   952.0            4561.0        1036.0\n",
       "Segment 1  8867.0  1897.0   2477.0  2088.0             294.0         681.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": {},
   "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:** According to the statistical description of the dataset, the representative point for Segment 0 has most purchases in the Milk(75%), Grocery(75%) and Detergents_Paper(75%) category compared to other categories, all having purchases in the 25-50% range. This point should represent big retail shops\n",
    "\n",
    "The representative point for Segment 1 has most purchases in Fresh(50%) and Frozen(50%) with low amount(25%) of purchases in all other categories. This point should represent small to medium range deli establishments/restaurants."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample point 0 predicted to be in Cluster 0\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** Sample point 0 and Sample point 2 seem to be close to the centroid of Cluster 0, judging by their high values of purchases in the Milk, Grocery, Detergents_paper and Delicatessen categories. Sample point 1 seems to be closer to centroid of Cluster 1, having low purchases in all categories except Fresh, Frozen and Delicatessen. \n",
    "\n",
    "The prediction puts point 1 in cluster 1, the segment representing deli shops which is consistent. It puts point 0 and point 2 in cluster 0, the segment representing mid to large retail stores which is also consistent. \n",
    "\n",
    "Point 0 was presumed to be a mid level warehouse and point 2, a big chain retail store in question 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this final section, you will investigate ways that you can make use of the clustered data. First, you will consider how the different groups of customers, the ***customer segments***, may be affected differently by a specific delivery scheme. Next, you will consider how giving a label to each customer (which *segment* that customer belongs to) can provide for additional features about the customer data. Finally, you will compare the ***customer segments*** to a hidden variable present in the data, to see whether the clustering identified certain relationships."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Question 10\n",
    "Companies will often run [A/B tests](https://en.wikipedia.org/wiki/A/B_testing) when making small changes to their products or services to determine whether making that change will affect its customers positively or negatively. The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively. *How can the wholesale distributor use the customer segments to determine which customers, if any, would react positively to the change in delivery service?*  \n",
    "**Hint:** Can we assume the change affects all customers equally? How can we determine which group of customers it affects the most?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** The distributor should run 2 A/B tests based on the two customer segments that have been identified. In each test, the distrubutor can form a test group and a control group comprising of customers from a single segment only. For example, in test 1, the control and test groups will have customers from Segment 0 only, and in test 2, from Segment 1 only. Then, in each test, the distributor should change the delivery service from the present 5 days a week to 3 days a week for the test group.\n",
    "\n",
    "The change should affect different customers differently. A small deli shop/restaurant would need fresh items purchased frequently and would not have much space to store them. Such customers would be happier with the 5 day delivery service. However, large retail stores have enough storage space and do not need supplies as frequently so they would not have a problem with the 3 day delivery service.\n",
    "\n",
    "So, the test group for test 1 should have more number of customers reacting positively to the change in delivery service compared to the test group for test 2. This should indicate that the wholesale distributor should change its delivery service for customers belonging to Segment 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 11\n",
    "Additional structure is derived from originally unlabeled data when using clustering techniques. Since each customer has a ***customer segment*** it best identifies with (depending on the clustering algorithm applied), we can consider *'customer segment'* as an **engineered feature** for the data. Assume the wholesale distributor recently acquired ten new customers and each provided estimates for anticipated annual spending of each product category. Knowing these estimates, the wholesale distributor wants to classify each new customer to a ***customer segment*** to determine the most appropriate delivery service.  \n",
    "*How can the wholesale distributor label the new customers using only their estimated product spending and the* ***customer segment*** *data?*  \n",
    "**Hint:** A supervised learner could be used to train on the original customers. What would be the target variable?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** The customer segment data can be used to train a supervised learner which takes as input the product expenditure for each customer and outputs the customer segment. We can use the customer segment assigned by the clustering algorithm to act as the label for our supervised algorithm. After training the supervised learner on customer segment data, it can be used to predict the segment of the new customer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Underlying Distributions\n",
    "\n",
    "At the beginning of this project, it was discussed that the `'Channel'` and `'Region'` features would be excluded from the dataset so that the customer product categories were emphasized in the analysis. By reintroducing the `'Channel'` feature to the dataset, an interesting structure emerges when considering the same PCA dimensionality reduction applied earlier to the original dataset.\n",
    "\n",
    "Run the code block below to see how each data point is labeled either `'HoReCa'` (Hotel/Restaurant/Cafe) or `'Retail'` the reduced space. In addition, you will find the sample points are circled in the plot, which will identify their labeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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F0j81s+OAPwHX+W08Bfik/k5mdg7e/Z2Id4/LgGJ/XT/gr0A+sB+wBhjVgjaJiEQNBUMi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W8VsvxMVJP+SRkJVo6c/MavsdsCTZ+FbOQqkUgkzRFIn6HWjAwVAwOF+P/bu//4uur6juPvT8ht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\nAAAAALFEMgQAAAAglkiGAAAAAMTS/wPWwztBt+x02gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd4aed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the clustering results based on 'Channel' data\n",
    "vs.channel_results(reduced_data, outliers, pca_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 12\n",
    "*How well does the clustering algorithm and number of clusters you've chosen compare to this underlying distribution of Hotel/Restaurant/Cafe customers to Retailer customers? Are there customer segments that would be classified as purely 'Retailers' or 'Hotels/Restaurants/Cafes' by this distribution? Would you consider these classifications as consistent with your previous definition of the customer segments?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** The clustering algorithm (K-Means) performs very well and the number of clusters is also correct in assuming that their are 2 underlying distributions. \n",
    "\n",
    "No, the segments of this distribution would not be 'purely' classified as 'Retailers' or 'Hotels/Restaurants/Cafes' as both segments also have points of the other segment among them.\n",
    "\n",
    "Although this 'soft' classification is not consistent with the type of clustering performed by K-Means, as it assumes each customer belongs to a specific segment with probability = 1.0, K-Means still performs pretty well and clusters most of the customers correctly."
   ]
  },
  {
   "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."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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 },
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