udemy-ML/20-Kmeans-Clustering/00-Kmeans-Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Copyright by Pierian Data Inc.</em></center>\n",
    "<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Means Clustering\n",
    "\n",
    "\n",
    "Let's work through an example of unsupervised learning - clustering customer data.\n",
    "\n",
    "## Goal:\n",
    "\n",
    "When working with unsupervised learning methods, its usually important to lay out a general goal. In our case, let's attempt to find reasonable clusters of customers for marketing segmentation and study. What we end up doing with those clusters would depend **heavily** on the domain itself, in this case, marketing.\n",
    "\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Data\n",
    "\n",
    "LINK: https://archive.ics.uci.edu/ml/datasets/bank+marketing\n",
    "\n",
    "   This dataset is public available for research. The details are described in [Moro et al., 2011]. \n",
    "\n",
    "\n",
    "      [Moro et al., 2011] S. Moro, R. Laureano and P. Cortez. Using Data Mining for Bank Direct Marketing: An Application of the CRISP-DM Methodology. \n",
    "      In P. Novais et al. (Eds.), Proceedings of the European Simulation and Modelling Conference - ESM'2011, pp. 117-121, Guimarães, Portugal, October, 2011. EUROSIS.\n",
    "\n",
    "      Available at: [pdf] http://hdl.handle.net/1822/14838\n",
    "                    [bib] http://www3.dsi.uminho.pt/pcortez/bib/2011-esm-1.txt\n",
    "     For more information, read [Moro et al., 2011]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    # bank client data:\n",
    "    1 - age (numeric)\n",
    "    2 - job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')\n",
    "    3 - marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)\n",
    "    4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')\n",
    "    5 - default: has credit in default? (categorical: 'no','yes','unknown')\n",
    "    6 - housing: has housing loan? (categorical: 'no','yes','unknown')\n",
    "    7 - loan: has personal loan? (categorical: 'no','yes','unknown')\n",
    "    # related with the last contact of the current campaign:\n",
    "    8 - contact: contact communication type (categorical: 'cellular','telephone')\n",
    "    9 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')\n",
    "    10 - day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')\n",
    "    11 - duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.\n",
    "    # other attributes:\n",
    "    12 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "    13 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)\n",
    "    14 - previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "    15 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')\n",
    "    # social and economic context attributes\n",
    "    16 - emp.var.rate: employment variation rate - quarterly indicator (numeric)\n",
    "    17 - cons.price.idx: consumer price index - monthly indicator (numeric)\n",
    "    18 - cons.conf.idx: consumer confidence index - monthly indicator (numeric)\n",
    "    19 - euribor3m: euribor 3 month rate - daily indicator (numeric)\n",
    "    20 - nr.employed: number of employees - quarterly indicator (numeric)\n",
    "    21 - y - has the client subscribed a term deposit? (binary: 'yes','no')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../DATA/bank-full.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  subscribed  \n",
       "0          93.994          -36.4      4.857       5191.0          no  \n",
       "1          93.994          -36.4      4.857       5191.0          no  \n",
       "2          93.994          -36.4      4.857       5191.0          no  \n",
       "3          93.994          -36.4      4.857       5191.0          no  \n",
       "4          93.994          -36.4      4.857       5191.0          no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
       "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'subscribed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             41188 non-null  int64  \n",
      " 1   job             41188 non-null  object \n",
      " 2   marital         41188 non-null  object \n",
      " 3   education       41188 non-null  object \n",
      " 4   default         41188 non-null  object \n",
      " 5   housing         41188 non-null  object \n",
      " 6   loan            41188 non-null  object \n",
      " 7   contact         41188 non-null  object \n",
      " 8   month           41188 non-null  object \n",
      " 9   day_of_week     41188 non-null  object \n",
      " 10  duration        41188 non-null  int64  \n",
      " 11  campaign        41188 non-null  int64  \n",
      " 12  pdays           41188 non-null  int64  \n",
      " 13  previous        41188 non-null  int64  \n",
      " 14  poutcome        41188 non-null  object \n",
      " 15  emp.var.rate    41188 non-null  float64\n",
      " 16  cons.price.idx  41188 non-null  float64\n",
      " 17  cons.conf.idx   41188 non-null  float64\n",
      " 18  euribor3m       41188 non-null  float64\n",
      " 19  nr.employed     41188 non-null  float64\n",
      " 20  subscribed      41188 non-null  object \n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous Feature Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=200)\n",
    "sns.histplot(data=df,x='age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=200)\n",
    "sns.histplot(data=df,x='age',hue='loan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pdays', ylabel='Count'>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=200)\n",
    "sns.histplot(data=df,x='pdays')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pdays', ylabel='Count'>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=200)\n",
    "sns.histplot(data=df[df['pdays']!=999],x='pdays')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Contact duration - contact with customer made, how long did call last?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 2000.0)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=200)\n",
    "sns.histplot(data=df,x='duration',hue='contact')\n",
    "plt.xlim(0,2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 15 - previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "* 16 - poutcome: outcome of the previous marketing campaign (categorical: \"unknown\",\"other\",\"failure\",\"success\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='previous', ylabel='count'>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=200)\n",
    "sns.countplot(data=df,x='previous',hue='contact')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='contact', ylabel='count'>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='contact')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4234"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df['previous'].value_counts()\n",
    "df['previous'].value_counts().sum()-36954\n",
    "# 36954 vs. 8257"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  subscribed  \n",
       "0          93.994          -36.4      4.857       5191.0          no  \n",
       "1          93.994          -36.4      4.857       5191.0          no  \n",
       "2          93.994          -36.4      4.857       5191.0          no  \n",
       "3          93.994          -36.4      4.857       5191.0          no  \n",
       "4          93.994          -36.4      4.857       5191.0          no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB/8AAASvCAYAAADv8jmtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAB7CAAAewgFu0HU+AADOd0lEQVR4nOzde7StVX0f/O8Pj6BgsDGoBz309RZeUBmNrwiJEkzatARNBBsvMYmVCDFGxWqiw1hja6wOFW3fVNukRrzFJiZoRUSxmN68YBWkNiavAbwOQSGCpkGRSw/M9489t+fJYu299rnstZmcz2eMZ6w5nznn75n7wH/fNZ9VrbUAAAAAAAAAAOM6YKs3AAAAAAAAAADsHeE/AAAAAAAAAAxO+A8AAAAAAAAAgxP+AwAAAAAAAMDghP8AAAAAAAAAMDjhPwAAAAAAAAAMTvgPAAAAAAAAAIMT/gMAAAAAAADA4IT/AAAAAAAAADA44T8AAAAAAAAADE74DwAAAAAAAACDE/4DAAAAAAAAwOCE/wAAAAAAAAAwOOE/AAAAAAAAAAxO+A8AAAAAAAAAgxP+AwAAAAAAAMDghP8AAAAAAAAAMLhtW70BmFVVByU5pnevTXLrFm4HAAAAAAAAYF+6S5J79/aft9Zu3hdFhf/cER2T5JKt3gQAAAAAAADAJntUks/si0Je+w8AAAAAAAAAg3Pynzuia1cbF198cQ4//PCt3AsAAAAAAADAPnP11VfnuOOOW+1eu97c3SH8547o1tXG4Ycfnh07dmzlXgAAAAAAAAA2y62Lp2yM1/4DAAAAAAAAwOCE/wAAAAAAAAAwOOE/AAAAAAAAAAxO+A8AAAAAAAAAgxP+AwAAAAAAAMDghP8AAAAAAAAAMDjhPwAAAAAAAAAMTvgPAAAAAAAAAIMT/gMAAAAAAADA4IT/AAAAAAAAADA44T8AAAAAAAAADE74DwAAAAAAAACDE/4DAAAAAAAAwOCE/wAAAAAAAAAwOOE/AAAAAAAAAAxO+A8AAAAAAAAAgxP+AwAAAAAAAMDghP8AAAAAAAAAMDjhPwAAAAAAAAAMTvgPAAAAAAAAAIMT/gMAAAAAAADA4IT/AAAAAAAAADA44T8AAAAAAAAADE74DwAAAAAAAACDE/4DAAAAAAAAwOCE/wAAAAAAAAAwOOE/AAAAAAAAAAxO+A8AAAAAAAAAgxP+AwAAAAAAAMDghP8AAAAAAAAAMDjhPwAAAAAAAAAMTvgPAAAAAAAAAIMT/gMAAAAAAADA4IT/AAAAAAAAADA44T8AAAAAAAAADE74DwAAAAAAAACDE/4DAAAAAAAAwOCE/wAAAAAAAAAwOOE/AAAAAAAAAAxO+A8AAAAAAAAAg9u21RuAzXLt7/2Hrd4CS3TvX/ulrd4CAAAAAAAAbBkn/wEAAAAAAABgcMJ/AAAAAAAAABic8B8AAAAAAAAABif8BwAAAAAAAIDBCf8BAAAAAAAAYHDCfwAAAAAAAAAYnPAfAAAAAAAAAAYn/AcAAAAAAACAwQn/AQAAAAAAAGBwwn8AAAAAAAAAGJzwHwAAAAAAAAAGJ/wHAAAAAAAAgMEJ/wEAAAAAAABgcMJ/AAAAAAAAABic8B8AAAAAAAAABif8BwAAAAAAAIDBCf8BAAAAAAAAYHDCfwAAAAAAAAAYnPAfAAAAAAAAAAYn/AcAAAAAAACAwQn/AQAAAAAAAGBwwn8AAAAAAAAAGJzwHwAAAAAAAAAGJ/wHAAAAAAAAgMHtt+F/Vd2nqn6mql5ZVR+uquuqqvXrHXtQ7+SqOreqrqqqm/vnuVV18m7U2FZVz66qj1fVtVV1Y1V9qareXFUP2406h/W/63NVdX2/Ptfv/dBu1Hl4f/aX+l6u7Xt7dlVt22gdAAAAAAAAADbX/hzg/tW+KFJVByT5/SSnzwzdv1+nVtXZSX61tXbbOnUOS3JBkkfNDD0oybOSPKOqntdaO3vBfo5P8v4k22eGjunXGVV1amvt4gV1fiXJv01y4OT23ZKc0K9frqrHt9auW68OAAAAAAAAAJtvvz35P+NrST6yh2tfnV3B/2eTPC3Jcf3zs/3+GUletVaBqrpLknOzK/h/X5KTkxyf5PlJvpnkoCRvXu9NAlV1RJLzsxL870xyVpIT+3VWv3d4kvOrasc6dR6X5N9nJfj/q76H4/ue3tenHZfk3L53AAAAAAAAALbQ/nzy/5VJLklySWvtr6rqAUm+sjsFqurIJC/q3c8kObG1dmPvX1JVH0jy0STHJnlxVb2ttfbFOaWekZXT9Enyu621507GLq6qDye5NMmhSd5YVUe31nbOqfPqJPfu7V9orb1nMvbxqro0yZ8kuU9Wvoxw2py/6a5J3pSVL4Zcn+QxrbUvTab8p6r6d0me0/f89CTvmLMXAAAAAAAAAJZkvz3531r7F621D7bW9ub1/y/Iri9QnDkJ/lef8b0kZ/butiQvXKPO6hcIvp3kxXP2+sUkr+ndhyR54uycqtqe5Bd798KZ4H+1zjlJLuzdp/c1s56YlZ8aSJLXzAT/q16c5K8nbQAAAAAAAAC20H4b/u+tqqokp/TuZa21T82b1+9f3run9HXTOkcmObp3z+lfGJjnHZP27cL/JE/Irv+eb19n66t1DuhrZp26xjO/r+/xnN59aP8bAAAAAAAAANgiwv8998Ak9+vtjy6Yuzp+/yQPmBk7Yc6822mtXZPkit59zJwpG6ozM7Zencv7M/e0DgAAAAAAAABLIvzfcw+dtC9bMHc6fvTM2J7UOaKqDlmjzt+sF9q31q5Ocv28vVTVPZIcsZt7uV0dAAAAAAAAAJZr2+IprGHHpH3VgrlXTtpHzIztSZ3q6y6fjK3WWVRjtc7D9tFeMqfOuqpqx4Ip23enHgAAAAAAAMD+Tvi/535g0v7ugrk3TNr32OQ6i2pM62zWXha5cvEUAAAAAAAAADbKa//33N0m7VsWzL150r77JtdZVGNaZ7P2AgAAAAAAAMASOfm/526atA9cMPegSfvGBXVuytoW1Tl4A3uZ1lm0l43UmFdnkUU/E7A9ySW7WRMAAAAAAABgvyX833PfmbQXvfb+kEl79nX6s3XWC/8X1Tl4A3uZ1lm0l43UmFdnXa21q9Ybr6rdKQcAAAAAAACw3/Pa/z03DbB3LJg7Pek++3v3e1Knzayb1llUY1pndi9f34O9zKsDAAAAAAAAwBIJ//fc5yftoxbMnY7/5T6oc2Vr7YY16tyzqravVaCqDk9y6Ly9tNa+k11B/t78TQAAAAAAAAAskfB/z30lyTd6+7EL5p7YP7+e5KszY5+YtNes0wP9I3v3ojlTNlRnZmy9Ov/3el8i2EAdAAAAAAAAAJZE+L+HWmstyXm9e1RV/ei8ef3+6in58/q6aZ0rsuvk/FOq6uA1HnnapH3unPEPJLmtt395na2v1rmtr5n1/jWe+X19j0/p3c/3vwEAAAAAAACALSL83zu/k+TW3n5TVd19Otj7b+rdnX3+PG/on/dKctbsYFU9OMlLe/eLmRP+t9auSfKHvXtSVT1pTp0nJzmpd9/V18w6N8mXe/ul/dmzXp/kBydtAAAAAAAAALbQtq3ewFapqhOSPGRy67BJ+yFVddp0fmvtHbM1WmtXVNXrk/xmkmOTXFRVr0vypSQPTvKSJI/o01/fWvvCGtt5Z5JnJnlMkuf21+2/JclfJzkuycuTHJqV0/rPb63tXKPOy5L8dJJ7J3l3VR2b5IN97GeS/EZvX5vkt+YVaK39n6o6M8n5/ZkXVdWrklyclcD/V5L8XJ/+iSTvWmMvAAAAAAAAACxJzbyFfr9RVe9I8oyNzm+t1Rp1DshKUP/MdZa/NcmzWmu3rTWhqg5LckGSR60x5eYkz2utnb3ePqvq+Ky8un/7GlOuSXJqa+3TC+r8SpJ/m+TANaZcnOTxrbXr1quzJ6pqR5Irk+TKK6/Mjh079qjOtb/3H/bltriDu/ev/dJWbwEAAAAAAAAWuuqqq3LEEUesdo9orV21L+p67f9eaq3d1lo7Pcnjk5yX5BtJbumf5yV5XGvtjPWC/17nuiSPTvK
      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=200)\n",
    "# https://stackoverflow.com/questions/46623583/seaborn-countplot-order-categories-by-count\n",
    "sns.countplot(data=df,x='job',order=df['job'].value_counts().index)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=200)\n",
    "# https://stackoverflow.com/questions/46623583/seaborn-countplot-order-categories-by-count\n",
    "sns.countplot(data=df,x='education',order=df['education'].value_counts().index)\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=200)\n",
    "# https://stackoverflow.com/questions/46623583/seaborn-countplot-order-categories-by-count\n",
    "sns.countplot(data=df,x='education',order=df['education'].value_counts().index,hue='default')\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='default', ylabel='count'>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x209136c9490>"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 1800x1800 with 110 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# THIS TAKES A LONG TIME!\n",
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation\n",
    "\n",
    "**UNSUPERVISED LEARNING REMINDER: NO NEED TO TRAIN TEST SPLIT!! NO LABEL TO \"TEST\" AGAINST!**\n",
    "\n",
    "We do however need to transform categorical features into numeric ones where it makes sense to do so, as well as scaling the data due to distance being a key factor in clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>subscribed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "1   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "2   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "3   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "4   may         mon  ...         1    999         0  nonexistent          1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  subscribed  \n",
       "0          93.994          -36.4      4.857       5191.0          no  \n",
       "1          93.994          -36.4      4.857       5191.0          no  \n",
       "2          93.994          -36.4      4.857       5191.0          no  \n",
       "3          93.994          -36.4      4.857       5191.0          no  \n",
       "4          93.994          -36.4      4.857       5191.0          no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>...</th>\n",
       "      <th>day_of_week_fri</th>\n",
       "      <th>day_of_week_mon</th>\n",
       "      <th>day_of_week_thu</th>\n",
       "      <th>day_of_week_tue</th>\n",
       "      <th>day_of_week_wed</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_success</th>\n",
       "      <th>subscribed_no</th>\n",
       "      <th>subscribed_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
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       "      <td>57</td>\n",
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       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
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       "      <td>5191.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
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       "      <td>999</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41183</th>\n",
       "      <td>73</td>\n",
       "      <td>334</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41184</th>\n",
       "      <td>46</td>\n",
       "      <td>383</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41185</th>\n",
       "      <td>56</td>\n",
       "      <td>189</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41186</th>\n",
       "      <td>44</td>\n",
       "      <td>442</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41187</th>\n",
       "      <td>74</td>\n",
       "      <td>239</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>94.767</td>\n",
       "      <td>-50.8</td>\n",
       "      <td>1.028</td>\n",
       "      <td>4963.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41188 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  duration  campaign  pdays  previous  emp.var.rate  cons.price.idx  \\\n",
       "0       56       261         1    999         0           1.1          93.994   \n",
       "1       57       149         1    999         0           1.1          93.994   \n",
       "2       37       226         1    999         0           1.1          93.994   \n",
       "3       40       151         1    999         0           1.1          93.994   \n",
       "4       56       307         1    999         0           1.1          93.994   \n",
       "...    ...       ...       ...    ...       ...           ...             ...   \n",
       "41183   73       334         1    999         0          -1.1          94.767   \n",
       "41184   46       383         1    999         0          -1.1          94.767   \n",
       "41185   56       189         2    999         0          -1.1          94.767   \n",
       "41186   44       442         1    999         0          -1.1          94.767   \n",
       "41187   74       239         3    999         1          -1.1          94.767   \n",
       "\n",
       "       cons.conf.idx  euribor3m  nr.employed  ...  day_of_week_fri  \\\n",
       "0              -36.4      4.857       5191.0  ...                0   \n",
       "1              -36.4      4.857       5191.0  ...                0   \n",
       "2              -36.4      4.857       5191.0  ...                0   \n",
       "3              -36.4      4.857       5191.0  ...                0   \n",
       "4              -36.4      4.857       5191.0  ...                0   \n",
       "...              ...        ...          ...  ...              ...   \n",
       "41183          -50.8      1.028       4963.6  ...                1   \n",
       "41184          -50.8      1.028       4963.6  ...                1   \n",
       "41185          -50.8      1.028       4963.6  ...                1   \n",
       "41186          -50.8      1.028       4963.6  ...                1   \n",
       "41187          -50.8      1.028       4963.6  ...                1   \n",
       "\n",
       "       day_of_week_mon  day_of_week_thu  day_of_week_tue  day_of_week_wed  \\\n",
       "0                    1                0                0                0   \n",
       "1                    1                0                0                0   \n",
       "2                    1                0                0                0   \n",
       "3                    1                0                0                0   \n",
       "4                    1                0                0                0   \n",
       "...                ...              ...              ...              ...   \n",
       "41183                0                0                0                0   \n",
       "41184                0                0                0                0   \n",
       "41185                0                0                0                0   \n",
       "41186                0                0                0                0   \n",
       "41187                0                0                0                0   \n",
       "\n",
       "       poutcome_failure  poutcome_nonexistent  poutcome_success  \\\n",
       "0                     0                     1                 0   \n",
       "1                     0                     1                 0   \n",
       "2                     0                     1                 0   \n",
       "3                     0                     1                 0   \n",
       "4                     0                     1                 0   \n",
       "...                 ...                   ...               ...   \n",
       "41183                 0                     1                 0   \n",
       "41184                 0                     1                 0   \n",
       "41185                 0                     1                 0   \n",
       "41186                 0                     1                 0   \n",
       "41187                 1                     0                 0   \n",
       "\n",
       "       subscribed_no  subscribed_yes  \n",
       "0                  1               0  \n",
       "1                  1               0  \n",
       "2                  1               0  \n",
       "3                  1               0  \n",
       "4                  1               0  \n",
       "...              ...             ...  \n",
       "41183              0               1  \n",
       "41184              1               0  \n",
       "41185              1               0  \n",
       "41186              0               1  \n",
       "41187              1               0  \n",
       "\n",
       "[41188 rows x 65 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating and Fitting a KMeans Model\n",
    "\n",
    "Note of our method choices here:\n",
    "\n",
    "* fit(X[, y, sample_weight])\n",
    "    * Compute k-means clustering.\n",
    "\n",
    "* fit_predict(X[, y, sample_weight])\n",
    "    * Compute cluster centers and predict cluster index for each sample.\n",
    "\n",
    "* fit_transform(X[, y, sample_weight])\n",
    "    * Compute clustering and transform X to cluster-distance space.\n",
    "\n",
    "* predict(X[, sample_weight])\n",
    "    * Predict the closest cluster each sample in X belongs to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = KMeans(n_clusters=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make sure to watch video to understand this line and fit() vs transform()\n",
    "cluster_labels = model.fit_predict(scaled_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IMPORTANT NOTE: YOUR 0s and 1s may be opposite of ours,\n",
    "# makes sense, the number values are not significant!\n",
    "cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41188"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(scaled_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41188"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cluster_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['Cluster'] = cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(X.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                    -0.019767\n",
       "duration                0.039581\n",
       "campaign               -0.129103\n",
       "pdays                  -0.267714\n",
       "previous                0.478493\n",
       "                          ...   \n",
       "poutcome_nonexistent   -0.544406\n",
       "poutcome_success        0.254406\n",
       "subscribed_no          -0.294472\n",
       "subscribed_yes          0.294472\n",
       "Cluster                 1.000000\n",
       "Name: Cluster, Length: 66, dtype: float64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.corr()['Cluster']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8IAAAVqCAYAAACVrbSWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAB7CAAAewgFu0HU+AAEAAElEQVR4nOzde7zldX3f+/cHB5Gr2KjBFMoYEEQtlRPUeAVKmz6SXRuNjwZzOPUSeZg0Fy9REMWemhudQKgaa04SNaK1iaQPa0wccvQYhQTEOFht7ZERMRnDNPYERRSGm8j3/LF/k1mZ7Nl7r8ueNes7z+fjsR7r+9v7u37f7x7978X3t6q1FgAAAAAAAADoxSHz3gAAAAAAAAAAzJIQDgAAAAAAAEBXhHAAAAAAAAAAuiKEAwAAAAAAANAVIRwAAAAAAACArgjhAAAAAAAAAHRFCAcAAAAAAACgK0I4AAAAAAAAAF0RwgEAAAAAAADoihAOAAAAAAAAQFeEcAAAAAAAAAC6IoQDAAAAAAAA0BUhHAAAAAAAAICuCOEAAAAAAAAAdEUIBwAAAAAAAKArQjgAAAAAAAAAXdk07w2wtqo6LMk/HC5vS/KdOW4HAAAAAAAAYJYekuRRw/jzrbX7pr2hEL4Y/mGSbfPeBAAAAAAAAMAGe0qSG6e9iUejAwAAAAAAANAVJ8IXw227B5/+9KfzmMc8Zp57AQAAAAAAAJiZr371q3nqU5+6+/K21eaulxC+GP7mO8Ef85jH5Pjjj5/nXgAAAAAAAAA2ynfWnrI2j0YHAAAAAAAAoCtCOAAAAAAAAABdEcIBAAAAAAAA6IoQDgAAAAAAAEBXhHAAAAAAAAAAuiKEAwAAAAAAANAVIRwAAAAAAACArgjhAAAAAAAAAHRFCAcAAAAAAACgK0I4AAAAAAAAAF0RwgEAAAAAAADoihAOAAAAAAAAQFeEcAAAAAAAAAC6IoQDAAAAAAAA0BUhHAAAAAAAAICuCOEAAAAAAAAAdEUIBwAAAAAAAKArQjgAAAAAAAAAXRHCAQAAAAAAAOiKEA4AAAAAAABAV4RwAAAAAAAAALoihAMAAAAAAADQFSEcAAAAAAAAgK4I4QAAAAAAAAB0RQgHAAAAAAAAoCtCOAAAAAAAAABdEcIBAAAAAAAA6IoQDgAAAAAAAEBXhHAAAAAAAAAAuiKEAwAAAAAAANAVIRwAAAAAAACArgjhAAAAAAAAAHRFCAcAAAAAAACgK0I4AAAAAAAAAF0RwgEAAAAAAADoihAOAAAAAAAAQFeEcAAAAAAAAAC6IoQDAAAAAAAA0JVN894AAAAAAAAAAItj88VbJ/7sji1LM9zJvjkRDgAAAAAAAEBXhHAAAAAAAAAAuiKEAwAAAAAAANAVIRwAAAAAAACArgjhAAAAAAAAAHRFCAcAAAAAAACgK0I4AAAAAAAAAF0RwgEAAAAAAADoyqZ5bwAAAAAAAACAyW2+eOtEn9uxZWnGOzlwOBEOAAAAAAAAQFeEcAAAAAAAAAC6IoQDAAAAAAAA0BUhHAAAAAAAAICuCOEAAAAAAAAAdEUIBwAAAAAAAKArQjgAAAAAAAAAXRHCAQAAAAAAAOiKEA4AAAAAAABAV4RwAAAAAAAAALoihAMAAAAAAADQFSEcAAAAAAAAgK4I4QAAAAAAAAB0RQgHAAAAAAAAoCtCOAAAAAAAAABdEcIBAAAAAAAA6IoQDgAAAAAAAEBXhHAAAAAAAAAAuiKEAwAAAAAAANAVIRwAAAAAAACArgjhAAAAAAAAAHRFCAcAAAAAAACgK0I4AAAAAAAAAF0RwgEAAAAAAADoihAOAAAAAAAAQFeEcAAAAAAAAAC60kUIr6oTq+qKqtpeVbuq6vaq2lZVF1bVERu05hFV9edV1YbXjo1YBwAAAAAAAIDxbJr3BqZVVc9N8r4kx4z8+IgkZw6vC6pqqbV2y4yX/oUkj53xPQEAAAAAAACY0kKfCK+qM5JcleUIfleSS5I8I8m5Sd4xTDslydaqOnrG674qyb1J7pzVfQEAAAAAAACY3qKfCH9rksOTPJDkB1prN4z87uNV9aUkl2U5hr8myZumXbCqHpLlyP6QJD+f5GVJZhbZAQAAAAAAgMW1+eKtE392x5alGe7k4LawJ8Kr6qlJnj1cvmuvCL7bFUluGsavrKpDZ7D0K5N8X5IvJvmVGdwPAAAAAAAAgBla2BCe5Hkj43evNKG19mCS9w6XxyY5Z5oFq+rELH83eJL8ZGvt/mnuBwAAAAAAAMDsLXIIf9bwvivJZ1aZd+3I+JlTrvnrSY5M8h9ba9dMeS8AAAAAAAAANsAif0f4acP7La21B1aZt32Fz4ytql6Y5IeSfCPL3zc+M1V1/BpTjpvlegAAAAAAAAA9W8gQXlUPS/LI4XLnanNba9+oql1ZPsl9woTrPSLJW4bLi1trt01yn1XcOuP7AQAAAAAAABy0FvXR6EePjO9ax/xdw/tRE653eZLvTnJDkndMeA8AAAAAAAAA9oOFPBGe5GEj4/vXMf++4f3wcReqquck+fEkDyT5ydZaG/ce67DWSfXjkmzbgHUBAAAAAAAAurOoIfzekfFD1zH/sOH9nnEWqarDkvxWkkry1tbafx/n8+vVWlv18e5VtRHLAgAAAAAAAHRpUR+NfufIeD2POz9yeF/PY9RHXZLk1Cx/h/e/HfOzAAAAAAAAAMzBQp4Ib63dW1VfT/JdSY5fbW5VPSJ7QvitYy71uuH9Y0meu4+T2bvvfWRVvXAY/3Vr7eNjrgUAAAAAAADADCxkCB98Icmzk5xcVZtaaw/sY97jR8Y3jbnG7seuv3R4reaRSX53GF+bRAgHAAAAAAAAmINFfTR6klw3vB+Z5PtWmXfWyPj6jdsOAAAAAAAAAAeCRQ7hvz8yXvG0dlUdkuRFw+UdST4xzgKttVrrleQrw/SvjPz87LH+EgAAAAAAAABmZmFDeGvt00n+dLh8WVU9fYVpr0ly2jB+a2vt26O/rKqzq6oNrys3brcAAAAAAAAA7C+L/B3hSfLKLD/u/PAkH62qS7N86vvwJC9M8vJh3s1JrpjLDgEAAAAAAADYrxY6hLfWPltV5yV5X5Jjkly6wrSbkyy11u7cr5sDAAAAAAAAYC4W9tHou7XW/jDJ6UnenOXofXeWvw/8xiSvS3JGa+2WuW0QAAAAAAAAgP1qoU+E79Za+0qSnxte43zumiQ15dqbp/k8AAAAAAAAALO18CfCAQAAAAAAAGCUEA4AAAAAAABAV4RwAAAAAAAAALoihAMAAAAAAADQFSEcAAAAAAAAgK4I4QAAAAAAAAB0RQgHAAAAAAAAoCtCOAAAAAAAAABdEcIBAAAAAAAA6IoQDgAAAAAAAEBXhHAAAAAAAAAAuiKEAwAAAAAAANCVTfPeAAAAAAAAAMBG2Xzx1ok+t2PL0ox3wv7kRDgAAAAAAAAAXRHCAQAAAAAAAOiKEA4AAAAAAABAV4RwAAAAAAAAALoihAMAAAAAAADQFSEcAAAAAAAAgK4I4QAAAAAAAAB0RQgHAAAAAAAAoCtCOAAAAAAAAABdEcIBAAAAAAAA6IoQDgAAAAAAAEBXhHAAAAAAAAAAuiKEAwAAAAAAANAVIRwAAAAAAACArgjhAAAAAAAAAHRFCAcAAAAAAACgK0I4AAAAAAAAAF0RwgEAAAAAAADoihAOAAAAAAAAQFeEcAAAAAAAAAC6IoQDAAAAAAAA0BUhHAAAAAAAAICuCOEAAAAAAAAAdEUIBwAAAAAAAKArQjgAAAAAAAAAXRHCAQAAAAAAAOiKEA4AAAAAAABAV4RwAAAAAAAAALoihAMAAAAAAADQFSEcAAAAAAAAgK4I4QAAAAAAAAB0RQgHAAAAAAAAoCtCOAAAAAAAAABdEcIBAAAAAAAA6IoQDgAAAAAAAEBXNs17AwAAAAAAAMDBYfPFWyf63I4tSzPeCb1zIhwAAAAAAACArgjhAAAAAAAAAHRFCAcAAAAAAACgK0I4AAAAAAAAAF0RwgEAAAAAAADoihAOAAAAAAAAQFeEcAAAAAAAAAC6IoQDAAAAAAA
      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6),dpi=200)\n",
    "X.corr()['Cluster'].iloc[:-1].sort_values().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choosing K Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "ssd = []\n",
    "\n",
    "for k in range(2,10):\n",
    "    \n",
    "    model = KMeans(n_clusters=k)\n",
    "    \n",
    "    \n",
    "    model.fit(scaled_X)\n",
    "    \n",
    "    #Sum of squared distances of samples to their closest cluster center.\n",
    "    ssd.append(model.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, ' Sum of Squared Distances')"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(2,10),ssd,'o--')\n",
    "plt.xlabel(\"K Value\")\n",
    "plt.ylabel(\" Sum of Squared Distances\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyzing SSE Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2469792.4095956706,\n",
       " 2370787.709348152,\n",
       " 2271502.7007717513,\n",
       " 2221128.900236805,\n",
       " 2145067.141554143,\n",
       " 2132468.751266735,\n",
       " 2039460.8832193925,\n",
       " 2005692.7454239195]"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             NaN\n",
       "1   -99004.700248\n",
       "2   -99285.008576\n",
       "3   -50373.800535\n",
       "4   -76061.758683\n",
       "5   -12598.390287\n",
       "6   -93007.868047\n",
       "7   -33768.137795\n",
       "dtype: float64"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change in SSD from previous K value!\n",
    "pd.Series(ssd).diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(ssd).diff().plot(kind='bar')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}