2_Customer_Segmentation_Report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1: Customer Segmentation Report\n",
    "\n",
    "The main bulk of your analysis will come in this part of the project. Here, you should use unsupervised learning techniques to describe the relationship between the demographics of the company's existing customers and the general population of Germany. By the end of this part, you should be able to describe parts of the general population that are more likely to be part of the mail-order company's main customer base, and which parts of the general population are less so.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import os\n",
    "import boto3\n",
    "import sagemaker\n",
    "\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "from utils import *\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sagemaker session, role\n",
    "session = sagemaker.Session()\n",
    "s3_client = boto3.client('s3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Azdias: (891221, 388)\n",
      "Customers: (191652, 388)\n"
     ]
    }
   ],
   "source": [
    "azdias, customers = read_s3_files(['azdias.pkl', 'customers.pkl'], s3_client)\n",
    "print('Azdias:', azdias.shape)\n",
    "print('Customers:', customers.shape)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "customers = customers.drop(['CUSTOMER_GROUP', 'ONLINE_PURCHASE', 'PRODUCT_GROUP'],\n",
    "                              axis = 1).copy()\n",
    "print(customers.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_lnr = azdias['LNR'].copy()\n",
    "azdias.drop('LNR', axis=1, inplace=True)\n",
    "customers_lnr = customers['LNR'].copy()\n",
    "customers.drop('LNR', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Pricipal Component Analysis\n",
    "Before training our machine learning model, we'll reduce the demension of our data for which we'll use **Incremental Principal Component Analysis.** Increamental PCA requires to split data into smaller batches, which are fed to IPCA one at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA()\n",
    "pca.fit(azdias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.84993237e-02, 4.81501876e-02, 4.45038665e-02, 3.58805671e-02,\n",
       "       2.18462939e-02, 1.93497285e-02, 1.69448687e-02, 1.60126196e-02,\n",
       "       1.56756823e-02, 1.37983890e-02, 1.27046218e-02, 1.17192844e-02,\n",
       "       1.13172468e-02, 1.02775680e-02, 9.98351095e-03, 9.21138245e-03,\n",
       "       8.94859292e-03, 8.46611517e-03, 8.33106321e-03, 7.94594561e-03,\n",
       "       7.75500828e-03, 7.55734079e-03, 7.27342427e-03, 7.05286117e-03,\n",
       "       6.95659409e-03, 6.65502620e-03, 6.65340183e-03, 6.10261563e-03,\n",
       "       5.80860532e-03, 5.71283512e-03, 5.44179470e-03, 5.13940484e-03,\n",
       "       5.09504918e-03, 5.06076521e-03, 4.90343557e-03, 4.85695975e-03,\n",
       "       4.64241900e-03, 4.61710424e-03, 4.52689177e-03, 4.47729260e-03,\n",
       "       4.35132582e-03, 4.25582236e-03, 4.15639352e-03, 4.08187570e-03,\n",
       "       4.06018189e-03, 4.03340366e-03, 3.90871442e-03, 3.78800456e-03,\n",
       "       3.68561256e-03, 3.68281648e-03, 3.65040187e-03, 3.57122803e-03,\n",
       "       3.52244757e-03, 3.50830626e-03, 3.46039271e-03, 3.42622507e-03,\n",
       "       3.35580733e-03, 3.31811334e-03, 3.26106206e-03, 3.23732455e-03,\n",
       "       3.19698416e-03, 3.15016113e-03, 3.12958068e-03, 3.07370074e-03,\n",
       "       3.06293900e-03, 3.04269632e-03, 3.02056180e-03, 2.97659865e-03,\n",
       "       2.94450316e-03, 2.92370543e-03, 2.89214089e-03, 2.88035821e-03,\n",
       "       2.87171971e-03, 2.85598591e-03, 2.84554238e-03, 2.83701624e-03,\n",
       "       2.81843019e-03, 2.80522881e-03, 2.79470844e-03, 2.78077280e-03,\n",
       "       2.75787665e-03, 2.74792854e-03, 2.73525521e-03, 2.72712194e-03,\n",
       "       2.71639445e-03, 2.71162987e-03, 2.69577775e-03, 2.67576103e-03,\n",
       "       2.66482606e-03, 2.65968131e-03, 2.65892602e-03, 2.65071772e-03,\n",
       "       2.63996269e-03, 2.63748753e-03, 2.62560102e-03, 2.61177700e-03,\n",
       "       2.60347610e-03, 2.59672129e-03, 2.59364560e-03, 2.58751434e-03,\n",
       "       2.58163007e-03, 2.57136104e-03, 2.53256692e-03, 2.51865320e-03,\n",
       "       2.51461130e-03, 2.47651266e-03, 2.42606687e-03, 2.42158445e-03,\n",
       "       2.39691116e-03, 2.37735890e-03, 2.36548960e-03, 2.35302828e-03,\n",
       "       2.34195087e-03, 2.32249175e-03, 2.31225480e-03, 2.30798203e-03,\n",
       "       2.26301877e-03, 2.25508211e-03, 2.23222818e-03, 2.21988230e-03,\n",
       "       2.21210455e-03, 2.20200786e-03, 2.15237806e-03, 2.13867056e-03,\n",
       "       2.12903704e-03, 2.12446745e-03, 2.11158972e-03, 2.10207289e-03,\n",
       "       2.09397912e-03, 2.08406726e-03, 2.07653053e-03, 2.06623395e-03,\n",
       "       2.04928333e-03, 2.03878833e-03, 2.02698880e-03, 2.01465560e-03,\n",
       "       2.00848864e-03, 2.00370721e-03, 1.99448921e-03, 1.97415248e-03,\n",
       "       1.95709856e-03, 1.94226411e-03, 1.94202329e-03, 1.93034335e-03,\n",
       "       1.90604499e-03, 1.90405893e-03, 1.88105278e-03, 1.87438206e-03,\n",
       "       1.86819027e-03, 1.84280120e-03, 1.83172543e-03, 1.82030227e-03,\n",
       "       1.81486287e-03, 1.81085250e-03, 1.79658288e-03, 1.78866597e-03,\n",
       "       1.77193950e-03, 1.76851262e-03, 1.75899852e-03, 1.74620360e-03,\n",
       "       1.73696367e-03, 1.73269217e-03, 1.71890385e-03, 1.71241868e-03,\n",
       "       1.71083436e-03, 1.70265052e-03, 1.67984679e-03, 1.67165045e-03,\n",
       "       1.66420323e-03, 1.65530307e-03, 1.65228302e-03, 1.64319294e-03,\n",
       "       1.62892188e-03, 1.62054415e-03, 1.61558188e-03, 1.60820352e-03,\n",
       "       1.60292667e-03, 1.59174832e-03, 1.58398272e-03, 1.57624485e-03,\n",
       "       1.55094149e-03, 1.54064982e-03, 1.53735323e-03, 1.52193800e-03,\n",
       "       1.51633705e-03, 1.50720558e-03, 1.50271010e-03, 1.48966728e-03,\n",
       "       1.48124606e-03, 1.46435433e-03, 1.44795893e-03, 1.43613068e-03,\n",
       "       1.42630072e-03, 1.40774321e-03, 1.39830005e-03, 1.39278164e-03,\n",
       "       1.38289787e-03, 1.37795299e-03, 1.37333900e-03, 1.36127146e-03,\n",
       "       1.34924279e-03, 1.33523339e-03, 1.32006769e-03, 1.31898034e-03,\n",
       "       1.29724774e-03, 1.29042199e-03, 1.27068353e-03, 1.26102916e-03,\n",
       "       1.24571668e-03, 1.22689795e-03, 1.22174822e-03, 1.20004172e-03,\n",
       "       1.19068500e-03, 1.16585461e-03, 1.13037202e-03, 1.12538800e-03,\n",
       "       1.11023136e-03, 1.07252169e-03, 1.06932015e-03, 1.06372020e-03,\n",
       "       1.05969904e-03, 1.04435222e-03, 1.03272592e-03, 1.01892284e-03,\n",
       "       1.00497822e-03, 9.85846363e-04, 9.71542680e-04, 9.50861741e-04,\n",
       "       9.47589802e-04, 9.40792227e-04, 9.20798077e-04, 9.14046983e-04,\n",
       "       9.02480034e-04, 8.92900513e-04, 8.78405654e-04, 8.75269078e-04,\n",
       "       8.54955906e-04, 8.50525124e-04, 8.38868295e-04, 8.32477629e-04,\n",
       "       8.24408409e-04, 8.03185016e-04, 7.96896915e-04, 7.94731538e-04,\n",
       "       7.87867006e-04, 7.81310804e-04, 7.76763051e-04, 7.71527603e-04,\n",
       "       7.67829637e-04, 7.47215791e-04, 7.41536966e-04, 7.35268255e-04,\n",
       "       7.27723730e-04, 7.24458087e-04, 7.14935739e-04, 7.11467729e-04,\n",
       "       6.99500939e-04, 6.87140288e-04, 6.75605504e-04, 6.69686937e-04,\n",
       "       6.66845599e-04, 6.50642331e-04, 6.44059898e-04, 6.42156416e-04,\n",
       "       6.37747969e-04, 6.33878849e-04, 6.24683223e-04, 6.18761123e-04,\n",
       "       6.12413993e-04, 6.05005829e-04, 5.95228064e-04, 5.90768877e-04,\n",
       "       5.77374011e-04, 5.72210549e-04, 5.68018467e-04, 5.64358193e-04,\n",
       "       5.55371231e-04, 5.51420204e-04, 5.44496598e-04, 5.39405393e-04,\n",
       "       5.30370184e-04, 5.26034176e-04, 5.25110635e-04, 5.21639500e-04,\n",
       "       4.96040005e-04, 4.94199403e-04, 4.88291346e-04, 4.85803777e-04,\n",
       "       4.78401457e-04, 4.76580562e-04, 4.67381855e-04, 4.63823920e-04,\n",
       "       4.59266155e-04, 4.54326714e-04, 4.45745846e-04, 4.41837313e-04,\n",
       "       4.36688529e-04, 4.35524114e-04, 4.30654123e-04, 4.22759227e-04,\n",
       "       4.17007284e-04, 4.15311152e-04, 4.07511981e-04, 4.03610787e-04,\n",
       "       3.95815693e-04, 3.86019263e-04, 3.82876844e-04, 3.75940666e-04,\n",
       "       3.71882226e-04, 3.67798191e-04, 3.58536038e-04, 3.48690976e-04,\n",
       "       3.45889278e-04, 3.44660353e-04, 3.41151208e-04, 3.36924265e-04,\n",
       "       3.35491682e-04, 3.28758686e-04, 3.24321924e-04, 3.22365302e-04,\n",
       "       3.16334951e-04, 3.07312268e-04, 3.03988887e-04, 2.95789218e-04,\n",
       "       2.92790181e-04, 2.90117256e-04, 2.88923463e-04, 2.85453800e-04,\n",
       "       2.80693667e-04, 2.79807706e-04, 2.73215035e-04, 2.66824732e-04,\n",
       "       2.64215728e-04, 2.62633737e-04, 2.60397574e-04, 2.56724253e-04,\n",
       "       2.54914117e-04, 2.51262224e-04, 2.49169422e-04, 2.46625586e-04,\n",
       "       2.45547609e-04, 2.43055283e-04, 2.34558746e-04, 2.31523739e-04,\n",
       "       2.28614537e-04, 2.20588445e-04, 2.18050703e-04, 2.15148909e-04,\n",
       "       2.12454963e-04, 2.11321740e-04, 2.06602810e-04, 2.02417070e-04,\n",
       "       2.01155663e-04, 1.99361836e-04, 1.98234819e-04, 1.97241716e-04,\n",
       "       1.96746014e-04, 1.95573151e-04, 1.91353343e-04, 1.91132425e-04,\n",
       "       1.84598263e-04, 1.83000336e-04, 1.78237372e-04, 1.74685451e-04,\n",
       "       1.71917514e-04, 1.54238590e-04, 1.35309143e-04, 1.32318793e-04,\n",
       "       1.26305816e-04, 1.15542990e-04, 1.14720520e-04, 1.14281140e-04,\n",
       "       1.08831671e-04, 1.08334169e-04, 9.70907378e-05, 8.95184821e-05,\n",
       "       8.42941673e-05, 6.03139370e-05, 5.94300254e-05, 5.56993621e-05,\n",
       "       5.51448915e-05, 3.21957527e-05, 2.63017286e-05, 1.14158607e-05,\n",
       "       4.81048331e-15, 2.43135356e-16, 8.97453145e-18])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "source": [
    "### PCA Model Attributes\n",
    "\n",
    "Three types of model attributes are contained within the PCA model.\n",
    "\n",
    "    mean: The mean that was subtracted from a component in order to center it.\n",
    "    v: The makeup of the principal components; (same as ‘components_’ in an sklearn PCA model).\n",
    "    s: The singular values of the components for the PCA transformation. This does not exactly give the % variance from the original feature space, but can give the % variance from the projected feature space.\n",
    "\n",
    "We are only interested in v and s.\n",
    "\n",
    "From s, we can get an approximation of the data variance that is covered in the first n principal components. The approximate explained variance is given by the formula: the sum of squared s values for all top n components over the sum over squared s values for all components:\n",
    "$$\\begin{equation*} \\frac{\\sum_{n}^{ } s_n^2}{\\sum s^2} \\end{equation*}$$\n",
    "\n",
    "From v, we can learn more about the combinations of original features that make up each principal component.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# get selected params\n",
    "s=pd.DataFrame(pca_model_params['s'].asnumpy())\n",
    "v=pd.DataFrame(pca_model_params['v'].asnumpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def explained_variance(s, n_top_components):\n",
    "    \"\"\"Calculates the approx. data variance that n_top_components captures.\n",
    "       :param s: A dataframe of singular values for top components; \n",
    "           the top value is in the last row.\n",
    "       :param n_top_components: An integer, the number of top components to use.\n",
    "       :return: The expected data variance covered by the n_top_components.\n",
    "    \"\"\"\n",
    "    start_idx = N_COMPONENTS - n_top_components\n",
    "    top_principal_components = s.iloc[start_idx:, :]\n",
    "    \n",
    "    variance = np.sum(np.square(top_principal_components)) / np.sum(np.square(s))\n",
    "    \n",
    "    return variance[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained variance for top 50 is: 0.606728732585907\n",
      "Explained variance for top 100 is: 0.7550870776176453\n",
      "Explained variance for top 150 is: 0.8589470386505127\n",
      "Explained variance for top 200 is: 0.9290238618850708\n",
      "Explained variance for top 250 is: 0.9682955145835876\n",
      "Explained variance for top 300 is: 0.9900360703468323\n",
      "Explained variance for top 350 is: 0.9999566078186035\n"
     ]
    }
   ],
   "source": [
    "exp_variance = []\n",
    "\n",
    "# calculate the explained variance\n",
    "for n in range(1, 354):\n",
    "    evar = explained_variance(s, n)\n",
    "    exp_variance.append(evar)\n",
    "    if n % 50 == 0:\n",
    "        print('Explained variance for top {} is: {}'.format(n, evar))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(pca.explained_variance_ratio_.cumsum(), marker='o',linestyle='--')\n",
    "plt.title(\"Exaplained Variance by Components\")\n",
    "plt.xlabel(\"Number of Component\")\n",
    "plt.ylabel(\"Cummulative Explained Variance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can notice **top 150 components** are able to explain 85% of the variance in our data. 80% is generally accepted threshold for data variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "def display_component(v, features_list, component_num, n_weights=10):\n",
    "    \n",
    "    # get index of component (last row - component_num)\n",
    "    row_idx = N_COMPONENTS - component_num\n",
    "\n",
    "    # get the list of weights from a row in v, dataframe\n",
    "    v_1_row = v.iloc[:, row_idx]\n",
    "    v_1 = np.squeeze(v_1_row.values)\n",
    "\n",
    "    # match weights to features in counties_scaled dataframe, using list comporehension\n",
    "    comps = pd.DataFrame(list(zip(v_1, features_list)), \n",
    "                         columns=['weights', 'features'])\n",
    "\n",
    "    # we'll want to sort by the largest n_weights\n",
    "    # weights can be neg/pos and we'll sort by magnitude\n",
    "    comps['abs_weights'] = comps['weights'].apply(lambda x: np.abs(x))\n",
    "    sorted_weight_data = comps.sort_values('abs_weights', ascending=False).head(n_weights)\n",
    "\n",
    "    # display using seaborn\n",
    "    ax = plt.subplots(figsize=(10,6))\n",
    "    ax = sns.barplot(data=sorted_weight_data, \n",
    "                    x=\"weights\", \n",
    "                    y=\"features\", \n",
    "                    palette=\"Blues_d\")\n",
    "    ax.set_title(\"PCA Component Makeup, Component #\" + str(component_num))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display makeup of first component\n",
    "num = 1\n",
    "display_component(v, azdias.columns.values, component_num=num, n_weights=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "del pca_model_params\n",
    "del s\n",
    "del v\n",
    "del exp_variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reducing components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_component(df, n=180):\n",
    "    pca = PCA(n_components=n).fit(df)\n",
    "    reduced_data = pca.transform(df)\n",
    "    reduced_data = pd.DataFrame(reduced_data)\n",
    "    print(pca.explained_variance_ratio_.sum())\n",
    "    return reduced_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8692946890991515\n",
      "(891221, 180)\n",
      "0.8703916942596288\n",
      "(191652, 180)\n"
     ]
    }
   ],
   "source": [
    "azdias_transformed = reduce_component(azdias)\n",
    "print(azdias_transformed.shape)\n",
    "customers_transformed = reduce_component(customers)\n",
    "print(customers_transformed.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At n = 150:\n",
    "- Explained variance: 81.70% 81.88%\n",
    "\n",
    "At n = 180:\n",
    "- Explained variance: 86.92% 87.04%\n",
    "\n",
    "At n = 200:\n",
    "- Explained variance: 89.93% 90.03%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_COMPONENTS = 180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=N_COMPONENTS)\n",
    "azdias_transformed = pca.fit_transform(azdias)\n",
    "customers_transformed = pca.transform(customers)\n",
    "azdias_transformed = pd.DataFrame(azdias_transformed)\n",
    "customers_transformed = pd.DataFrame(customers_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891221 entries, 0 to 891220\n",
      "Columns: 180 entries, 0 to 179\n",
      "dtypes: float32(180)\n",
      "memory usage: 612.0 MB\n"
     ]
    }
   ],
   "source": [
    "azdias_transformed.info(memory_usage=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/pca.save']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_file = 'models/pca.save'\n",
    "joblib.dump(pca, pca_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_transformed_sample = azdias_transformed.sample(20000)\n",
    "\n",
    "wcss = []\n",
    "for n in range(1, 21):\n",
    "    kmeans = KMeans(n_clusters=n, init='k-means++')\n",
    "    model = kmeans.fit(azdias_transformed_sample)\n",
    "    wcss.append(model.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,6))\n",
    "plt.plot(range(1,21), wcss, marker='o', linestyle='--')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.title('K-means with PCA Clustering')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "del azdias_transformed_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=5)\n",
    "kmodel = kmeans.fit(azdias_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making predictions for azdias\n",
      "Making predictions for customers\n"
     ]
    }
   ],
   "source": [
    "print(\"Making predictions for azdias...\")\n",
    "azdias_clusters = kmodel.predict(azdias_transformed)\n",
    "print(\"Making predictions for customers...\")\n",
    "customers_clusters = kmodel.predict(customers_transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['models/kmodel.save']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmodel_file = 'models/kmodel.save'\n",
    "joblib.dump(kmodel, kmodel_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "del azdias_transformed\n",
    "del customers_transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Segmentation Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias['Cluster'] = azdias_clusters\n",
    "customers['Cluster'] = customers_clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdias_proportions = Counter(azdias['Cluster'])\n",
    "customers_proportions = Counter(customers['Cluster'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, v in azdias_proportions.items():\n",
    "    azdias_proportions[k] = v / len(azdias)\n",
    "    \n",
    "for k, v in customers_proportions.items():\n",
    "    customers_proportions[k] = v / len(customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_props = pd.DataFrame(columns=[\"Dataset\", \"Cluster\", \"Proportions\", 'Difference'])\n",
    "for k, v in customers_proportions.items():\n",
    "    df_props = df_props.append({\"Dataset\" : \"customer\", \"Cluster\": k, \"Proportions\": v}, ignore_index=True)\n",
    "\n",
    "for k, v in azdias_proportions.items():\n",
    "    df_props = df_props.append({\"Dataset\" : \"general\", \"Cluster\": k, \"Proportions\": v}, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15,7))\n",
    "sns.barplot(x=\"Cluster\", y=\"Proportions\", hue=\"Dataset\", data=df_props);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['CUSTOMER_GROUP', 'ONLINE_PURCHASE', 'PRODUCT_GROUP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "customers[cols] = customers_whole[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving data locally\n",
    "data_dir = 'Data/arvato'\n",
    "\n",
    "if not os.path.exists(data_dir):\n",
    "    os.makedirs(data_dir)\n",
    "    \n",
    "azdias.to_pickle(os.path.join(data_dir, 'azdias.pkl'))\n",
    "customers.to_pickle(os.path.join(data_dir, 'customers.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-ap-south-1-714138043953/arvato-segmentation/customers.pkl'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Uploading to S3\n",
    "session.upload_data(os.path.join(data_dir, 'azdias.pkl'), key_prefix=prefix)\n",
    "session.upload_data(os.path.join(data_dir, 'customers.pkl'), key_prefix=prefix)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_mxnet_p36",
   "language": "python",
   "name": "conda_mxnet_p36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}