Project Part 4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e64d90b3",
   "metadata": {},
   "source": [
    "# Unsupervised Learning - Clustering - Credit Card Customer Data\n",
    "\n",
    "\n",
    "#### Brief description of the data set and its attributes:\n",
    "\n",
    "The Attributes Include:\n",
    "\n",
    "* Sl_No - Customer Serial Identification Number: Serves purpose of indexing values\n",
    "* Customer Key - Customer Key\n",
    "* AvgCreditLimit - Average Credit Card Limit For The Customer\n",
    "* TotalCreditCards - Total Credit Cards Owned by the Customer\n",
    "* Totalvisitsbank - Total Number of Bank Visits by the Customer\n",
    "* Totalvisitsonline - Total Visits Online by the Bank Customer\n",
    "* Totalcallsmade - Total Calls Made by the Customer to the Bank\n",
    "    \n",
    "at first glance we can segment the 5 features that i will use to 2 main groups\n",
    "\n",
    "1. Credit card usage - 2 features- AvgCreditLimit, TotalCreditCards\n",
    "2. service/marketing channels used - 3 features - Totalvisitsbank, Totalvisitsonline, Totalcallsmade\n",
    "    \n",
    "at first we can use basic EDA segmentation - distribution of Credit cards, Average limit\n",
    "\n",
    "    \n",
    "#### in  Total - 7 rows × 660 columns\n",
    "\n",
    "#### Main objective will focus on Clustering the customers\n",
    "\n",
    "I would like to set the main objective to be basic segmentation of the customers - Clustering.\n",
    "since the numbers of features that i will use - 5 in total(Sl_no & customer Key are not relevant) - there is no need to user dimensionality reduction.\n",
    "\n",
    "looking at the data itself - it will be required to scale the data - since AvgCreditLimit is around \n",
    "\n",
    "furthermore,\n",
    "I would like to segment them and understand how the features effect the clustering, \n",
    "\n",
    "Main Benefits:\n",
    "since features like visits/calls can make a great deal on understanding in which marketing/service channel we should focus by that we can understand by the number of Credit cards and the limit which are the \"top\" customers and which channels they use.\n",
    "\n",
    "all the above and the changes made to the service channel can increase \"Sales\" - in the forms of credit limit increase/# cards increase\n",
    " \n",
    " \n",
    "In this is notebook i will use 2 main models\n",
    "\n",
    "1. K-Means - running all inertia scores up to k=10 \n",
    "2. Agglomerative Clustering with n=2,3,4\n",
    "\n",
    "\n",
    "let's begin...\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435279e9",
   "metadata": {},
   "source": [
    "# Part 1a(Cleaning/Preparation):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87bc6728",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 0      87073\n",
       "1      38414\n",
       "2      17341\n",
       "3      40496\n",
       "4      47437\n",
       "       ...  \n",
       "655    51108\n",
       "656    60732\n",
       "657    53834\n",
       "658    80655\n",
       "659    80150\n",
       "Name: Customer Key, Length: 660, dtype: int64>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt, seaborn as sns\n",
    "#%matplotlib inline\n",
    "\n",
    "#import the dataframe from the file\n",
    "df = pd.read_csv('Credit Card Customer Data.csv')\n",
    "#check for values that are not unique in the Customer Key\n",
    "df['Customer Key'].unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9efab759",
   "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>Avg_Credit_Limit</th>\n",
       "      <th>Total_Credit_Cards</th>\n",
       "      <th>Total_visits_bank</th>\n",
       "      <th>Total_visits_online</th>\n",
       "      <th>Total_calls_made</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50000</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>99000</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>84000</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>145000</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>172000</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>167000</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>660 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Avg_Credit_Limit  Total_Credit_Cards  Total_visits_bank  \\\n",
       "0              100000                   2                  1   \n",
       "1               50000                   3                  0   \n",
       "2               50000                   7                  1   \n",
       "3               30000                   5                  1   \n",
       "4              100000                   6                  0   \n",
       "..                ...                 ...                ...   \n",
       "655             99000                  10                  1   \n",
       "656             84000                  10                  1   \n",
       "657            145000                   8                  1   \n",
       "658            172000                  10                  1   \n",
       "659            167000                   9                  0   \n",
       "\n",
       "     Total_visits_online  Total_calls_made  \n",
       "0                      1                 0  \n",
       "1                     10                 9  \n",
       "2                      3                 4  \n",
       "3                      1                 4  \n",
       "4                     12                 3  \n",
       "..                   ...               ...  \n",
       "655                   10                 0  \n",
       "656                   13                 2  \n",
       "657                    9                 1  \n",
       "658                   15                 0  \n",
       "659                   12                 2  \n",
       "\n",
       "[660 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove 2 columns that are not actual data features \n",
    "df = df.drop(columns=['Sl_No','Customer Key'])\n",
    "df\n",
    "# we have now the dataset without the columns that are not relevant for the modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9bad19d0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Avg_Credit_Limit       660\n",
       "Total_Credit_Cards     660\n",
       "Total_visits_bank      660\n",
       "Total_visits_online    660\n",
       "Total_calls_made       660\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "df.notna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8778ed7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(660, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a862f04e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Avg_Credit_Limit       int64\n",
       "Total_Credit_Cards     int64\n",
       "Total_visits_bank      int64\n",
       "Total_visits_online    int64\n",
       "Total_calls_made       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes\n",
    "#since no further cleaning/feature engineering is required - we will move on to the next step"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9108c84",
   "metadata": {},
   "source": [
    "---------------------------------------\n",
    "\n",
    "# Part 1b(EDA):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55109194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3000,   5000,   6000,   7000,   8000,   9000,  10000,  11000,\n",
       "        12000,  13000,  14000,  15000,  16000,  17000,  18000,  19000,\n",
       "        20000,  25000,  26000,  27000,  28000,  29000,  30000,  31000,\n",
       "        32000,  33000,  34000,  35000,  36000,  37000,  38000,  39000,\n",
       "        40000,  41000,  42000,  43000,  44000,  45000,  46000,  47000,\n",
       "        48000,  49000,  50000,  51000,  52000,  54000,  55000,  56000,\n",
       "        57000,  58000,  59000,  60000,  61000,  62000,  63000,  64000,\n",
       "        65000,  66000,  67000,  68000,  69000,  70000,  71000,  72000,\n",
       "        73000,  74000,  75000,  84000,  91000,  94000,  95000,  96000,\n",
       "        97000,  98000,  99000, 100000, 106000, 108000, 111000, 112000,\n",
       "       114000, 121000, 123000, 126000, 127000, 131000, 132000, 136000,\n",
       "       144000, 145000, 146000, 153000, 155000, 156000, 157000, 158000,\n",
       "       163000, 166000, 167000, 171000, 172000, 173000, 176000, 178000,\n",
       "       183000, 184000, 186000, 187000, 195000, 200000])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# before training the models i would like to understand furtermore the data\n",
    "\n",
    "#will check to see what unique values for the 'Avg_Credit_Limit' Feature\n",
    "df.Avg_Credit_Limit.sort_values(ascending=True).unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5dc34908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000      35\n",
       "6000      31\n",
       "9000      28\n",
       "13000     28\n",
       "19000     26\n",
       "          ..\n",
       "43000      1\n",
       "146000     1\n",
       "155000     1\n",
       "200000     1\n",
       "167000     1\n",
       "Name: Avg_Credit_Limit, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# brief overview of Credit card limit value counts\n",
    "df.Avg_Credit_Limit.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2ba93d46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.sort_values of 4     151\n",
       "6     117\n",
       "7     101\n",
       "5      74\n",
       "2      64\n",
       "1      59\n",
       "3      53\n",
       "10     19\n",
       "9      11\n",
       "8      11\n",
       "Name: Total_Credit_Cards, dtype: int64>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the Dist using Pandas of credit cards - \n",
    "df.Total_Credit_Cards.value_counts().sort_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ceb914ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the distribution of # of credit cards vs # of customers holding those credit cards\n",
    "plt.hist(df.Total_Credit_Cards)\n",
    "plt.xlabel('# of cards')\n",
    "plt.ylabel('# of customers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "80a1039a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the historgram of average credit limit\n",
    "plt.hist(df.Avg_Credit_Limit, bins=10)\n",
    "plt.title(\"Historgram of average credit limit\")\n",
    "plt.xlabel('Credit Limit')\n",
    "plt.ylabel('# of customers')\n",
    "plt.show()\n",
    "#we can see that most of the customer are on the left side - which makes sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30989ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_49bb0_row0_col0, #T_49bb0_row1_col1, #T_49bb0_row2_col2, #T_49bb0_row3_col3, #T_49bb0_row4_col4 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_49bb0_row0_col1 {\n",
       "  background-color: #cb3e38;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_49bb0_row0_col2 {\n",
       "  background-color: #7597f6;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_49bb0_row0_col3, #T_49bb0_row1_col0, #T_49bb0_row1_col4, #T_49bb0_row2_col3, #T_49bb0_row3_col2, #T_49bb0_row4_col1 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_49bb0_row0_col4 {\n",
       "  background-color: #f5c1a9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_49bb0_row1_col2 {\n",
       "  background-color: #edd1c2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_49bb0_row1_col3 {\n",
       "  background-color: #9fbfff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_49bb0_row2_col0 {\n",
       "  background-color: #6f92f3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_49bb0_row2_col1 {\n",
       "  background-color: #d9dce1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_49bb0_row2_col4 {\n",
       "  background-color: #f18f71;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_49bb0_row3_col0 {\n",
       "  background-color: #d55042;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_49bb0_row3_col1 {\n",
       "  background-color: #8fb1fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_49bb0_row3_col4 {\n",
       "  background-color: #7a9df8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_49bb0_row4_col0 {\n",
       "  background-color: #f7b396;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_49bb0_row4_col2 {\n",
       "  background-color: #d1493f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_49bb0_row4_col3 {\n",
       "  background-color: #86a9fc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_49bb0_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Avg_Credit_Limit</th>\n",
       "      <th class=\"col_heading level0 col1\" >Total_Credit_Cards</th>\n",
       "      <th class=\"col_heading level0 col2\" >Total_visits_bank</th>\n",
       "      <th class=\"col_heading level0 col3\" >Total_visits_online</th>\n",
       "      <th class=\"col_heading level0 col4\" >Total_calls_made</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_49bb0_level0_row0\" class=\"row_heading level0 row0\" >Avg_Credit_Limit</th>\n",
       "      <td id=\"T_49bb0_row0_col0\" class=\"data row0 col0\" >0.000000</td>\n",
       "      <td id=\"T_49bb0_row0_col1\" class=\"data row0 col1\" >0.608860</td>\n",
       "      <td id=\"T_49bb0_row0_col2\" class=\"data row0 col2\" >0.100312</td>\n",
       "      <td id=\"T_49bb0_row0_col3\" class=\"data row0 col3\" >0.551385</td>\n",
       "      <td id=\"T_49bb0_row0_col4\" class=\"data row0 col4\" >0.414352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49bb0_level0_row1\" class=\"row_heading level0 row1\" >Total_Credit_Cards</th>\n",
       "      <td id=\"T_49bb0_row1_col0\" class=\"data row1 col0\" >0.608860</td>\n",
       "      <td id=\"T_49bb0_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
       "      <td id=\"T_49bb0_row1_col2\" class=\"data row1 col2\" >0.315796</td>\n",
       "      <td id=\"T_49bb0_row1_col3\" class=\"data row1 col3\" >0.167758</td>\n",
       "      <td id=\"T_49bb0_row1_col4\" class=\"data row1 col4\" >0.651251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49bb0_level0_row2\" class=\"row_heading level0 row2\" >Total_visits_bank</th>\n",
       "      <td id=\"T_49bb0_row2_col0\" class=\"data row2 col0\" >0.100312</td>\n",
       "      <td id=\"T_49bb0_row2_col1\" class=\"data row2 col1\" >0.315796</td>\n",
       "      <td id=\"T_49bb0_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
       "      <td id=\"T_49bb0_row2_col3\" class=\"data row2 col3\" >0.551861</td>\n",
       "      <td id=\"T_49bb0_row2_col4\" class=\"data row2 col4\" >0.506016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49bb0_level0_row3\" class=\"row_heading level0 row3\" >Total_visits_online</th>\n",
       "      <td id=\"T_49bb0_row3_col0\" class=\"data row3 col0\" >0.551385</td>\n",
       "      <td id=\"T_49bb0_row3_col1\" class=\"data row3 col1\" >0.167758</td>\n",
       "      <td id=\"T_49bb0_row3_col2\" class=\"data row3 col2\" >0.551861</td>\n",
       "      <td id=\"T_49bb0_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
       "      <td id=\"T_49bb0_row3_col4\" class=\"data row3 col4\" >0.127299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_49bb0_level0_row4\" class=\"row_heading level0 row4\" >Total_calls_made</th>\n",
       "      <td id=\"T_49bb0_row4_col0\" class=\"data row4 col0\" >0.414352</td>\n",
       "      <td id=\"T_49bb0_row4_col1\" class=\"data row4 col1\" >0.651251</td>\n",
       "      <td id=\"T_49bb0_row4_col2\" class=\"data row4 col2\" >0.506016</td>\n",
       "      <td id=\"T_49bb0_row4_col3\" class=\"data row4 col3\" >0.127299</td>\n",
       "      <td id=\"T_49bb0_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9300ae3700>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the correlation between the features:\n",
    "df_corr = df.corr().abs()\n",
    "\n",
    "df_corr = df_corr.replace(to_replace=1.0, value=0)\n",
    "\n",
    "df_corr.style.background_gradient(cmap='coolwarm')\n",
    "# we can see that the value with the highest corr is 'Total_calls_made' & 'Total_Credit_Cards'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "53522248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Avg_Credit_Limit        Total_Credit_Cards\n",
       "Total_Credit_Cards        Total_calls_made\n",
       "Total_visits_bank      Total_visits_online\n",
       "Total_visits_online      Total_visits_bank\n",
       "Total_calls_made        Total_Credit_Cards\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d307f29",
   "metadata": {},
   "source": [
    "------------------------------------\n",
    "\n",
    "# Part 2:\n",
    "\n",
    "after EDA - the feature \"Avg_Credit_Limit\" needs to be scaled otherwise the clustering in KMeans which relies on distance will disrupt the model ability to cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "1532dde5",
   "metadata": {
    "scrolled": true
   },
   "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>Avg_Credit_Limit</th>\n",
       "      <th>Total_Credit_Cards</th>\n",
       "      <th>Total_visits_bank</th>\n",
       "      <th>Total_visits_online</th>\n",
       "      <th>Total_calls_made</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.492386</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.238579</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.238579</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.137056</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.492386</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>0.487310</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>0.411168</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>0.720812</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>0.857868</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>0.832487</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>660 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Avg_Credit_Limit  Total_Credit_Cards  Total_visits_bank  \\\n",
       "0            0.492386            0.111111                0.2   \n",
       "1            0.238579            0.222222                0.0   \n",
       "2            0.238579            0.666667                0.2   \n",
       "3            0.137056            0.444444                0.2   \n",
       "4            0.492386            0.555556                0.0   \n",
       "..                ...                 ...                ...   \n",
       "655          0.487310            1.000000                0.2   \n",
       "656          0.411168            1.000000                0.2   \n",
       "657          0.720812            0.777778                0.2   \n",
       "658          0.857868            1.000000                0.2   \n",
       "659          0.832487            0.888889                0.0   \n",
       "\n",
       "     Total_visits_online  Total_calls_made  \n",
       "0               0.066667               0.0  \n",
       "1               0.666667               0.9  \n",
       "2               0.200000               0.4  \n",
       "3               0.066667               0.4  \n",
       "4               0.800000               0.3  \n",
       "..                   ...               ...  \n",
       "655             0.666667               0.0  \n",
       "656             0.866667               0.2  \n",
       "657             0.600000               0.1  \n",
       "658             1.000000               0.0  \n",
       "659             0.800000               0.2  \n",
       "\n",
       "[660 rows x 5 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "mms = MinMaxScaler()\n",
    "\n",
    "scaled_df = pd.DataFrame(mms.fit_transform(df),columns=df.columns)\n",
    "\n",
    "#display the scaled data\n",
    "scaled_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50feef60",
   "metadata": {},
   "source": [
    "-------------\n",
    "\n",
    "# Part 3:\n",
    "\n",
    "### Train the Models:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81500bbf",
   "metadata": {},
   "source": [
    "### K-Means:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "7362f3ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Inertia')"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "inertia = []\n",
    "list_num_clusters = list(range(1,11))\n",
    "for num_clusters in list_num_clusters:\n",
    "    km = KMeans(n_clusters=num_clusters)\n",
    "    km.fit(scaled_df)\n",
    "    inertia.append(km.inertia_)\n",
    "    \n",
    "plt.plot(list_num_clusters,inertia)\n",
    "plt.title('Inertia for K-Means')\n",
    "plt.xlabel('Number of Clusters')\n",
    "plt.ylabel('Inertia')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e17dfb0",
   "metadata": {},
   "source": [
    "We can see that the inertia stabilizes at around k=4, therefor the recommended clustering is 4 groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "270e6793",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=4)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#run Kmeans with k=4\n",
    "km = KMeans(n_clusters=4)\n",
    "km.fit(scaled_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "63aed54a",
   "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>Avg_Credit_Limit</th>\n",
       "      <th>Total_Credit_Cards</th>\n",
       "      <th>Total_visits_bank</th>\n",
       "      <th>Total_visits_online</th>\n",
       "      <th>Total_calls_made</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>99000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>84000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>145000.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>172000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>167000.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>660 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Avg_Credit_Limit  Total_Credit_Cards  Total_visits_bank  \\\n",
       "0            100000.0                 2.0                1.0   \n",
       "1             50000.0                 3.0                0.0   \n",
       "2             50000.0                 7.0                1.0   \n",
       "3             30000.0                 5.0                1.0   \n",
       "4            100000.0                 6.0                0.0   \n",
       "..                ...                 ...                ...   \n",
       "655           99000.0                10.0                1.0   \n",
       "656           84000.0                10.0                1.0   \n",
       "657          145000.0                 8.0                1.0   \n",
       "658          172000.0                10.0                1.0   \n",
       "659          167000.0                 9.0                0.0   \n",
       "\n",
       "     Total_visits_online  Total_calls_made  \n",
       "0                    1.0               0.0  \n",
       "1                   10.0               9.0  \n",
       "2                    3.0               4.0  \n",
       "3                    1.0               4.0  \n",
       "4                   12.0               3.0  \n",
       "..                   ...               ...  \n",
       "655                 10.0               0.0  \n",
       "656                 13.0               2.0  \n",
       "657                  9.0               1.0  \n",
       "658                 15.0               0.0  \n",
       "659                 12.0               2.0  \n",
       "\n",
       "[660 rows x 5 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#inverse the scaling\n",
    "df_clustered = pd.DataFrame(mms.inverse_transform(scaled_df),columns=df.columns)\n",
    "df_clustered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "322ac21a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#add the cluster to the data frame\n",
    "df_clustered.insert(loc=5,column='Cluster',value=km.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2195b6f3",
   "metadata": {
    "scrolled": false
   },
   "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>Avg_Credit_Limit</th>\n",
       "      <th>Total_Credit_Cards</th>\n",
       "      <th>Total_visits_bank</th>\n",
       "      <th>Total_visits_online</th>\n",
       "      <th>Total_calls_made</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>99000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>84000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>145000.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>172000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>167000.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>660 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Avg_Credit_Limit  Total_Credit_Cards  Total_visits_bank  \\\n",
       "0            100000.0                 2.0                1.0   \n",
       "1             50000.0                 3.0                0.0   \n",
       "2             50000.0                 7.0                1.0   \n",
       "3             30000.0                 5.0                1.0   \n",
       "4            100000.0                 6.0                0.0   \n",
       "..                ...                 ...                ...   \n",
       "655           99000.0                10.0                1.0   \n",
       "656           84000.0                10.0                1.0   \n",
       "657          145000.0                 8.0                1.0   \n",
       "658          172000.0                10.0                1.0   \n",
       "659          167000.0                 9.0                0.0   \n",
       "\n",
       "     Total_visits_online  Total_calls_made  Cluster  \n",
       "0                    1.0               0.0        0  \n",
       "1                   10.0               9.0        1  \n",
       "2                    3.0               4.0        0  \n",
       "3                    1.0               4.0        0  \n",
       "4                   12.0               3.0        2  \n",
       "..                   ...               ...      ...  \n",
       "655                 10.0               0.0        2  \n",
       "656                 13.0               2.0        2  \n",
       "657                  9.0               1.0        2  \n",
       "658                 15.0               0.0        2  \n",
       "659                 12.0               2.0        2  \n",
       "\n",
       "[660 rows x 6 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the data with the clustering\n",
    "df_clustered"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ec39a35",
   "metadata": {},
   "source": [
    "#### Visualize the Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "02adc318",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a scatter of the clusters - Credit Limit vs. # Cards'\n",
    "plt.scatter(df_clustered.Total_Credit_Cards,df_clustered.Avg_Credit_Limit,c=df_clustered.Cluster)\n",
    "plt.xlabel('# of Credit Cards')\n",
    "plt.ylabel('Avg Credit')\n",
    "plt.title('Credit Limit vs. # Cards')\n",
    "plt.show()\n",
    "# we can see te obvious that the # of credit cards is correlated with the average credit limit."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89efd15b",
   "metadata": {},
   "source": [
    "we can see the obvious - that the # of credit cards is correlated with the average credit limit.\n",
    "However we must remember:\n",
    "\n",
    "The corr matrix showed that the higher correlation is between 'Total_calls_made' & 'Total_Credit_Cards'\n",
    "\n",
    "Let's plot that as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "089b536f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a scatter of the clusters - Total_calls_made' & 'Total_Credit_Cards\n",
    "plt.scatter(df_clustered.Total_Credit_Cards,df_clustered.Total_calls_made,c=df_clustered.Cluster)\n",
    "plt.xlabel('Total Credit Cards')\n",
    "plt.ylabel('total calls made')\n",
    "plt.title('Total Calls made vs. Total Credit Cards')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcb1b6e5",
   "metadata": {},
   "source": [
    "Preliminary conclusion: Credit Owner that has large amount of cards uses the telephone less,\n",
    "\n",
    "Finally - let's take a look at Visits on-line vs. Avg Card Limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "7b1733ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a scatter of the clusters - Visits online vs. Avg Card Limit\n",
    "plt.scatter(df_clustered.Total_visits_online,df_clustered.Avg_Credit_Limit,c=df_clustered.Cluster)\n",
    "plt.xlabel('Visits Online')\n",
    "plt.ylabel('Avg Card Limit')\n",
    "plt.title('Visits On-Line vs. Total Credit Cards')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43ff0f96",
   "metadata": {},
   "source": [
    "Preliminary conclusion: Credit Owner that has High credit limit tends to visit the on-line channel more often,\n",
    "and probably has more credit cards"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223931e6",
   "metadata": {},
   "source": [
    "### Agglomerative Clustering:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf91201",
   "metadata": {},
   "source": [
    "Run AgglomerativeClustering with n_clusters=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "ecc59816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'affinity': 'euclidean',\n",
       " 'compute_distances': False,\n",
       " 'compute_full_tree': True,\n",
       " 'connectivity': None,\n",
       " 'distance_threshold': None,\n",
       " 'linkage': 'ward',\n",
       " 'memory': None,\n",
       " 'n_clusters': 2}"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "ag = AgglomerativeClustering(n_clusters=2,linkage='ward', compute_full_tree=True)\n",
    "df_ag = ag.fit(scaled_df)\n",
    "#display the \"auto\" model parameters\n",
    "ag.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "2975e93e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#store the label for each parameter this case n=2\n",
    "ag2 = ag.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d030130a",
   "metadata": {},
   "source": [
    "Run AgglomerativeClustering with n_clusters=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "102af496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'affinity': 'euclidean',\n",
       " 'compute_distances': False,\n",
       " 'compute_full_tree': True,\n",
       " 'connectivity': None,\n",
       " 'distance_threshold': None,\n",
       " 'linkage': 'ward',\n",
       " 'memory': None,\n",
       " 'n_clusters': 3}"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "ag = AgglomerativeClustering(n_clusters=3,linkage='ward', compute_full_tree=True)\n",
    "df_ag = ag.fit(scaled_df)\n",
    "#display the \"auto\" model parameters\n",
    "ag.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "d7235181",
   "metadata": {},
   "outputs": [],
   "source": [
    "#store the label for each parameter this case n=3\n",
    "ag3 = ag.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08614bf3",
   "metadata": {},
   "source": [
    "Run AgglomerativeClustering with n_clusters=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "a12343c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'affinity': 'euclidean',\n",
       " 'compute_distances': False,\n",
       " 'compute_full_tree': True,\n",
       " 'connectivity': None,\n",
       " 'distance_threshold': None,\n",
       " 'linkage': 'ward',\n",
       " 'memory': None,\n",
       " 'n_clusters': 4}"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "\n",
    "ag = AgglomerativeClustering(n_clusters=4,linkage='ward', compute_full_tree=True)\n",
    "df_ag = ag.fit(scaled_df)\n",
    "#display the \"auto\" model parameters\n",
    "ag.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "157aa2a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#store the label for each parameter this case n=4\n",
    "ag4 = ag.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "16c40e67",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot insert ag_n2, already exists",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_4995/1145184532.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#add the diff n's to the DF\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_clustered\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ag_n2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mag2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mdf_clustered\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ag_n3'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mag3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf_clustered\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ag_n4'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mag4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36minsert\u001b[0;34m(self, loc, column, value, allow_duplicates)\u001b[0m\n\u001b[1;32m   4412\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_duplicates\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4413\u001b[0m             \u001b[0;31m# Should this be a different kind of error??\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4414\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"cannot insert {column}, already exists\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4415\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4416\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"loc must be int\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: cannot insert ag_n2, already exists"
     ]
    }
   ],
   "source": [
    "#add the diff n's to the DF\n",
    "df_clustered.insert(loc=6,column='ag_n2',value=ag2)\n",
    "df_clustered.insert(loc=7,column='ag_n3',value=ag3)\n",
    "df_clustered.insert(loc=8,column='ag_n4',value=ag4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "af055dbf",
   "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>Avg_Credit_Limit</th>\n",
       "      <th>Total_Credit_Cards</th>\n",
       "      <th>Total_visits_bank</th>\n",
       "      <th>Total_visits_online</th>\n",
       "      <th>Total_calls_made</th>\n",
       "      <th>Cluster</th>\n",
       "      <th>ag_n2</th>\n",
       "      <th>ag_n3</th>\n",
       "      <th>ag_n4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50000.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100000.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>99000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656</th>\n",
       "      <td>84000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>145000.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>172000.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>167000.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>660 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Avg_Credit_Limit  Total_Credit_Cards  Total_visits_bank  \\\n",
       "0            100000.0                 2.0                1.0   \n",
       "1             50000.0                 3.0                0.0   \n",
       "2             50000.0                 7.0                1.0   \n",
       "3             30000.0                 5.0                1.0   \n",
       "4            100000.0                 6.0                0.0   \n",
       "..                ...                 ...                ...   \n",
       "655           99000.0                10.0                1.0   \n",
       "656           84000.0                10.0                1.0   \n",
       "657          145000.0                 8.0                1.0   \n",
       "658          172000.0                10.0                1.0   \n",
       "659          167000.0                 9.0                0.0   \n",
       "\n",
       "     Total_visits_online  Total_calls_made  Cluster  ag_n2  ag_n3  ag_n4  \n",
       "0                    1.0               0.0        0      0      0      1  \n",
       "1                   10.0               9.0        1      1      1      0  \n",
       "2                    3.0               4.0        0      0      0      1  \n",
       "3                    1.0               4.0        0      1      1      0  \n",
       "4                   12.0               3.0        2      0      0      2  \n",
       "..                   ...               ...      ...    ...    ...    ...  \n",
       "655                 10.0               0.0        2      0      0      2  \n",
       "656                 13.0               2.0        2      0      0      2  \n",
       "657                  9.0               1.0        2      0      0      2  \n",
       "658                 15.0               0.0        2      0      0      2  \n",
       "659                 12.0               2.0        2      0      0      2  \n",
       "\n",
       "[660 rows x 9 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#display the DF \n",
    "df_clustered\n",
    "# we can see the different clustering from n=2 till n=4 using Agglomerative Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "a80650f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a scatter of the clusters for AG n=4\n",
    "#Visits online vs. Avg Card Limit\n",
    "plt.scatter(df_clustered.Total_visits_online,df_clustered.Avg_Credit_Limit,c=df_clustered.ag_n4)\n",
    "plt.xlabel('Visits Online')\n",
    "plt.ylabel('Avg Card Limit')\n",
    "plt.title('Total Visits On-Line vs. Total Credit Cards(AG when n=4)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2861fe70",
   "metadata": {},
   "source": [
    "Preliminary conclusion: Agglomerative Clustering Yields a very similar clustering as K Means\n",
    "\n",
    "Now let's have a look at the Bank visits vs the Credit Limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "d559e144",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot a scatter of the clusters for AG n=4\n",
    "#Visits online vs. Avg Card Limit\n",
    "plt.scatter(df_clustered.Total_visits_bank,df_clustered.Avg_Credit_Limit,c=df_clustered.ag_n4)\n",
    "plt.xlabel('Visits Online')\n",
    "plt.ylabel('Avg Card Limit')\n",
    "plt.title('Total Calls made vs. Total Credit Cards')\n",
    "plt.show()"
   ]
  },
  {
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   "id": "3581d5c3",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "\n",
    "# Insights and key findings:\n",
    "\n",
    "\n",
    "## Recommendatios:\n",
    "\n",
    "The Main recommendation to the decision makers will be to strengthen the On-line channel - marketing and service wise - since the majority of the high credit limit operates there, and there is a higher chance of \"high\" credit customers visiting and operating in this channel.\n",
    "\n",
    "this can result in improving the customer satisfaction with the credit services\n",
    "\n",
    "## Insights:\n",
    "\n",
    "* both models obtained similar results of clustering\n",
    "* the high Credit owners visits the On-Line channel the most\n",
    "* low credit owners still visits to the bank\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73816801",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Next steps:\n",
    "\n",
    "The next recommended steps:\n",
    "\n",
    "* continue collecting more data with various features to further understand the customer behavior in the On-Line Channel\n",
    "* improve the On-line channel's ability to support the customers\n"
   ]
  }
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