Creating_Customer_Segments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Engineer Nanodegree\n",
    "## Unsupervised Learning\n",
    "## Project 3: Creating Customer Segments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to the third project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!\n",
    "\n",
    "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.  \n",
    "\n",
    ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "\n",
    "In this project, you will analyze a dataset containing data on various customers' annual spending amounts (reported in *monetary units*) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.\n",
    "\n",
    "The dataset for this project can be found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers). For the purposes of this project, the features `'Channel'` and `'Region'` will be excluded in the analysis — with focus instead on the six product categories recorded for customers.\n",
    "\n",
    "Run the code block below to load the wholesale customers dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wholesale customers dataset has 440 samples with 6 features each.\n"
     ]
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import renders as rs\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Show matplotlib plots inline (nicely formatted in the notebook)\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the wholesale customers dataset\n",
    "try:\n",
    "    data = pd.read_csv(\"customers.csv\")\n",
    "    data.drop(['Region', 'Channel'], axis = 1, inplace = True)\n",
    "    print \"Wholesale customers dataset has {} samples with {} features each.\".format(*data.shape)\n",
    "except:\n",
    "    print \"Dataset could not be loaded. Is the dataset missing?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "In this section, you will begin exploring the data through visualizations and code to understand how each feature is related to the others. You will observe a statistical description of the dataset, consider the relevance of each feature, and select a few sample data points from the dataset which you will track through the course of this project.\n",
    "\n",
    "Run the code block below to observe a statistical description of the dataset. Note that the dataset is composed of six important product categories: **'Fresh'**, **'Milk'**, **'Grocery'**, **'Frozen'**, **'Detergents_Paper'**, and **'Delicatessen'**. Consider what each category represents in terms of products you could purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000    440.000000    440.000000    440.000000   \n",
       "mean    12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std     12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         3.000000     55.000000      3.000000     25.000000   \n",
       "25%      3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%      8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%     16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max    112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "\n",
       "       Detergents_Paper  Delicatessen  \n",
       "count        440.000000    440.000000  \n",
       "mean        2881.493182   1524.870455  \n",
       "std         4767.854448   2820.105937  \n",
       "min            3.000000      3.000000  \n",
       "25%          256.750000    408.250000  \n",
       "50%          816.500000    965.500000  \n",
       "75%         3922.000000   1820.250000  \n",
       "max        40827.000000  47943.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display a description of the dataset\n",
    "display(data.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Selecting Samples\n",
    "To get a better understanding of the customers and how their data will transform through the analysis, it would be best to select a few sample data points and explore them in more detail. In the code block below, add **three** indices of your choice to the `indices` list which will represent the customers to track. It is suggested to try different sets of samples until you obtain customers that vary significantly from one another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen samples of wholesale customers dataset:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3366</td>\n",
       "      <td>5403</td>\n",
       "      <td>12974</td>\n",
       "      <td>4400</td>\n",
       "      <td>5977</td>\n",
       "      <td>1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10253</td>\n",
       "      <td>1114</td>\n",
       "      <td>3821</td>\n",
       "      <td>397</td>\n",
       "      <td>964</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17546</td>\n",
       "      <td>4519</td>\n",
       "      <td>4602</td>\n",
       "      <td>1066</td>\n",
       "      <td>2259</td>\n",
       "      <td>2124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "0   3366  5403    12974    4400              5977          1744\n",
       "1  10253  1114     3821     397               964           412\n",
       "2  17546  4519     4602    1066              2259          2124"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Select three indices of your choice you wish to sample from the dataset\n",
    "indices = [10,15,20]\n",
    "\n",
    "# Create a DataFrame of the chosen samples\n",
    "samples = pd.DataFrame(data.loc[indices], columns = data.keys()).reset_index(drop = True)\n",
    "print \"Chosen samples of wholesale customers dataset:\"\n",
    "display(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "Consider the total purchase cost of each product category and the statistical description of the dataset above for your sample customers.  \n",
    "*What kind of establishment (customer) could each of the three samples you've chosen represent?*  \n",
    "**Hint:** Examples of establishments include places like markets, cafes, and retailers, among many others. Avoid using names for establishments, such as saying *\"McDonalds\"* when describing a sample customer as a restaurant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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8XFWlay5w25SUV7Vkyct6663/ltVq1RVX+Ck5eanS0/9f/TZ33RWr3NxsTZw4Ti6XS1On\nzpQkPfvsNM2ePVUmkzR4cKwCAgI0bdpsLVjwgnx9fdW+fQc98MBv9M03Oxoc02q1qlu3axQZeb23\nhtwqsZw1LgpLlgKnMRfgDSxnDTSPS/Uen/Dw4BY5F06ePKmnnnpcy5alys/Pv7nLueRYzhoAAACQ\nZLFYmv0XBpfLzp3fatGiBXrooTFtIvScD8EHAAAAMKhevW7U22+vb+4yWgQWNwAAAABgeAQfAAAA\nAIZH8AEAAABgeAQfAAAAAIbH4gYAAABoUy7VctaNKSo6rDFjRuu6666X2+2WyWRSz57XasKEZ71a\nC86O4AMAAIA2JT8/T6mL/j8FB4V5pb/yY8V6csr9Cg+PaXTbrl27avny171yXFwcgg8AAADanOCg\nMHXw8OXB3pSbm6333ntHJ05U66mnnlFBQYE++OBdWa1WRUZep4kTE/Xmm2/o669zJEl5ef/Ss89O\nU79+t2vBghdUWVmh2tpaTZo0Rd2799Do0cM1bNhwbd36N506dUqvvJKqK664oplH2TLwjA8AAABw\nmbjdZ7bt379PS5as0NVXd9XKlX/Q8uWv6Q9/+G8dOlSg3NxsPfbYf+nVV9/QxInPqkuXbrrjjsHa\nsGG9+vbtr1deSVVi4nS9+upSSadv47vmmu5asWKlwsOvUnb2l5d5hC0XV3wAAACAy+TgwQOaOHFc\n/TM+v/jFberRo6esVqvy8v6lq6/uIputnSQpOvoW7d27W9HRt+jEiRN6+eWX9MILC2S1WvXtt//Q\nsWPlysz8kySppqam/hhRUTdLkkJDw1RZWXn5B9lCEXwAAACAy+Tnz/jk5mZr7949kiSTyaS6uh8v\nCZ065VK7dqdD0LJlKfrNb0aqU6fOkiQfHx8988xU9ep14xnHsFh+/BHffbZLTG0Ut7oBAAAAl8n5\ncsjVV3fVoUPfqbq6WpL09dc5uu666/WXv3ymqqrv9ctf3le/7Q033Ki//nWTJGn//jxt2LD+ktZt\nBFzxAQAAQJtTfqy4Wfoymc79Wbt27fTkk0/r2WefksViUVTUzbrppt6Kj/+N/Pz8NWHCf8lkMunO\nO+9SXNxovfTScxo//veqq6vTM89M+eEIF3SstsjkbiXXv5zOiuYuAZIcjkD+LQAxF+Ad+/bt1YyV\nXyjAw5WlKssOKenxvoqI6Onlyi4O8wGtzaV6j094eDBzoQVwOALP2s4VHwAAALQpFoul2X9hgMuP\nZ3wAAAAAGB7BBwAAAIDhEXwAAAAAGB7BBwAAAIDhEXwAAAAAGB6rugEAAKBNuVTLWTemqOiwxowZ\nreuuu151dXWyWq1KSPitbrnl1rNuX1X1vXbu/Ea33trXq7Wez1/+8pnuvPOui9rnk08+1n//92vq\n3Plq1dXVyWKx6Nlnp6lr126XpkgPXVDw2bNnj8aPH69HH31UDz30UH375s2b9fvf/167du2SJKWn\np2vt2rWyWCwaMWKE4uLi5HK5NH36dBUWFspisSgpKUmdO3fWrl279Pzzz8tsNisyMlLPPffcpRkh\nAAAA8BP5+Xn6KmuBOoYHeaW/wqJj0uCZCg+PaXTbrl27avny1yVJhw4VaNq0ZzVv3gJ1797jjG13\n7fqnvvzy75c1+LzzzpqLDj6SNGRIrJ588mlJ0tdf52jp0oV65ZVUb5fXJI0Gn+rqas2fP1/9+vVr\n0F5TU6OVK1cqNDS0frvU1FSlpaXJarUqLi5OsbGxysrKUlBQkBYvXqwtW7YoJSVFS5cu1YIFCzRn\nzhz16tVLiYmJ2rx5swYOHHhpRgkAAAD8RMfwIHXtbG/WGjp16qxHHhmrtLQNiojoqT//OUMWi0UD\nB96hUaMe0tKlC1VVVaUuXbqqX7/blZw8Ty6XS2azRdOnz1ZoaJhGjx6uyMjrddttfRUWFq7ly1PU\nvn0HXX11F4WE2PXb3/5eK1em6h//+Fp1dXX6zW9G6q67YrVgwQtq376Ddu/epSNHijV37jx99dV2\n/etfezR79lTNmDFXc+bMkMt1SqdO1ejZZ6epZ8/ICxrXDTfcqEOHCiRJGzdmKC3tA1ksFl1zTXdN\nmTJTn3zysb74Yquqqr6X0+nUyJEPaujQ+7VjR65WrkyV1eqjsLAwTZ06S998s0PvvfeOTpyo1lNP\nPaNrr73O4693o8/42Gw2rVq1qj7g/OD111/Xww8/LB8fH0nSjh07FBUVJX9/f9lsNsXExCg7O1vb\ntm3TkCFDJEn9+/dXbm6uTp06pYKCAvXq1UuSNHjwYG3dutXjQQAAAACtUWTk9dq2bYs+/zxLr732\nplasWKm//OUzHTlSrPj4Mbrrrrt1//2/0qpVr+nBBxP0yiupGjFitN56a5Uk6fDhQo0d+7juvXeY\nXnvtVc2dO19LlqzQ3r27JUk7dnyt4uLDWrFipZYte01vv71KNTU1kiSXy6UlS15VXNwoZWb+SfHx\nCQoICNT8+Qv11VfbFRYWpuXLX9fcufNVVlZ2wWPasuWvuv760z/nnzx5QkuWvKrU1FU6cCBfeXn7\nJEn5+fu1cOErWrYsVatWvS63261lyxYrOXmJli1LVXBwiDZt+lSStH//Pi1ZsqJJoUe6gCs+ZrNZ\nvr6+Ddr279+v3bt3a+LEiVq4cKEkqaSkRHb7j6nZbrfL6XQ2aDeZTDKZTCopKVFwcPAZ2wIAAABt\nSVVVlfz8/PTddwc1ceI4ud1uVVVV6/Dhww22+/bbf+i77w7q7bdXye12Kzg4RJLUrl27+mdpiouL\n1KNHT0lS374DVFdXp2+/3aGdO3fW9y1JR4+WSJJ6975ZkhQaGqZ//nPnv490epsbb7xJq1a9psWL\nk3XHHXfqttvOf7vdp59u1K5d/1RdXZ06dHDo6acTJUmBgYGaNu1ZSdKBA/k6fvyYJCk6OkYmk0lB\nQcEKDAxUeXmZvvvuO82aNUVut1snTpxQcHCIOnRwqEePnrJam740gUc9JCcna/bs2ZJU/wX8ufO1\nm0ymc34OAAAAtBW7du1UTU2N+ve/XZMnz2jwWWFhQf2frVYfvfhisuz29g228fFpeIHiByaTqf7z\n++4bpocffvSMbSwWS/2ff/6zefv2HfT22+8pJ+crffTRh9q581s9+ujvzjmOnz7j84PTV5QWas2a\n9xUSEqKpUyfVf1ZXV9fg2CaTWQ6Ho/75px/k5mbLavU553EvxkUHn+LiYu3fv19TppxOY06nUwkJ\nCZo4caI2bdrUYLvo6GiFhoaqpKREkZGRcrlccrvdcjgcKi8vb7Dtz2+l+7mQED9ZrZbzboPLw+EI\nbO4SgBaBuYCmKisLaHIfdntAizgXW0INwIUqKwtQoZf7tNtPz+fzzYWamuOyWMz12xw8eFBpaR/o\nvffW65FHHlFgoI9sNpsWLFigKVOm6Morr5Cvr0UOR6BuuSVaOTnb9OCDD2rbtm0qLS3VvffeK5Pp\nx2OGhjpUWVmiLl266Ouvv1KfPn3Uv/+tevnll/XMM0+ppqZGixYt0uzZs9WunY+CgvzkcAQqKOgK\ntWvnU9+PwxGobdu26dSpUxo6dIhuueUmvfDCC+ccW2BgO11xhe8Zn5eXl8vX10fXXttFhw8f1t69\nu+Tv76PAwHbavfv/1KFDgMrKynTy5An17Hm1rFaLjh8/ooiICL3zzju67bbbFBzsJ5vN6pXvMRcd\nfMLCwrRx48b6vw8ePFjr1q3TyZMnNXv2bFVWVspkMik3N1ezZs1SRUWFMjIyNGDAAGVlZalPnz6y\nWCzq3r27cnJyFBMTo40bNyohIeG8xy0rq7r40cHrHI5AOZ0VzV0G0OyYC/CG0tJKr/TR3Oci8wGt\nTWlp5emV2LyksOiYOv57Pp9vLpSWfq/9+/frwQcfUk1Njerq6vTMM1NlsfgrLm60Ro16sH5xg2PH\nTuqqq7pp0aLFCggIVnz8b/XSSy/oo4/SZTKZNGvW83I6K+R2/3jM3/72cY0b96Q6duykjh276MQJ\nlzp37qGbborWr38dJ0n69a9HyOms0IkTp3TsWJWczgodO1atEydOyemsUETEtRo+/DeaNy9Z8+bN\n1muvvSGTyazHHnv8nGOrqDih6uqas3xuUUzMrfrVr36tHj2u1YMPJujFF+dr5MgH1aFDqP7rv57Q\noUOH9NhjT8jprNDkyTM1efJU+fr6qn37DrrrrnuVn1+okyddF/U95lwhyeRu5J6znTt3Kjk5WYWF\nhbJarQoLC9OKFSt05ZVXSpLuuusuffbZZ5KkjRs3atWqVTKbzUpISNC9996ruro6zZo1SwcOHJDN\nZlNycrLCwsK0b98+zZ07V263W71799a0adPOOwC+obYM/OcGnMZcgDfs27dXM1Z+oYCQTh7tX1l2\nSEmP91VERE8vV3ZxmA9obS7Ve3zCw4ObdS5s3/6Frr66m8LDw7Vo0QJFR9+iIUPuabZ6zuWTTz7W\n/v37zrg1zlvOFXwaveLTq1cvrVu37pyf/xB6JCk2NlaxsbENPjebzUpKSjpjv4iICL377ruNHR4A\nAADwKovF0uy/MLgU3G5p5sxE+fn5y25v79H7eM7F5XJp0qTx9c8O/aBLl65nPJvUUjV9eQQAAAAA\nze622/o2uvqap6xWq1599Q2v9PXLX97nlX4uVqPv8QEAAACA1o7gAwAAAMDwCD4AAAAADI/gAwAA\nAMDwWNwAAAAAbcqlWs66MUVFhzVmzGhdd931qqurk9VqVULCb3XLLbeedfvVq1cqODhEN93UW5s3\n/0Vjxz5+wfVUVX2vnTu/0a23XprFDlojgg8AAADalPz8PL3w560KDPfsHVo/V1F0SM/dLYWHxzS6\nbdeuXbV8+euSpEOHCjRt2rOaN2+Bunfvcc59eva8Vj17XntRNe3a9U99+eXfCT4/QfABAABAmxMY\n3klBnbo2aw2dOnXWI4+MVVraBkVE9NSf/5whi8WigQPv0KhRD9Vvl5ubrbS0DZo//2VlZPxRaWkf\nyGy2aNSohzR48BC9//47+stfsuR2u9Wv3wA9+ujvtHTpQlVVValLl67q1+92JSfPk8vlktls0fTp\nsxUaGqZXXlmk3bt3qa6uTr/61W/0y1/ed9a2zz/P0vvvvyur1arrrrtB48c/rU8++Vj/+MfXKisr\nVUHBd3rwwQTde++wZvxqNo7gAwAAADSTyMjr9dprr6qg4Du99tqbkqQnnhirQYOGNNjOZDKpqqpK\nb7/9ptat+0AnT57USy89r8GDh8hkMum1196UyWTSiBEPaOTIeMXHj9H+/ft0//2/UnLyi3rwwQTd\ncsut2rZti956a5WeeGKCtm3bog8++Egul0sZGR/r+PHjZ7RVV1drzZrVWrnybVmtVs2dO0PffvsP\nSVJe3j698cZbOngwX889N5PgAwAAAODsqqqq5Ofnp+++O6iJE8fJ7XaruvqEDh8+fMa2Bw7sV7du\n3eTj4yMfHx8lJS2WJNlsNo0f/3tZLBYdP16u48ePN9jv22//oe++O6i3314lt9ut4OAQXXnllerS\npatmzJisQYPu0n/+532yWq3/bkvUoEFDdM8992rv3t06cqRIzz77lNxut77//nsVFZ2u7cYbb5Ik\nORxh+v777y/xV6rpCD4AAABAM9m1a6dqamrUv//tmjx5RoPPsrO/bPB3i8Wiurq6Bm1FRUV6//31\nWrNmvWy2dhozZtQZx7BaffTii8my29s3aF+0aJn27t2tP/85QxkZf9SSJSvOaHviiYmKjLxBKSnL\nG+z7yScfy2Kx1P/d7fZo+JcVy1kDAAAAl8lPA8KhQwXasOE9rVixUtnZ23Xy5Am53W4tW5aimpqa\nM/bt0qWbDh48qBMnTujkyZOaNGm8jh0rl91ul83WTrt371JxcZFOnaqRyWSSy+WSJPXqdaP++tdN\nkqTs7O369NNMFRUd1ocfvq+ePSP15JNP6/jx4yoqKjqjrUuXrjpwYL/Ky8slSW+++YZKSkrONjKv\nf628jSs+AAAAaHMqig55t6+bLmyhhO++O6CJE8eppqZGdXV1SkycrtDQMI0aFa/x4x+XxWLRf/zH\nnfL19T1j33bt2umxxx7X008/IZPJpFGj4tWz57Vq1+4KPfnk7xQVdbMeeODXWrLkZU2cmKjXX1+h\n0NAwjR1ihR8jAAAgAElEQVT7uF566QV9+ulGmUwmzZr1vDp0cOibb/6hzz7bKF9fm+67b5g6dOhw\nRpvNZtPEiYmaPHmifH19de21kerQocNZRmZq4lfx0jO53a3hwpTkdFY0dwmQ5HAE8m8BiLkA79i3\nb69mrPxCASGeLalbWXZISY/3VURETy9XdnGYD2htLtV7fMLDg5kLLYDDEXjWdq74tCHemOR2e28v\nVQMAANA8LBZLs//CAJcfwacNyc/P09ZJE3WVn59H+x+uqpJ9zWqFhFzl5coAAACAS4vg08Zc5een\nLgFnv/wHAAAAGBWrugEAAAAwPIIPAAAAAMMj+AAAAAAwPIIPAAAAAMMj+AAAAAAwPIIPAAAAAMMj\n+AAAAAAwPIIPAAAAAMMj+AAAAAAwPIIPAAAAAMMj+AAAAAAwvAsKPnv27NHdd9+td999V5J0+PBh\n/fa3v1VCQoLGjh2ro0ePSpLS09MVFxenUaNG6cMPP5QkuVwuTZ48WfHx8UpISFBBQYEkadeuXRo9\nerTi4+P1wgsvXIqxAQAAAICkCwg+1dXVmj9/vvr161fftmzZMo0ePVrr1q3TXXfdpbfeekvV1dVK\nTU3VmjVrtHbtWq1Zs0bHjx/Xxx9/rKCgIK1fv17jxo1TSkqKJGnBggWaM2eO1q9fr+PHj2vz5s2X\nbpQAAAAA2rRGg4/NZtOqVasUGhpa3/b8888rNjZWkmS321VeXq4dO3YoKipK/v7+stlsiomJUXZ2\ntrZt26YhQ4ZIkvr376/c3FydOnVKBQUF6tWrlyRp8ODB2rp166UYHwAAAAA0HnzMZrN8fX0btLVr\n104mk0l1dXVav3697rvvPpWUlMhut9dvY7fb5XQ6G7SbTCaZTCaVlJQoODj4jG0BAAAA4FKwerpj\nXV2dpkyZon79+qlv3776+OOPG3zudrvPup/b7ZbJZDrn5wAAAGhbamtrlZ+f16Q+unXrLovF4qWK\nYEQeB58ZM2bommuu0ZNPPilJCg0NbXDVpri4WNHR0QoNDVVJSYkiIyPlcrnkdrvlcDhUXl7eYNuf\n3kp3NiEhfrJaOZmboqwsQPu90I/DEeiFXoDWj7mApiorC2hyH3Z7QIs4F1tCDWi99uzZo6cXpcsv\n6Pw/D55L1bEjWpcUr2uvvdbLlV085kLL5VHwSU9Pl6+vr5566qn6tt69e2vOnDmqrKyUyWRSbm6u\nZs2apYqKCmVkZGjAgAHKyspSnz59ZLFY1L17d+Xk5CgmJkYbN25UQkLCeY9ZVlblSan4idLSSq/0\n43RWeKUfoDVzOAKZC2gyb3xfLi2tbPZzkfmApiotrZRfUKgCQjo1qY/mPg+ZCy3DucJno8Fn586d\nSk5OVmFhoaxWqzIzM1VaWipfX18lJCTIZDKpR48emjt3rhITEzV27FiZzWZNmDBBAQEBGjp0qLZs\n2aL4+HjZbDYlJydLkmbOnKm5c+fK7Xard+/eDVaNAwAAAABvajT49OrVS+vWrbugzmJjY+tXe/uB\n2WxWUlLSGdtGRETUvxcIAAAAAC6lC3qBKQAAAAC0ZgQfAAAAAIZH8AEAAABgeAQfAAAAAIZH8AEA\nAABgeAQfAAAAAIZH8AEAAABgeAQfAAAAAIZH8AEAAABgeAQfAAAAAIZH8AEAAABgeAQfAAAAAIZH\n8AEAAABgeAQfAAAAAIZH8AEAAABgeAQfAAAAAIZH8AEAAABgeAQfAAAAAIZH8AEAAABgeAQfAAAA\nAIZH8AEAAABgeAQfAAAAAIZH8AEAAABgeAQfAAAAAIZH8AEAAABgeAQfAAAAAIZH8AEAAABgeNbm\nLgBA21JbW6v8/Lwm99OtW3dZLBYvVAQAANqCCwo+e/bs0fjx4/Xoo4/qoYceUlFRkaZMmSK32y2H\nw6GFCxfKx8dH6enpWrt2rSwWi0aMGKG4uDi5XC5Nnz5dhYWFslgsSkpKUufOnbVr1y49//zzMpvN\nioyM1HPPPXepxwqgBcjPz9PTi9LlFxTqcR9Vx45o2ZRhiojo6cXKAACAkTUafKqrqzV//nz169ev\nvm3ZsmVKSEhQbGysli5dqrS0ND3wwANKTU1VWlqarFar4uLiFBsbq6ysLAUFBWnx4sXasmWLUlJS\ntHTpUi1YsEBz5sxRr169lJiYqM2bN2vgwIGXdLAAWga/oFAFhHRq7jIAAEAb0ugzPjabTatWrVJo\n6I+/nf3yyy81aNAgSdKgQYO0detW7dixQ1FRUfL395fNZlNMTIyys7O1bds2DRkyRJLUv39/5ebm\n6tSpUyooKFCvXr0kSYMHD9bWrVsvxfgAAAAAoPHgYzab5evr26CturpaPj4+kqT27dvryJEjOnr0\nqOx2e/02drtdTqdTJSUl9e0mk0kmk0klJSUKDg4+Y1sAAAAAuBSavKqb2+2+6HaTyXTOzwEAAADA\n2zxa1c3f3181NTXy9fVVcXGxwsLCFBoa2uCqTXFxsaKjoxUaGqqSkhJFRkbK5XLVL4hQXl7eYNuf\n3kp3NiEhfrJaWcGpKcrKArTfC/04HIFe6AVtVVlZgFf6sdsDmv1cbO7jo/XzxnxoCXNBYj6gaZgL\nuBw8Cj79+vVTZmam7r//fmVmZmrgwIGKiorS7NmzVVlZKZPJpNzcXM2aNUsVFRXKyMjQgAEDlJWV\npT59+shisah79+7KyclRTEyMNm7cqISEhPMes6ysyqMB4kelpZVe6cfprPBKP2ibvHUelpZWNuu5\n6HAEMhfQZN6YD809FyTmA5qOuQBvOlf4bDT47Ny5U8nJySosLJTValVmZqYWL16s6dOn64MPPlDH\njh01fPhwWSwWJSYmauzYsTKbzZowYYICAgI0dOhQbdmyRfHx8bLZbEpOTpYkzZw5U3PnzpXb7Vbv\n3r0brBoHAAAAAN7UaPDp1auX1q1bd0b76tWrz2iLjY1VbGxsgzaz2aykpKQzto2IiNC77757MbUC\nAAAAgEeavLgBAAAAALR0BB8AAAAAhkfwAQAAAGB4BB8AAAAAhkfwAQAAAGB4BB8AAAAAhkfwAQAA\nAGB4BB8AAAAAhkfwAQAAAGB4BB8AAAAAhkfwAQAAAGB4BB8AAAAAhkfwAQAAAGB4BB8AAAAAhkfw\nAQAAAGB4BB8AAAAAhkfwAQAAAGB4BB8AAAAAhkfwAQAAAGB4BB8AAAAAhkfwAQAAAGB4BB8AAAAA\nhkfwAQAAAGB4BB8AAAAAhkfwAQAAAGB4BB8AAAAAhkfwAQAAAGB4BB8AAAAAhmf1ZKeqqipNmzZN\nx44d06lTpzR+/Hj16NFDU6ZMkdvtlsPh0MKFC+Xj46P09HStXbtWFotFI0aMUFxcnFwul6ZPn67C\nwkJZLBYlJSWpc+fO3h4bAAAAAEjyMPj87//+r7p3765JkybpyJEjeuSRR3TzzTfr4Ycf1j333KOl\nS5cqLS1NDzzwgFJTU5WWliar1aq4uDjFxsYqKytLQUFBWrx4sbZs2aKUlBQtXbrU22MDAAAAAEke\n3uoWEhKisrIySdKxY8dkt9u1fft2DR48WJI0aNAgbd26VTt27FBUVJT8/f1ls9kUExOj7Oxsbdu2\nTUOGDJEk9e/fXzk5OV4aDgAAAACcyaPgM3ToUBUWFio2NlYJCQmaOnWqqqur5ePjI0lq3769jhw5\noqNHj8put9fvZ7fb5XQ6VVJSUt9uMplkNpvlcrm8MBwAAAAAOJNHt7qlp6erY8eOWrVqlXbv3q0Z\nM2Y0+Nztdp91v3O119XVeVIGAAAAAFwQj4JPTk6OBg4cKEmKjIyU0+nUFVdcoZqaGvn6+qq4uFhh\nYWEKDQ2V0+ms36+4uFjR0dEKDQ1VSUmJIiMj66/0WK3nLyUkxE9Wq8WTcvFvZWUB2u+FfhyOQC/0\ngraqrCzAK/3Y7QHNfi429/HR+nljPrSEuSAxH9A0LWEu1NbWat++fU2swY+50IJ5FHy6du2qr7/+\nWnfffbcOHTokf39/3XbbbcrIyNCwYcOUmZmpgQMHKioqSrNnz1ZlZaVMJpNyc3M1a9YsVVRUKCMj\nQwMGDFBWVpb69OnT6DHLyqo8KRU/UVpa6ZV+nM4Kr/SDtslb52FpaWWznosORyBzAU3mjfnQ3HNB\nYj6g6VrCXNi3b6+2Tpqoq/z8PNr/cFWV7luzWiEhV3lcA7zjXOHTo+AzatQozZw5UwkJCaqtrdW8\nefN0zTXXaNq0adqwYYM6duyo4cOHy2KxKDExUWPHjpXZbNaECRMUEBCgoUOHasuWLYqPj5fNZlNy\ncnKTBgcAAAA01VV+fuoSwBUbo/Io+Pj5+emVV145o3316tVntMXGxio2NrZBm9lsVlJSkieHBgAA\nAICL5tGqbgAAAADQmhB8AAAAABgewQcAAACA4RF8AAAAABgewQcAAACA4RF8AAAAABgewQcAAACA\n4RF8AAAAABgewQcAAACA4RF8AAAAABgewQcAAACA4RF8AAAAABgewQcAAACA4RF8AAAAABgewQcA\nAACA4RF8AAAAABgewQcAAACA4RF8AAAAABgewQcAAACA4RF8AAAAABgewQcAAACA4RF8AAAAABge\nwQcAAACA4RF8AAAAABgewQcAAACA4RF8AAAAABgewQcAAACA4RF8AAAAABie1dMd09PT9eabb8pq\ntWrixImKjIzUlClT5Ha75XA4tHDhQvn4+Cg9PV1r166VxWLRiBEjFBcXJ5fLpenTp6uwsFAWi0VJ\nSUnq3LmzN8cFAAAAAPU8uuJTXl6uP/zhD3r//ff1xhtv6LPPPtOyZcuUkJCgd955R126dFFaWpqq\nq6uVmpqqNWvWaO3atVqzZo2OHz+ujz/+WEFBQVq/fr3GjRunlJQUb48LAAAAAOp5FHy2bt2qAQMG\n6IorrlCHDh00b948ffnllxo0aJAkadCgQdq6dat27NihqKgo+fv7y2azKSYmRtnZ2dq2bZuGDBki\nSerfv79ycnK8NyIAAAAA+BmPbnU7dOiQqqur9cQTT6iiokLjx4/XiRMn5OPjI0lq3769jhw5oqNH\nj8put9fvZ7fb5XQ6VVJSUt9uMplkNpvlcrlktXp85x0AAAAAnJNHScPtdtff7nbo0CGNGTNGbre7\nwefn2u9s6urqPCkDAAAAAC6IR8GnQ4cOio6Oltls1tVXXy1/f39ZrVbV1NTI19dXxcXFCgsLU2ho\nqJxOZ/1+xcXFio6OVmhoqEpKShQZGSmXy3W6kEau9oSE+MlqtXhSLv6trCxA+73Qj8MR6NF+tbW1\n2rdvX5OPHxERIYuFc6G1KisL8Eo/dnuAx+eitzT38dH6eWM+tIS5IDEf0DQtYS40989JuPQ8Cj4D\nBgzQzJkz9fvf/17l5eWqqqrS7bffroyMDA0bNkyZmZkaOHCgoqKiNHv2bFVWVspkMik3N1ezZs1S\nRUWFMjIyNGDAAGVlZalPnz6NHrOsrMqTUvETpaWVXunH6azwaL99+/bqq6wF6hge5PGxC4uO6ReD\nZyoioqfHfaB5ees8LC2t9Phc9AaHI7BZjw9j8MZ8aO65IDEf0HQtYS40989J8J5zhU+Pgk9YWJju\nuecejRw5UiaTSXPnztWNN96oqVOnasOGDerYsaOGDx8ui8WixMREjR07VmazWRMmTFBAQICGDh2q\nLVu2KD4+XjabTcnJyU0aHFqPjuFB6trZ3viGAAAAgBd5vJrAyJEjNXLkyAZtq1evPmO72NhYxcbG\nNmgzm81KSkry9NAAAABAPXddnQ4ePNCkPpq6P1o+llEDAABAq1Zd4dSKrzPkf8jz52ucuw4rUb5e\nrAotDcEHAAAArZ6/I1CBHYM93v/7I8clnfJeQWhxPHqBKQAAAAC0JgQfAAAAAIZH8AEAAABgeAQf\nAAAAAIbH4gatRG1trfLz85rUB8s0AgAAoK0i+LQS+fl5mpo+V/7neBPthWCZRgAAALRVBJ9WhGUa\nAQAAAM/wjA8AAAAAwyP4AAAAADA8gg8AAAAAwyP4AAAAADA8gg8AAAAAwyP4AAAAADA8gg8AAAAA\nwyP4AAAAADA8gg8AAAAAwyP4AAAAADA8gg8AAAAAwyP4AAAAADA8a3MXAAAAmkdtba3y8/Oa3I/d\n3tsL1QDApUXwAQCgjcrPz9PWSRN1lZ+fx30crqqSfc1qhYRc5cXKAMD7CD4AALRS7ro6HTx4wOP9\nDx48oKv8/NQlINCLVQFAy0TwAQCglaqucGrF1xnyP+RZcHHuOqxE+Xq5KgBomQg+uGB1brf279+v\n0tJKj/Y/ePAAJxwAeJm/I1CBHYM92vf7I8clnfJuQQDQQvFzKC5YcXWVMld+oeCgMI/2P1iwU2NG\ne7koAAAA4AIQfHBRgoPC1CGkk0f7lh0rlnTQuwUBAAAAF4D3+AAAAAAwvCYFn5MnT+ruu+/WRx99\npKKiIiUkJOjhhx/WpEmTdOrU6XuG09PTFRcXp1GjRunDDz+UJLlcLk2ePFnx8fFKSEhQQUFB00cC\nAAAAAOfQpOCTmpqq4ODTD1QuW7ZMCQkJeuedd9SlSxelpaWpurpaqampWrNmjdauXas1a9bo+PHj\n+vjjjxUUFKT169dr3LhxSklJ8cpgAAAAAOBsPA4+eXl5ysvL0x133CG3263t27dr0KBBkqRBgwZp\n69at2rFjh6KiouTv7y+bzaaYmBhlZ2dr27ZtGjJkiCSpf//+ysnJ8c5oAAAAAOAsPA4+L7/8sqZP\nn17/9+rqavn4+EiS2rdvryNHjujo0aOy2+3129jtdjmdTpWUlNS3m0wmmc1muVwuT0sBAAAAgPPy\nKPh89NFHio6OVqdOZ1/dy+12X1R7XV2dJ2UAAAAAwAXxaDnrzz//XAUFBdq0aZOKi4vl4+MjPz8/\n1dTUyNfXV8XFxQoLC1NoaKicTmf9fsXFxYqOjlZoaKhKSkoUGRlZf6XHaj1/KSEhfrJaLZ6Uawhl\nZQHNXUKLYbcHyOHw7C3laH7eOpdbwnnQ3MdH62ek7+3MBzQFcwGXg0fBZ+nSpfV/XrFihTp37qyc\nnBxlZGRo2LBhyszM1MCBAxUVFaXZs2ersrJSJpNJubm5mjVrlioqKpSRkaEBAwYoKytLffr0afSY\nZWVVnpRqGKWllc1dQotRWlopp7OiucuAh7x1Ljf3eeBwBHIeosmM9L2d+YCmYC7Am84VPr32AtOJ\nEydq6tSp2rBhgzp27Kjhw4fLYrEoMTFRY8eOldls1oQJExQQEKChQ4dqy5Ytio+Pl81mU3JysrfK\nAIBG1dbWKj8/r0l92O29vVQNAAC4HJocfJ566qn6P69evfqMz2NjYxUbG9ugzWw2KykpqamHBgCP\n5OfnaeukibrKz8+j/Q9XVcm+ZrVCQq7ycmUAAOBS8doVHwBoTa7y81OXAO7DBgCgrWjSC0wBAAAA\noDUg+AAAAAAwPIIPAAAAAMMj+AAAAAAwPIIPAAAAAMNjVbfLpKnvDTl48IAXqwEAAADaFoLPZZKf\nn6enF6XLLyjUo/2PFvxTnX7p5aIAAACANoLgcxn5BYUqIKSTR/tWHSuWVOLdggAAAIA2gmd8AAAA\nABgewQcAAACA4RF8AAAAABgez/gAAAAATVTndmv//v0qLa30uI9u3brLYrF4sSr8FMEHAAAAaKLi\n6iplrvxCwUFhHu1ffqxYT065XxERPb1cGX5A8AEAAAC8IDgoTB08XMEXlx7BBwAAAGhmdXV1TX5h\nPbfKnR/BBwAAAGhmxyqcOvKvLFkrgzzav7DomDR4JrfKnQfBBwAAAGgBOoYHqWtne3OXYVgsZw0A\nAADA8Ag+AAAAAAyP4AMAAADA8Ag+AAAAAAyP4AMAAADA8Ag+AAAAAAyP4AMAAADA8Ag+AAAAAAyP\n4AMAAADA8KzNXQAAAGi7amtrlZ+f16Q+unXrLovF4qWKABiVx8Fn4cKFysnJUW1trR5//HHddNNN\nmjJlitxutxwOhxYuXCgfHx+lp6dr7dq1slgsGjFihOLi4uRyuTR9+nQVFhbKYrEoKSlJnTt39ua4\nAABAK5Cfn6evshaoY3iQR/sXFh2TBs9URERPL1cGwGg8Cj5///vftW/fPr3//vsqLy/X8OHD1bdv\nXz388MO65557tHTpUqWlpemBBx5Qamqq0tLSZLVaFRcXp9jYWGVlZSkoKEiLFy/Wli1blJKSoqVL\nl3p7bAAAoBXoGB6krp3tHu1bW1engwcPNOn4XDEC2gaPgs9tt92m3r17S5KuvPJKVVVVafv27Zo3\nb54kadCgQVq9erW6deumqKgo+fv7S5JiYmKUnZ2tbdu26Ve/+pUkqX///po5c6Y3xgIAANqY4iMV\n+uPhQwo85tn+FUWH9Nzd4ooR0AZ4FHxMJpPatWsnSfrwww9155136m9/+5t8fHwkSe3bt9eRI0d0\n9OhR2e0//gbHbrfL6XSqpKSkvt1kMslsNsvlcslq5ZEjAABwcQLDOymoU9fmLgNAC9ekVd0+/fRT\npaWlac6cOXK73fXtP/3zT52rva6uril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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc60250d190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import Seaborn, a very powerful library for Data Visualisation\n",
    "import seaborn as sns\n",
    "samples_bar = samples.append(data.describe().loc['mean'])\n",
    "samples_bar.index = indices + ['mean']\n",
    "_ = samples_bar.plot(kind='bar', figsize=(14,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** sample2 represents customer that purchases fresh food as the amount of the fresh food is greater than the mean of the fresh food purchase by all 3 customers.\n",
    "sample1 represents customer that purchases fresh food but at the small scale as the amount of the fresh food is lower than the mean of the grocery purchase by all 3 customers but is fresh food greatest in the spending of sample1 customer.\n",
    "sample0 represents customer that purchases grocery as the amount of the grocery is greater than the mean of the grocery purchase by all 3 customers."
   ]
  },
  {
   "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": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.173438009379\n"
     ]
    }
   ],
   "source": [
    "# TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature\n",
    "#new_data = None\n",
    "new_data = data.drop('Milk', axis=1)\n",
    "#print data['Milk']\n",
    "\n",
    "# TODO: Split the data into training and testing sets using the given feature as the target\n",
    "#X_train, X_test, y_train, y_test = (None, None, None, None)\n",
    "\n",
    "from sklearn.cross_validation import train_test_split, ShuffleSplit\n",
    "X_train, X_test, y_train, y_test = train_test_split(new_data, data.Milk, test_size=0.25, random_state=42)\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "# TODO: Create a decision tree regressor and fit it to the training set\n",
    "#regressor = None\n",
    "regressor = DecisionTreeRegressor(random_state=0)\n",
    "regressor.fit(X_train, y_train)\n",
    "# TODO: Report the score of the prediction using the testing set\n",
    "from sklearn.metrics import r2_score\n",
    "y_predict = regressor.predict(X_test)\n",
    "score = r2_score(y_test, y_predict)\n",
    "print score\n",
    "#score = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "*Which feature did you attempt to predict? What was the reported prediction score? Is this feature is necessary for identifying customers' spending habits?*  \n",
    "**Hint:** The coefficient of determination, `R^2`, is scored between 0 and 1, with 1 being a perfect fit. A negative `R^2` implies the model fails to fit the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** I attempted to predict 'Milk' feature. Score was 0.173. This feature seems to be important in identifying the customers' spending habits because according to the R^2 score the data without 'Milk' feature is not fitted well enough to predict 'Milk' feature as dependent variable and so 'Milk' feature is unrelated to the other features in the dataset."
   ]
  },
  {
   "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": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3hm4xMRnG62skFE3Q3LqbaDxGc+tuqpUShw/10NHml/HDm9jAyBTo7WQyKRob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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6009dcb90>"
      ]
     },
     "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:** Detergent_papers and grocery features seem to be correlated. \n",
    "Milk and grocery features seem to be correlated.\n",
    "Detergent_papers and milk features seem to be correlated. \n",
    "This denies the suspicions about the relevance of the feature attempted to predicted as the Milk feature is correlated to grocery and detergent paper. The dataset is skewed to the right as most of the data points lie near zero values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "In this section, you will preprocess the data to create a better representation of customers by performing a scaling on the data and detecting (and optionally removing) outliers. Preprocessing data is often times a critical step in assuring that results you obtain from your analysis are significant and meaningful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Feature Scaling\n",
    "If data is not normally distributed, especially if the mean and median vary significantly (indicating a large skew), it is most [often appropriate](http://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics) to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a [Box-Cox test](http://scipy.github.io/devdocs/generated/scipy.stats.boxcox.html), which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Assign a copy of the data to `log_data` after applying a logarithm scaling. Use the `np.log` function for this.\n",
    " - Assign a copy of the sample data to `log_samples` after applying a logrithm scaling. Again, use `np.log`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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U63XMRo1OT8MmOon6XViNCgI6Fyf92GwWXFaNSk1C7VYxCToTYRfXLp+nVK6w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+Tc9DD1RlcPye1y9FN7zXqRdDq1J9PlfniY4/CJo/OUYfeE3z3Bt/9790noYU4FtjtHG9EMFs8U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Uk4P9SFyaDbND7JpTHpZKgUctYSCU50t+TnihnXMqvJLBolG5yYd0Nj7O8dP35jnutTAd79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\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DGwVO9wgrayFmTz+HRqgzNebmBz9bZnJykhGPg5ngKMlUlpvL6yytrlEQRZzuUZKZXD/j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\n/yO5nrE8Fg6Ha7Jitjvp7hl5bayYSBXxMRpyDHG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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6009e8d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Scale the data using the natural logarithm\n",
    "#log_data = None\n",
    "log_data = np.log(data)\n",
    "#print np.log(data)\n",
    "# TODO: Scale the sample data using the natural logarithm\n",
    "#log_samples = None\n",
    "log_samples = np.log(samples)\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": 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>0</th>\n",
       "      <td>8.121480</td>\n",
       "      <td>8.594710</td>\n",
       "      <td>9.470703</td>\n",
       "      <td>8.389360</td>\n",
       "      <td>8.695674</td>\n",
       "      <td>7.463937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.235326</td>\n",
       "      <td>7.015712</td>\n",
       "      <td>8.248267</td>\n",
       "      <td>5.983936</td>\n",
       "      <td>6.871091</td>\n",
       "      <td>6.021023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.772581</td>\n",
       "      <td>8.416046</td>\n",
       "      <td>8.434246</td>\n",
       "      <td>6.971669</td>\n",
       "      <td>7.722678</td>\n",
       "      <td>7.661056</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "0  8.121480  8.594710  9.470703  8.389360          8.695674      7.463937\n",
       "1  9.235326  7.015712  8.248267  5.983936          6.871091      6.021023\n",
       "2  9.772581  8.416046  8.434246  6.971669          7.722678      7.661056"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the log-transformed sample data\n",
    "display(log_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Outlier Detection\n",
    "Detecting outliers in the data is extremely important in the data preprocessing step of any analysis. The presence of outliers can often skew results which take into consideration these data points. There are many \"rules of thumb\" for what constitutes an outlier in a dataset. Here, we will use [Tukey's Method for identfying outliers](http://datapigtechnologies.com/blog/index.php/highlighting-outliers-in-your-data-with-the-tukey-method/): An *outlier step* is calculated as 1.5 times the interquartile range (IQR). A data point with a feature that is beyond an outlier step outside of the IQR for that feature is considered abnormal.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Assign the value of the 25th percentile for the given feature to `Q1`. Use `np.percentile` for this.\n",
    " - Assign the value of the 75th percentile for the given feature to `Q3`. Again, use `np.percentile`.\n",
    " - Assign the calculation of an outlier step for the given feature to `step`.\n",
    " - Optionally remove data points from the dataset by adding indices to the `outliers` list.\n",
    "\n",
    "**NOTE:** If you choose to remove any outliers, ensure that the sample data does not contain any of these points!  \n",
    "Once you have performed this implementation, the dataset will be stored in the variable `good_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Fresh':\n",
      "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",
      "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",
      "Grocery\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>9.923192</td>\n",
       "      <td>7.036148</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>8.390949</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>6.882437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "75   9.923192  7.036148  1.098612  8.390949          1.098612      6.882437\n",
       "154  6.432940  4.007333  4.919981  4.317488          1.945910      2.079442"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Frozen':\n",
      "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",
      "Detergents_Paper\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>9.923192</td>\n",
       "      <td>7.036148</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>8.390949</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>6.882437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>9.428190</td>\n",
       "      <td>6.291569</td>\n",
       "      <td>5.645447</td>\n",
       "      <td>6.995766</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>7.711101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicatessen\n",
       "75   9.923192  7.036148  1.098612  8.390949          1.098612      6.882437\n",
       "161  9.428190  6.291569  5.645447  6.995766          1.098612      7.711101"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points considered outliers for the feature 'Delicatessen':\n",
      "Delicatessen\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2.197225</td>\n",
       "      <td>7.335634</td>\n",
       "      <td>8.911530</td>\n",
       "      <td>5.164786</td>\n",
       "      <td>8.151333</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.248504</td>\n",
       "      <td>9.724899</td>\n",
       "      <td>10.274568</td>\n",
       "      <td>6.511745</td>\n",
       "      <td>6.728629</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>4.941642</td>\n",
       "      <td>9.087834</td>\n",
       "      <td>8.248791</td>\n",
       "      <td>4.955827</td>\n",
       "      <td>6.967909</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>8.034955</td>\n",
       "      <td>8.997147</td>\n",
       "      <td>9.021840</td>\n",
       "      <td>6.493754</td>\n",
       "      <td>6.580639</td>\n",
       "      <td>3.583519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>10.519646</td>\n",
       "      <td>8.875147</td>\n",
       "      <td>9.018332</td>\n",
       "      <td>8.004700</td>\n",
       "      <td>2.995732</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>6.432940</td>\n",
       "      <td>4.007333</td>\n",
       "      <td>4.919981</td>\n",
       "      <td>4.317488</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>2.079442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>10.514529</td>\n",
       "      <td>10.690808</td>\n",
       "      <td>9.911952</td>\n",
       "      <td>10.505999</td>\n",
       "      <td>5.476464</td>\n",
       "      <td>10.777768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>5.789960</td>\n",
       "      <td>6.822197</td>\n",
       "      <td>8.457443</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>5.811141</td>\n",
       "      <td>2.397895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>7.798933</td>\n",
       "      <td>8.987447</td>\n",
       "      <td>9.192075</td>\n",
       "      <td>8.743372</td>\n",
       "      <td>8.148735</td>\n",
       "      <td>1.098612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>6.368187</td>\n",
       "      <td>6.529419</td>\n",
       "      <td>7.703459</td>\n",
       "      <td>6.150603</td>\n",
       "      <td>6.860664</td>\n",
       "      <td>2.890372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>6.871091</td>\n",
       "      <td>8.513988</td>\n",
       "      <td>8.106515</td>\n",
       "      <td>6.842683</td>\n",
       "      <td>6.013715</td>\n",
       "      <td>1.945910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>10.602965</td>\n",
       "      <td>6.461468</td>\n",
       "      <td>8.188689</td>\n",
       "      <td>6.948897</td>\n",
       "      <td>6.077642</td>\n",
       "      <td>2.890372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>10.663966</td>\n",
       "      <td>5.655992</td>\n",
       "      <td>6.154858</td>\n",
       "      <td>7.235619</td>\n",
       "      <td>3.465736</td>\n",
       "      <td>3.091042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>7.431892</td>\n",
       "      <td>8.848509</td>\n",
       "      <td>10.177932</td>\n",
       "      <td>7.283448</td>\n",
       "      <td>9.646593</td>\n",
       "      <td>3.610918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fresh       Milk    Grocery     Frozen  Detergents_Paper  \\\n",
       "66    2.197225   7.335634   8.911530   5.164786          8.151333   \n",
       "109   7.248504   9.724899  10.274568   6.511745          6.728629   \n",
       "128   4.941642   9.087834   8.248791   4.955827          6.967909   \n",
       "137   8.034955   8.997147   9.021840   6.493754          6.580639   \n",
       "142  10.519646   8.875147   9.018332   8.004700          2.995732   \n",
       "154   6.432940   4.007333   4.919981   4.317488          1.945910   \n",
       "183  10.514529  10.690808   9.911952  10.505999          5.476464   \n",
       "184   5.789960   6.822197   8.457443   4.304065          5.811141   \n",
       "187   7.798933   8.987447   9.192075   8.743372          8.148735   \n",
       "203   6.368187   6.529419   7.703459   6.150603          6.860664   \n",
       "233   6.871091   8.513988   8.106515   6.842683          6.013715   \n",
       "285  10.602965   6.461468   8.188689   6.948897          6.077642   \n",
       "289  10.663966   5.655992   6.154858   7.235619          3.465736   \n",
       "343   7.431892   8.848509  10.177932   7.283448          9.646593   \n",
       "\n",
       "     Delicatessen  \n",
       "66       3.295837  \n",
       "109      1.098612  \n",
       "128      1.098612  \n",
       "137      3.583519  \n",
       "142      1.098612  \n",
       "154      2.079442  \n",
       "183     10.777768  \n",
       "184      2.397895  \n",
       "187      1.098612  \n",
       "203      2.890372  \n",
       "233      1.945910  \n",
       "285      2.890372  \n",
       "289      3.091042  \n",
       "343      3.610918  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(154, 3), (128, 2), (65, 2), (66, 2), (75, 2)]\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "import pandas as pd\n",
    "l = []\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 = None\n",
    "    Q1 = np.percentile(log_data[feature], 25)\n",
    "    #print Q1\n",
    "    \n",
    "    # TODO: Calculate Q3 (75th percentile of the data) for the given feature\n",
    "    #Q3 = None\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 = None\n",
    "    step = 1.5 * (Q3 - Q1)\n",
    "    # Display the outliers\n",
    "    print \"Data points considered outliers for the feature '{}':\".format(feature)\n",
    "    print feature\n",
    "    display(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))])\n",
    "    l.append(pd.DataFrame(log_data[~((log_data[feature] >= Q1 - step) & (log_data[feature] <= Q3 + step))]).index)\n",
    "print Counter([item for sublist in l for item in sublist]).most_common(5)\n",
    "\n",
    "# OPTIONAL: Select the indices for data points you wish to remove\n",
    "outliers  = [75,154,66,65]\n",
    "\n",
    "# Remove the outliers, if any were specified\n",
    "good_data = log_data.drop(log_data.index[outliers]).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 4\n",
    "*Are there any data points considered outliers for more than one feature 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": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** 128, 75, 154, 65 and 66 data points were considered outliers for 2 or 3 features.These outliers should be removed because they are present in the multiple features. \n",
    "\n",
    "Unsupervised techniques like k-means can be quite sensitive to outliers in your data set. The reason is simply that k-means tries to optimize the sum of squares. And thus a large deviation (such as of an outlier) gets a lot of weight. (source http://stackoverflow.com/questions/13989419/removing-outliers-from-a-k-mean-cluster)\n",
    "\n",
    " Whereas outlier like data points of 343, 289 and many more are present in the single features so they should not be removed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Transformation\n",
    "In this section you will use principal component analysis (PCA) to draw conclusions about the underlying structure of the wholesale customer data. Since using PCA on a dataset calculates the dimensions which best maximize variance, we will find which compound combinations of features best describe customers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Implementation: PCA\n",
    "\n",
    "Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the `good_data` to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the *explained variance ratio* of each dimension — how much variance within the data is explained by that dimension alone. Note that a component (dimension) from PCA can be considered a new \"feature\" of the space, however it is a composition of the original features present in the data.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Import `sklearn.decomposition.PCA` and assign the results of fitting PCA in six dimensions with `good_data` to `pca`.\n",
    " - Apply a PCA transformation of the sample log-data `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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0d0RERJa+PT095ePjo8jISPKgiHE0D8qUKaOgoKCr9tG6dWt5eXlJkk6cOKGIiAjVq1dP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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0bfd8a7950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Apply PCA by fitting the good data with the same number of dimensions as features\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=6)\n",
    "pca.fit(good_data)\n",
    "\n",
    "\n",
    "\n",
    "# TODO: 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 5\n",
    "*How much variance in the data is explained* ***in total*** *by the first and second principal component? What about the first four principal components? Using the visualization provided above, discuss what the first four dimensions best represent in terms of customer spending.*  \n",
    "**Hint:** A positive increase in a specific dimension corresponds with an *increase* of the *positive-weighted* features and a *decrease* of the *negative-weighted* features. The rate of increase or decrease is based on the indivdual feature weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension 1    0.4380\n",
      "Dimension 2    0.7080\n",
      "Dimension 3    0.8302\n",
      "Dimension 4    0.9311\n",
      "Dimension 5    0.9797\n",
      "Dimension 6    1.0001\n",
      "Name: Explained Variance, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print pca_results['Explained Variance'].cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**Total Variance by first and second pc is around 0.7. \n",
    "First four pc has total variance of around 0.931.\n",
    "The first principal component is made up of large positive weights in Detergents_Paper, and lesser but still sizeable positive weights on Grocery and Milk. It also correlates with a decrease in Fresh and Frozen. This pattern might represent spending in household staples products that are purchased together.\n",
    "\n",
    "The second principal component is made up of large positive weights in fresh, frozen and delicateseen products along with very small positive weights on the remaining products.\n",
    "\n",
    "The third principal component is made up of large positive weights in fresh products. It also correlates with a decrease in Fresh, Milk and Delicateseen. \n",
    "\n",
    "\"The fourth principal component is made up of large positive weights in frozen products. It also correlates with a decrease in Fresh and Delicateseen. "
   ]
  },
  {
   "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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "      <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>2.0830</td>\n",
       "      <td>0.7314</td>\n",
       "      <td>-0.8195</td>\n",
       "      <td>1.0346</td>\n",
       "      <td>-0.5663</td>\n",
       "      <td>-0.2434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.4230</td>\n",
       "      <td>-0.8631</td>\n",
       "      <td>1.1662</td>\n",
       "      <td>-0.8646</td>\n",
       "      <td>-0.7490</td>\n",
       "      <td>-0.3839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.8293</td>\n",
       "      <td>1.1075</td>\n",
       "      <td>0.3521</td>\n",
       "      <td>-0.8153</td>\n",
       "      <td>-0.4907</td>\n",
       "      <td>0.3171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2  Dimension 3  Dimension 4  Dimension 5  \\\n",
       "0       2.0830       0.7314      -0.8195       1.0346      -0.5663   \n",
       "1      -0.4230      -0.8631       1.1662      -0.8646      -0.7490   \n",
       "2       0.8293       1.1075       0.3521      -0.8153      -0.4907   \n",
       "\n",
       "   Dimension 6  \n",
       "0      -0.2434  \n",
       "1      -0.3839  \n",
       "2       0.3171  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sample log-data after having a PCA transformation applied\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation: Dimensionality Reduction\n",
    "When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the *cumulative explained variance ratio* is extremely important for knowing how many dimensions are necessary for the problem. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.\n",
    "\n",
    "In the code block below, you will need to implement the following:\n",
    " - Assign the results of fitting PCA in two dimensions with `good_data` to `pca`.\n",
    " - Apply a PCA transformation of `good_data` using `pca.transform`, and assign the reuslts to `reduced_data`.\n",
    " - Apply a PCA transformation of the sample log-data `log_samples` using `pca.transform`, and assign the results to `pca_samples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
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       "<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>1.756030</td>\n",
       "      <td>0.022463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.782589</td>\n",
       "      <td>0.837947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.871300</td>\n",
       "      <td>1.657090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.165280</td>\n",
       "      <td>1.426609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.769154</td>\n",
       "      <td>2.430305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.081426</td>\n",
       "      <td>0.350490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.128691</td>\n",
       "      <td>-0.247633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.559804</td>\n",
       "      <td>0.940858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.865232</td>\n",
       "      <td>-0.638384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.867729</td>\n",
       "      <td>0.714125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.083046</td>\n",
       "      <td>0.731438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-1.013639</td>\n",
       "      <td>0.017195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.231549</td>\n",
       "      <td>1.280874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.881916</td>\n",
       "      <td>1.293794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.331716</td>\n",
       "      <td>0.938519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.422961</td>\n",
       "      <td>-0.863063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.802168</td>\n",
       "      <td>-1.981860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-0.263768</td>\n",
       "      <td>0.568928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.378552</td>\n",
       "      <td>1.832513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.000324</td>\n",
       "      <td>-0.455726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.829337</td>\n",
       "      <td>1.107515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-1.747444</td>\n",
       "      <td>-0.184486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.124228</td>\n",
       "      <td>2.802720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2.787239</td>\n",
       "      <td>3.691693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2.054980</td>\n",
       "      <td>2.524676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.238790</td>\n",
       "      <td>-1.585923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-1.912334</td>\n",
       "      <td>0.388796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-2.415134</td>\n",
       "      <td>-0.592393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3.561385</td>\n",
       "      <td>1.053592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-0.575696</td>\n",
       "      <td>1.089933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>0.472951</td>\n",
       "      <td>0.387818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>-0.746295</td>\n",
       "      <td>0.052392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>2.181137</td>\n",
       "      <td>-5.364735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>0.063214</td>\n",
       "      <td>1.708630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>-0.555599</td>\n",
       "      <td>-0.011539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1.590853</td>\n",
       "      <td>0.835366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>2.548711</td>\n",
       "      <td>-0.084820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1.922601</td>\n",
       "      <td>-0.506053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>3.304816</td>\n",
       "      <td>-2.407903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>0.333015</td>\n",
       "      <td>0.118923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>3.032772</td>\n",
       "      <td>-1.733771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1.449388</td>\n",
       "      <td>1.060798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>-0.448500</td>\n",
       "      <td>0.739482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>0.415505</td>\n",
       "      <td>0.465835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>1.070144</td>\n",
       "      <td>-0.104230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>-2.381223</td>\n",
       "      <td>1.777886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>2.063979</td>\n",
       "      <td>1.617304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>-0.468468</td>\n",
       "      <td>2.630248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>0.125879</td>\n",
       "      <td>-0.700533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>-1.424994</td>\n",
       "      <td>-1.142159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>0.084912</td>\n",
       "      <td>-0.400560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>0.200877</td>\n",
       "      <td>1.703918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>-0.129104</td>\n",
       "      <td>-0.549821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>-0.937438</td>\n",
       "      <td>-0.604715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>1.001929</td>\n",
       "      <td>0.399250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>-0.660962</td>\n",
       "      <td>2.850567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>-3.197819</td>\n",
       "      <td>1.957292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>3.736173</td>\n",
       "      <td>0.890983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>-1.674393</td>\n",
       "      <td>0.433088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>-0.719331</td>\n",
       "      <td>-3.699931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>436 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Dimension 1  Dimension 2\n",
       "0       1.756030     0.022463\n",
       "1       1.782589     0.837947\n",
       "2       1.871300     1.657090\n",
       "3      -1.165280     1.426609\n",
       "4       0.769154     2.430305\n",
       "5       1.081426     0.350490\n",
       "6       1.128691    -0.247633\n",
       "7       1.559804     0.940858\n",
       "8       0.865232    -0.638384\n",
       "9       2.867729     0.714125\n",
       "10      2.083046     0.731438\n",
       "11     -1.013639     0.017195\n",
       "12      2.231549     1.280874\n",
       "13      1.881916     1.293794\n",
       "14      2.331716     0.938519\n",
       "15     -0.422961    -0.863063\n",
       "16      2.802168    -1.981860\n",
       "17     -0.263768     0.568928\n",
       "18      1.378552     1.832513\n",
       "19      1.000324    -0.455726\n",
       "20      0.829337     1.107515\n",
       "21     -1.747444    -0.184486\n",
       "22      0.124228     2.802720\n",
       "23      2.787239     3.691693\n",
       "24      2.054980     2.524676\n",
       "25      1.238790    -1.585923\n",
       "26     -1.912334     0.388796\n",
       "27     -2.415134    -0.592393\n",
       "28      3.561385     1.053592\n",
       "29     -0.575696     1.089933\n",
       "..           ...          ...\n",
       "406     0.472951     0.387818\n",
       "407    -0.746295     0.052392\n",
       "408     2.181137    -5.364735\n",
       "409     0.063214     1.708630\n",
       "410    -0.555599    -0.011539\n",
       "411     1.590853     0.835366\n",
       "412     2.548711    -0.084820\n",
       "413     1.922601    -0.506053\n",
       "414     3.304816    -2.407903\n",
       "415     0.333015     0.118923\n",
       "416     3.032772    -1.733771\n",
       "417     1.449388     1.060798\n",
       "418    -0.448500     0.739482\n",
       "419     0.415505     0.465835\n",
       "420     1.070144    -0.104230\n",
       "421    -2.381223     1.777886\n",
       "422     2.063979     1.617304\n",
       "423    -0.468468     2.630248\n",
       "424     0.125879    -0.700533\n",
       "425    -1.424994    -1.142159\n",
       "426     0.084912    -0.400560\n",
       "427     0.200877     1.703918\n",
       "428    -0.129104    -0.549821\n",
       "429    -0.937438    -0.604715\n",
       "430     1.001929     0.399250\n",
       "431    -0.660962     2.850567\n",
       "432    -3.197819     1.957292\n",
       "433     3.736173     0.890983\n",
       "434    -1.674393     0.433088\n",
       "435    -0.719331    -3.699931\n",
       "\n",
       "[436 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Apply PCA by fitting the good data with only two dimensions\n",
    "from sklearn.decomposition import PCA\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 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'])\n",
    "(reduced_data)"
   ]
  },
  {
   "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": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dimension 1</th>\n",
       "      <th>Dimension 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0830</td>\n",
       "      <td>0.7314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.4230</td>\n",
       "      <td>-0.8631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.8293</td>\n",
       "      <td>1.1075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dimension 1  Dimension 2\n",
       "0       2.0830       0.7314\n",
       "1      -0.4230      -0.8631\n",
       "2       0.8293       1.1075"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display sample log-data after applying PCA transformation in two dimensions\n",
    "display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering\n",
    "\n",
    "In this section, you will choose to use either a K-Means clustering algorithm or a Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data. You will then recover specific data points from the clusters to understand their significance by transforming them back into their original dimension and scale. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "*What are the advantages to using a K-Means clustering algorithm? What are the advantages to using a Gaussian Mixture Model clustering algorithm? Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** K-means is computationally time inexpensive. It is also easy to implement and can be intuitive because it is non-probablistic clustering.\n",
    "GMM clustering has good initializaton like kmeans can be used for initialization. \n",
    "K-Means is much faster and much more scalable compared to GMM since GMM must also learn covariance matrices and probability assignments for clusters in addition to the cluster means (and cluster assignments) required for K-Means! But since our dataset is quite small and scalability is not an issue, one could reasonably use GMM since the data also seems to cluster with some overlap.\n",
    "\n",
    "K-Means can be used for preliminary analysis as it is faster, once implemented GMM can implemented in the next step while using the cluster assignments and centers obtained from KMeans as the initialisation for GMM and see if there is significant improvement in the performance."
   ]
  },
  {
   "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": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.424516155105\n"
     ]
    }
   ],
   "source": [
    "# TODO: Apply your clustering algorithm of choice to the reduced data \n",
    "from sklearn.cluster import KMeans\n",
    "km = KMeans(n_clusters=2)\n",
    "\n",
    "\n",
    "clusterer = km.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",
    "#print centers\n",
    "\n",
    "# TODO: Predict the cluster for each transformed sample data point\n",
    "sample_preds = clusterer.predict(pca_samples)\n",
    "\n",
    "# TODO: Calculate the mean silhouette coefficient for the number of clusters chosen\n",
    "from sklearn import metrics\n",
    "score = metrics.silhouette_score(reduced_data, preds, metric='euclidean')\n",
    "print score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7\n",
    "*Report the silhouette score for several cluster numbers you tried. Of these, which number of clusters has the best silhouette score?* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** With 2 clusters score is 0.424516155105 \n",
    "With 3 clusters score is 0.400704837144\n",
    "With 4 clusters score is 0.331533324246\n",
    "With 5 clusters score is 0.35102021277"
   ]
  },
  {
   "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": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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0fpcUHx+P/v37Q0NDQ265ubk5evbsKXwued2uXr0aLVu2REREBHbt2oWgoCCc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j0df/uhA5LCZ+nSV3nuL38jIyPTYx0gf4iPjyXthtEBu87lFOoUKCdcvzvVROmSw1AVp5XLpTB2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0bfd88d2d0>"
      ]
     },
     "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": 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>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>9026.0</td>\n",
       "      <td>1922.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2114.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>693.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment 1</th>\n",
       "      <td>3793.0</td>\n",
       "      <td>7878.0</td>\n",
       "      <td>12077.0</td>\n",
       "      <td>917.0</td>\n",
       "      <td>4574.0</td>\n",
       "      <td>982.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Fresh    Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Segment 0  9026.0  1922.0   2500.0  2114.0             298.0         693.0\n",
       "Segment 1  3793.0  7878.0  12077.0   917.0            4574.0         982.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Inverse transform the centers\n",
    "log_centers = pca.inverse_transform(centers)\n",
    "\n",
    "# TODO: Exponentiate the centers\n",
    "true_centers = np.exp(log_centers)\n",
    "\n",
    "# Display the true centers\n",
    "segments = ['Segment {}'.format(i) for i in range(0,len(centers))]\n",
    "true_centers = pd.DataFrame(np.round(true_centers), columns = data.keys())\n",
    "true_centers.index = segments\n",
    "display(true_centers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 8\n",
    "Consider the total purchase cost of each product category for the representative data points above, and reference the statistical description of the dataset at the beginning of this project. *What set of establishments could each of the customer segments represent?*  \n",
    "**Hint:** A customer who is assigned to `'Cluster X'` should best identify with the establishments represented by the feature set of `'Segment X'`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:**Segment 0 fresh and frozen products a lot. While Segment 1 uses Milk and detergent paper. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### Question 9\n",
    "*For each sample point, which customer segment from* ***Question 8*** *best represents it? Are the predictions for each sample point consistent with this?*\n",
    "\n",
    "Run the code block below to find which cluster each sample point is predicted to be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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 0\n",
      "Sample point 2 predicted to be in Cluster 1\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": {
    "collapsed": false
   },
   "source": [
    "**Answer:** Sample point 0 is consistent since their spending is most on milk and detergent paper.\n",
    "Sample point 1 is consistent since their spending is most on fresh products.\n",
    "Sample point 2 is inconsistent since their spending is most on fresh products."
   ]
  },
  {
   "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 reach 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:** In A/B testing companies will divide customers into group A and B randomly and independently. Group A will be control group while changes will be shown to the group B. However the change does not affect all the customers equally in group B. \n",
    "\n",
    "If the wholesale distributor can segment the customers by clustering let's say after clustering 2 customer segments are created. Then the wholesale distributer can perform A/B testing on each customer segment i.e. A/B testing will performed two times. And there will be more homogeniety due to the customer segmentation."
   ]
  },
  {
   "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:** In the unsupervised learning we have segmented the customer, we can then label them with the relevant target variable, for example target variable can be 'purchases on particular item'. Since we will then have labelled data we can then carry out classification task for the prediction of the new customers that purchases which item the most as the target variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing Underlying Distributions\n",
    "\n",
    "At the beginning of this project, it was discussed that the `'Channel'` and `'Region'` features would be excluded from the dataset so that the customer product categories were emphasized in the analysis. By reintroducing the `'Channel'` feature to the dataset, an interesting structure emerges when considering the same PCA dimensionality reduction applied earlier to the original dataset.\n",
    "\n",
    "Run the code block below to see how each data point is labeled either `'HoReCa'` (Hotel/Restaurant/Cafe) or `'Retail'` the reduced space. In addition, you will find the sample points are circled in the plot, which will identify their labeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Egtdffx02NjZ48OCB+N1wdnbGzZs38dZbb0EikaBjx45ioLh48WJ8/vnn2Lx5MwYNGiQ+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\nQ6CDrS/Pv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0bfcf52550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the clustering results based on 'Channel' data\n",
    "rs.channel_results(reduced_data, outliers, pca_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 12\n",
    "*How well does the clustering algorithm and number of clusters you've chosen compare to this underlying distribution of Hotel/Restaurant/Cafe customers to Retailer customers? Are there customer segments that would be classified as purely 'Retailers' or 'Hotels/Restaurants/Cafes' by this distribution? Would you consider these classifications as consistent with your previous definition of the customer segments?*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer:** There are green points which are in the red region so it is difficult tp classify customer segments as purely 'Retailers' or 'Hotels/Restaurants/Cafes'. \n",
    "It is inconsistent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \n",
    "**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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